Enhancing <scp>MM</scp>/P(G)<scp>BSA</scp> Methods: Integration of Formulaic Entropy for Improved Binding Free Energy Calculations

Balancing computational efficiency and precision, MM/P(G)BSA methods have been widely employed in the estimation of binding free energies within biological systems. However, the entropy contribution to the binding free energy is often neglected in MM/P(G)BSA calculations, due to the computational cost of conventional methods such as normal mode analysis (NMA). In this work, the entropy effect using a formulaic entropy can be computed from one single structure according to variations in the polar and non-polar solvents accessible surface areas and the count of rotatable bonds in ligands. It was incorporated into MM/P(G)BSA methods to enhance their performance. Extensive benchmarking reveals that the integration of formulaic entropy systematically elevates the performance of both MM/PBSA and MM/GBSA without incurring additional computational expenses. In addition, we found the inclusion of dispersion can deteriorate the correlation performance (R<sub>p</sub>) but reduce the root mean square error (RMSE) of both MM/PBSA and MM/GBSA. Notably, MM/PBSA_S, including the formulaic entropy but excluding the dispersion, surpasses all other MM/P(G)BSA methods across a spectrum of datasets. Our investigation furnishes a valuable and practical MM/P(G)BSA method, optimizing binding free energy calculations for a variety of biological systems.

Authors

Institutions

Publication Details

Journal
Journal of Computational Chemistry
Published
2025-04-08
DOI
https://doi.org/10.1002/jcc.70093
Citations
5
Primary Topic
Protein Structure and Dynamics
Type
article
Field-Weighted Citation Impact
4.03

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Enhancing MM/P(G)BSA Methods: Integration of Formulaic Entropy for Improved Binding Free Energy Calculations

Pengfei Li, Lina Dong, Binju Wang
5 citations
Journal of Computational Chemistry
Protein Structure and Dynamics
4.03
article

Enhancing MM/P(G)BSA Methods: Integration of Formulaic Entropy for Improved Binding Free Energy Calculations

Pengfei Li, Lina Dong, Binju Wang
article en
5 citations

Abstract

Balancing computational efficiency and precision, MM/P(G)BSA methods have been widely employed in the estimation of binding free energies within biological systems. However, the entropy contribution to the binding free energy is often neglected in MM/P(G)BSA calculations, due to the computational cost of conventional methods such as normal mode analysis (NMA). In this work, the entropy effect using a formulaic entropy can be computed from one single structure according to variations in the polar and non-polar solvents accessible surface areas and the count of rotatable bonds in ligands. It was incorporated into MM/P(G)BSA methods to enhance their performance. Extensive benchmarking reveals that the integration of formulaic entropy systematically elevates the performance of both MM/PBSA and MM/GBSA without incurring additional computational expenses. In addition, we found the inclusion of dispersion can deteriorate the correlation performance (R<sub>p</sub>) but reduce the root mean square error (RMSE) of both MM/PBSA and MM/GBSA. Notably, MM/PBSA_S, including the formulaic entropy but excluding the dispersion, surpasses all other MM/P(G)BSA methods across a spectrum of datasets. Our investigation furnishes a valuable and practical MM/P(G)BSA method, optimizing binding free energy calculations for a variety of biological systems.

Journal of Computational ChemistryVol. 46(10)
Xiamen University (CN), Loyola University Chicago (US)
Loyola University Chicago, National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities
Affordable and clean energy
Openalex Percentile: Top 7%
Protein Structure and Dynamics
4.03
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Enhancing <scp>MM</scp>/P(G)<scp>BSA</scp> Methods: Integration of Formulaic Entropy for Improved Binding Free Energy Calculations — Pengfei Li, Lina Dong, et al. · Journal of Computational Chemistry (2025) | TGRS Research Map | TGRS