State Function-Based Correction: A Simple and Efficient Free-Energy Correction Algorithm for Large-Scale Relative Binding Free-Energy Calculations DOI
Runduo Liu,

Yijun Lai,

Yufen Yao

и другие.

The Journal of Physical Chemistry Letters, Год журнала: 2025, Номер unknown, С. 5763 - 5768

Опубликована: Июнь 3, 2025

Free-energy perturbation-based relative binding free-energy (FEP-RBFE) calculations have become an important tool in drug discovery, but inherent computational errors require corrections based on fundamental physical principles to improve prediction accuracy. Traditional correction methods enforce consistency by identifying cycles perturbation graphs, their cost grows exponentially with network size due the combinatorial explosion of cycles. This severely limits applicability modern where large-scale FEP-RBFE screens involving hundreds thousands ligands are increasingly common. We present efficient and straightforward State Function-based Correction (SFC) algorithm, which leverages state function property free energy without requiring cycle identification. eliminates bottlenecks, scaling as O(P × N), P is number edges graph N molecules. In contrast graph-based such weighted closure (WCC), SFC maintains consistent efficiency across increasing sizes, enabling handling large networks up 50 000 molecules or even more─useful for high-throughput applications. Furthermore, incorporates uncertainty-aware weighting further enhance performance. These advantages position RBFE method better support aimed at lead optimization.

Язык: Английский

The Physics-AI Dialogue in Drug Design DOI Creative Commons
Pablo Andrés Vargas-Rosales, Amedeo Caflisch

RSC Medicinal Chemistry, Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

A long path has led from the determination of first protein structure in 1960 to recent breakthroughs science. Protein prediction and design methodologies based on machine learning (ML) have been recognized with 2024 Nobel prize Chemistry, but they would not possible without previous work input many domain scientists. Challenges remain application ML tools for structural ensembles their usage within software pipelines by crystallography or cryogenic electron microscopy. In drug discovery workflow, techniques are being used diverse areas such as scoring docked poses, generation molecular descriptors. As become more widespread, novel applications emerge which can profit large amounts data available. Nevertheless, it is essential balance potential advantages against environmental costs deployment decide if when best apply it. For hit lead optimization efficiently interpolate between compounds chemical series free energy calculations dynamics simulations seem be superior designing derivatives. Importantly, complementarity and/or synergism physics-based methods (e.g., force field-based simulation models) data-hungry growing strongly. Current evolved decades research. It now necessary biologists, physicists, computer scientists fully understand limitations ensure that exploited design.

Язык: Английский

Процитировано

1
A thermodynamic cycle to predict the competitive inhibition outcomes of an evolving enzyme DOI Creative Commons
Ebru Çetin, Haleh Abdizadeh, Ali Rana Atılgan

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2025, Номер unknown

Опубликована: Фев. 7, 2025

Abstract Understanding competitive inhibition at the molecular level is essential for unraveling dynamics of enzyme-inhibitor interactions and predicting evolutionary outcomes resistance mutations. In this study, we present a framework linking to alchemical free energy perturbation (FEP) calculations, focusing on E. coli dihydrofolate reductase (DHFR) its by trimethoprim (TMP). Using thermodynamic cycles, relate experimentally measured binding constants ( K i m ) differences associated with wild-type mutant forms DHFR mean error 0.9 kcal/mol, providing insights into underpinnings TMP resistance. Our findings highlight importance local conformational in inhibition. Mutations affect substrate inhibitor affinities differently, influencing fitness landscape under selective pressure from TMP. FEP simulations reveal that mutations stabilize inhibitor-bound or substrate-bound states through specific structural and/or dynamical effects. The interplay these effects showcases significant epistasis certain cases. ability separately assess provides valuable insights, allowing more precise interpretation mutation epistatic interactions. Furthermore, identify key challenges simulations, including convergence issues arising charge-changing long-range allosteric By integrating computational experimental data, provide an effective approach functional impact their contributions landscapes. These pave way constructing robust mutational scanning protocols designing therapeutic strategies against resistant bacterial strains.

