Computational Modeling of β-Secretase 1 (BACE-1) Inhibitors Using Ligand Based Approaches

Journal of Chemical Information and Modeling

Volumn 56, Issue 10, 2016, Pages 1936-1949

  Subramanian, Govindan ^a   Ramsundar, Bharath ^b   Pande, Vijay ^c   Denny, Rajiah Aldrin ^d  

^a ZOETIS   (United States)

^b Stanford University   (United States)

^c STANFORD UNIVERSITY   (United States)

^d PFIZER INC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BINDING ENERGY; COMPUTATIONAL CHEMISTRY; DEEP LEARNING; DEEP NEURAL NETWORKS; LIGANDS; MOLECULAR GRAPHICS; STATISTICAL TESTS;

MACHINE LEARNING APPROACHES; MACHINE LEARNING METHODS; MOLECULAR FINGERPRINT; QUALITATIVE CLASSIFICATION; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS; SCIENTIFIC LITERATURE; STATISTICAL TECHNIQUES; TOPOLOGICAL DESCRIPTORS;

LEARNING SYSTEMS;

ASPARTIC PROTEINASE; BACE1 PROTEIN, HUMAN; LIGAND; MOLECULAR LIBRARY; SECRETASE;

ANTAGONISTS AND INHIBITORS; ARTIFICIAL NEURAL NETWORK; CHEMISTRY; COMPUTER SIMULATION; DRUG DEVELOPMENT; HUMAN; MACHINE LEARNING; METABOLISM; MOLECULAR LIBRARY; MOLECULAR MODEL; PHARMACOLOGY; PROCEDURES; QUANTITATIVE STRUCTURE ACTIVITY RELATION;

AMYLOID PRECURSOR PROTEIN SECRETASES; ASPARTIC ACID ENDOPEPTIDASES; COMPUTER SIMULATION; DRUG DISCOVERY; HUMANS; LIGANDS; MACHINE LEARNING; MODELS, MOLECULAR; NEURAL NETWORKS (COMPUTER); QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; SMALL MOLECULE LIBRARIES;

EID: 84992694543     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/acs.jcim.6b00290     Document Type: Article

References (85)

