Force field optimization guided by small molecule crystal lattice data enables consistent sub-Angstrom protein-ligand docking

By Hahnbeom Park, Guangfeng Zhou, Minkyung Baek, David Baker, Frank DiMaio. 2021

79 citations recorded by the source corpus.

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 1,386 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 1,112 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.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Joseph J. Noh
2. Frank DiMaio
3. Masha Karelina
4. Ron O. Dror
5. Ivan V. Anishchenko
6. Minkyung Baek

In Materials Chemistry

1. Minkyung Baek
2. Ivan V. Anishchenko
3. A. Kang
4. Frank DiMaio
5. Ron O. Dror
6. David Baker
7. Sergey Ovchinnikov
8. Yang Zhang

In Molecular Biology

1. Minkyung Baek
2. Ivan V. Anishchenko
3. Frank DiMaio
4. A. Kang
5. Jue Wang
6. Ryan McHugh
7. A. Bera
8. Ron O. Dror

Citations in the corpus

• Generalized Biomolecular Modeling and Design with RoseTTAFold All-Atom (2023, positive): “We found that predictions for protein – small molecule complexes in our recent PDB set with lower computedbindingenergies(Rosetta D G )( 32 , 33 ) were more accurate (Fig.”
• How accurately can one predict drug binding modes using AlphaFold models? (2023, methodology): “For Rosetta docking, we used the GALigandDock protocol (Park et al., 2021).”