ArticleInternational journal of molecular sciences2021
SAAFEC-SEQ: A Sequence-Based Method for Predicting the Effect of Single Point Mutations on Protein Thermodynamic Stability.
Article in International journal of molecular sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers, 1 of them a synthesis that pooled it.
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Who cites it
68 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Time Series Analysis of SARS-CoV-2 Genomes and Correlations among Highly Prevalent Mutations.Microbiology spectrum · 2022Pooled it
- Persistent sheaf Laplacian analysis of protein stability and solubility changes upon mutation.Protein science : a publication of the Protein Society · 2026Article
- Molecular profiling and computational mutational analysis of bla-NDM-1 in clinical Acinetobacter baumannii isolates.BMC microbiology · 2026Article
- dUTPase is essential in zebrafish development and possesses several single-nucleotide variants with pronounced structural and functional consequences.FEBS open bio · 2026Article
- MuFaDDG: a sequence-based multiscale feature fusion framework for protein stability changes prediction.Bioinformatics (Oxford, England) · 2026Article
- The CHRNA5 rs16969968 variant is associated with MMP-9 expression and inflammatory signaling in COPD in a West Bengal population, India.Frontiers in pharmacology · 2026Article
- A dual functional inhibitory molecule to combat GyrA-ParC and associated mutants in fluoroquinolone-resistantFrontiers in bioinformatics · 2026Article
- Genomic Characterization of SARS-CoV-2 NB.1.8.1 and PQ.2 from the Infants and Young Children with Gastrointestinal Symptoms.Infection and drug resistance · 2026Article
- Generalizable and scalable protein stability prediction with rewired protein generative models.Nature communications · 2025Article
- A Family with Meester-Loeys Syndrome Caused by a Novel Missense Variant in theInternational journal of molecular sciences · 2025Article
- PPAC: Predicting Protein-Protein Affinity Changes Induced by Amino Acid Mutations Using Protein Large Language Models.ACS omega · 2025Article
- Integrative structural profiling and ligand optimisation across the transthyretin mutational landscape.NPJ systems biology and applications · 2025Article
- CDKL5 Deficiency Disorder: Revealing the Molecular Mechanism of Pathogenic Variants.International journal of molecular sciences · 2025Article
- Genotype Distribution and Migration Patterns of Hepatitis C Virus in Shandong Province, China: Molecular Epidemiology and Phylogenetic Study.JMIR medical informatics · 2025Article
- Impact of SARS-CoV-2 Variant NSP6 on Pathogenicity: Genetic Analysis and Cell Biology.Current issues in molecular biology · 2025Article
- Development of a β-glucosidase improved for glucose retroinhibition for cellulosic ethanol production: an integrated bioinformatics and genetic engineering approach.Biotechnology for biofuels and bioproducts · 2025Article
- Adoption of an in-silico analysis approach to assess the functional and structural impacts of rpoB-encoded protein mutations on Chlamydia pneumoniae sensitivity to antibiotics.BMC microbiology · 2025Article
- Shared-weight graph framework for comprehensive protein stability prediction across diverse mutation types.Briefings in bioinformatics · 2025Article
- Assessing the predicted impact of single amino acid substitutions in MAPK proteins for CAGI6 challenges.Human genetics · 2025Article
- Assessing the predicted impact of single amino acid substitutions in calmodulin for CAGI6 challenges.Human genetics · 2025Article
8 more citing papers are in PubMed but not listed here.
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3 authors.
Funding
Abstract
Modeling the effect of mutations on protein thermodynamics stability is useful for protein engineering and understanding molecular mechanisms of disease-causing variants. Here, we report a new development of the SAAFEC method, the SAAFEC-SEQ, which is a gradient boosting decision tree machine learning method to predict the change of the folding free energy caused by amino acid substitutions. The method does not require the 3D structure of the corresponding protein, but only its sequence and, thus, can be applied on genome-scale investigations where structural information is very sparse. SAAFEC-SEQ uses physicochemical properties, sequence features, and evolutionary information features to make the predictions. It is shown to consistently outperform all existing state-of-the-art sequence-based methods in both the Pearson correlation coefficient and root-mean-squared-error parameters as benchmarked on several independent datasets. The SAAFEC-SEQ has been implemented into a web server and is available as stand-alone code that can be downloaded and embedded into other researchers' code.
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