ArticlePLoS computational biology2020
PremPS: Predicting the impact of missense mutations on protein stability.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 141 papers.
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141 citing papers in PubMed.
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- Deciphering gain-of-function from loss-of-function variants with AlphaMissense: A case study with the mechanosensitive PIEZO1 ion channel protein.Biochemistry and biophysics reports · 2026Article
- CRISPR-Cas9-based gene editing as a proof-of-concept approach in an inborn error of immunity caused by a DCLRE1C variant.Immunologic research · 2026Article
- Cadherin-23 mutations cause calcium-dependent, allele-sensitive mechanosensory defects.iScience · 2026Article
- An interpretable molecular framework for predicting cancer driver missense mutations.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Investigation of Genetic Aetiology in Intellectual Developmental Disorder with Trio-Whole Exome Sequencing Approach.Noro psikiyatri arsivi · 2026Article
- Generalizable and scalable protein stability prediction with rewired protein generative models.Nature communications · 2025Article
- Insights into Caspase-6 Mutations and Neuropathology in Huntington's Disease and Psychiatric Disorders: A Structural Genomics and Drug Repurposing Approach.Molecular neurobiology · 2025Article
81 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Computational methods that predict protein stability changes induced by missense mutations have made a lot of progress over the past decades. Most of the available methods however have very limited accuracy in predicting stabilizing mutations because existing experimental sets are dominated by mutations reducing protein stability. Moreover, few approaches could consistently perform well across different test cases. To address these issues, we developed a new computational method PremPS to more accurately evaluate the effects of missense mutations on protein stability. The PremPS method is composed of only ten evolutionary- and structure-based features and parameterized on a balanced dataset with an equal number of stabilizing and destabilizing mutations. A comprehensive comparison of the predictive performance of PremPS with other available methods on nine benchmark datasets confirms that our approach consistently outperforms other methods and shows considerable improvement in estimating the impacts of stabilizing mutations. A protein could have multiple structures available, and if another structure of the same protein is used, the predicted change in stability for structure-based methods might be different. Thus, we further estimated the impact of using different structures on prediction accuracy, and demonstrate that our method performs well across different types of structures except for low-resolution structures and models built based on templates with low sequence identity. PremPS can be used for finding functionally important variants, revealing the molecular mechanisms of functional influences and protein design. PremPS is freely available at https://lilab.jysw.suda.edu.cn/research/PremPS/, which allows to do large-scale mutational scanning and takes about four minutes to perform calculations for a single mutation per protein with ~ 300 residues and requires ~ 0.4 seconds for each additional mutation.
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