ArticleJournal of computational chemistry2025
Evaluation of Physics-Based Protein Design Methods for Predicting Single Residue Effects on Peptide Binding Specificities.
Article in Journal of computational chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Adversarial Sequence Mutations in AlphaFold and ESMFold Reveal Nonphysical Structural Invariance, Confidence Failures, and Concerns for Protein Design.Computational and structural biotechnology journal · 2026Article
- Evaluation of Physics-Based Protein Design Methods for Predicting Single Residue Effects on Peptide Binding Specificities.Journal of computational chemistry · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Understanding the interactions that make up protein-protein or protein-peptide interfaces is a crucial step towards applications in biotechnology. The mutation of a single residue can have a strong impact on binding affinity and specificity, which is difficult to capture in sampling and scoring. Many established computational methods provide an estimate of binding or non-binding; however, comparing highly similar ligands is an important and significantly more challenging problem. Here we evaluated the capability of predicting ligand binding specificity using three established but conceptually different physics-based methods for protein design. As a model system, we analyzed the binding of peptides to designed armadillo repeat proteins, where a single residue of the peptide was changed systematically, leading to affinity changes in the range of 1-1000 nM. We critically assessed the prediction accuracy of the computational methods. While a good correlation with experimentally determined data was observed in several cases, specific biases in the prediction performance of each method were identified.
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Registered trials
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