Volume 27, 2025, Pages 4418-4430Author links open overlay panelLiangxu Xie, Guoming Bao, Dawei Zhang, Lei Xu, Xiaojun Xu, Shan ChangShow moreHighlights•

Evaluated impact of different data partitioning strategies on predicting mutation-induced changes in binding free energy.

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UniProt-based partitioning reduces model prediction accuracy, highlighting potential overestimation from conventional methods.

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Proposed an anchor-query partitioning framework, leveraging limited reference data to improve predictive generalization.

Abstract

Accurate prediction of the relative free energy of protein-ligand binding, especially regarding protein mutations, is vital for drug design and interpreting drug resistance. However, machine learning (ML) / deep learning (DL) methods often struggle with generalization due to dataset partitioning strategy. Random data partitioning potentially produces spuriously high correlations that inflate performance estimates. UniProt-based splitting preserves data independence but lacks high prediction accuracy. In this study, we first evaluate six distinct ML/DL models on the MdrDB database using two data partitioning methods. Protein sequences are embedded using the ESM-2 protein large language model, integrating wild-type and mutant features. Although all models show high predictive correlations (Pearson coefficients up to 0.70) under random partitioning, their performance declines with UniProt-based partitioning. To address this issue, we propose a query-anchor pairwise learning framework, utilizing known states as anchor points for predicting unknown query states. The proposed method is validated across three systems, revealing that even a small amount of reference data can significantly enhance prediction accuracy. This enhancement suggests that leveraging known states as anchor points allows for more precise predicting of unknown query states.

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Mutant proteins

Relative free energy

Protein language model

Data partitioning strategy

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© 2025 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.