计算方法在蛋白-蛋白相互作用小分子调节剂研究发现中的应用
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摘要
蛋白-蛋白相互作用(protein-protein interactions, PPIs)涉及各个重要的生物通路以及生理过程,作为一类新的药物靶标,近年来得到了学术界和工业界的广泛关注。经过多年的研究,人们更好地了解了PPIs界面的特点,也有小分子抑制剂成功进入临床研究乃至上市,其中计算机辅助的合理设计方法功不可没。本文综述了计算方法应用在PPIs热点残基预测、PPIs小分子调节剂的虚拟筛选策略方面的一些成功案例。但以现有的计算方法研究PPIs仍有诸多挑战。针对PPIs作用特点的计算方法以及在PPIs筛选中更多地考虑蛋白柔性将是今后研究的发展方向。
Protein-protein interactions(PPIs) are of utmost importance and are implicated in almost all signaling pathways and biological processes, and have received extensive attention from academia and industry as a new class of drug target. Over years of research, computer-aided rational design has facilitated the characterization of the protein-protein interfaces and the discovery and development of small-molecule inhibitors, some of which have entered clinical research and market. This review summarizes some computational approaches applied in prediction of hot spots in protein-protein interfaces and virtual screening strategies against PPIs. As protein-protein interaction prediction is still very challenging for current computational approaches, more sophisticated and specific computational approaches considering protein flexibility for proteinprotein interactions are necessary and paramount for drug discovery of PPIs.
Protein-protein interactions(PPIs) are of utmost importance and are implicated in almost all signaling pathways and biological processes, and have received extensive attention from academia and industry as a new class of drug target. Over years of research, computer-aided rational design has facilitated the characterization of the protein-protein interfaces and the discovery and development of small-molecule inhibitors, some of which have entered clinical research and market. This review summarizes some computational approaches applied in prediction of hot spots in protein-protein interfaces and virtual screening strategies against PPIs. As protein-protein interaction prediction is still very challenging for current computational approaches, more sophisticated and specific computational approaches considering protein flexibility for proteinprotein interactions are necessary and paramount for drug discovery of PPIs.
引文
[1]Drews J. Genomic sciences and the medicine of tomorrow. Nat Biotechnol, 1996, 14(11):1516-1518
[2]Drews J, Ryser S. The role of innovation in drug development. Nat Biotechnol, 1997, 15(13):1318-1319
[3]Hopkins AL,GroomCR. Thedruggablegenome.Nat Rev Drug Discov, 2002, 1(9):727-730
[4]WellsJA,McClendonCL.Reachingforhigh-hanging fruitindrugdiscoveryatprotein-proteininterfaces.Nature, 2007, 450(7172):1001-1009
[5]Cheng AC,ColemanRG,SmythKT,etal.Structurebasedmaximalaffinitymodelpredictssmall-molecule druggability. Nat Biotechnol, 2007, 25(1):71-75
[6]Huang N, Jacobson MP. Binding-site assessment by virtual fragment screening. PLoS One, 2010, 5(4):e10109
[7]Bogan AA, Thorn KS. Anatomy of hot spots in protein interfaces. J Mol Biol, 1998, 280(1):1-9
[8]Clackson T, Wells JA. A hot spot of binding energy in a hormone-receptorinterface.Science,1995,267(5196):383-386
[9]ChoKI,KimD,LeeD. Afeature-basedapproachto modeling protein-protein interaction hot spots. Nucleic Acids Res, 2009, 37(8):2672-2687
[10]Darnell SJ, Page D, Mitchell JC. An automated decisiontree approach to predicting protein interaction hot spots.Proteins, 2007, 68(4):813-823
[11]del Sol A, O'Meara P. Small-world network approach to identify key residues in protein-protein interaction. Proteins, 2005, 58(3):672-682
[12]GrosdidierS,Fernandez-RecioJ.Identificationofhotspot residues in protein-protein interactions by computational docking. BMC Bioinformatics, 2008, 9:447
[13]Guerois R, Nielsen JE, Serrano L. Predicting changes in the stability of proteins and protein complexes:a study of more than 1000 mutations. J Mol Biol, 2002, 320(2):369-387
[14]Huo S, Massova I, Kollman PA. Computational alanine scanningofthe1:1humangrowthhormone-receptor complex. J Comput Chem, 2002, 23(1):15-27