1. Sliwoski, G.; Kothiwale, S.; Meiler, J.; Lowe, E. W., Jr. Computational Methods in Drug Discovery Pharmacol. Rev. 2014, 66, 334-395 10.1124/pr.112.007336
2. Ekins, S.; Mestres, J.; Testa, B. In silico Pharmacology for Drug Discovery: Methods for Virtual Ligand Screening and Profiling Br. J. Pharmacol. 2007, 152, 9-20 10.1038/sj.bjp.0707305
3. Ekins, S.; Mestres, J.; Testa, B. In silico Pharmacology for Drug Discovery: Applications to Targets and Beyond Br. J. Pharmacol. 2007, 152, 21-37 10.1038/sj.bjp.0707306
4. Macalino, S. J. Y.; Gosu, V.; Hong, S.; Choi, S. Role of Computer-aided Drug Design in Modern Drug Discovery Arch. Pharmacal Res. 2015, 38, 1686-1702 10.1007/s12272-015-0640-5
5. Anderson, A. C. The Process of Structure-Based Drug Design Chem. Biol. 2003, 10, 787-797 10.1016/j.chembiol.2003.09.002
6. Kuntz, I. D.; Blaney, J. M.; Oatley, S. J.; Langridge, R.; Ferrin, T. E. A Geometric Approach to Macromolecule-ligand Interactions J. Mol. Biol. 1982, 161, 269-288 10.1016/0022-2836(82)90153-X
7. Kitchen, D. B.; Decornez, H.; Furr, J. R.; Bajorath, J. Docking and Scoring in Virtual Screening for Drug Discovery: Methods and Applications Nat. Rev. Drug Discovery 2004, 3, 935-949 10.1038/nrd1549
8. Warren, G. L.; Andrews, C. W.; Capelli, A.-M.; Clarke, B.; LaLonde, J.; Lambert, M. H.; Lindvall, M.; Nevins, N.; Semus, S. F.; Senger, S. et al. A Critical Assessment of Docking Programs and Scoring Functions J. Med. Chem. 2006, 49, 5912-5931 10.1021/jm050362n
9. Homeyer, N.; Stoll, F.; Hillisch, A.; Gohlke, H. Binding Free Energy Calculations for Lead Optimization: Assesment of Their Accuracy in an Industrial Drug Design Context J. Chem. Theory Comput. 2014, 10, 3331-3344 10.1021/ct5000296
10. Wang, L.; Wu, Y.; Deng, Y.; Kim, B.; Pierce, L.; Krilov, G.; Lupyan, D.; Robinson, S.; Dahlgren, M. K.; Greenwood, J. et al. Accurate and Reliable Prediction of Relative Ligand Binding Potency in Prospective Drug Discovery by Way of a Modern Free-Energy Calculation Protocol and Force Field J. Am. Chem. Soc. 2015, 137, 2695-2703 10.1021/ja512751q
11. Aldeghi, M.; Heifetz, A.; Bodkin, M. J.; Knapp, S.; Biggin, P. Accurate Calculation of the Absolute Free Energy of Binding for Drug Molecules Chem. Sci. 2016, 7, 207-218 10.1039/C5SC02678D
12. De Vivo, M.; Masetti, M.; Bottegoni, G.; Cavalli, A. Role of Molecular Dynamics and Related Methods in Drug Discovery J. Med. Chem. 2016, 59, 4035-4061 10.1021/acs.jmedchem.5b01684
13. Craig, I. R.; Essex, J. W.; Spiegel, K. Ensemble Docking into Multiple Crystallographically Derived Protein Structures: An Evaluation Based on the Statistical Analysis of Enrichments J. Chem. Inf. Model. 2010, 50, 511-524 10.1021/ci900407c
14. Korb, O.; Olsson, T. S. G.; Bowden, S. J.; Hall, R. J.; Verdonk, M. L.; Liebeschuetz, J. W.; Cole, J. C. Potential and Limitations of Ensemble Docking J. Chem. Inf. Model. 2012, 52, 1262-1274 10.1021/ci2005934
15. Grebner, C.; Iegre, J.; Ulander, J.; Edman, K.; Hogner, A.; Tyrchan, C. Binding Mode and Induced Fit Predictions for Prospective Computational Drug Design J. Chem. Inf. Model. 2016, 56, 774-787 10.1021/acs.jcim.5b00744
