Name
Abstract | ||
|---|---|---|
Even a single amino acid substitution in a protein can be the cause of a debilitating disease. Experimentally studying the effects of all possible multiple mutations in a protein is infeasible since it requires a combinatorial number of mutants to be engineered and assessed. Computational methods for studying the impact of single amino acid substitutions do not scale to handling the number of mutants that are possible for two amino acid substitutions. We present an approach for reducing the amount of mutation samples that need to be used to predict the impact of pairwise amino acid substitutions. We evaluate the effectiveness of our method by generating exhaustive mutations in silico for 8 proteins with 2 amino acid substitutions, analyzing the mutants via rigidity analysis, and comparing the predictions from a sample of the mutants to that in the exhaustive dataset. We show it is possible to approximate the effect of the two amino acid substitutions using as little as 25% of the exhaustive mutations, which is further improved by imposing a low rank constraint.
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Year | Venue | Field |
|---|---|---|
2018 | BCB | Pairwise comparison,Amino acid,Computer science,Bioinformatics,Mutant,Computational biology,Mutation,In silico |
DocType | ISBN | Citations |
Conference | 978-1-4503-5794-4 | 0 |
PageRank | References | Authors |
0.34 | 13 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicholas Majeske | 1 | 1 | 0.70 |
| Filip Jagodzinski | 2 | 71 | 14.83 |
| Brian Hutchinson | 3 | 137 | 13.23 |
| Tanzima Islam | 4 | 0 | 0.34 |