蛋白质结构预测方法学研究
|
摘要
随着人类基因组测序计划的完成,生命科学的重心开始转移到对基因的表达产物蛋白质的研究上来,蛋白质组学已成为后基因组时代的研究前沿和热点领域。蛋白质与配体相互作用以及蛋白质的结构与功能关系是后基因组时代研究的核心内容。研究蛋白质受体与配体间相互作用与识别对揭示细胞中蛋白质的分子生物学机理、计算机辅助药物设计和复合物结构预测都具有重要的意义。
由于实验测定蛋白质复合物结构存在较大的困难,近年来,随着计算机处理能力的不断增强以及理论模拟方法的迅速发展和广泛应用,计算机分子模拟方法已经成为研究蛋白质受体与其配体相互作用机制的重要手段。本论文采用分子对接和氨基酸网络方法等分子模拟方法,对蛋白质间相互作用与识别机制进行了一系列基础性的研究工作。论文内容主要包括以下几个方面:
(1)蛋白质分子对接打分函数的研究
提出了两个打分函数。一个是针对Others类型蛋白质复合物的组合打分函数ComScore。ComScore由原子接触势、范德华和静电相互作用能组成,采用多元线性回归拟合权重。对CAPRI比赛的benchmark 1.0中的17个复合物的对接结构测试结果表明,该组合打分基本能够体现Others类型复合物的相互作用特征,反映出复合物形成前后的能量变化关系,具备一定的从大量采集构象中筛选获得有效结构的能力。ComScore被用于CAPRI的第9-12轮的打分比赛中,在第9轮和第11轮都取得了好的成绩。
另一个是基于氨基酸网络的打分函数。蛋白质复合物拓扑结构给蛋白质-蛋白质相互作用机理的研究很多启示。在本论文中,建立了蛋白质的残基网络,其中蛋白质的残基被视为节点,残基之间的接触视为连接。根据残基类型将蛋白质复合物的残基网络分成两种类型,即疏水和亲水残基网络。分析这两种不同类型的网络,发现他们都具有小世界的性质。通过分析网络参量发现,正确结合的复合物构象比错误结合的结构具有更高的界面度值和更低的网络特征路径长度。这些性质反映出正确结合的复合物结构有更好的几何和残基类型互补,同时正确的结合模式对于保证天然蛋白质复合物的特征路径长度起着重要作用。此外,建立了两个基于网络参量的打分项,它们能够很好地反映复合物整体形状和残基类型互补特性。将基于网络的打分项与其他打分函数项进行组合后,提出了一个新的多项打分函数HPNCscore,它能够将RosettaDock组合打分函数的区分能力提高12%。上述工作能够扩展我们对蛋白质-蛋白质结合机制的了解,并可以用于蛋白质结构设计的工作。
(2)蛋白质分子对接搜索方法的改进
分子对接需要在尽量短的时间里搜索到能量低的结构,因此分子对接方法研究的另一个重要问题是快速有效的搜索算法,即采用新的理论和计算方法提升现有程序的计算效率。Autodock 3.0是一个被广泛采用的分子对接程序,它由美国Scripps研究所Olson等人开发,在预测蛋白质受体和配体间结合模式上取得了很好的成绩。本论文在分析Autodock 3.0串行程序的基础上提出并实现了5种不同的并行方案,从正确性、参数分析(包括5个不同输入参数)、并行进程数量的影响等多个角度对5个并行方案进行了测试和分析。在正确性测试中,并行方案五和原始串行程序分别应用于10个不同蛋白质-小分子体系的对接,对接结果比较证实了并行程序的正确性和可靠性。在参数分析测试中,通过改变能量评价次数、种群个体数、局部搜索概率、局部搜索迭代次数和对接次数等5个不同输入参数,分析了它们对不同方案的影响,这些测试将对并行程序在虚拟筛选中的应用起到指导作用。在并行进程数量测试中,第五个混合的并行方案由于结合多种方案的特点,随着进程数量的增加,程序依然能够充分合理地安排进程资源,保持较高的并行效率。并行化改造能够有效地提升Autodock分子对接软件的计算效率,将为计算机辅助药物设计和虚拟筛选提供一些帮助。
另外,还采用蚁群算法对Autodock 3.0程序进行了改进,替换了原程序中进行全局搜索的遗传算法。在22个蛋白质-小分子体系上测试发现,蚁群算法能够有效地改善程序的搜索结果。同时,不管是否采用局部搜索的算法,蚁群算法比遗传算法都具有更好的性能和更快的收敛速度。新的优化算法-蚁群算法的引入将对分子对接软件的改进提供一些新的参考。
(3)蛋白质氨基酸网络研究
蛋白质分子的三维结构可以被视为由氨基酸组成的复杂网络,对网络性质的分析能够帮助理解蛋白质结构和功能之间的关系。由于蛋白质的氨基酸网络是在蛋白质折叠过程中形成的,通常的网络模型难以解释其演化的机制。基于蛋白质折叠的观点,提出了一个氨基酸网络的演化模型。在此模型中,演化从天然蛋白质的氨基酸序列开始,由两个基本假设进行引导,即近邻偏好性规则和能量偏好性规则。研究发现近邻偏好性规则主要决定通常的网络性质,而能量偏好性规则主要决定特殊的生物学结构特征。应用于天然蛋白质体系发现,该模型能够很好地模拟出氨基酸网络的性质。
另外,建立并研究了蛋白质保守残基的无向网络。标识蛋白质结合界面是蛋白质-蛋白质相互作用预测以及蛋白质分类的重要环节。在本论文中,蛋白质结构被视为一个无向网络,其中保守性残基为网络节点,残基之间的接触视为连接。研究发现,保守性残基网络具有介于规则网络和随机网络之间的聚集系数和特征路径长度,属于小世界类型的网络。蛋白质复合物界面的残基比表面残基通常具有大的度值和低的聚集系数。此外,还发现了保守残基的空间聚集是一个普遍现象。保守性残基网络的性质将能够给蛋白质-蛋白质界面预测提供一些新的参量。
With the completion of Human Genome Project (HGP), the life science has focused on the study of the gene expressed products, i.e. proteins, and proteomics researches have become the pioneering and hot themes of post-genomic era. The core of the post-genomic era research has gone into the interactions between protein and ligand and the relationship between protein structure and function. The studies of the interactions and recognition between protein receptor and ligand are very important for understanding the molecular biology mechanism of proteins in cell, the computer-aided drug design and the structure prediction of protein-protein complex.
It is difficult to determine a protein complex structure through experimental methods. Recently, with the continuous progress of the computers’processing ability, as well as the rapid development and extensive application of theoretical simulation, molecular modeling methods have become important tools for exploring the interaction mechanism of protein receptor with ligand. In this thesis, a series of studies on the mechanism of interactions and recognition of protein have been done by using the molecular docking and amino acid network methods. The content of the thesis contain the following major aspects:
(1) Study on the scoring function of protein molecular docking
Two scoring functions were proposed. One was a combinatorial scoring function, ComScore, which was specially designed for the Others-type protein-protein complexes. ComScore was composed of the atomic contact energy, van der Waals and electrostatic interaction energies, in which the weight of each item was fit through the multiple linear regression approach. The test result on 17 Others-type complexes from CAPRI benchmark 1.0 demonstrated that the combinatorial scoring function can delineate the interaction feature of the Others-type complexes, reflect the energy change during the complex formation, and have a certain capacity to discriminate effective structures from numbers of the docked decoys. ComScore was used in the scoring test for CAPRI rounds 9-12 with the good results in rounds 9 and 11.
The other one was a scoring function based on the amino acid network. The topology study on protein-protein complexes will give some insights into the mechanisms of protein-protein interactions. In this thesis, residue networks were constructed by defining the amino acid residues as the vertices and atom contacts between them as the edges. The residue network of a protein complex was divided into two types of networks, i.e., the hydrophobic and the hydrophilic residue networks. Analyzing these two different types of networks, we find that these networks are of small-world properties. Furthermore, through analyzing the network parameters, it is found that the correct binding complex conformations are of both higher sum of the interface degree values and lower characteristic path length than those of the incorrect ones. These features reflect that the correct binding complex conformations have better geometric and residue type complementarity, and the correct binding modes are very important for preserving the characteristic path lengths of the native protein complexes. In addition, two scoring terms are proposed based on the network parameters, in which the characteristics of the entire complex shape and residue type complementarity are taken into account. These network-based scoring terms have also been used in conjunction with other scoring terms, and the new multi-term scoring HPNCscore has been devised. It can improve the discrimination of the combined scoring function of RosettaDock more than 12 %. This work can enhance our knowledge of the mechanisms of protein-protein interactions and recognition and also be used in protein design.
(2) Improvement on the searching algorithm of protein molecular docking
It is required for molecular docking to search structures with lower energy in less time. Therefore, another important issue of molecular docking is how to have the efficient searching algorithms. In other words, the efficiency of docking programs will be improved by using new theories and computational methods. AutoDock 3.0 is a widely used docking program developed by the Professor Olson’s group at the Scripps Research Institute, which has achieved great success in the prediction of the binding modes and conformations between protein receptor and ligand. In this thesis, based on the analysis of the algorithm of AutoDock 3.0, we proposed 5 parallel methods using the message passing interface (MPI) library. We tested and analyzed these methods for reliability, parameter analysis (including 5 input parameters) and the influences of the numbers of processors. In the reliability test, the parallel scheme 5 and the serial program were applied to 10 protein-ligand systems and the docking results indicate the validity and reliability of the parallel programs. In the parameter analysis, we changed 5 different parameters, including the numbers of energy evaluations, the population sizes, the frequencies of local search, the iteration numbers of local search and the numbers of docking runs. The influences of those parameters on the different 5 schemes were analyzed, which will guide for the parallel program in the virtual screening. In the test of the numbers of processors, the hybrid parallel scheme 5 has the characteristic of other schemes. With the processor increasing, the hybrid scheme can effectively use the processors and keep higher speedup and parallel efficiency. The parallel improvement can enhance the efficiency of the molecular docking program AutoDock 3.0, which can give some help to the computer-aided drug design and virtual screening.
In addition, instead of the global search of Genetic Algorithm (GA), we improved AutoDock 3.0 by using the Ant Colony Optimization (ACO) method. Tested on the 22 protein-ligand systems, it is found that the ACO method can make an improvement for the searching results. Meanwhile, it is found that whether with the local search or not, the performance of ACO is obviously better than that of GA and the energy convergence rate of ACO is also quicker than that of GA. The new search technology, the ACO method, has been introduced and it will give some advices for the improvement of docking programs.
(3) Study on the amino acid networks of proteins
The three-dimensional structure of a protein can be treated as a complex network composed of amino acids, and the network properties can help us to understand the relationship between structure and function of protein. Since the amino acid network of a protein is formed in the process of protein folding, it is difficult for the general network models to explain its evolving mechanism. Based on the perspectives of protein folding, we proposed an evolving model for the amino acid networks. In our model, the evolution starts from the amino acid sequence of a native protein and it is guided by two generic assumptions, i.e. the neighbor preferential rule and the energy preferential rule. It is found that the neighbor preferential rule predominates the general network properties and the energy preferential rule predominates the specific biological structure characteristics. Applied on native proteins, our model can mimic the features of the amino acid networks well.
In addition, the conservation residue network was constructed and studied. Identifying protein interface is crucial for the prediction of protein-protein interactions and for protein functional classification. In this thesis, the protein structure was modeled as an undirected graph with the conservation amino-acid residues as the vertices and atom contacts between them as the edges. It is found that the conservation residue networks are characterized by intermediate values of clustering coefficient and characteristic path length, which are the typical property of the small-world networks. The residues on the protein interfaces typically have higher degree values and lower clustering coefficient values than that of the surface residues. Additionally, it is detected that the spatial clustering of the conservation residues is a general phenomenon. These results indicate that the conservation residue network propensities can give us some new parameters in protein–protein interface prediction.
由于实验测定蛋白质复合物结构存在较大的困难,近年来,随着计算机处理能力的不断增强以及理论模拟方法的迅速发展和广泛应用,计算机分子模拟方法已经成为研究蛋白质受体与其配体相互作用机制的重要手段。本论文采用分子对接和氨基酸网络方法等分子模拟方法,对蛋白质间相互作用与识别机制进行了一系列基础性的研究工作。论文内容主要包括以下几个方面:
(1)蛋白质分子对接打分函数的研究
提出了两个打分函数。一个是针对Others类型蛋白质复合物的组合打分函数ComScore。ComScore由原子接触势、范德华和静电相互作用能组成,采用多元线性回归拟合权重。对CAPRI比赛的benchmark 1.0中的17个复合物的对接结构测试结果表明,该组合打分基本能够体现Others类型复合物的相互作用特征,反映出复合物形成前后的能量变化关系,具备一定的从大量采集构象中筛选获得有效结构的能力。ComScore被用于CAPRI的第9-12轮的打分比赛中,在第9轮和第11轮都取得了好的成绩。
另一个是基于氨基酸网络的打分函数。蛋白质复合物拓扑结构给蛋白质-蛋白质相互作用机理的研究很多启示。在本论文中,建立了蛋白质的残基网络,其中蛋白质的残基被视为节点,残基之间的接触视为连接。根据残基类型将蛋白质复合物的残基网络分成两种类型,即疏水和亲水残基网络。分析这两种不同类型的网络,发现他们都具有小世界的性质。通过分析网络参量发现,正确结合的复合物构象比错误结合的结构具有更高的界面度值和更低的网络特征路径长度。这些性质反映出正确结合的复合物结构有更好的几何和残基类型互补,同时正确的结合模式对于保证天然蛋白质复合物的特征路径长度起着重要作用。此外,建立了两个基于网络参量的打分项,它们能够很好地反映复合物整体形状和残基类型互补特性。将基于网络的打分项与其他打分函数项进行组合后,提出了一个新的多项打分函数HPNCscore,它能够将RosettaDock组合打分函数的区分能力提高12%。上述工作能够扩展我们对蛋白质-蛋白质结合机制的了解,并可以用于蛋白质结构设计的工作。
(2)蛋白质分子对接搜索方法的改进
分子对接需要在尽量短的时间里搜索到能量低的结构,因此分子对接方法研究的另一个重要问题是快速有效的搜索算法,即采用新的理论和计算方法提升现有程序的计算效率。Autodock 3.0是一个被广泛采用的分子对接程序,它由美国Scripps研究所Olson等人开发,在预测蛋白质受体和配体间结合模式上取得了很好的成绩。本论文在分析Autodock 3.0串行程序的基础上提出并实现了5种不同的并行方案,从正确性、参数分析(包括5个不同输入参数)、并行进程数量的影响等多个角度对5个并行方案进行了测试和分析。在正确性测试中,并行方案五和原始串行程序分别应用于10个不同蛋白质-小分子体系的对接,对接结果比较证实了并行程序的正确性和可靠性。在参数分析测试中,通过改变能量评价次数、种群个体数、局部搜索概率、局部搜索迭代次数和对接次数等5个不同输入参数,分析了它们对不同方案的影响,这些测试将对并行程序在虚拟筛选中的应用起到指导作用。在并行进程数量测试中,第五个混合的并行方案由于结合多种方案的特点,随着进程数量的增加,程序依然能够充分合理地安排进程资源,保持较高的并行效率。并行化改造能够有效地提升Autodock分子对接软件的计算效率,将为计算机辅助药物设计和虚拟筛选提供一些帮助。
另外,还采用蚁群算法对Autodock 3.0程序进行了改进,替换了原程序中进行全局搜索的遗传算法。在22个蛋白质-小分子体系上测试发现,蚁群算法能够有效地改善程序的搜索结果。同时,不管是否采用局部搜索的算法,蚁群算法比遗传算法都具有更好的性能和更快的收敛速度。新的优化算法-蚁群算法的引入将对分子对接软件的改进提供一些新的参考。
(3)蛋白质氨基酸网络研究
蛋白质分子的三维结构可以被视为由氨基酸组成的复杂网络,对网络性质的分析能够帮助理解蛋白质结构和功能之间的关系。由于蛋白质的氨基酸网络是在蛋白质折叠过程中形成的,通常的网络模型难以解释其演化的机制。基于蛋白质折叠的观点,提出了一个氨基酸网络的演化模型。在此模型中,演化从天然蛋白质的氨基酸序列开始,由两个基本假设进行引导,即近邻偏好性规则和能量偏好性规则。研究发现近邻偏好性规则主要决定通常的网络性质,而能量偏好性规则主要决定特殊的生物学结构特征。应用于天然蛋白质体系发现,该模型能够很好地模拟出氨基酸网络的性质。
另外,建立并研究了蛋白质保守残基的无向网络。标识蛋白质结合界面是蛋白质-蛋白质相互作用预测以及蛋白质分类的重要环节。在本论文中,蛋白质结构被视为一个无向网络,其中保守性残基为网络节点,残基之间的接触视为连接。研究发现,保守性残基网络具有介于规则网络和随机网络之间的聚集系数和特征路径长度,属于小世界类型的网络。蛋白质复合物界面的残基比表面残基通常具有大的度值和低的聚集系数。此外,还发现了保守残基的空间聚集是一个普遍现象。保守性残基网络的性质将能够给蛋白质-蛋白质界面预测提供一些新的参量。
With the completion of Human Genome Project (HGP), the life science has focused on the study of the gene expressed products, i.e. proteins, and proteomics researches have become the pioneering and hot themes of post-genomic era. The core of the post-genomic era research has gone into the interactions between protein and ligand and the relationship between protein structure and function. The studies of the interactions and recognition between protein receptor and ligand are very important for understanding the molecular biology mechanism of proteins in cell, the computer-aided drug design and the structure prediction of protein-protein complex.
It is difficult to determine a protein complex structure through experimental methods. Recently, with the continuous progress of the computers’processing ability, as well as the rapid development and extensive application of theoretical simulation, molecular modeling methods have become important tools for exploring the interaction mechanism of protein receptor with ligand. In this thesis, a series of studies on the mechanism of interactions and recognition of protein have been done by using the molecular docking and amino acid network methods. The content of the thesis contain the following major aspects:
(1) Study on the scoring function of protein molecular docking
Two scoring functions were proposed. One was a combinatorial scoring function, ComScore, which was specially designed for the Others-type protein-protein complexes. ComScore was composed of the atomic contact energy, van der Waals and electrostatic interaction energies, in which the weight of each item was fit through the multiple linear regression approach. The test result on 17 Others-type complexes from CAPRI benchmark 1.0 demonstrated that the combinatorial scoring function can delineate the interaction feature of the Others-type complexes, reflect the energy change during the complex formation, and have a certain capacity to discriminate effective structures from numbers of the docked decoys. ComScore was used in the scoring test for CAPRI rounds 9-12 with the good results in rounds 9 and 11.
The other one was a scoring function based on the amino acid network. The topology study on protein-protein complexes will give some insights into the mechanisms of protein-protein interactions. In this thesis, residue networks were constructed by defining the amino acid residues as the vertices and atom contacts between them as the edges. The residue network of a protein complex was divided into two types of networks, i.e., the hydrophobic and the hydrophilic residue networks. Analyzing these two different types of networks, we find that these networks are of small-world properties. Furthermore, through analyzing the network parameters, it is found that the correct binding complex conformations are of both higher sum of the interface degree values and lower characteristic path length than those of the incorrect ones. These features reflect that the correct binding complex conformations have better geometric and residue type complementarity, and the correct binding modes are very important for preserving the characteristic path lengths of the native protein complexes. In addition, two scoring terms are proposed based on the network parameters, in which the characteristics of the entire complex shape and residue type complementarity are taken into account. These network-based scoring terms have also been used in conjunction with other scoring terms, and the new multi-term scoring HPNCscore has been devised. It can improve the discrimination of the combined scoring function of RosettaDock more than 12 %. This work can enhance our knowledge of the mechanisms of protein-protein interactions and recognition and also be used in protein design.
(2) Improvement on the searching algorithm of protein molecular docking
It is required for molecular docking to search structures with lower energy in less time. Therefore, another important issue of molecular docking is how to have the efficient searching algorithms. In other words, the efficiency of docking programs will be improved by using new theories and computational methods. AutoDock 3.0 is a widely used docking program developed by the Professor Olson’s group at the Scripps Research Institute, which has achieved great success in the prediction of the binding modes and conformations between protein receptor and ligand. In this thesis, based on the analysis of the algorithm of AutoDock 3.0, we proposed 5 parallel methods using the message passing interface (MPI) library. We tested and analyzed these methods for reliability, parameter analysis (including 5 input parameters) and the influences of the numbers of processors. In the reliability test, the parallel scheme 5 and the serial program were applied to 10 protein-ligand systems and the docking results indicate the validity and reliability of the parallel programs. In the parameter analysis, we changed 5 different parameters, including the numbers of energy evaluations, the population sizes, the frequencies of local search, the iteration numbers of local search and the numbers of docking runs. The influences of those parameters on the different 5 schemes were analyzed, which will guide for the parallel program in the virtual screening. In the test of the numbers of processors, the hybrid parallel scheme 5 has the characteristic of other schemes. With the processor increasing, the hybrid scheme can effectively use the processors and keep higher speedup and parallel efficiency. The parallel improvement can enhance the efficiency of the molecular docking program AutoDock 3.0, which can give some help to the computer-aided drug design and virtual screening.
In addition, instead of the global search of Genetic Algorithm (GA), we improved AutoDock 3.0 by using the Ant Colony Optimization (ACO) method. Tested on the 22 protein-ligand systems, it is found that the ACO method can make an improvement for the searching results. Meanwhile, it is found that whether with the local search or not, the performance of ACO is obviously better than that of GA and the energy convergence rate of ACO is also quicker than that of GA. The new search technology, the ACO method, has been introduced and it will give some advices for the improvement of docking programs.
(3) Study on the amino acid networks of proteins
The three-dimensional structure of a protein can be treated as a complex network composed of amino acids, and the network properties can help us to understand the relationship between structure and function of protein. Since the amino acid network of a protein is formed in the process of protein folding, it is difficult for the general network models to explain its evolving mechanism. Based on the perspectives of protein folding, we proposed an evolving model for the amino acid networks. In our model, the evolution starts from the amino acid sequence of a native protein and it is guided by two generic assumptions, i.e. the neighbor preferential rule and the energy preferential rule. It is found that the neighbor preferential rule predominates the general network properties and the energy preferential rule predominates the specific biological structure characteristics. Applied on native proteins, our model can mimic the features of the amino acid networks well.
In addition, the conservation residue network was constructed and studied. Identifying protein interface is crucial for the prediction of protein-protein interactions and for protein functional classification. In this thesis, the protein structure was modeled as an undirected graph with the conservation amino-acid residues as the vertices and atom contacts between them as the edges. It is found that the conservation residue networks are characterized by intermediate values of clustering coefficient and characteristic path length, which are the typical property of the small-world networks. The residues on the protein interfaces typically have higher degree values and lower clustering coefficient values than that of the surface residues. Additionally, it is detected that the spatial clustering of the conservation residues is a general phenomenon. These results indicate that the conservation residue network propensities can give us some new parameters in protein–protein interface prediction.
引文
1 S. Fields. Proteomics: Proteomics in genomeland. Science. 2001, 291(5507): 1221-1224
2 A. Abbott. And now for the proteome. Nature. 2001, 409(6822): 747-747
3 A. Sali, R. Glaeser, T. Earnest, and W. Baumeister. From words to literature in structural proteomics. Nature. 2003, 422(6928): 216-225
4 A. M. Edwards, B. Kus, R. Jansen, D. Greenbaum, J. Greenblatt, and M. Gerstein. Bridging structural biology and genomics: Assessing protein interaction data with known complexes. Trends in Genetics. 2002, 18(10): 529-536
5 W. L. Jorgensen. The many roles of computation in drug discovery. Science. 2004, 303(5665): 1813-1818
6 C. A. Taft, V. B. Da Silva, and C. Da Silva. Current topics in computer-aided drug design. Journal of Pharmaceutical Sciences. 2008, 97(3): 1089-1098
7 P. Prathipati, A. Dixit, and A. K. Saxena. Computer-aided drug design: Integration of structure-based and ligand-based approaches in drug design. Current Computer-Aided Drug Design. 2007, 3(2): 133-148
8 H. M. Berman, J. Westbrook, Z. Feng, G. Gilliland, T. N. Bhat, H. Weissig, I. N. Shindyalov, and P. E. Bourne. The Protein Data Bank. Nucleic Acids Research. 2000, 28(1): 235-242
9 R. B. Russell, F. Alber, P. Aloy, F. P. Davis, D. Korkin, M. Pichaud, M. Topf, and A. Sali. A structural perspective on protein-protein interactions. Current Opinion in Structural Biology. 2004, 14(3): 313-324
10 J. Janin, K. Henrick, J. Moult, L. Ten Eyck, M. J. E. Sternberg, S. Vajda, I. Vasker, and S. J. Wodak. CAPRI: A Critical Assessment of PRedicted Interactions. Proteins-Structure Function and Bioinformatics. 2003, 52(1): 2-9
11 C. J. Camacho, S. R. Kimura, C. DeLisi, and S. Vajda. Kinetics of desolvation-mediated protein-protein binding. Biophysical Journal. 2000, 78(3): 1094-1105
12 C. J. Camacho, Z. P. Weng, S. Vajda, and C. DeLisi. Free energy landscapes of encounter complexes in protein-protein association. Biophysical Journal. 1999, 76(3): 1166-1178
13 G. Schreiber and A. R. Fersht. Rapid, electrostatically assisted association of proteins.Nature Structural Biology. 1996, 3(5): 427-431
14 S. R. Kimura, R. C. Brower, S. Vajda, and C. J. Camacho. Dynamical view of the positions of key side chains in protein-protein recognition. Biophysical Journal. 2001, 80(2): 635-642
15 E. Fischer. Einfluss der configuration auf die wirkung der enzyme. Berichte der Deutschen Chemischen Gesellschaft. 1894, 27: 3189-3232
16 D. E. Koshland. Application of a theory of enzyme specificity to protein synthesis. Proceedings of the National Academy of Sciences of the United States of America. 1958, 44: 98-104
17 P. N. Palma, L. Krippahl, J. E. Wampler, and J. J. G. Moura. BiGGER: A new (soft) docking algorithm for predicting protein interactions. Proteins-Structure Function and Genetics. 2000, 39(4): 372-384
18 J. Nocedal and S. J. Wright, Numerical optimization, Springer-Verlag, New York (1999).
19 J. H. Holland, Adaptation in natural and artificial system, Ann Arbor: University of Michigan Press (1975).
20 N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller. Equations of state calculations by fast computing machines. Journal of Chemical Physics. 1953, 21(6): 1087-1092
21 E. J. Gardiner, P. Willett, and P. J. Artymiuk. Protein docking using a genetic algorithm. Proteins-Structure Function and Genetics. 2001, 44(1): 44-56
22 J. S. Taylor and R. M. Burnett. DARWIN: A program for docking flexible molecules. Proteins-Structure Function and Genetics. 2000, 41(2): 173-191
23 C. Wang, O. Schueler-Furman, and D. Baker. Improved side-chain modeling for protein-protein docking. Protein Science. 2005, 14(5): 1328-1339
24 J. J. Gray, S. Moughon, C. Wang, O. Schueler-Furman, B. Kuhlman, C. A. Rohl, and D. Baker. Protein-protein docking with simultaneous optimization of rigid-body displacement and side-chain conformations. Journal of Molecular Biology. 2003, 331(1): 281-299
25 E. Katchalski-katzir, I. Shariv, M. Eisenstein, A. A. Friesem, C. Aflalo, and I. A. Vakser. Molecular surface recognition: Determination of geometric fit between proteins and their ligands by correlation techniques. Proceedings of the National Academy of Sciences of the United States of America. 1992, 89(6): 2195-2199
26 R. Chen, L. Li, and Z. P. Weng. ZDOCK: An initial-stage protein-docking algorithm. Proteins-Structure Function and Genetics. 2003, 52(1): 80-87
27 I. A. Vakser. Evaluation of GRAMM low-resolution docking methodology on the hemagglutinin-antibody complex. Proteins-Structure Function and Genetics. 1997: 226-230
28 J. G. Mandell, V. A. Roberts, M. E. Pique, V. Kotlovyi, J. C. Mitchell, E. Nelson, I. Tsigelny, and L. F. Ten Eyck. Protein docking using continuum electrostatics and geometric fit. Protein Engineering. 2001, 14(2): 105-113
29 C. J. Camacho and D. W. Gatchell. Successful discrimination of protein interactions. Proteins-Structure Function and Genetics. 2003, 52(1): 92-97
30 S. R. Comeau, D. W. Gatchell, S. Vajda, and C. J. Camacho. ClusPro: An automated docking and discrimination method for the prediction of protein complexes. Bioinformatics. 2004, 20(1): 45-50
31 A. Berchanski, B. Shapira, and M. Eisenstein. Hydrophobic complementarity in protein-protein docking. Proteins-Structure Function and Bioinformatics. 2004, 56(1): 130-142
32 P. Aloy, E. Querol, F. X. Aviles, and M. J. E. Sternberg. Automated structure-based prediction of functional sites in proteins: Applications to assessing the validity of inheriting protein function from homology in genome annotation and to protein docking. Journal of Molecular Biology. 2001, 311(2): 395-408
33 C. Zhang, S. Liu, Q. Q. Zhu, and Y. Q. Zhou. A knowledge-based energy function for protein-ligand, protein-protein, and protein-DNA complexes. Journal of Medicinal Chemistry. 2005, 48(7): 2325-2335
34 W. D. Cornell, P. Cieplak, C. I. Bayly, I. R. Gould, K. M. Merz, D. M. Ferguson, D. C. Spellmeyer, T. Fox, J. W. Caldwell, and P. A. Kollman. A second generation force field for the simulation of proteins, nucleic acids, and organic molecules. Journal of the American Chemical Society. 1996, 118(9): 2309-2309
35 S. J. Weiner, P. A. Kollman, D. A. Case, U. C. Singh, C. Ghio, G. Alagona, S. Profeta, and P. Weiner. A new force-field for molecular mechanical simulation of nucleic-acids and proteins. Journal of the American Chemical Society. 1984, 106(3): 765-784
36 B. R. Brooks, R. E. Bruccoleri, B. D. Olafson, D. J. States, S. Swaminathan, and M. Karplus.Charmm: A program for macromolecular energy, minimization, and dynamics calculations. Journal of Computational Chemistry. 1983, 4: 187-217
37 I. D. Kuntz, J. M. Blaney, S. J. Oatley, R. Langridge, and T. E. Ferrin. A geometric approach to macromolecule-ligand interactions. Journal of Molecular Biology. 1982, 161(2): 269-288
38 B. A. Luty, Z. R. Wasserman, P. F. W. Stouten, C. N. Hodge, M. Zacharias, and J. A. McCammon. A molecular mechanics grid method for evaluation of ligand-receptor interactions. Journal of Computational Chemistry. 1995, 16(4): 454-464
39 K. Komatsu, Y. Kurihara, M. Iwadate, M. Takeda-Shitaka, and H. Umeyama. Evaluation of the third solvent clusters fitting procedure for the prediction of protein-protein interactions based on the results at the CAPRI blind docking study. Proteins-Structure Function and Genetics. 2003, 52(1): 15-18
40 D. M. Lorber, M. K. Udo, and B. K. Shoichet. Protein-protein docking with multiple residue conformations and residue substitutions. Protein Science. 2002, 11(6): 1393-1408
41 C. H. Li, X. H. Ma, W. Z. Chen, and C. X. Wang. A protein-protein docking algorithm dependent on the type of complexes. Protein Engineering. 2003, 16(4): 265-269
42 H. J. Bohm. Ludi: Rule-based automatic design of new substituents for enzyme-inhibitor leads. Journal of Computer-Aided Molecular Design. 1992, 6(6): 593-606
43 R. X. Wang, L. Liu, L. H. Lai, and Y. Q. Tang. SCORE: A new empirical method for estimating the binding affinity of a protein-ligand complex. Journal of Molecular Modeling. 1998, 4(12): 379-394
44 J. Fernandez-Recio, M. Totrov, and R. Abagyan. Identification of protein-protein interaction sites from docking energy landscapes. Journal of Molecular Biology. 2004, 335(3): 843-865
45 X. H. Ma, C. H. Li, L. Z. Shen, X. Q. Gong, W. Z. Chen, and C. X. Wang. Biologically enhanced sampling geometric docking and backbone flexibility treatment with multiconformational superposition. Proteins-Structure Function and Bioinformatics. 2005, 60(2): 319-323
46 R. Norel, F. Sheinerman, D. Petrey, and B. Honig. Electrostatic contributions to protein-protein interactions: Fast energetic filters for docking and their physical basis. Protein Science. 2001, 10(11): 2147-2161
47 C. Zhang, S. Liu, and Y. Q. Zhou. Accurate and efficient loop selections by theDFIRE-based all-atom statistical potential. Protein Science. 2004, 13(2): 391-399
48 G. Moont, H. A. Gabb, and M. J. E. Sternberg. Use of pair potentials across protein interfaces in screening predicted docked complexes. Proteins-Structure Function and Genetics. 1999, 35(3): 364-373
49 C. Zhang, G. Vasmatzis, J. L. Cornette, and C. DeLisi. Determination of atomic desolvation energies from the structures of crystallized proteins. Journal of Molecular Biology. 1997, 267(3): 707-726
50 C. Dominguez, R. Boelens, and A. Bonvin. HADDOCK: A protein-protein docking approach based on biochemical or biophysical information. Journal of the American Chemical Society. 2003, 125(7): 1731-1737
51 G. M. Morris, D. S. Goodsell, R. S. Halliday, R. Huey, W. E. Hart, R. K. Belew, and A. J. Olson. Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. Journal of Computational Chemistry. 1998, 19(14): 1639-1662
52 C. M. Venkatachalam, X. Jiang, T. Oldfield, and M. Waldman. LigandFit: A novel method for the shape-directed rapid docking of ligands to protein active sites. Journal of Molecular Graphics & Modelling. 2003, 21(4): 289-307
53 M. Rarey, B. Kramer, T. Lengauer, and G. Klebe. A fast flexible docking method using an incremental construction algorithm. Journal of Molecular Biology. 1996, 261(3): 470-489
54 T. A. Halgren, R. B. Murphy, R. A. Friesner, H. S. Beard, L. L. Frye, W. T. Pollard, and J. L. Banks. Glide: A new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. Journal of Medicinal Chemistry. 2004, 47(7): 1750-1759
55 R. A. Friesner, J. L. Banks, R. B. Murphy, T. A. Halgren, J. J. Klicic, D. T. Mainz, M. P. Repasky, E. H. Knoll, M. Shelley, J. K. Perry, D. E. Shaw, P. Francis, and P. S. Shenkin. Glide: A new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. Journal of Medicinal Chemistry. 2004, 47(7): 1739-1749
56 G. Jones, P. Willett, R. C. Glen, A. R. Leach, and R. Taylor. Development and validation of a genetic algorithm for flexible docking. Journal of Molecular Biology. 1997, 267(3): 727-748
57 Z. Zsoldos, D. Reid, A. Simon, B. S. Sadjad, and A. P. Johnson. eHITS: An innovative approach to the docking and scoring function problems. Current Protein & Peptide Science. 2006, 7(5): 421-435
58 J. J. Gray. High-resolution protein-protein docking. Current Opinion in Structural Biology. 2006, 16(2): 183-193
59 V. Mohan, A. C. Gibbs, M. D. Cummings, E. P. Jaeger, and R. L. DesJarlais. Docking: Successes and challenges. Current Pharmaceutical Design. 2005, 11(3): 323-333
60 G. A. Petsko. Structure and mechanism in protein science: A guide to enzyme catalysis and protein folding. Nature. 1999, 401(6749): 115-116
61 T. Lazaridis and M. Karplus. ''New view'' of protein folding reconciled with the old through multiple unfolding simulations. Science. 1997, 278(5345): 1928-1931
62 F. L. Heppner, C. Musahl, I. Arrighi, M. A. Klein, T. Rulicke, B. Oesch, R. M. Zinkernagel, U. Kalinke, and A. Aguzzi. Prevention of scrapie pathogenesis by transgenic expression of anti-prion protein antibodies. Science. 2001, 294(5540): 178-182
63 M. F. Perutz, M. G. Rossmann, A. F. Cullis, G. Muirhead, G. Will, and T. North. Structure of hemoglobin: The three-dimensional fourier synthesis at 5.5 ? resolution, obtained by X-ray analysis. Nature. 1960, 185: 416-422