16. Hansen, N.; van Gunsteren, W. F. Practical Aspects of Free-energy Calculations: A Review J. Chem. Theory Comput. 2014, 10, 2632-47 10.1021/ct500161f
17. Marillet, S.; Boudinot, P.; Cazals, F. High-resolution Crystal Structures Leverage Protein Binding Affinity Predictions Proteins: Struct., Funct., Genet. 2016, 84, 9-20 10.1002/prot.24946
18. Hansch, C.; Maloney, P.; Fujita, T.; Muir, R. Correlation of Biological Activity of Phenoxyacetic Acids with Hammett Substituent Constants and Partition Coefficients Nature 1962, 194, 178-180 10.1038/194178b0
19. Cherkasov, A.; Muratov, E. N.; Fourches, D.; Varnek, A.; Baskin, I. I.; Cronin, M.; Dearden, J.; Gramatica, P.; Martin, Y. C.; Todeschini, R. et al. QSAR Modeling: Where Have you Been? Where are you Going to J. Med. Chem. 2014, 57, 4977-5010 10.1021/jm4004285
20. Sanders, M. P. A.; Barbosa, A. J. M.; Zarzycka, B.; Nicolaes, G. A. F.; Klomp, J. P. G.; Vlieg, J.; Rio, A. D. Comparative Analysis of Pharmacophore Screening Tools J. Chem. Inf. Model. 2012, 52, 1607-1620 10.1021/ci2005274
21. Wolber, G.; Sippl, W. Pharmacophore Identification and Pseudo-receptor Modeling. In The Practice of Medicinal Chemistry, Fourth ed.; Wermuth, C. G.; Aldous, D.; Raboisson, P.; Rognan, D., Eds.; Academic Press: London, United Kingdom, 2015; pp 489-510.
22. Cramer, R. D., III; Patterson, D. E.; Bunce, J. D. Comparative Molecular Field Analysis (CoMFA). 1. Effect of Shape on Binding of Steroids to Carrier Proteins J. Am. Chem. Soc. 1988, 110, 5959-5967 10.1021/ja00226a005
23. Drwal, M. N.; Griffith, R. Combination of Ligand- and Structure-based Methods in Virtual Screening Drug Discovery Today: Technol. 2013, 10, e395-e401 10.1016/j.ddtec.2013.02.002
24. Broccatelli, F.; Brown, N. Best of Both Worlds: On the Complementarity of Ligand-based and Structure-based Virtual Screening J. Chem. Inf. Model. 2014, 54, 1634-1641 10.1021/ci5001604
25. Huang, S.-Y.; Li, M.; Wang, J.; Pan, Y. HybridDock: A Hybrid Protein-Ligand Docking Protocol Integrating Protein- and Ligand-based Approaches J. Chem. Inf. Model. 2016, 56, 1078-1087 10.1021/acs.jcim.5b00275
26. Prathipati, P.; Mizuguchi, K. Integration of Ligand and Structure Based Approaches for CSAR-2014 J. Chem. Inf. Model. 2016, 56, 974-987 10.1021/acs.jcim.5b00477
27. Klebe, G.; Abraham, U. Comparative Molecular Similarity Index Analysis (CoMSIA) to Study Hydrogen-bonding Properties and to Score Combinatorial Libraries J. Comput.-Aided Mol. Des. 1999, 13, 1-10 10.1023/A:1008047919606
28. Cappel, D.; Dixon, S. L.; Sherman, W.; Duan, J. Exploring Conformational Search Protocols for Ligand-based Virtual Screening and 3-D QSAR Modeling J. Comput.-Aided Mol. Des. 2015, 29, 165-182 10.1007/s10822-014-9813-4
29. Subramanian, G.; Rao, S. N. An Integrated Computational Workflow for Efficient and Quantitative Modeling of Renin Inhibitors Bioorg. Med. Chem. 2012, 20, 851-858 10.1016/j.bmc.2011.11.063
30. Urniaz, R. D.; Jóźwiak, K. X-ray Crystallographic Structures as a Source of Ligand Alignment in 3D-QSAR J. Chem. Inf. Model. 2013, 53, 1406-1414 10.1021/ci400004e
