In silico functional dissection of saturation mutagenesis: Interpreting the relationship between phenotypes and changes in protein stability, interactions and activity
Open accessAbstract
Despite interest in associating polymorphisms with clinical or experimental phenotypes, functional interpretation of mutation data has lagged behind generation of data from modern high-throughput techniques and the accurate prediction of the molecular impact of a mutation remains a non-trivial task. We present here an integrated knowledge-driven computational workflow designed to evaluate the effects of experimental and disease missense mutations on protein structure and interactions. We exemplify its application with analyses of saturation mutagenesis of DBR1 and Gal4 and show that the experimental phenotypes for over 80% of the mutations correlate well with predicted effects of mutations on protein stability and RNA binding affinity. We also show that analysis of mutations in VHL using our workflow provides valuable insights into the effects of mutations, and their links to the risk of developing renal carcinoma. Taken together the analyses of the three examples demonstrate that structural bioinformatics tools, when applied in a systematic, integrated way, can rapidly analyse a given system to provide a powerful approach for predicting structural and functional effects of thousands of mutations in order to reveal molecular mechanisms leading to a phenotype. Missense or non-synonymous mutations are nucleotide substitutions that alter the amino acid sequence of a protein. Their effects can range from modifying transcription, translation, processing and splicing, localization, changing stability of the protein, altering its dynamics or interactions with other proteins, nucleic acids and ligands, including small molecules and metal ions. The advent of high-throughput techniques including sequencing and saturation mutagenesis has provided large amounts of phenotypic data linked to mutations. However, one of the hurdles has been understanding and quantifying the effects of a particular mutation, and how they translate into a given phenotype. One approach to overcome this is to use robust, accurate and scalable computational methods to understand and correlate structural effects of mutations with disease.
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The graph-based signatures concept has proven to be a powerful approach and has been widely applied to the study of protein structure, including how mutations alter protein stability, dynamics and interactions with other molecules.
“Our previously described graph-based signatures concept has proven to be a powerful approach and has been widely applied to the study of protein structure, including how mutations alter protein stability (20,26), dynamics (27) and interactions with other molecules (20,28–34).”
From mCSM-PPI2: predicting the effects of mutations on protein–protein interactions (2019) · cites this paper as [30]
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- Authors
- Pires, D. E. V., Chen, J., Blundell, T. L., & Ascher, D. B.
- Affiliations
- University of Cambridge (GB)
- Corresponding author
- Pires, Douglas E. V.
- Ascher, David B.
- Venue
- Scientific Reports
- Published
- 2016-01-22
- Volume
- 6
- Issue
- 1
- Pages
- 19848-19848
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC4726175
- OpenAlex
- W2418388116
- Cited by
- 102
- References
- 48
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 5 · 2023: 6 · 2022: 8 · 2021: 7 · 2020: 14 · 2019: 14 · 2018: 16 · 2017: 9 · 2016: 12
- FWCI
- 13.72
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Americanae (AECID Library) (submittedVersion, other-oa)
- Arca - Repositório Institucional da Fiocruz (submittedVersion, other-oa)
- Minerva Access (University of Melbourne) (submittedVersion, cc-by)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, other-oa)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- Funders
- Wellcome Trust
- Research Councils UK
- Conselho Nacional de Desenvolvimento Científico e Tecnológico
- Fundação de Amparo à Pesquisa do Estado de Minas Gerais
- Fundação Oswaldo Cruz
- Conselho Nacional das Fundações Estaduais de Amparo à Pesquisa
- Medical Research Council (MR/N501864/1)
- National Health and Medical Research Council (1072476)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Keywords
- saturation mutagenesis, missense mutations, non-synonymous mutations, protein stability, RNA binding affinity, protein-protein interaction, DBR1, GAL4, VHL, renal carcinoma, mutation effect prediction
- MeSH terms
- Carcinoma, Renal Cell, Computer Simulation, DNA, DNA Mutational Analysis, DNA-Binding Proteins, von Hippel-Lindau Disease, Humans, Kidney Neoplasms, Mutation, Phenotype, Protein Binding, Regression Analysis, Risk Factors, RNA Nucleotidyltransferases, Transcription Factors, Mutagenesis, Saccharomyces cerevisiae Proteins, Protein Stability, Protein Interaction Maps
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