[15]Kortemme T, Baker D. A simple physical model for binding energy hot spots in protein-protein complexes. Proc Natl Acad Sci USA, 2002, 99(22):14116-14121
[16]Li J, Liu Q.'Double water exclusion':a hypothesis refining the O-ring theory for the hot spots at protein interfaces.Bioinformatics, 2009, 25(6):743-750
[17]MoreiraIS,FernandesPA,RamosMJ.Computational alanine scanning mutagenesis--an improved methodological approach. J Comput Chem, 2007, 28(3):644-654
[18]Ofran Y,RostB.Protein-proteininteractionhotspots carved into sequences. PLoS Comput Biol, 2007, 3(7):e119
[19]Tuncbag N, Gursoy A, Keskin O. Identification of computational hot spots in protein interfaces:combining solvent accessibility and inter-residue potentials improves the accuracy. Bioinformatics, 2009, 25(12):1513-1520
[20]Kortemme T, Kim DE, Baker D. Computational alanine scanning of protein-protein interfaces. Sci STKE, 2004,2004(219):pl2
[21]Kim DE, Chivian D, Baker D. Protein structure prediction and analysis using the Robetta server. Nucleic Acids Res, 2004, 32(Web Server issue):W526-531
[22]Gao Y,WangR,LaiL.Structure-basedmethodfor analyzing protein-protein interfaces. J Mol Model, 2004,10(1):44-54
[23]Brenke R, Kozakov D, Chuang GY, et al. Fragment-based identification of druggable'hot spots'of proteins using Fourierdomaincorrelationtechniques.Bioinformatics,2009, 25(5):621-627
[24]Guharoy M, Chakrabarti P. Empirical estimation of the energetic contribution of individual interface residues in structures of protein-protein complexes. J Comput Aided Mol Des, 2009, 23(9):645-654
[25]Yogurtcu ON, Erdemli SB, Nussinov R, et al. Restricted mobility of conserved residues in protein-protein interfaces in molecular simulations. Biophys J, 2008, 94(9):3475-3485
[26]Assi SA, Tanaka T, Rabbitts TH, et al. PCRPi:Presaging Critical Residues in Protein interfaces, a new computational tool to chart hot spots in protein interfaces. Nucleic Acids Res, 2010, 38(6):e86
[27]Lise S, Archambeau C, Pontil M, et al. Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods. BMC Bioinformatics, 2009, 10:365
[28]Lise S, Buchan D, Pontil M, et al. Predictions of hot spot residues at protein-protein interfaces using support vector machines. PLoS One, 2011, 6(2):e16774
[29]Koes DR, Camacho CJ. Small-molecule inhibitor starting points learned from protein-protein interaction inhibitor structure. Bioinformatics, 2012, 28(6):784-791
[30]Zhu X, Mitchell JC. KFC2:a knowledge-based hot spot prediction method based on interface solvation, atomic density,andplasticityfeatures.Proteins,2011,79(9):2671-2683
[31]Wang L, Liu ZP, Zhang XS, et al. Prediction of hot spots inproteininterfacesusingarandomforestmodelwith hybrid features. Protein Eng Des Sel, 2012, 25(3):119-126
[32]Qiao Y, Xiong Y, Gao H, et al. Protein-protein interface hot spots prediction based on a hybrid feature selection strategy. BMC Bioinformatics, 2018, 19(1):14
[33]TuncbagN,SalmanFS,KeskinO,etal. Analysisand network representation of hotspots in protein interfaces using minimum cut trees. Proteins, 2010, 78(10):2283-2294
[34]Ozbek P, Soner S, Haliloglu T. Hot spots in a network of functional sites. PLoS One, 2013, 8(9):e74320
[35]Wolber G, Langer T. LigandScout:3-D pharmacophores derivedfromprotein-boundligandsandtheiruseas virtual screening filters. J Chem Inf Model, 2005, 45(1):160-169
[36]Brenk R, Naerum L, Gradler U, et al. Virtual screening for submicromolar leads of tRNA-guanine transglycosylase based on a new unexpected binding mode detected by crystal structure analysis. J Med Chem, 2003, 46(7):1133-1143
[37]Ahlstrom MM, Ridderstrom M, Luthman K, et al. Virtual screening and scaffold hopping based on GRID molecular interaction fields. J Chem Inf Model, 2005, 45(5):1313-1323
[38]CarlsonHA,MasukawaKM,RubinsK,etal.Developing a dynamic pharmacophore model for HIV-1 integrase. J Med Chem, 2000, 43(11):2100-2114
