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FoldX force field revisited, an improved version

Javier Delgado, Raul Reche, Damiano Cianferoni, Gabriele Orlando, Rob van der Kant, Frédéric Rousseau, Joost Schymkowitz, Luís Serrano

2025Bioinformatics54 citationsDOIOpen Access PDF

Abstract

MOTIVATION: The FoldX force field was originally validated with a database of 1000 mutants at a time when there were few high-resolution structures. Here, we have manually curated a database of 5556 mutants affecting protein stability, resulting in 2484 highly confident mutations denominated FoldX stability dataset (FSD), represented in non-redundant X-ray structures with <2.5 Å resolution, not involving duplicates, metals, or prosthetic groups. Using this database, we have created a new version of the FoldX force field by introducing pi stacking, pH dependency for all charged residues, improving aromatic-aromatic interactions, modifying the Ncap contribution and α-helix dipole, recalibrating the side-chain entropy of methionine, adjusting the H-bond parameters, and modifying the solvation contribution of tryptophan and others. RESULTS: These changes have led to significant improvements for the prediction of specific mutants involving the above residues/interactions and a statistically significant increase of FoldX predictions, as well as for the majority of the 20 aa. Removing all training sets data from FSD [Validation FoldX Stability Dataset (VFSD) dataset] resulted in improved predictions from R = 0.693 (RMSE = 1.277 kcal/mol) to R = 0.706 (RMSE = 1.252 kcal/mol) when compared with the previously released version. FoldX achieves 95% accuracy considering an error of ±0.85 kcal/mol in prediction and an area under the curve = 0.78 for the VFSD, predicting the sign of the energy change upon mutation. AVAILABILITY AND IMPLEMENTATION: FoldX versions 4.1 and 5.1 are freely available for academics at https://foldxsuite.crg.eu/.

Topics & Concepts

Force field (fiction)StackingMutantComputer scienceSolvationStability (learning theory)Mean squared errorChemistryDatabaseComputational biologyArtificial intelligenceMathematicsMachine learningMoleculeStatisticsBiologyBiochemistryOrganic chemistryGeneProtein Structure and DynamicsEnzyme Structure and FunctionBacterial Genetics and Biotechnology
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