64 J. C. Kendrew, G. Bodo, H. M. Dintzis, H. Parrish, H. Wyckoff, and D. C. Phillips. A three-dimensional model of the myoglobin molecule obtained by X-ray analysis. Nature. 1958, 181: 662~666
65 T. Dandekar and R. Konig. Computational methods for the prediction of protein folds. Biochimica Et Biophysica Acta-Protein Structure and Molecular Enzymology. 1997, 1343(1): 1-15
66 J. Moult. The current state of the art in protein structure prediction. Current Opinion in Biotechnology. 1996, 7(4): 422-427
67 A. V. Finkelstein and O. V. Galzitskaya. Physics of protein folding. Physics of Life Reviews. 2004, 1(1): 23-56
68 C. B. Anfinsen. Principles that govern the folding of protein chains. Science. 1973, 181: 223-230
69 C. Levinthal. Are there pathways for protein folding? Journal of Chemical Physics. 1968, 65: 44-45
70 K. A. Dill and H. S. Chan. From Levinthal to pathways to funnels. Nature Structural Biology. 1997, 4(1): 10-19
71 P. G. Wolynes, J. N. Onuchic, and D. Thirumalai. Navigating the folding routes. Science. 1995, 267(5204): 1619-1620
72 J. D. Bryngelson, J. N. Onuchic, N. D. Socci, and P. G. Wolynes. Funnels, pathways, and the energy landscape of protein folding: A synthesis. Proteins-Structure Function and Genetics. 1995, 21(3): 167-195
73 K. Iguchi. A toy model for understanding the conceptual framework of protein folding: Rubik's magic snake model. Modern Physics Letters B. 1998, 12(13): 499-506
74 J. H. Werner, R. B. Dyer, R. M. Fesinmeyer, and N. H. Andersen. Dynamics of the primary processes of protein folding: Helix nucleation. Journal of Physical Chemistry B. 2002, 106(2): 487-494
75 R. H. Zhou, X. H. Huang, C. J. Margulis, and B. J. Berne. Hydrophobic collapse in multidomain protein folding. Science. 2004, 305(5690): 1605-1609
76 S. A. Islam, M. Karplus, and D. L. Weaver. The role of sequence and structure in protein folding kinetics: The diffusion-collision model applied to proteins L and G. Structure. 2004, 12(10): 1833-1845
77 R. L. Baldwin and G. D. Rose. Is protein folding hierarchic? II. Folding intermediates and transition states. Trends in Biochemical Sciences. 1999, 24(2): 77-83
78 P. S. Kim and R. L. Baldwin. Specific intermediates in the folding reactions of small proteins and the mechanism of protein folding. Annual Review of Biochemistry. 1982, 51: 459-489
79 C. M. Dobson, A. Sali, and M. Karplus. Protein folding: A perspective from theory and experiment. Angewandte Chemie-International Edition. 1998, 37(7): 868-893
80 L. W. Bergman and W. M. Kuehl. Formation of an intrachain disulfide bond on nascent immunoglobulin light chains. Journal of Biological Chemistry. 1979, 254(18): 8869-8876
81 J. M. Yon. Protein folding in the post-genomic era. Journal of Cellular and Molecular Medicine. 2002, 6(3): 307-327
82 M. Faloutsos, P. Faloutsos, and C. Faloutsos. On power-law relationships of the Internet topology. Acm Sigcomm'99 Conference: Applications, Technologies, Architectures, and Protocols for Computer Communications. 1999, 29(4): 251-262
83 R. Albert, H. Jeong, and A. L. Barabasi. Internet: Diameter of the World-Wide Web. Nature.1999, 401(6749): 130-131
84 A. L. Barabasi and E. Bonabeau. Scale-free networks. Scientific American. 2003, 288(5): 60-69
85 L. A. N. Amaral, A. Scala, M. Barthelemy, and H. E. Stanley. Classes of small-world networks. Proceedings of the National Academy of Sciences of the United States of America. 2000, 97(21): 11149-11152
86 F. J. Muller, L. C. Laurent, D. Kostka, I. Ulitsky, R. Williams, C. Lu, I. H. Park, M. S. Rao, R. Shamir, P. H. Schwartz, N. O. Schmidt, and J. F. Loring. Regulatory networks define phenotypic classes of human stem cell lines. Nature. 2008, 455(7211): 401-U455
87 M. E. J. Newman. Clustering and preferential attachment in growing networks. Physical Review E. 2001, 64(2): 025102
88 M. E. J. Newman. The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences of the United States of America. 2001, 98(2): 404-409
89 M. Karonski. A review of random graphs. Journal of Graph Theory. 1982, 6(4): 349-389
90 P. Erd?s and A. Rényi. On the evolution of random graphs. Publications of the Mathematical Institute of the Hungarian Academy of Sciences. 1960, 5: 17-61
91 S. Boccaletti, V. Latora, Y. Moreno, M. Chavez, and D. U. Hwang. Complex networks: Structure and dynamics. Physics Reports-Review Section of Physics Letters. 2006, 424(4-5): 175-308
92 D. J. Watts. The 'new' science of networks. Annual Review of Sociology 2004, 30: 243-270
93 S. H. Strogatz. Exploring complex networks. Nature. 2001, 410(6825): 268-276
94 D. J. Watts and S. H. Strogatz. Collective dynamics of 'small-world' networks. Nature. 1998, 393(6684): 440-442
95 M. E. J. Newman. Mixing patterns in networks. Physical Review E. 2003, 67(2): 026126
96 L. C. Freeman. Set of measures of centrality based on betweenness. Sociometry. 1977, 40(1): 35-41
97 M. E. J. Newman. Assortative mixing in networks. Physical Review Letters. 2002, 89(20): 208701
98 C. M. Song, S. Havlin, and H. A. Makse. Self-similarity of complex networks. Nature. 2005, 433(7024): 392-395
99 M. E. J. Newman and D. J. Watts. Renormalization group analysis of the small-world network model. Physics Letters A. 1999, 263(4-6): 341-346
100 A. L. Barabasi, R. Albert, and H. Jeong. Mean-field theory for scale-free random networks. Physica A. 1999, 272(1-2): 173-187
101 X. Li and G. R. Chen. A local-world evolving network model. Physica a-Statistical Mechanics and Its Applications. 2003, 328(1-2): 274-286
102 M. Vendruscolo, N. V. Dokholyan, E. Paci, and M. Karplus. Small-world view of the amino acids that play a key role in protein folding. Physical Review E. 2002, 65(6): 061910
103 N. V. Dokholyan, L. Li, F. Ding, and E. I. Shakhnovich. Topological determinants of protein folding. Proceedings of the National Academy of Sciences of the United States of America. 2002, 99(13): 8637-8641
104 A. R. Atilgan, P. Akan, and C. Baysal. Small-world communication of residues and significance for protein dynamics. Biophysical Journal. 2004, 86(1): 85-91
105 G. Amitai, A. Shemesh, E. Sitbon, M. Shklar, D. Netanely, I. Venger, and S. Pietrokovski. Network analysis of protein structures identifies functional residues. Journal of Molecular Biology. 2004, 344(4): 1135-1146
106 D. J. Jacobs, A. J. Rader, L. A. Kuhn, and M. F. Thorpe. Protein flexibility predictions using graph theory. Proteins-Structure Function and Genetics. 2001, 44(2): 150-165
107 A. del Sol, H. Fujihashi, D. Amoros, and R. Nussinov. Residues crucial for maintaining short paths in network communication mediate signaling in proteins. Molecular Systems Biology. 2006, 2: 0019
108 A. del Sol and P. O'Meara. Small-world network approach to identify key residues in protein-protein interaction. Proteins-Structure Function and Bioinformatics. 2005, 58(3): 672-682
109 A. del Sol, H. Fujihashi, and P. O'Meara. Topology of small-world networks of protein-protein complex structures. Bioinformatics. 2005, 21(8): 1311-1315
110 M. D. Daily, T. J. Upadhyaya, and J. J. Gray. Contact rearrangements form coupled networks from local motions in allosteric proteins. Proteins-Structure Function and Bioinformatics. 2008, 71(1): 455-466
111 F. Rao and A. Caflisch. The protein folding network. Journal of Molecular Biology. 2004,342(1): 299-306
112 J. P. K. Doye and C. P. Massen. Characterizing the network topology of the energy landscapes of atomic clusters. Journal of Chemical Physics. 2005, 122(8): 084105
113 A. Scala, L. A. N. Amaral, and M. Barthelemy. Small-world networks and the conformation space of a short lattice polymer chain. Europhysics Letters. 2001, 55(4): 594-600
114 A. C. Gavin, M. Bosche, R. Krause, P. Grandi, M. Marzioch, A. Bauer, J. Schultz, J. M. Rick, A. M. Michon, C. M. Cruciat, M. Remor, C. Hofert, M. Schelder, M. Brajenovic, H. Ruffner, A. Merino, K. Klein, M. Hudak, D. Dickson, T. Rudi, V. Gnau, A. Bauch, S. Bastuck, B. Huhse, C. Leutwein, M. A. Heurtier, R. R. Copley, A. Edelmann, E. Querfurth, V. Rybin, G. Drewes, M. Raida, T. Bouwmeester, P. Bork, B. Seraphin, B. Kuster, G. Neubauer, and G. Superti-Furga. Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature. 2002, 415(6868): 141-147
115 T. Maniatis and R. Reed. An extensive network of coupling among gene expression machines. Nature. 2002, 416(6880): 499-506
116 G. Frigerio, M. Burri, D. Bopp, S. Baumgartner, and M. Noll. Structure of the segmentation gene paired and the drosophila prd gene set as part of a gene network. Cell. 1986, 47(5): 735-746
117 C. Bode, I. A. Kovacs, M. S. Szalay, R. Palotai, T. Korcsmaros, and P. Csermely. Network analysis of protein dynamics. Febs Letters. 2007, 581(15): 2776-2782
118 A. V. Finkelstein and M. A. Roytberg. Computation of biopolymers: A general approach to different problems. Biosystems. 1993, 30(1-3): 1-19
119 R. M. Jackson. Comparison of protein-protein interactions in serine protease-inhibitor and antibody-antigen complexes: Implications for the protein docking problem. Protein Science. 1999, 8(3): 603-613
120 S. Vajda and C. J. Camacho. Protein-protein docking: Is the glass half-full or half-empty? Trends in Biotechnology. 2004, 22(3): 110-116
121 R. Chen, J. Mintseris, J. Janin, and Z. P. Weng. A protein-protein docking benchmark. Proteins-Structure Function and Genetics. 2003, 52(1): 88-91
122 J. Menetrey, M. Perderiset, J. Cicolari, T. Dubois, N. Elkhatib, F. El Khadali, M. Franco, P. Chavrier, and A. Houdusse. Structural basis for ARF1-mediated recruitment of ARHGAP21to Golgi membranes. Embo Journal. 2007, 26(7): 1953-1962
123 B. Kuhlman and D. Baker. Native protein sequences are close to optimal for their structures. Proceedings of the National Academy of Sciences of the United States of America. 2000, 97(19): 10383-10388
124 R. P. Bahadur, P. Chakrabarti, F. Rodier, and J. Janin. Dissecting subunit interfaces in homodimeric proteins. Proteins-Structure Function and Genetics. 2003, 53(3): 708-719
125 P. Chakrabarti and J. Janin. Dissecting protein-protein recognition sites. Proteins-Structure Function and Genetics. 2002, 47(3): 334-343
126 M. Nayal and B. Honig. On the nature of cavities on protein surfaces: Application to the identification of drug-binding sites. Proteins-Structure Function and Bioinformatics. 2006, 63(4): 892-906
127 T. A. Binkowski, A. Joachimiak, and J. Liang. Protein surface analysis for function annotation in high-throughput structural genomics pipeline. Protein Science. 2005, 14(12): 2972-2981
128 C. M. W. Ho and G. R. Marshall. Cavity search: An algorithm for the isolation and display of cavity-like binding regions. Journal of Computer-Aided Molecular Design. 1990, 4(4): 337-354
129 G. Nicola and I. A. Vakser. A simple shape characteristic of protein-protein recognition. Bioinformatics. 2007, 23(7): 789-792
130 J. Foote and A. Raman. A relation between the principal axes of inertia and ligand binding. Proceedings of the National Academy of Sciences of the United States of America. 2000, 97(3): 978-983
131 R. Norel, S. L. Lin, H. J. Wolfson, and R. Nussinov. Shape complementarity at protein-protein interfaces. Biopolymers. 1994, 34(7): 933-940
132 P. H. Walls and M. J. E. Sternberg. New algorithm to model protein-protein recognition based on surface complementarity. Applications to antibody-antigen docking. Journal of Molecular Biology. 1992, 228(1): 277-297
133 Y. Levy, S. S. Cho, J. N. Onuchic, and P. G. Wolynes. A survey of flexible protein binding mechanisms and their transition states using native topology based energy landscapes. Journal of Molecular Biology. 2005, 346(4): 1121-1145
134 Y. Levy, P. G. Wolynes, and J. N. Onuchic. Protein topology determines binding mechanism. Proceedings of the National Academy of Sciences of the United States of America. 2004, 101(2): 511-516
135 M. Aftabuddin and S. Kundu. Hydrophobic, hydrophilic, and charged amino acid networks within protein. Biophysical Journal. 2007, 93(1): 225-231
