Sampling and refinement protocols for template-based macrocycle docking: 2018 D3R Grand Challenge 4

We describe a new template-based method for docking flexible ligands such as macrocycles to proteins. It combines Monte-Carlo energy minimization on the manifold, a fast manifold search method, with BRIKARD for complex flexible ligand searching, and with the MELD accelerator of Replica-Exchange Molecular Dynamics simulations for atomistic degrees of freedom. Here we test the method in the Drug Design Data Resource blind Grand Challenge competition. This method was among the best performers in the competition, giving sub-angstrom prediction quality for the majority of the targets.

[1]  Mohammad Moghadasi,et al.  Protein–ligand docking using FFT based sampling: D3R case study , 2017, Journal of Computer-Aided Molecular Design.

[2]  Darko Butina,et al.  Unsupervised Data Base Clustering Based on Daylight's Fingerprint and Tanimoto Similarity: A Fast and Automated Way To Cluster Small and Large Data Sets , 1999, J. Chem. Inf. Comput. Sci..

[3]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[4]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[5]  Matthew P Jacobson,et al.  Exhaustive Conformational Sampling of Complex Fused Ring Macrocycles Using Inverse Kinematics. , 2016, Journal of chemical theory and computation.

[6]  Petras J. Kundrotas,et al.  Template-Based Modeling of Protein-RNA Interactions , 2016, PLoS Comput. Biol..

[7]  Sergei Grudinin,et al.  Convex-PL: a novel knowledge-based potential for protein-ligand interactions deduced from structural databases using convex optimization , 2017, Journal of Computer-Aided Molecular Design.

[8]  Ioannis Ch. Paschalidis,et al.  The Impact of Side-Chain Packing on Protein Docking Refinement , 2015, J. Chem. Inf. Model..

[9]  Gianni De Fabritiis,et al.  KDEEP: Protein-Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks , 2018, J. Chem. Inf. Model..

[10]  Daisuke Kihara,et al.  Improved performance in CAPRI round 37 using LZerD docking and template‐based modeling with combined scoring functions , 2018, Proteins.

[11]  Dima Kozakov,et al.  Energy Minimization on Manifolds for Docking Flexible Molecules. , 2015, Journal of chemical theory and computation.

[12]  Daisuke Kihara,et al.  Prediction of homoprotein and heteroprotein complexes by protein docking and template‐based modeling: A CASP‐CAPRI experiment , 2016, Proteins.

[13]  Sereina Riniker,et al.  Better Informed Distance Geometry: Using What We Know To Improve Conformation Generation , 2015, J. Chem. Inf. Model..

[14]  Viktor Hornak,et al.  HIV-1 protease flaps spontaneously open and reclose in molecular dynamics simulations. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[15]  D. Baker,et al.  Efficient minimization of angle‐dependent potentials for polypeptides in internal coordinates , 2003, Proteins.

[16]  J. Treanor,et al.  Beta-secretase cleavage of Alzheimer's amyloid precursor protein by the transmembrane aspartic protease BACE. , 1999, Science.

[17]  T. Cheatham,et al.  Determination of Alkali and Halide Monovalent Ion Parameters for Use in Explicitly Solvated Biomolecular Simulations , 2008, The journal of physical chemistry. B.

[18]  D. Baker,et al.  An orientation-dependent hydrogen bonding potential improves prediction of specificity and structure for proteins and protein-protein complexes. , 2003, Journal of molecular biology.

[19]  Xu Hong,et al.  P3DOCK: a protein-RNA docking webserver based on template-based and template-free docking , 2019, Bioinform..

[20]  C. Simmerling,et al.  ff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SB. , 2015, Journal of chemical theory and computation.

[21]  Cong Liu,et al.  Monte Carlo on the manifold and MD refinement for binding pose prediction of protein–ligand complexes: 2017 D3R Grand Challenge , 2018, Journal of Computer-Aided Molecular Design.

[22]  T. Halgren Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94 , 1996, J. Comput. Chem..

[23]  Didier Rognan,et al.  Ranking docking poses by graph matching of protein–ligand interactions: lessons learned from the D3R Grand Challenge 2 , 2017, Journal of Computer-Aided Molecular Design.

[24]  Arthur J. Olson,et al.  AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading , 2009, J. Comput. Chem..

[25]  David Ryan Koes,et al.  Convolutional neural network scoring and minimization in the D3R 2017 community challenge , 2018, Journal of Computer-Aided Molecular Design.

[26]  Gregory Landrum,et al.  RDKit: Open-source cheminformatics. Release 2014.03.1 , 2014 .

[27]  Christopher I. Bayly,et al.  Fast, efficient generation of high‐quality atomic charges. AM1‐BCC model: II. Parameterization and validation , 2002, J. Comput. Chem..

[28]  Dima Kozakov,et al.  Rigid Body Energy Minimization on Manifolds for Molecular Docking. , 2012, Journal of chemical theory and computation.

[29]  B. Berne,et al.  Replica exchange with solute scaling: a more efficient version of replica exchange with solute tempering (REST2). , 2011, The journal of physical chemistry. B.

[30]  Paolo Tosco,et al.  Bringing the MMFF force field to the RDKit: implementation and validation , 2014, Journal of Cheminformatics.

[31]  Andrey Alekseenko,et al.  Template‐based modeling by ClusPro in CASP13 and the potential for using co‐evolutionary information in docking , 2019, Proteins.

[32]  Ioannis Ch. Paschalidis,et al.  Focused grid‐based resampling for protein docking and mapping , 2016, J. Comput. Chem..

[33]  Guo-Wei Wei,et al.  Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges , 2018, Journal of Computer-Aided Molecular Design.

[34]  Rui Duan,et al.  Lessons learned from participating in D3R 2016 Grand Challenge 2: compounds targeting the farnesoid X receptor , 2017, Journal of Computer-Aided Molecular Design.

[35]  Andreas Verras,et al.  Performance of multiple docking and refinement methods in the pose prediction D3R prospective Grand Challenge 2016 , 2017, Journal of Computer-Aided Molecular Design.

[36]  Alberto Perez,et al.  Accelerating molecular simulations of proteins using Bayesian inference on weak information , 2015, Proceedings of the National Academy of Sciences.

[37]  Jorge Nocedal,et al.  On the limited memory BFGS method for large scale optimization , 1989, Math. Program..

[38]  W. L. Jorgensen,et al.  Comparison of simple potential functions for simulating liquid water , 1983 .

[39]  Alberto Perez,et al.  Determining protein structures by combining semireliable data with atomistic physical models by Bayesian inference , 2015, Proceedings of the National Academy of Sciences.

[40]  Petras J. Kundrotas,et al.  Modeling CAPRI targets 110‐120 by template‐based and free docking using contact potential and combined scoring function , 2018, Proteins.

[41]  Junmei Wang,et al.  Development and testing of a general amber force field , 2004, J. Comput. Chem..