ArticleComputational and structural biotechnology journal2023
Accurate protein stability predictions from homology models.
Article in Computational and structural biotechnology journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed, 30 citations in OpenAlex.
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- Exploration of Fingerprints and Data Mining-based Prediction of Some Bioactive Compounds fromCurrent computer-aided drug design · 2025Article
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- An allelic atlas of immunoglobulin heavy chain variable regions reveals antibody binding epitope preference resilient to SARS-CoV-2 mutation escape.Frontiers in immunology · 2024Article
- Systematic identification of conditionally folded intrinsically disordered regions by AlphaFold2.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Article
- Target-template relationships in protein structure prediction and their effect on the accuracy of thermostability calculations.Protein science : a publication of the Protein Society · 2023Article
- RosettaDDGPrediction for high-throughput mutational scans: From stability to binding.Protein science : a publication of the Protein Society · 2023Article
- Chemoinformatics and artificial intelligence colloquium: progress and challenges in developing bioactive compounds.Journal of cheminformatics · 2022Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Calculating changes in protein stability (ΔΔG) has been shown to be central for predicting the consequences of single amino acid substitutions in protein engineering as well as interpretation of genomic variants for disease risk. Structure-based calculations are considered most accurate, however the tools used to calculate ΔΔGs have been developed on experimentally resolved structures. Extending those calculations to homology models based on related proteins would greatly extend their applicability as large parts of e.g. the human proteome are not structurally resolved. In this study we aim to investigate the accuracy of ΔΔG values predicted on homology models compared to crystal structures. Specifically, we identified four proteins with a large number of experimentally tested ΔΔGs and templates for homology modeling across a broad range of sequence identities, and selected three methods for ΔΔG calculations to test. We find that ΔΔG-values predicted from homology models compare equally well to experimental ΔΔGs as those predicted on experimentally established crystal structures, as long as the sequence identity of the model template to the target protein is at least 40%. In particular, the Rosetta cartesian_ddg protocol is robust against the small perturbations in the structure which homology modeling introduces. In an independent assessment, we observe a similar trend when using ΔΔGs to categorize variants as low or wild-type-like abundance. Overall, our results show that stability calculations performed on homology models can substitute for those on crystal structures with acceptable accuracy as long as the model is built on a template with sequence identity of at least 40% to the target protein.
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