136 M. Aftabuddin and S. Kundu. Weighted and unweighted network of amino acids within protein. Physica a-Statistical Mechanics and Its Applications. 2006, 369(2): 895-904
137 S. Kundu. Amino acid network within protein. Physica a-Statistical Mechanics and Its Applications. 2005, 346(1-2): 104-109
138 J. Li, J. Wang, and W. Wang. Identifying folding nucleus based on residue contact networks of proteins. Proteins-Structure Function and Bioinformatics. 2008, 71(4): 1899-1907
139 X. Jiao, S. Chang, C. H. Li, W. Z. Chen, and C. X. Wang. Construction and application of the weighted amino acid network based on energy. Physical Review E. 2007, 75(5): 051903
140 K. V. Brinda and S. Vishveshwara. Oligomeric protein structure networks: insights into protein-protein interactions. BMC Bioinformatics. 2005, 6: 296-310
141 Z. J. Hu, D. Bowen, W. M. Southerland, A. del Sol, Y. P. Pan, R. Nussinov, and B. Y. Ma. Ligand binding and circular permutation modify residue interaction network in DHFR. Plos Computational Biology. 2007, 3(6): 1097-1107
142 J. Mintseris, K. Wiehe, B. Pierce, R. Anderson, R. Chen, J. Janin, and Z. P. Weng. Protein-protein docking benchmark 2.0: An update. Proteins-Structure Function and Bioinformatics. 2005, 60(2): 214-216
143 S. J. Hubbard and J. M. Thornton. NACCESS [computer program]. Department of Biochemistry and Molecular Biology, University College London. 1993:
144 S. Jones and J. M. Thornton. Principles of protein-protein interactions. Proceedings of the National Academy of Sciences of the United States of America. 1996, 93(1): 13-20
145 S. J. Sun, R. Brem, H. S. Chan, and K. A. Dill. Designing amino acid sequences to fold with good hydrophobic cores. Protein Engineering. 1995, 8(12): 1205-1213
146 L. H. Greene and V. A. Higman. Uncovering network systems within protein structures. Journal of Molecular Biology. 2003, 334(4): 781-791
147 R. Mendez, R. Leplae, L. De Maria, and S. J. Wodak. Assessment of blind predictions ofprotein-protein interactions: Current status of docking methods. Proteins-Structure Function and Genetics. 2003, 52(1): 51-67
148 C. J. Camacho, D. W. Gatchell, S. R. Kimura, and S. Vajda. Scoring docked conformations generated by rigid-body protein-protein docking. Proteins-Structure Function and Genetics. 2000, 40(3): 525-537
149 R. Mendez, R. Leplae, M. F. Lensink, and S. J. Wodak. Assessment of CAPRI predictions in rounds 3-5 shows progress in docking procedures. Proteins-Structure Function and Bioinformatics. 2005, 60(2): 150-169
150 J. Janin and C. Chothia. The structure of protein-protein recognition sites. Journal of Biological Chemistry. 1990, 265(27): 16027-16030
151 T. R. Gamble, F. F. Vajdos, S. H. Yoo, D. K. Worthylake, M. Houseweart, W. I. Sundquist, and C. P. Hill. Crystal structure of human cyclophilin A bound to the amino-terminal domain of HIV-1 capsid. Cell. 1996, 87(7): 1285-1294
152 A. Colorni, M. Dorigo, and V. Maniezzo. Distributed optimization by ant colonies. The First European conference on Artificial Life. 1991: 134-142
153 J. Kennedy and R. Eberhart. Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks. 1995, 4: 1942-1948
154 D. W. Walker and J. J. Dongarra. MPI: A standard message passing interface. Supercomputer. 1996, 12(1): 56-68
155 J. Dongarra, D. Walker, E. Lusk, B. Knighten, M. Snir, A. Geist, S. Otto, R. Hempel, E. Lusk, W. Gropp, J. Cownie, T. Skjellum, L. Clarke, R. Littlefield, M. Sears, S. Husslederman, E. Anderson, S. Berryman, J. Feeney, D. Frye, L. Hart, A. Ho, J. Kohl, P. Madams, C. Mosher, P. Pierce, E. Schikuta, R. G. Voigt, R. Babb, R. Bjornson, V. Fernando, I. Glendinning, T. Haupt, C. T. H. Ho, S. Krauss, A. Mainwaring, D. Nessett, S. Ranka, A. Singh, D. Weeks, J. Baron, N. Doss, S. Fineberg, A. Greenberg, D. Heller, G. Howell, B. Leary, O. McBryan, P. Pacheco, P. Rigsbee, A. Sussman, S. Wheat, E. Barszcz, A. Elster, J. Flower, R. Harrison, T. Henderson, J. Kapenga, A. Maccabe, P. McKinley, H. Palmer, A. Robison, R. Tomlinson, and S. Zenith. Special issue on mpi: A message-passing interface standard. International Journal of Supercomputer Applications and High Performance Computing. 1994, 8(3-4): 165-414
156陈国良.并行计算——结构·算法·编程.高等教育出版社. 1999:
157 S. J. Weiner, P. A. Kollman, D. T. Nguyen, and D. A. Case. An all atom force-field for simulations of proteins and nucleic-acids. Journal of Computational Chemistry. 1986, 7(2): 230-252
158 D. S. Goodsell and A. J. Olson. Automated docking of substrates to proteins by simulated annealing. Proteins-Structure Function and Genetics. 1990, 8(3): 195-202
159 C. Perez and A. R. Ortiz. Evaluation of docking functions for protein-ligand docking. Journal of Medicinal Chemistry. 2001, 44(23): 3768-3785
160 B. G. Turner and M. F. Summers. Structural biology of HIV. Journal of Molecular Biology. 1999, 285(1): 1-32
161 K. V. Brinda and S. Vishveshwara. A network representation of protein structures: Implications for protein stability. Biophysical Journal. 2005, 89(6): 4159-4170
162 M. M. Gromiha and S. Selvaraj. Inter-residue interactions in protein folding and stability. Progress in Biophysics & Molecular Biology. 2004, 86(2): 235-277
163 A. L. Barabasi and R. Albert. Emergence of scaling in random networks. Science. 1999, 286(5439): 509-512
164 E. Shakhnovich. Protein folding thermodynamics and dynamics: Where physics, chemistry, and biology meet. Chemical Reviews. 2006, 106(5): 1559-1588
165 K. W. Plaxco, K. T. Simons, and D. Baker. Contact order, transition state placement and the refolding rates of single domain proteins. Journal of Molecular Biology. 1998, 277(4): 985-994
166 K. A. Dill. Theory for the folding and stability of globular proteins. Biochemistry. 1985, 24(6): 1501-1509
167 O. B. Ptitsyn and A. A. Rashin. Stagewise mechanism of protein folding. Doklady Akademii Nauk SSSR. 1973, 213: 473-475
168 L. Mirny and E. Shakhnovich. Protein folding theory: From lattice to all-atom models. Annual Review of Biophysics and Biomolecular Structure. 2001, 30: 361-396
169 J. N. Onuchic, H. Nymeyer, A. E. Garcia, J. Chahine, and N. D. Socci. The energy landscape theory of protein folding: Insights into folding mechanisms and scenarios. Advances in Protein Chemistry. 2000, 53: 87-152
170 J. N. Onuchic, Z. LutheySchulten, and P. G. Wolynes. Theory of protein folding: The energy landscape perspective. Annual Review of Physical Chemistry. 1997, 48: 545-600
171 D. Baker. A surprising simplicity to protein folding. Nature. 2000, 405(6782): 39-42
172 D. E. Makarov and K. W. Plaxco. The topomer search model: A simple, quantitative theory of two-state protein folding kinetics. Protein Science. 2003, 12(1): 17-26
173 S. Miyazawa and R. L. Jernigan. Residue-residue potentials with a favorable contact pair term and an unfavorable high packing density term, for simulation and threading. Journal of Molecular Biology. 1996, 256(3): 623-644
174 Z. Gugolya, Z. Dosztanyi, and I. Simon. Interresidue interactions in protein classes. Proteins-Structure Function and Genetics. 1997, 27(3): 360-366
175 V. I. Abkevich, A. M. Gutin, and E. I. Shakhnovich. Specific nucleus as the transition state for protein folding: Evidence from the lattice model. Biochemistry. 1994, 33(33): 10026-10036
176 S. H. White and R. E. Jacobs. Nucleation, rapid folding, and globular intrachain regions in proteins. . Proceedings of the National Academy of Sciences of the United States of America. 1973, 70(3): 697-701
177 A. Y. Istomin, D. J. Jacobs, and D. R. Livesay. On the role of structural class of a protein with two-state folding kinetics in determining correlations between its size, topology, and folding rate. Protein Science. 2007, 16(11): 2564-2569
178 M. E. J. Newman. The structure and function of complex networks. Siam Review. 2003, 45(2): 167-256
179 S. N. Dorogovtsev and J. F. F. Mendes. Evolution of networks. Advances in Physics. 2002, 51(4): 1079-1187
180 S. A. Teichmann, A. G. Murzin, and C. Chothia. Determination of protein function, evolution and interactions by structural genomics. Current Opinion in Structural Biology. 2001, 11(3): 354-363
181 A. Gutteridge, G. J. Bartlett, and J. M. Thornton. Using a neural network and spatial clustering to predict the location of active sites in enzymes. Journal of Molecular Biology. 2003, 330(4): 719-734
182 S. Jones and J. M. Thornton. Analysis of protein-protein interaction sites using surfacepatches. Journal of Molecular Biology. 1997, 272(1): 121-132
183 C. Chothia and J. Janin. Principles of protein-protein recognition. Nature. 1975, 256(5520): 705-708
184 O. Lichtarge, H. R. Bourne, and F. E. Cohen. An evolutionary trace method defines binding surfaces common to protein families. Journal of Molecular Biology. 1996, 257(2): 342-358
185 H. Liao, W. Yeh, D. Chiang, R. L. Jernigan, and B. Lustig. Protein sequence entropy is closely related to packing density and hydrophobicity. Protein Engineering Design & Selection. 2005, 18(2): 59-64
186 L. A. Mirny and E. I. Shakhnovich. Universally conserved positions in protein folds: Reading evolutionary signals about stability, folding kinetics and function. Journal of Molecular Biology. 1999, 291(1): 177-196
187 M. Landau, I. Mayrose, Y. Rosenberg, F. Glaser, E. Martz, T. Pupko, and N. Ben-Tal. ConSurf 2005: The projection of evolutionary conservation scores of residues on protein structures. Nucleic Acids Research. 2005, 33: W299-W302
2 A. Abbott. And now for the proteome. Nature. 2001, 409(6822): 747-747
3 A. Sali, R. Glaeser, T. Earnest, and W. Baumeister. From words to literature in structural proteomics. Nature. 2003, 422(6928): 216-225
4 A. M. Edwards, B. Kus, R. Jansen, D. Greenbaum, J. Greenblatt, and M. Gerstein. Bridging structural biology and genomics: Assessing protein interaction data with known complexes. Trends in Genetics. 2002, 18(10): 529-536
5 W. L. Jorgensen. The many roles of computation in drug discovery. Science. 2004, 303(5665): 1813-1818
6 C. A. Taft, V. B. Da Silva, and C. Da Silva. Current topics in computer-aided drug design. Journal of Pharmaceutical Sciences. 2008, 97(3): 1089-1098
7 P. Prathipati, A. Dixit, and A. K. Saxena. Computer-aided drug design: Integration of structure-based and ligand-based approaches in drug design. Current Computer-Aided Drug Design. 2007, 3(2): 133-148
8 H. M. Berman, J. Westbrook, Z. Feng, G. Gilliland, T. N. Bhat, H. Weissig, I. N. Shindyalov, and P. E. Bourne. The Protein Data Bank. Nucleic Acids Research. 2000, 28(1): 235-242
9 R. B. Russell, F. Alber, P. Aloy, F. P. Davis, D. Korkin, M. Pichaud, M. Topf, and A. Sali. A structural perspective on protein-protein interactions. Current Opinion in Structural Biology. 2004, 14(3): 313-324
10 J. Janin, K. Henrick, J. Moult, L. Ten Eyck, M. J. E. Sternberg, S. Vajda, I. Vasker, and S. J. Wodak. CAPRI: A Critical Assessment of PRedicted Interactions. Proteins-Structure Function and Bioinformatics. 2003, 52(1): 2-9
11 C. J. Camacho, S. R. Kimura, C. DeLisi, and S. Vajda. Kinetics of desolvation-mediated protein-protein binding. Biophysical Journal. 2000, 78(3): 1094-1105
12 C. J. Camacho, Z. P. Weng, S. Vajda, and C. DeLisi. Free energy landscapes of encounter complexes in protein-protein association. Biophysical Journal. 1999, 76(3): 1166-1178
13 G. Schreiber and A. R. Fersht. Rapid, electrostatically assisted association of proteins.Nature Structural Biology. 1996, 3(5): 427-431
14 S. R. Kimura, R. C. Brower, S. Vajda, and C. J. Camacho. Dynamical view of the positions of key side chains in protein-protein recognition. Biophysical Journal. 2001, 80(2): 635-642
15 E. Fischer. Einfluss der configuration auf die wirkung der enzyme. Berichte der Deutschen Chemischen Gesellschaft. 1894, 27: 3189-3232
16 D. E. Koshland. Application of a theory of enzyme specificity to protein synthesis. Proceedings of the National Academy of Sciences of the United States of America. 1958, 44: 98-104
17 P. N. Palma, L. Krippahl, J. E. Wampler, and J. J. G. Moura. BiGGER: A new (soft) docking algorithm for predicting protein interactions. Proteins-Structure Function and Genetics. 2000, 39(4): 372-384
