Rational Discovery of GSK3-Beta Modulators Aided by Protein Pocket Prediction and High-Throughput Molecular Docking

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• 关键词：Computational biology ; Drug discovery ; GSK3 ; beta ; Virtual screening ; Protein binding pocket prediction
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9876
• 期：1
• 页码：429-439
• 全文大小：1,018 KB
• 参考文献：1.Sliwoski, G., Kothiwale, S., Meiler, J., Lowe Jr., E.W.: Computational methods in drug discovery. Pharmacol. Rev. 66, 334–395 (2014)CrossRef
  2.Dolezal, R., Sobeslav, V., Hornig, O., Balik, L., Korabecny, J., Kuca, K.: HPC cloud technologies for virtual screening in drug discovery. In: Nguyen, N.T., Trawiński, B., Kosala, R. (eds.) ACIIDS 2015. LNCS, vol. 9012, pp. 440–449. Springer, Heidelberg (2015)
  3.Kubinyi, H.: Success Stories of Computer-Aided Design. Computer Applications in Pharmaceutical Research and Development. John Wiley & Sons Inc., New York (2006)CrossRef
  4.Lyne, P.D.: Structure-based virtual screening: an overview. Drug. Discov. Today 7, 1047–1055 (2002)CrossRef
  5.Dolezal, R., Ramalho, T.C., França, T.C., Kuca, K.: Parallel flexible molecular docking in computational chemistry on high performance computing clusters. In: Núñez, M., et al. (eds.) ICCCI 2015. LNCS, vol. 9330, pp. 418–427. Springer, Heidelberg (2015). doi:10.​1007/​978-3-319-24306-1_​41 CrossRef
  6.Campbell, S.J., Gold, N.D., Jackson, R.M., Westhead, D.R.: Ligand binding: functional site location, similarity and docking. Curr. Opin. Struct. Biol. 13, 389–395 (2003)CrossRef
  7.Laurie, A.T., Jackson, R.M.: Methods for the prediction of protein-ligand binding sites for structure-based drug design and virtual ligand screening. Curr. Protein Pept. Sci. 7, 395–406 (2006)CrossRef
  8.Le Guilloux, V., Schmidtke, P., Tuffery, P.: Fpocket: an open source platform for ligand pocket detection. BMC Bioinform. 10, 168 (2009)CrossRef
  9.Labute, P., Santavy, M.: Locating binding sites in protein structures. J. Chem. Comput. Group (2007)
  10.Palomo, V., Soteras, I., Perez, D.I., Perez, C., Gil, C., Campillo, N.E., Martinez, A.: Exploring the binding sites of glycogen synthase kinase 3. Identification and characterization of allosteric modulation cavities. J. Med. Chem. 54, 8461–8470 (2011)CrossRef
  
• 作者单位：Rafael Dolezal (17) (18)
  Michaela Melikova (17) (18)
  Jakub Mesicek (17) (18)
  Kamil Kuca (17) (18)
  
  17. Center for Basic and Applied Research, Faculty of Informatics and Management, University of Hradec Kralove, Rokitanskeho 62, 50003, Hradec Kralove, Czech Republic
  18. Biomedical Research Center, University Hospital Hradec Kralove, Sokolska 581, 500 05, Hradec Kralove, Czech Republic
  
• 丛书名：Computational Collective Intelligence
• ISBN：978-3-319-45246-3
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9876

文摘

Over the last three decades, computer-aided drug design (CADD) methods have attracted increasing attention of medicinal chemists especially due to their potential to penetrate into the molecular level of drugs’ mechanism of action. So far, CADD techniques have significantly contributed to rational development of more than two hundred novel drugs. Brute force of supercomputers has enabled chemists to screen virtual ligand libraries of millions of chemical structures in affordable time span while indicating which compounds should be prioritized in further preclinical research and which can be eliminated a priori. A prominent position in CADD is held by structure-based methods that analyze 3D structures of biological targets to find optimal small binding molecules modulating the target’s bioactivity. In the current work, we have performed a protein binding pocket screening on an X-ray model of human Glycogen Synthase Kinase 3 beta (GSK3β) employing an algorithm based on Voronoi tessellation. The found binding sites were analyzed and compared with the results of surface screening of the GSK3β model by molecular docking based calculations. Finally, the revealed binding sites were exploited in a structure-based virtual screening supported by pleasingly parallelized calculations on a peta-flops-scale supercomputer. The most promising GSK3β modulators resulting from the in silico screening have been proposed for in vitro testing.