[39]BarillariC,MarcouG,RognanD.Hot-spots-guided receptor-basedpharmacophores(HS-Pharm):aknowledge-based approach to identify ligand-anchoring atoms in protein cavities and prioritize structure-based pharmacophores. J Chem Inf Model, 2008, 48(7):1396-1410
[40]CaporuscioF,Tafi A,GonzalezE,etal. Adynamic target-basedpharmacophoricmodelmappingtheCD4binding site on HIV-1 gp120 to identify new inhibitors of gp120-CD4 protein-protein interactions. Bioorg Med Chem Lett, 2009, 19(21):6087-6091
[41]Tuffery P, Derreumaux P. Flexibility and binding affinity inprotein-ligand,protein-proteinandmulti-component protein interactions:limitations of current computational approaches. J R Soc Interface, 2012, 9(66):20-33
[42]Mukherjee P, Desai P, Zhou YD, et al. Targeting the BH3domainmediatedprotein-proteininteractionofBclxL through virtual screening. J Chem Inf Model, 2010,50(5):906-923
[43]Jones G, Willett P, Glen RC, et al. Development and validation of a genetic algorithm for flexible docking. J Mol Biol, 1997, 267(3):727-748
[44]BillardC.BH3mimetics:statusofthefieldandnew developments. Mol Cancer Ther, 2013, 12(9):1691-1700
[45]KortagereS, Welsh WJ,MorriseyJM,etal.Structurebased design of novel small-molecule inhibitors of Plasmodiumfalciparum.JChemInfModel,2010,50(5):840-849
[46]Corradi V,ManciniM,ManettiF,etal.Identification of the first non-peptidic small molecule inhibitor of the c-Abl/14-3-3protein-proteininteractionsabletodrive sensitive and Imatinib-resistant leukemia cells to apoptosis.Bioorg Med Chem Lett, 2010, 20(20):6133-6137
[47]Geppert T, Bauer S, Hiss JA, et al. Immunosuppressive smallmoleculediscoveredbystructure-basedvirtual screeningforinhibitorsofprotein-proteininteractions.Angew Chem Int Ed Engl, 2012, 51(1):258-261
[48]Joce C, Stahl JA, Shridhar M, et al. Application of a novel insilicohigh-throughputscreentoidentifyselective inhibitorsforprotein-proteininteractions.BioorgMed Chem Lett, 2010, 20(18):5411-5413
[49]Khanna M, Wang F, Jo I, et al. Targeting multiple conformations leads to small molecule inhibitors of the uPAR.uPAprotein-proteininteractionthatblockcancercell invasion. ACS Chem Biol, 2011, 6(11):1232-1243
[50]VerdonkML,Berdini V,HartshornMJ,etal. Virtual screening using protein-ligand docking:avoiding artificialenrichment.JChemInfComputSci,2004,44(3):793-806
[51]Friesner RA, Murphy RB, Repasky MP, et al. Extra precision glide:docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. J Med Chem, 2006, 49(21):6177-6196
[52]Sherman W, Day T, Jacobson MP, et al. Novel procedure for modeling ligand/receptor induced fit effects. J Med Chem, 2006, 49(2):534-553
[53]Higueruelo AP, Jubb H,Blundell TL. TIMBAL v2:update of a database holding small molecules modulating protein-protein interactions. Database(Oxford), 2013, 2013:bat039
[54]Labbe CM, Kuenemann MA, Zarzycka B, et al. iPPI-DB:an online database of modulators of protein-protein interactions. Nucleic Acids Res, 2016, 44(D1):D542-547
[55]Basse MJ, Betzi S, Morelli X, et al. 2P2Idb v2:update of a structural database dedicated to orthosteric modulation of protein-protein interactions. Database(Oxford), 2016,2016:baw007
[2]Drews J, Ryser S. The role of innovation in drug development. Nat Biotechnol, 1997, 15(13):1318-1319
[3]Hopkins AL,GroomCR. Thedruggablegenome.Nat Rev Drug Discov, 2002, 1(9):727-730
[4]WellsJA,McClendonCL.Reachingforhigh-hanging fruitindrugdiscoveryatprotein-proteininterfaces.Nature, 2007, 450(7172):1001-1009
[5]Cheng AC,ColemanRG,SmythKT,etal.Structurebasedmaximalaffinitymodelpredictssmall-molecule druggability. Nat Biotechnol, 2007, 25(1):71-75
[6]Huang N, Jacobson MP. Binding-site assessment by virtual fragment screening. PLoS One, 2010, 5(4):e10109
[7]Bogan AA, Thorn KS. Anatomy of hot spots in protein interfaces. J Mol Biol, 1998, 280(1):1-9
[8]Clackson T, Wells JA. A hot spot of binding energy in a hormone-receptorinterface.Science,1995,267(5196):383-386