18 J. Nocedal and S. J. Wright, Numerical optimization, Springer-Verlag, New York (1999).
19 J. H. Holland, Adaptation in natural and artificial system, Ann Arbor: University of Michigan Press (1975).
20 N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller. Equations of state calculations by fast computing machines. Journal of Chemical Physics. 1953, 21(6): 1087-1092
21 E. J. Gardiner, P. Willett, and P. J. Artymiuk. Protein docking using a genetic algorithm. Proteins-Structure Function and Genetics. 2001, 44(1): 44-56
22 J. S. Taylor and R. M. Burnett. DARWIN: A program for docking flexible molecules. Proteins-Structure Function and Genetics. 2000, 41(2): 173-191
23 C. Wang, O. Schueler-Furman, and D. Baker. Improved side-chain modeling for protein-protein docking. Protein Science. 2005, 14(5): 1328-1339
24 J. J. Gray, S. Moughon, C. Wang, O. Schueler-Furman, B. Kuhlman, C. A. Rohl, and D. Baker. Protein-protein docking with simultaneous optimization of rigid-body displacement and side-chain conformations. Journal of Molecular Biology. 2003, 331(1): 281-299
25 E. Katchalski-katzir, I. Shariv, M. Eisenstein, A. A. Friesem, C. Aflalo, and I. A. Vakser. Molecular surface recognition: Determination of geometric fit between proteins and their ligands by correlation techniques. Proceedings of the National Academy of Sciences of the United States of America. 1992, 89(6): 2195-2199
26 R. Chen, L. Li, and Z. P. Weng. ZDOCK: An initial-stage protein-docking algorithm. Proteins-Structure Function and Genetics. 2003, 52(1): 80-87
27 I. A. Vakser. Evaluation of GRAMM low-resolution docking methodology on the hemagglutinin-antibody complex. Proteins-Structure Function and Genetics. 1997: 226-230
28 J. G. Mandell, V. A. Roberts, M. E. Pique, V. Kotlovyi, J. C. Mitchell, E. Nelson, I. Tsigelny, and L. F. Ten Eyck. Protein docking using continuum electrostatics and geometric fit. Protein Engineering. 2001, 14(2): 105-113
29 C. J. Camacho and D. W. Gatchell. Successful discrimination of protein interactions. Proteins-Structure Function and Genetics. 2003, 52(1): 92-97
30 S. R. Comeau, D. W. Gatchell, S. Vajda, and C. J. Camacho. ClusPro: An automated docking and discrimination method for the prediction of protein complexes. Bioinformatics. 2004, 20(1): 45-50
31 A. Berchanski, B. Shapira, and M. Eisenstein. Hydrophobic complementarity in protein-protein docking. Proteins-Structure Function and Bioinformatics. 2004, 56(1): 130-142
32 P. Aloy, E. Querol, F. X. Aviles, and M. J. E. Sternberg. Automated structure-based prediction of functional sites in proteins: Applications to assessing the validity of inheriting protein function from homology in genome annotation and to protein docking. Journal of Molecular Biology. 2001, 311(2): 395-408
33 C. Zhang, S. Liu, Q. Q. Zhu, and Y. Q. Zhou. A knowledge-based energy function for protein-ligand, protein-protein, and protein-DNA complexes. Journal of Medicinal Chemistry. 2005, 48(7): 2325-2335
34 W. D. Cornell, P. Cieplak, C. I. Bayly, I. R. Gould, K. M. Merz, D. M. Ferguson, D. C. Spellmeyer, T. Fox, J. W. Caldwell, and P. A. Kollman. A second generation force field for the simulation of proteins, nucleic acids, and organic molecules. Journal of the American Chemical Society. 1996, 118(9): 2309-2309
35 S. J. Weiner, P. A. Kollman, D. A. Case, U. C. Singh, C. Ghio, G. Alagona, S. Profeta, and P. Weiner. A new force-field for molecular mechanical simulation of nucleic-acids and proteins. Journal of the American Chemical Society. 1984, 106(3): 765-784
36 B. R. Brooks, R. E. Bruccoleri, B. D. Olafson, D. J. States, S. Swaminathan, and M. Karplus.Charmm: A program for macromolecular energy, minimization, and dynamics calculations. Journal of Computational Chemistry. 1983, 4: 187-217
37 I. D. Kuntz, J. M. Blaney, S. J. Oatley, R. Langridge, and T. E. Ferrin. A geometric approach to macromolecule-ligand interactions. Journal of Molecular Biology. 1982, 161(2): 269-288
38 B. A. Luty, Z. R. Wasserman, P. F. W. Stouten, C. N. Hodge, M. Zacharias, and J. A. McCammon. A molecular mechanics grid method for evaluation of ligand-receptor interactions. Journal of Computational Chemistry. 1995, 16(4): 454-464
39 K. Komatsu, Y. Kurihara, M. Iwadate, M. Takeda-Shitaka, and H. Umeyama. Evaluation of the third solvent clusters fitting procedure for the prediction of protein-protein interactions based on the results at the CAPRI blind docking study. Proteins-Structure Function and Genetics. 2003, 52(1): 15-18
40 D. M. Lorber, M. K. Udo, and B. K. Shoichet. Protein-protein docking with multiple residue conformations and residue substitutions. Protein Science. 2002, 11(6): 1393-1408
41 C. H. Li, X. H. Ma, W. Z. Chen, and C. X. Wang. A protein-protein docking algorithm dependent on the type of complexes. Protein Engineering. 2003, 16(4): 265-269
42 H. J. Bohm. Ludi: Rule-based automatic design of new substituents for enzyme-inhibitor leads. Journal of Computer-Aided Molecular Design. 1992, 6(6): 593-606
43 R. X. Wang, L. Liu, L. H. Lai, and Y. Q. Tang. SCORE: A new empirical method for estimating the binding affinity of a protein-ligand complex. Journal of Molecular Modeling. 1998, 4(12): 379-394
44 J. Fernandez-Recio, M. Totrov, and R. Abagyan. Identification of protein-protein interaction sites from docking energy landscapes. Journal of Molecular Biology. 2004, 335(3): 843-865
45 X. H. Ma, C. H. Li, L. Z. Shen, X. Q. Gong, W. Z. Chen, and C. X. Wang. Biologically enhanced sampling geometric docking and backbone flexibility treatment with multiconformational superposition. Proteins-Structure Function and Bioinformatics. 2005, 60(2): 319-323
46 R. Norel, F. Sheinerman, D. Petrey, and B. Honig. Electrostatic contributions to protein-protein interactions: Fast energetic filters for docking and their physical basis. Protein Science. 2001, 10(11): 2147-2161
47 C. Zhang, S. Liu, and Y. Q. Zhou. Accurate and efficient loop selections by theDFIRE-based all-atom statistical potential. Protein Science. 2004, 13(2): 391-399
48 G. Moont, H. A. Gabb, and M. J. E. Sternberg. Use of pair potentials across protein interfaces in screening predicted docked complexes. Proteins-Structure Function and Genetics. 1999, 35(3): 364-373
49 C. Zhang, G. Vasmatzis, J. L. Cornette, and C. DeLisi. Determination of atomic desolvation energies from the structures of crystallized proteins. Journal of Molecular Biology. 1997, 267(3): 707-726
50 C. Dominguez, R. Boelens, and A. Bonvin. HADDOCK: A protein-protein docking approach based on biochemical or biophysical information. Journal of the American Chemical Society. 2003, 125(7): 1731-1737
51 G. M. Morris, D. S. Goodsell, R. S. Halliday, R. Huey, W. E. Hart, R. K. Belew, and A. J. Olson. Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. Journal of Computational Chemistry. 1998, 19(14): 1639-1662
52 C. M. Venkatachalam, X. Jiang, T. Oldfield, and M. Waldman. LigandFit: A novel method for the shape-directed rapid docking of ligands to protein active sites. Journal of Molecular Graphics & Modelling. 2003, 21(4): 289-307
53 M. Rarey, B. Kramer, T. Lengauer, and G. Klebe. A fast flexible docking method using an incremental construction algorithm. Journal of Molecular Biology. 1996, 261(3): 470-489
54 T. A. Halgren, R. B. Murphy, R. A. Friesner, H. S. Beard, L. L. Frye, W. T. Pollard, and J. L. Banks. Glide: A new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. Journal of Medicinal Chemistry. 2004, 47(7): 1750-1759
55 R. A. Friesner, J. L. Banks, R. B. Murphy, T. A. Halgren, J. J. Klicic, D. T. Mainz, M. P. Repasky, E. H. Knoll, M. Shelley, J. K. Perry, D. E. Shaw, P. Francis, and P. S. Shenkin. Glide: A new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. Journal of Medicinal Chemistry. 2004, 47(7): 1739-1749
56 G. Jones, P. Willett, R. C. Glen, A. R. Leach, and R. Taylor. Development and validation of a genetic algorithm for flexible docking. Journal of Molecular Biology. 1997, 267(3): 727-748
57 Z. Zsoldos, D. Reid, A. Simon, B. S. Sadjad, and A. P. Johnson. eHITS: An innovative approach to the docking and scoring function problems. Current Protein & Peptide Science. 2006, 7(5): 421-435
58 J. J. Gray. High-resolution protein-protein docking. Current Opinion in Structural Biology. 2006, 16(2): 183-193
59 V. Mohan, A. C. Gibbs, M. D. Cummings, E. P. Jaeger, and R. L. DesJarlais. Docking: Successes and challenges. Current Pharmaceutical Design. 2005, 11(3): 323-333
60 G. A. Petsko. Structure and mechanism in protein science: A guide to enzyme catalysis and protein folding. Nature. 1999, 401(6749): 115-116
61 T. Lazaridis and M. Karplus. ''New view'' of protein folding reconciled with the old through multiple unfolding simulations. Science. 1997, 278(5345): 1928-1931
62 F. L. Heppner, C. Musahl, I. Arrighi, M. A. Klein, T. Rulicke, B. Oesch, R. M. Zinkernagel, U. Kalinke, and A. Aguzzi. Prevention of scrapie pathogenesis by transgenic expression of anti-prion protein antibodies. Science. 2001, 294(5540): 178-182
63 M. F. Perutz, M. G. Rossmann, A. F. Cullis, G. Muirhead, G. Will, and T. North. Structure of hemoglobin: The three-dimensional fourier synthesis at 5.5 ? resolution, obtained by X-ray analysis. Nature. 1960, 185: 416-422
64 J. C. Kendrew, G. Bodo, H. M. Dintzis, H. Parrish, H. Wyckoff, and D. C. Phillips. A three-dimensional model of the myoglobin molecule obtained by X-ray analysis. Nature. 1958, 181: 662~666
65 T. Dandekar and R. Konig. Computational methods for the prediction of protein folds. Biochimica Et Biophysica Acta-Protein Structure and Molecular Enzymology. 1997, 1343(1): 1-15
66 J. Moult. The current state of the art in protein structure prediction. Current Opinion in Biotechnology. 1996, 7(4): 422-427
67 A. V. Finkelstein and O. V. Galzitskaya. Physics of protein folding. Physics of Life Reviews. 2004, 1(1): 23-56
68 C. B. Anfinsen. Principles that govern the folding of protein chains. Science. 1973, 181: 223-230
69 C. Levinthal. Are there pathways for protein folding? Journal of Chemical Physics. 1968, 65: 44-45
70 K. A. Dill and H. S. Chan. From Levinthal to pathways to funnels. Nature Structural Biology. 1997, 4(1): 10-19
