1/10/2024 0 Comments Automute protein![]() Approaches and resources for prediction of the effects of non-synonymous single nucleotide polymorphisms on protein function and interactions. S Teng, E Michonova-Alexova, and E Alexov. Annual Reviews of Genomics Human Genetics, 7:61–80, 2006. Predicting the effects of amnio acid substitutions on protein functions. Deep mutational scanning: a new style of protein science. Computational alanine scanning of protein-protein interfaces. Tanja Kortemme, David E Kim, and David Baker. High-resolution epitope mapping of hgh-receptor interactions by alanine-scanning mutagenesis. Journal of Molecular Biology, 320(2):369–387, 2002.īrian C Cunningham and James A Wells. Predicting changes in the stability of proteins and protein complexes: A study of more than 1000 mutations. Nucleic Acids Research, 39(Web Server Issue):W215–W222, 2011. Sdm-a server for predicting effects of mutations on protein stability and malfunction. Protein Engineering, 10(1):7–21, 1997.ĬL Worth, R Preissner, and L Blundell. Prediction of the stability of protein mutants based on structural environment-dependent amino acid substitutions and propensity tables. Prediction of protein stability changes for single-site mutations using support vector machines. Predicting protein stability changes upon mutation using database-derived potentials: Solvent accessibility determines the importance of local versus non-local interactions along the sequence. Protherm and pronit : Thermodynamic databases for proteins and protein-nucleic acid interactions. Contributions of hydrogen bonds of Thr 157 to the thermodynamic stability of phage T4 lysozyme. The response of T4 lysozyme to large-to-small substitutions within the core and its relation to the hydrophobic effect. We demonstrate that this approach outperforms previous attempts at low sampling percentages with a variety of biologically inspired and random sampling strategies. Here we introduce a new tailored approach that decomposes the result into two components, the low-rank and sparse matrix. Previously, we sampled a subset of the ground truth and attempted a reconstruction of the data using singular value decomposition. We explore how to reconstruct from the exhaustive sets the information about which pairwise mutations are impactful, with the added goal that we sample as few decoys as possible. In this work we have generated in silico for several proteins the sets of exhaustive mutants with two amino acid substitutions. However due to the combinatorial increase in the count of possible decoys with two mutations, even computational approaches have their limitations. Computational approaches are available for generating mutant decoys and assessing them en masse to infer which mutations are impactful. Performing point mutations via mutagenesis experiments on physical proteins to infer the effects of the mutations is time consuming even for a single amino acid substitution, let alone for all possible mutations. Assessing how an amino acid mutation impacts protein structure and stability provides insights that advance drug design efforts for combating a variety of debilitating diseases.
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