ArticleMolecules (Basel, Switzerland)2018
Predicting the Effect of Single and Multiple Mutations on Protein Structural Stability.
Article in Molecules (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 41 citations in OpenAlex.
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- A framework for identifying and prioritizing SNPs in genes of the hypothalamic- pituitary-gonadal axis in Guzerat cattle using amplicon-based NGS.BMC genomic data · 2025Article
- Analysis of Correlation Effects of Double Mutations in Enzymes: A Revised Residual-Contact Network Clique Model.International journal of molecular sciences · 2024Article
- How pairs of insertion mutations impact protein structure: an exhaustive computational study.Bioinformatics advances · 2024Article
- Two sequence- and two structure-based ML models have learned different aspects of protein biochemistry.Scientific reports · 2023Article
- Two sequence- and two structure-based ML models have learned different aspects of protein biochemistry.bioRxiv : the preprint server for biology · 2023Article
- Using machine learning to predict the effects and consequences of mutations in proteins.Current opinion in structural biology · 2023Review
- Topological data analysis of protein structure and inter/intra-molecular interaction changes attributable to amino acid mutations.Computational and structural biotechnology journal · 2023Article
- The role of data imbalance bias in the prediction of protein stability change upon mutation.PloS one · 2023Article
- Elucidating the Structural Impacts of Protein InDels.Biomolecules · 2022Article
- Analysis of Protein-Protein Interactions for Intermolecular Bond Prediction.Molecules (Basel, Switzerland) · 2022Article
- DiffBond: A Method for Predicting Intermolecular Bond Formation.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2021Article
- KEAP1 Cancer Mutants: A Large-Scale Molecular Dynamics Study of Protein Stability.International journal of molecular sciences · 2021Article
- Integrating Rigidity Analysis into the Exploration of Protein Conformational Pathways Using RRT* and MC.Molecules (Basel, Switzerland) · 2021Article
- Deep Learning in Proteomics.Proteomics · 2020Review
- Evaluating Protein Engineering Thermostability Prediction Tools Using an Independently Generated Dataset.ACS omega · 2020Article
- PETRA: Drug Engineering via Rigidity Analysis.Molecules (Basel, Switzerland) · 2020Article
- Robust Prediction of Single and Multiple Point Protein Mutations Stability Changes.Biomolecules · 2019Article
- Amino-Acid Network Clique Analysis of Protein Mutation Non-Additive Effects: A Case Study of Lysozme.International journal of molecular sciences · 2018Article
- Molecular dynamics approach to identification of new OGG1 cancer-associated somatic variants with impaired activity.The Journal of biological chemistryArticle
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predicting how a point mutation alters a protein's stability can guide pharmaceutical drug design initiatives which aim to counter the effects of serious diseases. Conducting mutagenesis studies in physical proteins can give insights about the effects of amino acid substitutions, but such wet-lab work is prohibitive due to the time as well as financial resources needed to assess the effect of even a single amino acid substitution. Computational methods for predicting the effects of a mutation on a protein structure can complement wet-lab work, and varying approaches are available with promising accuracy rates. In this work we compare and assess the utility of several machine learning methods and their ability to predict the effects of single and double mutations. We in silico generate mutant protein structures, and compute several rigidity metrics for each of them. We use these as features for our Support Vector Regression (SVR), Random Forest (RF), and Deep Neural Network (DNN) methods. We validate the predictions of our in silico mutations against experimental Δ Δ G stability data, and attain Pearson Correlation values upwards of 0.71 for single mutations, and 0.81 for double mutations. We perform ablation studies to assess which features contribute most to a model's success, and also introduce a voting scheme to synthesize a single prediction from the individual predictions of the three models.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.