71 P. G. Wolynes, J. N. Onuchic, and D. Thirumalai. Navigating the folding routes. Science. 1995, 267(5204): 1619-1620
72 J. D. Bryngelson, J. N. Onuchic, N. D. Socci, and P. G. Wolynes. Funnels, pathways, and the energy landscape of protein folding: A synthesis. Proteins-Structure Function and Genetics. 1995, 21(3): 167-195
73 K. Iguchi. A toy model for understanding the conceptual framework of protein folding: Rubik's magic snake model. Modern Physics Letters B. 1998, 12(13): 499-506
74 J. H. Werner, R. B. Dyer, R. M. Fesinmeyer, and N. H. Andersen. Dynamics of the primary processes of protein folding: Helix nucleation. Journal of Physical Chemistry B. 2002, 106(2): 487-494
75 R. H. Zhou, X. H. Huang, C. J. Margulis, and B. J. Berne. Hydrophobic collapse in multidomain protein folding. Science. 2004, 305(5690): 1605-1609
76 S. A. Islam, M. Karplus, and D. L. Weaver. The role of sequence and structure in protein folding kinetics: The diffusion-collision model applied to proteins L and G. Structure. 2004, 12(10): 1833-1845
77 R. L. Baldwin and G. D. Rose. Is protein folding hierarchic? II. Folding intermediates and transition states. Trends in Biochemical Sciences. 1999, 24(2): 77-83
78 P. S. Kim and R. L. Baldwin. Specific intermediates in the folding reactions of small proteins and the mechanism of protein folding. Annual Review of Biochemistry. 1982, 51: 459-489
79 C. M. Dobson, A. Sali, and M. Karplus. Protein folding: A perspective from theory and experiment. Angewandte Chemie-International Edition. 1998, 37(7): 868-893
80 L. W. Bergman and W. M. Kuehl. Formation of an intrachain disulfide bond on nascent immunoglobulin light chains. Journal of Biological Chemistry. 1979, 254(18): 8869-8876
81 J. M. Yon. Protein folding in the post-genomic era. Journal of Cellular and Molecular Medicine. 2002, 6(3): 307-327
82 M. Faloutsos, P. Faloutsos, and C. Faloutsos. On power-law relationships of the Internet topology. Acm Sigcomm'99 Conference: Applications, Technologies, Architectures, and Protocols for Computer Communications. 1999, 29(4): 251-262
83 R. Albert, H. Jeong, and A. L. Barabasi. Internet: Diameter of the World-Wide Web. Nature.1999, 401(6749): 130-131
84 A. L. Barabasi and E. Bonabeau. Scale-free networks. Scientific American. 2003, 288(5): 60-69
85 L. A. N. Amaral, A. Scala, M. Barthelemy, and H. E. Stanley. Classes of small-world networks. Proceedings of the National Academy of Sciences of the United States of America. 2000, 97(21): 11149-11152
86 F. J. Muller, L. C. Laurent, D. Kostka, I. Ulitsky, R. Williams, C. Lu, I. H. Park, M. S. Rao, R. Shamir, P. H. Schwartz, N. O. Schmidt, and J. F. Loring. Regulatory networks define phenotypic classes of human stem cell lines. Nature. 2008, 455(7211): 401-U455
87 M. E. J. Newman. Clustering and preferential attachment in growing networks. Physical Review E. 2001, 64(2): 025102
88 M. E. J. Newman. The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences of the United States of America. 2001, 98(2): 404-409
89 M. Karonski. A review of random graphs. Journal of Graph Theory. 1982, 6(4): 349-389
90 P. Erd?s and A. Rényi. On the evolution of random graphs. Publications of the Mathematical Institute of the Hungarian Academy of Sciences. 1960, 5: 17-61
91 S. Boccaletti, V. Latora, Y. Moreno, M. Chavez, and D. U. Hwang. Complex networks: Structure and dynamics. Physics Reports-Review Section of Physics Letters. 2006, 424(4-5): 175-308
92 D. J. Watts. The 'new' science of networks. Annual Review of Sociology 2004, 30: 243-270
93 S. H. Strogatz. Exploring complex networks. Nature. 2001, 410(6825): 268-276
94 D. J. Watts and S. H. Strogatz. Collective dynamics of 'small-world' networks. Nature. 1998, 393(6684): 440-442
95 M. E. J. Newman. Mixing patterns in networks. Physical Review E. 2003, 67(2): 026126
96 L. C. Freeman. Set of measures of centrality based on betweenness. Sociometry. 1977, 40(1): 35-41
97 M. E. J. Newman. Assortative mixing in networks. Physical Review Letters. 2002, 89(20): 208701
98 C. M. Song, S. Havlin, and H. A. Makse. Self-similarity of complex networks. Nature. 2005, 433(7024): 392-395
99 M. E. J. Newman and D. J. Watts. Renormalization group analysis of the small-world network model. Physics Letters A. 1999, 263(4-6): 341-346
100 A. L. Barabasi, R. Albert, and H. Jeong. Mean-field theory for scale-free random networks. Physica A. 1999, 272(1-2): 173-187
101 X. Li and G. R. Chen. A local-world evolving network model. Physica a-Statistical Mechanics and Its Applications. 2003, 328(1-2): 274-286
102 M. Vendruscolo, N. V. Dokholyan, E. Paci, and M. Karplus. Small-world view of the amino acids that play a key role in protein folding. Physical Review E. 2002, 65(6): 061910
103 N. V. Dokholyan, L. Li, F. Ding, and E. I. Shakhnovich. Topological determinants of protein folding. Proceedings of the National Academy of Sciences of the United States of America. 2002, 99(13): 8637-8641
104 A. R. Atilgan, P. Akan, and C. Baysal. Small-world communication of residues and significance for protein dynamics. Biophysical Journal. 2004, 86(1): 85-91
105 G. Amitai, A. Shemesh, E. Sitbon, M. Shklar, D. Netanely, I. Venger, and S. Pietrokovski. Network analysis of protein structures identifies functional residues. Journal of Molecular Biology. 2004, 344(4): 1135-1146
106 D. J. Jacobs, A. J. Rader, L. A. Kuhn, and M. F. Thorpe. Protein flexibility predictions using graph theory. Proteins-Structure Function and Genetics. 2001, 44(2): 150-165
107 A. del Sol, H. Fujihashi, D. Amoros, and R. Nussinov. Residues crucial for maintaining short paths in network communication mediate signaling in proteins. Molecular Systems Biology. 2006, 2: 0019
108 A. del Sol and P. O'Meara. Small-world network approach to identify key residues in protein-protein interaction. Proteins-Structure Function and Bioinformatics. 2005, 58(3): 672-682
109 A. del Sol, H. Fujihashi, and P. O'Meara. Topology of small-world networks of protein-protein complex structures. Bioinformatics. 2005, 21(8): 1311-1315
110 M. D. Daily, T. J. Upadhyaya, and J. J. Gray. Contact rearrangements form coupled networks from local motions in allosteric proteins. Proteins-Structure Function and Bioinformatics. 2008, 71(1): 455-466
111 F. Rao and A. Caflisch. The protein folding network. Journal of Molecular Biology. 2004,342(1): 299-306
112 J. P. K. Doye and C. P. Massen. Characterizing the network topology of the energy landscapes of atomic clusters. Journal of Chemical Physics. 2005, 122(8): 084105
113 A. Scala, L. A. N. Amaral, and M. Barthelemy. Small-world networks and the conformation space of a short lattice polymer chain. Europhysics Letters. 2001, 55(4): 594-600
114 A. C. Gavin, M. Bosche, R. Krause, P. Grandi, M. Marzioch, A. Bauer, J. Schultz, J. M. Rick, A. M. Michon, C. M. Cruciat, M. Remor, C. Hofert, M. Schelder, M. Brajenovic, H. Ruffner, A. Merino, K. Klein, M. Hudak, D. Dickson, T. Rudi, V. Gnau, A. Bauch, S. Bastuck, B. Huhse, C. Leutwein, M. A. Heurtier, R. R. Copley, A. Edelmann, E. Querfurth, V. Rybin, G. Drewes, M. Raida, T. Bouwmeester, P. Bork, B. Seraphin, B. Kuster, G. Neubauer, and G. Superti-Furga. Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature. 2002, 415(6868): 141-147
115 T. Maniatis and R. Reed. An extensive network of coupling among gene expression machines. Nature. 2002, 416(6880): 499-506
116 G. Frigerio, M. Burri, D. Bopp, S. Baumgartner, and M. Noll. Structure of the segmentation gene paired and the drosophila prd gene set as part of a gene network. Cell. 1986, 47(5): 735-746
117 C. Bode, I. A. Kovacs, M. S. Szalay, R. Palotai, T. Korcsmaros, and P. Csermely. Network analysis of protein dynamics. Febs Letters. 2007, 581(15): 2776-2782
118 A. V. Finkelstein and M. A. Roytberg. Computation of biopolymers: A general approach to different problems. Biosystems. 1993, 30(1-3): 1-19
119 R. M. Jackson. Comparison of protein-protein interactions in serine protease-inhibitor and antibody-antigen complexes: Implications for the protein docking problem. Protein Science. 1999, 8(3): 603-613
120 S. Vajda and C. J. Camacho. Protein-protein docking: Is the glass half-full or half-empty? Trends in Biotechnology. 2004, 22(3): 110-116
121 R. Chen, J. Mintseris, J. Janin, and Z. P. Weng. A protein-protein docking benchmark. Proteins-Structure Function and Genetics. 2003, 52(1): 88-91
122 J. Menetrey, M. Perderiset, J. Cicolari, T. Dubois, N. Elkhatib, F. El Khadali, M. Franco, P. Chavrier, and A. Houdusse. Structural basis for ARF1-mediated recruitment of ARHGAP21to Golgi membranes. Embo Journal. 2007, 26(7): 1953-1962
123 B. Kuhlman and D. Baker. Native protein sequences are close to optimal for their structures. Proceedings of the National Academy of Sciences of the United States of America. 2000, 97(19): 10383-10388
124 R. P. Bahadur, P. Chakrabarti, F. Rodier, and J. Janin. Dissecting subunit interfaces in homodimeric proteins. Proteins-Structure Function and Genetics. 2003, 53(3): 708-719
125 P. Chakrabarti and J. Janin. Dissecting protein-protein recognition sites. Proteins-Structure Function and Genetics. 2002, 47(3): 334-343
126 M. Nayal and B. Honig. On the nature of cavities on protein surfaces: Application to the identification of drug-binding sites. Proteins-Structure Function and Bioinformatics. 2006, 63(4): 892-906
127 T. A. Binkowski, A. Joachimiak, and J. Liang. Protein surface analysis for function annotation in high-throughput structural genomics pipeline. Protein Science. 2005, 14(12): 2972-2981
128 C. M. W. Ho and G. R. Marshall. Cavity search: An algorithm for the isolation and display of cavity-like binding regions. Journal of Computer-Aided Molecular Design. 1990, 4(4): 337-354
129 G. Nicola and I. A. Vakser. A simple shape characteristic of protein-protein recognition. Bioinformatics. 2007, 23(7): 789-792
130 J. Foote and A. Raman. A relation between the principal axes of inertia and ligand binding. Proceedings of the National Academy of Sciences of the United States of America. 2000, 97(3): 978-983
131 R. Norel, S. L. Lin, H. J. Wolfson, and R. Nussinov. Shape complementarity at protein-protein interfaces. Biopolymers. 1994, 34(7): 933-940