Язык: Английский

Процитировано

0
Covalent-Allosteric Inhibitors: Do We Get the Best of Both Worlds? DOI
Hui Tao, Bo Yang,

Atena Farhangian

и другие.

Journal of Medicinal Chemistry, Год журнала: 2025, Номер unknown

Опубликована: Фев. 12, 2025

Covalent-allosteric inhibitors (CAIs) may achieve the best of both worlds: increased potency, long-lasting effects, and reduced drug resistance typical covalent ligands, along with enhanced specificity decreased toxicity inherent in allosteric modulators. Therefore, CAIs can be an effective strategy to transform many undruggable targets into druggable ones. However, are challenging design. In this perspective, we analyze discovery known targeting three protein families: phosphatases, kinases, GTPases. We also discuss how computational methods tools play a role addressing practical challenges rational CAI

Язык: Английский

Процитировано

0
Discovery of Novel Pyridin-2-yl Urea Inhibitors Targeting ASK1 Kinase and Its Binding Mode by Absolute Protein–Ligand Binding Free Energy Calculations DOI Open Access
Lingzhi Wang,

Yalei Gao,

Yuying Chen

и другие.

International Journal of Molecular Sciences, Год журнала: 2025, Номер 26(4), С. 1527 - 1527

Опубликована: Фев. 12, 2025

Apoptosis signal-regulating kinase 1 (ASK1), a key component of the mitogen-activated protein (MAPK) cascades, has been identified as promising therapeutic target owing to its critical role in signal transduction pathways. In this study, we proposed novel pyridin-2-yl urea inhibitors exhibiting favorable physicochemical properties. The potency these compounds was validated through vitro bioassays. inhibition (IC50) compound 2 1.55 ± 0.27 nM, which comparable known clinical inhibitor, Selonsertib. To further optimize hit compounds, two possible binding modes were initially predicted by molecular docking. Absolute free energy (BFE) calculations based on dynamics simulations discriminated modes, presenting good tendency with bioassay results. This strategy, underpinned BFE calculations, great potential expedite drug discovery process targeting ASK1 kinase.

Язык: Английский

Процитировано

0
FEP-SPell-ABFE: An Open-Source Automated Alchemical Absolute Binding Free-Energy Calculation Workflow for Drug Discovery DOI
Pengfei Li,

Tianlei Pu,

Ye Mei

и другие.

Journal of Chemical Information and Modeling, Год журнала: 2025, Номер unknown

Опубликована: Март 3, 2025

The binding affinity between a drug molecule and its target, measured by the absolute free energy (ABFE), is crucial factor in lead discovery phase of development. Recent research has highlighted potential silico ABFE predictions to directly aid development allowing for ranking prioritization promising candidates. This work introduces an open-source Python workflow called FEP-SPell-ABFE, designed automate calculations with minimal user involvement. requires only three key inputs: receptor protein structure PDB format, candidate ligands SDF configuration file (config.yaml) that governs both molecular dynamics simulation parameters. It produces ranked list along their energies comma-separated values (CSV) format. leverages SLURM (Simple Linux Utility Resource Management) automating task execution resource allocation across modules. A usage example several benchmark systems validation are provided. FEP-SPell-ABFE workflow, practical example, publicly accessible on GitHub at https://github.com/freeenergylab/FEP-SPell-ABFE, distributed under MIT License.

Язык: Английский

Процитировано

0
QuantumBind-RBFE: Accurate Relative Binding Free Energy Calculations Using Neural Network Potentials DOI
Francesc Sabanés Zariquiey, Stephen E. Farr, Stefan H. Doerr

и другие.