31. Therese, P. J.; Manvar, D.; Kondepudi, S.; Battu, M. B.; Sriram, D.; Basu, A.; Yogeeswari, P.; Kaushik-Basu, N. Multiple e-Pharmacophore Modeling, 3D-QSAR, and High-throughput Virtual Screening of Hepatitis C Virus NS5B Polymerase Inhibitors J. Chem. Inf. Model. 2014, 54, 539-552 10.1021/ci400644r
32. Cramer, R. D.; Wendt, B. Template CoMFA: The 3D-QSAR Grail? J. Chem. Inf. Model. 2014, 54, 660-671 10.1021/ci400696v
33. Cramer, R. D. Template CoMFA Applied to 116 Biological Targets J. Chem. Inf. Model. 2014, 54, 2147-2156 10.1021/ci500230a
34. Haass, C.; Selkoe, D. J. Soluble Protein Oligomers in Neurodegenaration: Lessons from the Alzheimer's Amyloid beta-peptide Nat. Rev. Mol. Cell Biol. 2007, 8, 101-112 10.1038/nrm2101
35. Walsh, D. M.; Selkoe, D. J. A beta Oligomers-a Decade of Discovery J. Neurochem. 2007, 101, 1172-1184 10.1111/j.1471-4159.2006.04426.x
36. Cummings, J. L.; Morstorf, T.; Zhong, K. Alzheimer's Disease Drug-development Pipeline: Few Candidates, Frequent Failures Alzheimer's Res. Ther. 2014, 6, 37 10.1186/alzrt269
37. Jarvis, L. M. Alzheimer's Next Chapter Chem. Eng. News 2015, 93 (22) 11-15 10.1021/cen-09322-cover
38. Hong, L.; Koelsch, G.; Lin, X.; Wu, S.; Terzyan, S.; Ghosh, A. K.; Zhang, X. C.; Tang, J. Structure of the Protease Domain of Memapsin 2 (β-secretase) Complexed with Inhibitor Science 2000, 290, 150-153 10.1126/science.290.5489.150
39. Xu, Y.; Li, M. J.; Greenblatt, H.; Chen, W.; Paz, A.; Dym, O.; Peleg, Y.; Chen, T.; Shen, X.; He, J. et al. Flexibility of the Flap in the Active Site of BACE1 as Revealed by Crystal Structures and Molecular Dynamics Simulations Acta Crystallogr., Sect. D: Biol. Crystallogr. 2012, D68, 13-25 10.1107/S0907444911047251
40. Shimizu, H.; Tosaki, A.; Kaneko, K.; Hisano, T.; Sakurai, T.; Nukina, N. Crystal Structure of an Active Form of BACE1, an Enzyme Responsible for Amyloid β Protein Production Mol. Cell. Biol. 2008, 28, 3663-3671 10.1128/MCB.02185-07
41. Manoharan, P.; Chennoju, K.; Ghoshal, N. Target Specific Proteochemometric Model Development for BACE1-Protein Flexibility and Structural Water are Critical in Virtual Screening Mol. BioSyst. 2015, 11, 1955-1972 10.1039/C5MB00088B
42. Dominguez, J. L.; Christopeit, T.; Villaverde, M. C.; Gossas, T.; Otero, J. M.; Nyström, S.; Baraznenok, V.; Lindström, E.; Danielson, U. H.; Sussman, F. Effect of the Protonation State of the Titratable Residues on the Inhibitor Affinity of BACE-1 Biochemistry 2010, 49, 7255-7263 10.1021/bi100637n
43. Ellis, C. R.; Shen, J. pH-Dependent Population Shift Regulates BACE1 Activity and Inhibition J. Am. Chem. Soc. 2015, 137, 9543-9546 10.1021/jacs.5b05891
44. Geschwindner, S.; Ulander, J.; Johansson, P. Ligand Binding Thermodynamics in Drug Discovery: Still a Hot Tip? J. Med. Chem. 2015, 58, 6321-6335 10.1021/jm501511f
45. RCSB Protein Data Bank. http://www.rcsb.org/ (accessed Jan 30, 2014).
46. Kramer, C.; Dahl, G.; Tyrchan, C.; Ulander, J. A Comprehensive Company Database Analysis of Biological Assay Variability Drug Discovery Today 2016, 21, 1213-1221 10.1016/j.drudis.2016.03.015
47. ChEMBL. https://www.ebi.ac.uk/chembl (accessed Mar 30, 2014).
48. DeepChem datasets. https://github.com/deepchem/deepchem/tree/master/datasets (accessed May 3, 2016).