132 P. H. Walls and M. J. E. Sternberg. New algorithm to model protein-protein recognition based on surface complementarity. Applications to antibody-antigen docking. Journal of Molecular Biology. 1992, 228(1): 277-297
133 Y. Levy, S. S. Cho, J. N. Onuchic, and P. G. Wolynes. A survey of flexible protein binding mechanisms and their transition states using native topology based energy landscapes. Journal of Molecular Biology. 2005, 346(4): 1121-1145
134 Y. Levy, P. G. Wolynes, and J. N. Onuchic. Protein topology determines binding mechanism. Proceedings of the National Academy of Sciences of the United States of America. 2004, 101(2): 511-516
135 M. Aftabuddin and S. Kundu. Hydrophobic, hydrophilic, and charged amino acid networks within protein. Biophysical Journal. 2007, 93(1): 225-231
136 M. Aftabuddin and S. Kundu. Weighted and unweighted network of amino acids within protein. Physica a-Statistical Mechanics and Its Applications. 2006, 369(2): 895-904
137 S. Kundu. Amino acid network within protein. Physica a-Statistical Mechanics and Its Applications. 2005, 346(1-2): 104-109
138 J. Li, J. Wang, and W. Wang. Identifying folding nucleus based on residue contact networks of proteins. Proteins-Structure Function and Bioinformatics. 2008, 71(4): 1899-1907
139 X. Jiao, S. Chang, C. H. Li, W. Z. Chen, and C. X. Wang. Construction and application of the weighted amino acid network based on energy. Physical Review E. 2007, 75(5): 051903
140 K. V. Brinda and S. Vishveshwara. Oligomeric protein structure networks: insights into protein-protein interactions. BMC Bioinformatics. 2005, 6: 296-310
141 Z. J. Hu, D. Bowen, W. M. Southerland, A. del Sol, Y. P. Pan, R. Nussinov, and B. Y. Ma. Ligand binding and circular permutation modify residue interaction network in DHFR. Plos Computational Biology. 2007, 3(6): 1097-1107
142 J. Mintseris, K. Wiehe, B. Pierce, R. Anderson, R. Chen, J. Janin, and Z. P. Weng. Protein-protein docking benchmark 2.0: An update. Proteins-Structure Function and Bioinformatics. 2005, 60(2): 214-216
143 S. J. Hubbard and J. M. Thornton. NACCESS [computer program]. Department of Biochemistry and Molecular Biology, University College London. 1993:
144 S. Jones and J. M. Thornton. Principles of protein-protein interactions. Proceedings of the National Academy of Sciences of the United States of America. 1996, 93(1): 13-20
145 S. J. Sun, R. Brem, H. S. Chan, and K. A. Dill. Designing amino acid sequences to fold with good hydrophobic cores. Protein Engineering. 1995, 8(12): 1205-1213
146 L. H. Greene and V. A. Higman. Uncovering network systems within protein structures. Journal of Molecular Biology. 2003, 334(4): 781-791
147 R. Mendez, R. Leplae, L. De Maria, and S. J. Wodak. Assessment of blind predictions ofprotein-protein interactions: Current status of docking methods. Proteins-Structure Function and Genetics. 2003, 52(1): 51-67
148 C. J. Camacho, D. W. Gatchell, S. R. Kimura, and S. Vajda. Scoring docked conformations generated by rigid-body protein-protein docking. Proteins-Structure Function and Genetics. 2000, 40(3): 525-537
149 R. Mendez, R. Leplae, M. F. Lensink, and S. J. Wodak. Assessment of CAPRI predictions in rounds 3-5 shows progress in docking procedures. Proteins-Structure Function and Bioinformatics. 2005, 60(2): 150-169
150 J. Janin and C. Chothia. The structure of protein-protein recognition sites. Journal of Biological Chemistry. 1990, 265(27): 16027-16030
151 T. R. Gamble, F. F. Vajdos, S. H. Yoo, D. K. Worthylake, M. Houseweart, W. I. Sundquist, and C. P. Hill. Crystal structure of human cyclophilin A bound to the amino-terminal domain of HIV-1 capsid. Cell. 1996, 87(7): 1285-1294
152 A. Colorni, M. Dorigo, and V. Maniezzo. Distributed optimization by ant colonies. The First European conference on Artificial Life. 1991: 134-142
153 J. Kennedy and R. Eberhart. Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks. 1995, 4: 1942-1948
154 D. W. Walker and J. J. Dongarra. MPI: A standard message passing interface. Supercomputer. 1996, 12(1): 56-68
155 J. Dongarra, D. Walker, E. Lusk, B. Knighten, M. Snir, A. Geist, S. Otto, R. Hempel, E. Lusk, W. Gropp, J. Cownie, T. Skjellum, L. Clarke, R. Littlefield, M. Sears, S. Husslederman, E. Anderson, S. Berryman, J. Feeney, D. Frye, L. Hart, A. Ho, J. Kohl, P. Madams, C. Mosher, P. Pierce, E. Schikuta, R. G. Voigt, R. Babb, R. Bjornson, V. Fernando, I. Glendinning, T. Haupt, C. T. H. Ho, S. Krauss, A. Mainwaring, D. Nessett, S. Ranka, A. Singh, D. Weeks, J. Baron, N. Doss, S. Fineberg, A. Greenberg, D. Heller, G. Howell, B. Leary, O. McBryan, P. Pacheco, P. Rigsbee, A. Sussman, S. Wheat, E. Barszcz, A. Elster, J. Flower, R. Harrison, T. Henderson, J. Kapenga, A. Maccabe, P. McKinley, H. Palmer, A. Robison, R. Tomlinson, and S. Zenith. Special issue on mpi: A message-passing interface standard. International Journal of Supercomputer Applications and High Performance Computing. 1994, 8(3-4): 165-414
156陈国良.并行计算——结构·算法·编程.高等教育出版社. 1999:
157 S. J. Weiner, P. A. Kollman, D. T. Nguyen, and D. A. Case. An all atom force-field for simulations of proteins and nucleic-acids. Journal of Computational Chemistry. 1986, 7(2): 230-252
158 D. S. Goodsell and A. J. Olson. Automated docking of substrates to proteins by simulated annealing. Proteins-Structure Function and Genetics. 1990, 8(3): 195-202
159 C. Perez and A. R. Ortiz. Evaluation of docking functions for protein-ligand docking. Journal of Medicinal Chemistry. 2001, 44(23): 3768-3785
160 B. G. Turner and M. F. Summers. Structural biology of HIV. Journal of Molecular Biology. 1999, 285(1): 1-32
161 K. V. Brinda and S. Vishveshwara. A network representation of protein structures: Implications for protein stability. Biophysical Journal. 2005, 89(6): 4159-4170
162 M. M. Gromiha and S. Selvaraj. Inter-residue interactions in protein folding and stability. Progress in Biophysics & Molecular Biology. 2004, 86(2): 235-277
163 A. L. Barabasi and R. Albert. Emergence of scaling in random networks. Science. 1999, 286(5439): 509-512
164 E. Shakhnovich. Protein folding thermodynamics and dynamics: Where physics, chemistry, and biology meet. Chemical Reviews. 2006, 106(5): 1559-1588
165 K. W. Plaxco, K. T. Simons, and D. Baker. Contact order, transition state placement and the refolding rates of single domain proteins. Journal of Molecular Biology. 1998, 277(4): 985-994
166 K. A. Dill. Theory for the folding and stability of globular proteins. Biochemistry. 1985, 24(6): 1501-1509
167 O. B. Ptitsyn and A. A. Rashin. Stagewise mechanism of protein folding. Doklady Akademii Nauk SSSR. 1973, 213: 473-475
168 L. Mirny and E. Shakhnovich. Protein folding theory: From lattice to all-atom models. Annual Review of Biophysics and Biomolecular Structure. 2001, 30: 361-396
169 J. N. Onuchic, H. Nymeyer, A. E. Garcia, J. Chahine, and N. D. Socci. The energy landscape theory of protein folding: Insights into folding mechanisms and scenarios. Advances in Protein Chemistry. 2000, 53: 87-152
170 J. N. Onuchic, Z. LutheySchulten, and P. G. Wolynes. Theory of protein folding: The energy landscape perspective. Annual Review of Physical Chemistry. 1997, 48: 545-600
171 D. Baker. A surprising simplicity to protein folding. Nature. 2000, 405(6782): 39-42
172 D. E. Makarov and K. W. Plaxco. The topomer search model: A simple, quantitative theory of two-state protein folding kinetics. Protein Science. 2003, 12(1): 17-26
173 S. Miyazawa and R. L. Jernigan. Residue-residue potentials with a favorable contact pair term and an unfavorable high packing density term, for simulation and threading. Journal of Molecular Biology. 1996, 256(3): 623-644
174 Z. Gugolya, Z. Dosztanyi, and I. Simon. Interresidue interactions in protein classes. Proteins-Structure Function and Genetics. 1997, 27(3): 360-366
175 V. I. Abkevich, A. M. Gutin, and E. I. Shakhnovich. Specific nucleus as the transition state for protein folding: Evidence from the lattice model. Biochemistry. 1994, 33(33): 10026-10036
176 S. H. White and R. E. Jacobs. Nucleation, rapid folding, and globular intrachain regions in proteins. . Proceedings of the National Academy of Sciences of the United States of America. 1973, 70(3): 697-701
177 A. Y. Istomin, D. J. Jacobs, and D. R. Livesay. On the role of structural class of a protein with two-state folding kinetics in determining correlations between its size, topology, and folding rate. Protein Science. 2007, 16(11): 2564-2569
178 M. E. J. Newman. The structure and function of complex networks. Siam Review. 2003, 45(2): 167-256
179 S. N. Dorogovtsev and J. F. F. Mendes. Evolution of networks. Advances in Physics. 2002, 51(4): 1079-1187
180 S. A. Teichmann, A. G. Murzin, and C. Chothia. Determination of protein function, evolution and interactions by structural genomics. Current Opinion in Structural Biology. 2001, 11(3): 354-363
181 A. Gutteridge, G. J. Bartlett, and J. M. Thornton. Using a neural network and spatial clustering to predict the location of active sites in enzymes. Journal of Molecular Biology. 2003, 330(4): 719-734
182 S. Jones and J. M. Thornton. Analysis of protein-protein interaction sites using surfacepatches. Journal of Molecular Biology. 1997, 272(1): 121-132
183 C. Chothia and J. Janin. Principles of protein-protein recognition. Nature. 1975, 256(5520): 705-708
184 O. Lichtarge, H. R. Bourne, and F. E. Cohen. An evolutionary trace method defines binding surfaces common to protein families. Journal of Molecular Biology. 1996, 257(2): 342-358
185 H. Liao, W. Yeh, D. Chiang, R. L. Jernigan, and B. Lustig. Protein sequence entropy is closely related to packing density and hydrophobicity. Protein Engineering Design & Selection. 2005, 18(2): 59-64
186 L. A. Mirny and E. I. Shakhnovich. Universally conserved positions in protein folds: Reading evolutionary signals about stability, folding kinetics and function. Journal of Molecular Biology. 1999, 291(1): 177-196
187 M. Landau, I. Mayrose, Y. Rosenberg, F. Glaser, E. Martz, T. Pupko, and N. Ben-Tal. ConSurf 2005: The projection of evolutionary conservation scores of residues on protein structures. Nucleic Acids Research. 2005, 33: W299-W302