[9]ChoKI,KimD,LeeD. Afeature-basedapproachto modeling protein-protein interaction hot spots. Nucleic Acids Res, 2009, 37(8):2672-2687
[10]Darnell SJ, Page D, Mitchell JC. An automated decisiontree approach to predicting protein interaction hot spots.Proteins, 2007, 68(4):813-823
[11]del Sol A, O'Meara P. Small-world network approach to identify key residues in protein-protein interaction. Proteins, 2005, 58(3):672-682
[12]GrosdidierS,Fernandez-RecioJ.Identificationofhotspot residues in protein-protein interactions by computational docking. BMC Bioinformatics, 2008, 9:447
[13]Guerois R, Nielsen JE, Serrano L. Predicting changes in the stability of proteins and protein complexes:a study of more than 1000 mutations. J Mol Biol, 2002, 320(2):369-387
[14]Huo S, Massova I, Kollman PA. Computational alanine scanningofthe1:1humangrowthhormone-receptor complex. J Comput Chem, 2002, 23(1):15-27
[15]Kortemme T, Baker D. A simple physical model for binding energy hot spots in protein-protein complexes. Proc Natl Acad Sci USA, 2002, 99(22):14116-14121
[16]Li J, Liu Q.'Double water exclusion':a hypothesis refining the O-ring theory for the hot spots at protein interfaces.Bioinformatics, 2009, 25(6):743-750
[17]MoreiraIS,FernandesPA,RamosMJ.Computational alanine scanning mutagenesis--an improved methodological approach. J Comput Chem, 2007, 28(3):644-654
[18]Ofran Y,RostB.Protein-proteininteractionhotspots carved into sequences. PLoS Comput Biol, 2007, 3(7):e119
[19]Tuncbag N, Gursoy A, Keskin O. Identification of computational hot spots in protein interfaces:combining solvent accessibility and inter-residue potentials improves the accuracy. Bioinformatics, 2009, 25(12):1513-1520
[20]Kortemme T, Kim DE, Baker D. Computational alanine scanning of protein-protein interfaces. Sci STKE, 2004,2004(219):pl2
[21]Kim DE, Chivian D, Baker D. Protein structure prediction and analysis using the Robetta server. Nucleic Acids Res, 2004, 32(Web Server issue):W526-531
[22]Gao Y,WangR,LaiL.Structure-basedmethodfor analyzing protein-protein interfaces. J Mol Model, 2004,10(1):44-54
[23]Brenke R, Kozakov D, Chuang GY, et al. Fragment-based identification of druggable'hot spots'of proteins using Fourierdomaincorrelationtechniques.Bioinformatics,2009, 25(5):621-627
[24]Guharoy M, Chakrabarti P. Empirical estimation of the energetic contribution of individual interface residues in structures of protein-protein complexes. J Comput Aided Mol Des, 2009, 23(9):645-654
[25]Yogurtcu ON, Erdemli SB, Nussinov R, et al. Restricted mobility of conserved residues in protein-protein interfaces in molecular simulations. Biophys J, 2008, 94(9):3475-3485
[26]Assi SA, Tanaka T, Rabbitts TH, et al. PCRPi:Presaging Critical Residues in Protein interfaces, a new computational tool to chart hot spots in protein interfaces. Nucleic Acids Res, 2010, 38(6):e86
[27]Lise S, Archambeau C, Pontil M, et al. Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods. BMC Bioinformatics, 2009, 10:365
[28]Lise S, Buchan D, Pontil M, et al. Predictions of hot spot residues at protein-protein interfaces using support vector machines. PLoS One, 2011, 6(2):e16774
[29]Koes DR, Camacho CJ. Small-molecule inhibitor starting points learned from protein-protein interaction inhibitor structure. Bioinformatics, 2012, 28(6):784-791
[30]Zhu X, Mitchell JC. KFC2:a knowledge-based hot spot prediction method based on interface solvation, atomic density,andplasticityfeatures.Proteins,2011,79(9):2671-2683
[31]Wang L, Liu ZP, Zhang XS, et al. Prediction of hot spots inproteininterfacesusingarandomforestmodelwith hybrid features. Protein Eng Des Sel, 2012, 25(3):119-126
[32]Qiao Y, Xiong Y, Gao H, et al. Protein-protein interface hot spots prediction based on a hybrid feature selection strategy. BMC Bioinformatics, 2018, 19(1):14
[33]TuncbagN,SalmanFS,KeskinO,etal. Analysisand network representation of hotspots in protein interfaces using minimum cut trees. Proteins, 2010, 78(10):2283-2294