49. BIOVIA Pipeline Pilot. http://accelrys.com/products/collaborative-science/biovia-pipeline-pilot/ (accessed May 3, 2016).
50. Abad-Zapatero, C. et al. Ligand Efficiency Indices for Effective mapping of Chemico-biological Space: The concept of an Atlas-like Representation Drug Discovery Today 2010, 15, 804-811 10.1016/j.drudis.2010.08.004
51. MAESTRO; Schrödinger, LLC: New York, NY, USA, 2014.
52. MOE; Chemical Computing Group: Montreal, Canada, 2014.
53. Canvas; Schrödinger, LLC: New York, NY, USA, 2014.
54. Sastry, M.; Lowrie, J. F.; Dixon, S. L.; Sherman, W. Large-Scale Systematic Analysis of 2D Fingerprint Methods and Parameters to Improve Virtual Screening Enrichments J. Chem. Inf. Model. 2010, 50, 771-784 10.1021/ci100062n
55. An, Y.; Sherman, W.; Dixon, S. L. Kernel-based Partial Least Squares: Application to Fingerprint-based QSAR with Model Visualization J. Chem. Inf. Model. 2013, 53, 2312-2321 10.1021/ci400250c
56. Ramsundar, B.; Kearnes, S.; Riley, P.; Webster, D.; Konerding, D.; Pande, V. Massively Multitask Networks for Drug Discovery. Cornell University Library. http://arxiv.org/abs/1502.02072 (accessed May 3, 2016).
57. Sheridan, R. P. The Relative Importance of Domain Applicability Metrics for Estimating Prediction Errors in QSAR Varies with Training Set Diversity J. Chem. Inf. Model. 2015, 55, 1098-1107 10.1021/acs.jcim.5b00110
58. Rogers, D.; Hahn, M. Extended-connectivity Fingerprints J. Chem. Inf. Model. 2010, 50, 742-754 10.1021/ci100050t
59. Bemis, B. W.; Murcko, M. A. The Properties of Known Drugs. 1. Molecular Frameworks J. Med. Chem. 1996, 39, 2887-2893 10.1021/jm9602928
60. Kohonen, T. Self-Organizing Maps, 3 rd ed.; Springer-Verlag: Berlin, 2001.
61. Osolodkin, D. I.; Radchenko, E. V.; Orlov, A. A.; Voronkov, A. E.; Palyulin, V. A.; Zefirov, N. S. Progress in Visual Representations of Chemical Space Expert Opin. Drug Discovery 2015, 10, 959-973 10.1517/17460441.2015.1060216
62. Sander, T.; Freyss, J.; von Korff, M.; Rufener, C. DataWarrior: An Open-source Program for Chemistry Aware Data Visualization and Analysis J. Chem. Inf. Model. 2015, 55, 460-473 10.1021/ci500588j
63. Guha, R.; van Drie, J. H. Structure-activity Landscape Index: Identifying and Quantifying Activity Cliffs J. Chem. Inf. Model. 2008, 48, 646-658 10.1021/ci7004093
64. Deepchem Home Page. https://github.com/deepchem/deepchem (accessed May 3, 2016).
65. Bergstra, J.; Bengio, Y. Random Search for Hyper-Parameter Optimization J. Machine Learn. Res. 2012, 12, 281-305
66. Abad-Zapatero, C.; Metz, J. T. Ligand Efficiency Indices as Guideposts for Drug Discovery Drug Discovery Today 2005, 10, 464-469 10.1016/S1359-6446(05)03386-6
67. Abad-Zapatero, C. Ligand Efficiency Indices for Effective Drug Discovery Expert Opin. Drug Discovery 2007, 2, 469-488 10.1517/17460441.2.4.469
68. Mignani, S.; Huber, S.; Tomás, H.; Rodrigues, J.; Majoral, J.-P. Compound High-quality Criteria: A New Vision to Guide the Development of Drugs, Current Situation Drug Discovery Today 2016, 21, 573-584 10.1016/j.drudis.2016.01.005
69. Srivastava, N.; Hinton, G.; Krizhevsky, A.; Sutskever, I.; Salakhutdinov, R. Dropout: A Simple Way to Prevent Neural Networks from Overfitting J. Mach. Learn. Res. 2014, 15, 1929-1958
