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Force Field Optimization Guided by Small Molecule Crystal Lattice Data Enables Consistent Sub-Angstrom Protein-Ligand Docking.

ABSTRACT: Accurate and rapid calculation of protein-small molecule interaction free energies is critical for computational drug discovery. Because of the large chemical space spanned by drug-like molecules, classical force fields contain thousands of parameters describing atom-pair distance and torsional preferences; each parameter is typically optimized independently on simple representative molecules. Here, we describe a new approach in which small molecule force field parameters are jointly optimized guided by the rich source of information contained within thousands of available small molecule crystal structures. We optimize parameters by requiring that the experimentally determined molecular lattice arrangements have lower energy than all alternative lattice arrangements. Thousands of independent crystal lattice-prediction simulations were run on each of 1386 small molecule crystal structures, and energy function parameters of an implicit solvent energy model were optimized, so native crystal lattice arrangements had the lowest energy. The resulting energy model was implemented in Rosetta, together with a rapid genetic algorithm docking method employing grid-based scoring and receptor flexibility. The success rate of bound structure recapitulation in cross-docking on 1112 complexes was improved by more than 10% over previously published methods, with solutions within <1 Å in over half of the cases. Our results demonstrate that small molecule crystal structures are a rich source of information for guiding molecular force field development, and the improved Rosetta energy function should increase accuracy in a wide range of small molecule structure prediction and design studies.

SUBMITTER: Park H

PROVIDER: S-EPMC8218654 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:Protein-ligand docking has emerged as a particularly important tool in drug design and development, and flexible ligand docking is a widely used method for docking simulations. Many docking software packages can simulate flexible ligand docking, and among them, Autodock is widely used. Focusing on the search algorithm used in Autodock, many new optimization approaches have been proposed over the last few decades. However, despite the large number of alternatives, we are still lacking a search method with high robustness and high performance. RESULTS:In this paper, in conjunction with the popular Autodock software, a novel hybrid version of the random drift particle swarm optimization (RDPSO) algorithm, called diversity-guided Lamarckian RDPSO (DGLRDPSO), is proposed to further enhance the performance and robustness of flexible ligand docking. In this algorithm, a novel two-phase diversity control (2PDC) strategy and an efficient local search strategy are used to improve the search ability and robustness of the RDPSO algorithm. By using the PDBbind coreset v.2016 and 24 complexes with apo-structures, the DGLRDPSO algorithm is compared with the Lamarckian genetic algorithm (LGA), Lamarckian particle swarm optimization (LPSO) and Lamarckian random drift particle swarm optimization (LRDPSO). The experimental results show that the 2PDC strategy is able to enhance the robustness and search performance of the proposed algorithm; for test cases with different numbers of torsions, the DGLRDPSO outperforms the LGA and LPSO in finding both low-energy and small-RMSD docking conformations with high robustness in most cases. CONCLUSION:The DGLRDPSO algorithm has good search performance and a high possibility of finding a conformation with both a low binding free energy and a small RMSD. Among all the tested algorithms, DGLRDPSO has the best robustness in solving both holo- and apo-structure docking problems with different numbers of torsions, which indicates that the proposed algorithm is a reliable choice for the flexible ligand docking in Autodock software.

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Force Field Optimization Guided by Small Molecule Crystal Lattice Data Enables Consistent Sub-Angstrom Protein-Ligand Docking.

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