Protein-ligand binding free-energy calculations using molecular dynamics (MD) simulations have emerged as a powerful tool for in silico drug design. Here, we present results obtained with the ARROW force field (FF)─a multipolar polarizable and physics-based model with all parameters fitted entirely to high-level ab initio quantum mechanical (QM) calculations. ARROW has already proven its ability to determine solvation free energy of arbitrary neutral compounds with unprecedented accuracy. The ARROW FF parameterization is now extended to include coverage of all amino acids including charged groups, allowing molecular simulations of a series of protein-ligand systems and prediction of their relative binding free energies. We ensure adequate sampling by applying a novel technique that is based on coupling the Hamiltonian Replica exchange (HREX) with a conformation reservoir generated via potential softening and nonequilibrium MD. ARROW provides predictions with near chemical accuracy (mean absolute error of ∼0.5 kcal/mol) for two of the three protein systems studied here (MCL1 and Thrombin). The third protein system (CDK2) reveals the difficulty in accurately describing dimer interaction energies involving polar and charged species. Overall, for all of the three protein systems studied here, ARROW FF predicts relative binding free energies of ligands with a similar accuracy level as leading nonpolarizable force fields.
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http://dx.doi.org/10.1021/acs.jctc.2c00930 | DOI Listing |
Molecules
January 2025
Dipartimento di Scienze Matematiche, Informatiche e Fisiche (DMIF), University of Udine, 33100 Udine, Italy.
(1) Background: Electrostatics plays a capital role in protein-protein and protein-ligand interactions. Implicit solvent models are widely used to describe electrostatics and complementarity at interfaces. Electrostatic complementarity at the interface is not trivial, involving surface potentials rather than the charges of surfacial contacting atoms.
View Article and Find Full Text PDFPhys Chem Chem Phys
January 2025
Center for Advanced Materials Research, Beijing Normal University at Zhuhai, Zhuhai, 519087, China.
Understanding the molecular mechanism of inhibitor binding to prostate-specific membrane antigen (PSMA) is of fundamental importance for designing targeted drugs for prostate cancer. Here we designed a series of PSMA-targeting inhibitors with distinct molecular structures, which were synthesized and characterized using both experimental and computational approaches. Microsecond molecular dynamics simulations revealed the structural and thermodynamic details of PSMA-inhibitor interactions.
View Article and Find Full Text PDFEXCLI J
November 2024
Department of Herbal Pharmacology, College of Korean Medicine, Gachon University, 1342 Seongnamdae-ro, Sujeong-gu, Seongnam-si, 13120, Korea.
Hepatocellular carcinoma (HCC) is the fifth leading cause of cancer related deaths globally. Despite advancements in treatment, drug resistance and adverse side effects have spurred the search for novel therapeutic strategies. This study aimed to investigate how the can inhibit key targets involved in HCC progression.
View Article and Find Full Text PDFChem Biol Interact
January 2025
Department of Informatics and Information Science, University of Konstanz, Germany; Faculty of Information Technology, Monash University, Australia. Electronic address:
Microcystins (MCs) occur frequently during cyanobacterial blooms worldwide, representing a group of currently about 300 known MC congeners, which are structurally highly similar. Human exposure to MCs via contaminated water, food or dietary supplements can lead to severe intoxications with ensuing high morbidity and in some cases mortality. Currently, one MC congener (MC-LR) is almost exclusively considered for risk assessment (RA) by the WHO.
View Article and Find Full Text PDFJ Med Chem
January 2025
Hangzhou Carbonsilicon AI Technology Company Limited, Hangzhou 310018, Zhejiang, China.
Applying artificial intelligence techniques to flexibly model the binding between the ligand and protein has attracted extensive interest in recent years, but their applicability remains improved. In this study, we have developed CarsiDock-Flex, a novel two-step flexible docking paradigm that generates binding poses directly from predicted structures. CarsiDock-Flex consists of an equivariant deep learning-based model termed CarsiInduce to refine ESMFold-predicted protein pockets with the induction of specific ligands and our existing CarsiDock algorithm to redock the ligand into the induced binding pockets.
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