70. Mendenhall, J.; Meiler, J. Improving Quantitative Structure-activity Relationship Models using Artificial Neural Networks Trained with Dropout J. Comput.-Aided Mol. Des. 2016, 30, 177-189 10.1007/s10822-016-9895-2
71. Gleeson, M. P.; Hersey, A.; Hannongbua, S. In-silico ADME Models: A General Assessment of their Utility in Drug Discovery Applications Curr. Top. Med. Chem. 2011, 11, 358-381 10.2174/156802611794480927
72. Peach, M. L.; Zakharov, A. V.; Liu, R.; Pugliese, A.; Tawa, G.; Wallqvist, A.; Nicklaus, M. C. Computational Tools and Resources for Metabolism-related Property Predictions. 1. Overview of Publicly Available (free and commercial) Databases and Software Future Med. Chem. 2012, 4, 1907-1932 10.4155/fmc.12.150
73. Kabsch, W.; Sander, C. How Good are Predictions of Protein Secondary Structure? FEBS Lett. 1983, 155, 179-182 10.1016/0014-5793(82)80597-8
74. Drozdetskiy, A.; Cole, C.; Procter, J.; Barton, G. J. JPred4: a protein Secondary Structure Prediction Server Nucleic Acids Res. 2015, 43, W389-W394 10.1093/nar/gkv332
75. Chou, P. Y.; Fasman, G. D. Conformational Parameters for Amino Acids in Helical, β-sheet, and Random Coil Regions Calculated from Proteins Biochemistry 1974, 13, 211-222 10.1021/bi00699a001
76. Ambure, P.; Roy, K. Advances in Quantitative Structure-activity relationship models of anti-Alzheimer's Agents Expert Opin. Drug Discovery 2014, 9, 697-723 and references cited therein 10.1517/17460441.2014.909404
77. Wu, Q.; Li, X.; Gao, Q.; Wang, J.; Li, Y.; Yang, L. Interaction mechanism Exploration of HEA Derivatives as BACE1 Inhibitors by in silico Analysis Mol. BioSyst. 2016, 12, 1151-1165 and references cited therein 10.1039/C5MB00859J
78. Cramer, R. D. Template CoMFA Generates Single 3D-QSAR models that, for Twelve of Twelve Biological Targets, predict All ChEMBL-Tabulated Affinities PLoS One 2015, 10, e0129307 10.1371/journal.pone.0129307
79. Ciordia, M.; Perez-Benito, L.; Delgado, F.; Trabanco, A. A.; Tresadern, G. Application of Free Energy Perturbation for the Design of BACE1 Inhibitors J. Chem. Inf. Model. 2016, 56, 1856 10.1021/acs.jcim.6b00220
80. Xu, Y.; Dai, Z.; Chen, F.; Gao, S.; Pei, J.; Lai, L. Deep Learning for Drug-induced Liver Injury J. Chem. Inf. Model. 2015, 55, 2085-2093 10.1021/acs.jcim.5b00238
81. Gawehn, E.; Hiss, J. A.; Schneider, G. Deep Learning in Drug Discovery Mol. Inf. 2016, 35, 3-14 10.1002/minf.201501008
82. Mamoshina, P.; Vieira, A.; Putin, E.; Zhavoronkov, A. Applications of Deep Learning in Biomedicine Mol. Pharmaceutics 2016, 13, 1445-1454 10.1021/acs.molpharmaceut.5b00982
83. Fujita, T.; Winkler, D. A. Understanding the Roles of the "Two QSARs J. Chem. Inf. Model. 2016, 56, 269-274 10.1021/acs.jcim.5b00229
84. Xu, Y.; Dai, Z.; Chen, F.; Gao, S.; Pei, J.; Lai, L. Deep Learning for Drug-Induced Liver Injury J. Chem. Inf. Model. 2015, 55, 2085-2093 10.1021/acs.jcim.5b00238
85. Segall, M. D.; Yusof, I.; Champness, E. J. Avoiding Missed Opportunities by Analyzing the Sensitivity of Our Decisions J. Med. Chem. 2016, 59, 4267-4277 10.1021/acs.jmedchem.5b01921