Journal of Chemical Information and Modeling, Год журнала: 2025, Номер unknown

Опубликована: Апрель 8, 2025

Accurate prediction of protein-ligand binding affinities is crucial in drug discovery, particularly during hit-to-lead and lead optimization phases, however, limitations ligand force fields continue to impact accuracy. In this work, we validate relative free energy (RBFE) accuracy using neural network potentials (NNPs) for the ligands. We utilize a novel NNP model, AceFF 1.0, based on TensorNet architecture small molecules that broadens applicability diverse drug-like compounds, including all important chemical elements supporting charged molecules. Using established benchmarks, show overall improved correlation affinity predictions compared with GAFF2 molecular mechanics ANI2-x NNPs. Slightly less but comparable correlations OPLS4. also can run simulations at 2 fs time step, least two times larger than previous models, providing significant speed gains. The results promise further evolutions calculations NNPs while demonstrating its practical use already current generation. code model are publicly available research use.

Язык: Английский

Процитировано

0
Enhancing the understandings on SARS-CoV-2 main protease (Mpro) mutants from molecular dynamics and machine learning DOI
Jiawen Wang, Juan Xie,

Yi Yu

и другие.

International Journal of Biological Macromolecules, Год журнала: 2025, Номер unknown, С. 143076 - 143076

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0
A Thermodynamic Cycle to Predict the Competitive Inhibition Outcomes of an Evolving Enzyme DOI Creative Commons
Ebru Çetin, Haleh Abdizadeh, Ali Rana Atılgan

и другие.

Journal of Chemical Theory and Computation, Год журнала: 2025, Номер unknown

Опубликована: Апрель 23, 2025

Understanding competitive inhibition at the molecular level is essential for unraveling dynamics of enzyme-inhibitor interactions and predicting evolutionary outcomes resistance mutations. In this study, we present a framework linking to alchemical free energy perturbation (FEP) calculations, focusing on Escherichia coli dihydrofolate reductase (DHFR) its by trimethoprim (TMP). Using thermodynamic cycles, relate experimentally measured binding constants (Ki Km) differences associated with wild-type mutant forms DHFR mean error 0.9 kcal/mol, providing insight into underpinnings TMP resistance. Our findings highlight importance local conformational in inhibition. Mutations affect substrate inhibitor affinities differently, influencing fitness landscape under selective pressure from TMP. FEP simulations reveal that mutations stabilize inhibitor-bound or substrate-bound states through specific structural and/or dynamical effects. The interplay these effects showcases significant molecular-level epistasis certain cases. ability separately assess provides valuable insights, allowing more precise interpretation mutation epistatic interactions. Furthermore, identify key challenges simulations, including convergence issues arising charge-changing long-range allosteric By integrating computational experimental data, provide an effective approach functional impact their contributions landscapes. These insights pave way constructing robust mutational scanning protocols designing therapeutic strategies against resistant bacterial strains.

Язык: Английский

Процитировано

0
State Function-Based Correction: A Simple and Efficient Free-Energy Correction Algorithm for Large-Scale Relative Binding Free-Energy Calculations DOI
Runduo Liu,

Yijun Lai,

Yufen Yao

и другие.

The Journal of Physical Chemistry Letters, Год журнала: 2025, Номер unknown, С. 5763 - 5768

Опубликована: Июнь 3, 2025

Free-energy perturbation-based relative binding free-energy (FEP-RBFE) calculations have become an important tool in drug discovery, but inherent computational errors require corrections based on fundamental physical principles to improve prediction accuracy. Traditional correction methods enforce consistency by identifying cycles perturbation graphs, their cost grows exponentially with network size due the combinatorial explosion of cycles. This severely limits applicability modern where large-scale FEP-RBFE screens involving hundreds thousands ligands are increasingly common. We present efficient and straightforward State Function-based Correction (SFC) algorithm, which leverages state function property free energy without requiring cycle identification. eliminates bottlenecks, scaling as O(P × N), P is number edges graph N molecules. In contrast graph-based such weighted closure (WCC), SFC maintains consistent efficiency across increasing sizes, enabling handling large networks up 50 000 molecules or even more─useful for high-throughput applications. Furthermore, incorporates uncertainty-aware weighting further enhance performance. These advantages position RBFE method better support aimed at lead optimization.

Язык: Английский

Процитировано

0