Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening

Abstract

This work introduces a number of algebraic topology approaches, including multi-component persistent homology, multi-level persistent homology, and electrostatic persistence for the representation, characterization, and description of small molecules and biomolecular complexes. In contrast to the conventional persistent homology, multi-component persistent homology retains critical chemical and biological information during the topological simplification of biomolecular geometric complexity. Multi-level persistent homology enables a tailored topological description of inter- and/or intra-molecular interactions of interest. Electrostatic persistence incorporates partial charge information into topological invariants. These topological methods are paired with Wasserstein distance to characterize similarities between molecules and are further integrated with a variety of machine learning algorithms, including k-nearest neighbors, ensemble of trees, and deep convolutional neural networks, to manifest their descriptive and predictive powers for protein-ligand binding analysis and virtual screening of small molecules. Extensive numerical experiments involving 4,414 protein-ligand complexes from the PDBBind database and 128,374 ligand-target and decoy-target pairs in the DUD database are performed to test respectively the scoring power and the discriminatory power of the proposed topological learning strategies. It is demonstrated that the present topological learning outperforms other existing methods in protein-ligand binding affinity prediction and ligand-decoy discrimination.

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     Mysinger MM, Shoichet BK. Rapid context-dependent ligand desolvation in molecular docking. Journal of chemical information and modeling. 2010;50(9):1561–1573. doi: 10.1021/ci100214a

     Not in our library10.1021/ci100214a

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     Irwin JJ, Shoichet BK. ZINC- a free database of commercially available compounds for virtual screening. Journal of chemical information and modeling. 2005;45(1):177–182. doi: 10.1021/ci049714

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     Armstrong MS, Morris GM, Finn PW, Sharma R, Moretti L, Cooper RI, et al ElectroShape: fast molecular similarity calculations incorporating shape, chirality and electrostatics. Journal of computer-aided molecular design. 2010;24(9):789–801. doi: 10.1007/s10822-010-9374-0

     Not in our library10.1007/s10822-010-9374-0

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     Xiang Z, Honig B. Extending the accuracy limits of prediction for side-chain conformations. J Mol Biol. 2001;311(2):421–430–405. doi: 10.1006/jmbi.2001.4865

     Not in our library10.1006/jmbi.2001.4865

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     Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, et al AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. Journal of computational chemistry. 2009;30(16):2785–2791. doi: 10.1002/jcc.21256

     Not in our library10.1002/jcc.21256

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     Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Computat Chem. 2010;31(2):455–461.

     Not in our library10.1002/jcc.21334

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     Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research. 2011;12:2825–2830.

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     Neves MA, Totrov M, Abagyan R. Docking and scoring with ICM: the benchmarking results and strategies for improvement. Journal of computer-aided molecular design. 2012;26(6):675–686. doi: 10.1007/s10822-012-9547-0

     Not in our library10.1007/s10822-012-9547-0

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     Cross JB, Thompson DC, Rai BK, Baber JC, Fan KY, Hu Y, et al Comparison of several molecular docking programs: pose prediction and virtual screening accuracy. Journal of chemical information and modeling. 2009;49(6):1455–1474. doi: 10.1021/ci900056c

     Not in our library10.1021/ci900056c

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     Edelsbrunner H, Harer JL. Computational topology An introduction. Providence, RI: American Mathematical Society (AMS) xii, 241 p.; 2010.

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     Kasson PM, Zomorodian A, Park S, Singhal N, Guibas LJ, Pande VS. Persistent voids: a new structural metric for membrane fusion. Bioinformatics. 2007;23(14):1753–1759. doi: 10.1093/bioinformatics/btm250

     Not in our library10.1093/bioinformatics/btm250

119. 119.

     Xia KL, Wei GW. Multidimensional persistence in biomolecular data. Journal of Computational Chemistry. 2015;36:1502–1520. doi: 10.1002/jcc.23953

     Not in our library10.1002/jcc.23953

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     Miteva MA, Guyon F, Tufféry P. Frog2: Efficient 3D conformation ensemble generator for small compounds. Nucleic acids research. 2010;38(suppl 2):W622–W627. doi: 10.1093/nar/gkq325

     Not in our library10.1093/nar/gkq325

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     Xia KL, Opron K, Wei GW. Multiscale multiphysics and multidomain models—Flexibility and Rigidity. Journal of Chemical Physics. 2013;139:194109 doi: 10.1063/1.4830404

     Not in our library10.1063/1.4830404

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     Cohen-Steiner D, Edelsbrunner H, Harer J. Stability of persistence diagrams. In: Proceedings of the twenty-first annual symposium on Computational geometry. ACM; 2005. p. 263–271.

     Not in our library

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     Burago D, Burago Y, Ivanov S. A course in metric geometry. vol. 33 American Mathematical Society Providence, RI; 2001.

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     Tian K, Yang X, Kong Q, Yin C, He RL, Yau SST. Two dimensional Yau-Hausdorff distance with applications on comparison of DNA and protein sequences. PloS one. 2015;10(9):e0136577 doi: 10.1371/journal.pone.0136577

     Not in our library10.1371/journal.pone.0136577

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     Chollet F. Keras; 2015. https://github.com/fchollet/keras.

     Not in our library

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     Theano Development Team. Theano: A Python framework for fast computation of mathematical expressions. arXiv e-prints. 2016;abs/1605.02688.

     Not in our library

Cited by papers we hold 2

• Collaborative Approach between Explainable Artificial Intelligence and Simplified Chemical Interactions to Explore Active Ligands for Cyclin-Dependent Kinase 2 · 2022
  Not checked
• Computational Screening of Potential Inhibitors of Desulfobacter postgatei for Pyrite Scale Prevention in Oil and Gas Wells · 2021
  Not checked

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