[34]Ozbek P, Soner S, Haliloglu T. Hot spots in a network of functional sites. PLoS One, 2013, 8(9):e74320
[35]Wolber G, Langer T. LigandScout:3-D pharmacophores derivedfromprotein-boundligandsandtheiruseas virtual screening filters. J Chem Inf Model, 2005, 45(1):160-169
[36]Brenk R, Naerum L, Gradler U, et al. Virtual screening for submicromolar leads of tRNA-guanine transglycosylase based on a new unexpected binding mode detected by crystal structure analysis. J Med Chem, 2003, 46(7):1133-1143
[37]Ahlstrom MM, Ridderstrom M, Luthman K, et al. Virtual screening and scaffold hopping based on GRID molecular interaction fields. J Chem Inf Model, 2005, 45(5):1313-1323
[38]CarlsonHA,MasukawaKM,RubinsK,etal.Developing a dynamic pharmacophore model for HIV-1 integrase. J Med Chem, 2000, 43(11):2100-2114
[39]BarillariC,MarcouG,RognanD.Hot-spots-guided receptor-basedpharmacophores(HS-Pharm):aknowledge-based approach to identify ligand-anchoring atoms in protein cavities and prioritize structure-based pharmacophores. J Chem Inf Model, 2008, 48(7):1396-1410
[40]CaporuscioF,Tafi A,GonzalezE,etal. Adynamic target-basedpharmacophoricmodelmappingtheCD4binding site on HIV-1 gp120 to identify new inhibitors of gp120-CD4 protein-protein interactions. Bioorg Med Chem Lett, 2009, 19(21):6087-6091
[41]Tuffery P, Derreumaux P. Flexibility and binding affinity inprotein-ligand,protein-proteinandmulti-component protein interactions:limitations of current computational approaches. J R Soc Interface, 2012, 9(66):20-33
[42]Mukherjee P, Desai P, Zhou YD, et al. Targeting the BH3domainmediatedprotein-proteininteractionofBclxL through virtual screening. J Chem Inf Model, 2010,50(5):906-923
[43]Jones G, Willett P, Glen RC, et al. Development and validation of a genetic algorithm for flexible docking. J Mol Biol, 1997, 267(3):727-748
[44]BillardC.BH3mimetics:statusofthefieldandnew developments. Mol Cancer Ther, 2013, 12(9):1691-1700
[45]KortagereS, Welsh WJ,MorriseyJM,etal.Structurebased design of novel small-molecule inhibitors of Plasmodiumfalciparum.JChemInfModel,2010,50(5):840-849
[46]Corradi V,ManciniM,ManettiF,etal.Identification of the first non-peptidic small molecule inhibitor of the c-Abl/14-3-3protein-proteininteractionsabletodrive sensitive and Imatinib-resistant leukemia cells to apoptosis.Bioorg Med Chem Lett, 2010, 20(20):6133-6137
[47]Geppert T, Bauer S, Hiss JA, et al. Immunosuppressive smallmoleculediscoveredbystructure-basedvirtual screeningforinhibitorsofprotein-proteininteractions.Angew Chem Int Ed Engl, 2012, 51(1):258-261
[48]Joce C, Stahl JA, Shridhar M, et al. Application of a novel insilicohigh-throughputscreentoidentifyselective inhibitorsforprotein-proteininteractions.BioorgMed Chem Lett, 2010, 20(18):5411-5413
[49]Khanna M, Wang F, Jo I, et al. Targeting multiple conformations leads to small molecule inhibitors of the uPAR.uPAprotein-proteininteractionthatblockcancercell invasion. ACS Chem Biol, 2011, 6(11):1232-1243
[50]VerdonkML,Berdini V,HartshornMJ,etal. Virtual screening using protein-ligand docking:avoiding artificialenrichment.JChemInfComputSci,2004,44(3):793-806
[51]Friesner RA, Murphy RB, Repasky MP, et al. Extra precision glide:docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. J Med Chem, 2006, 49(21):6177-6196
[52]Sherman W, Day T, Jacobson MP, et al. Novel procedure for modeling ligand/receptor induced fit effects. J Med Chem, 2006, 49(2):534-553
[53]Higueruelo AP, Jubb H,Blundell TL. TIMBAL v2:update of a database holding small molecules modulating protein-protein interactions. Database(Oxford), 2013, 2013:bat039
[54]Labbe CM, Kuenemann MA, Zarzycka B, et al. iPPI-DB:an online database of modulators of protein-protein interactions. Nucleic Acids Res, 2016, 44(D1):D542-547
[55]Basse MJ, Betzi S, Morelli X, et al. 2P2Idb v2:update of a structural database dedicated to orthosteric modulation of protein-protein interactions. Database(Oxford), 2016,2016:baw007