Improving the predictive performance of binding affinities and poses for protein-cyclic peptide complexes through fine-tuned MM/PBSA(GBSA)-based methods.

ABSTRACT: Cyclic peptides represent a highly promising class of biopharmaceutical scaffolds. The screening of cyclic peptides against protein targets can be greatly facilitated using computational approaches, especially molecular docking. However, it remains a crucial challenge to accurately predict protein-cyclic peptide (P-cp) interactions employing scoring functions of molecular docking. End-point approaches, such as molecular mechanics generalized Born surface area (MM/GBSA) and molecular mechanics Poisson-Boltzmann surface area (MM/PBSA), provide theoretically more robust frameworks than conventional scoring functions, but their reliability in predicting binding affinities and discriminating native-like binding poses for P-cp complexes remains poorly quantified. Herein, we comprehensively asses

SUBMITTER: Zhao H 

PROVIDER: S-EPMC12665037 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature