Presenter Information

Nick MajeskeFollow

Presentation Title

Elucidating Mutation Sensitive Site Pairs in a Wildtype Protein’s Polypeptide Chain

Presentation Type

Oral Presentation

Abstract

The specific sequence of amino acids in a polypeptide chain dictates the three-dimensional structure, and hence function, of a protein. Mutagenesis experiments on physical proteins involving amino acid substitutions provide insights enabling pharmaceutical companies to design medicines to combat a variety of debilitating diseases. However, such wet lab work is prohibitive, because even studying the effects of a single mutation may require weeks of work. Computational approaches for performing exhaustive screens of the effects of single mutations have been developed, but methods for conducting a systematic, exhaustive screen of the effects of all possible multiple mutations were not previously available due to the large number of mutant protein structures that would need to be analyzed. In this work we motivate and demonstrate a proof of concept approach for conducting protein stability analysis for in-silico experiments in which all possible mutant structures with 2, or pairwise, amino acid substitutions are performed. We generated two datasets of mutant structures containing exhaustive pairwise amino acid substitutions for protein structures 1HHP and 1CRN. These two exhaustive sets for the proteins contain 373,635 and 1,751,211 unique pairwise protein mutant forms respectively. Via a statistical and outlier analysis of the stability of the mutants relative to the wild type, we are able to identify those pairwise mutations that have the greatest impact on the protein’s stability. The findings of these experiment provide insight into specific amino acid sites that are most structurally sensitive to mutation.

Start Date

10-5-2018 9:30 AM

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May 10th, 9:30 AM

Elucidating Mutation Sensitive Site Pairs in a Wildtype Protein’s Polypeptide Chain

The specific sequence of amino acids in a polypeptide chain dictates the three-dimensional structure, and hence function, of a protein. Mutagenesis experiments on physical proteins involving amino acid substitutions provide insights enabling pharmaceutical companies to design medicines to combat a variety of debilitating diseases. However, such wet lab work is prohibitive, because even studying the effects of a single mutation may require weeks of work. Computational approaches for performing exhaustive screens of the effects of single mutations have been developed, but methods for conducting a systematic, exhaustive screen of the effects of all possible multiple mutations were not previously available due to the large number of mutant protein structures that would need to be analyzed. In this work we motivate and demonstrate a proof of concept approach for conducting protein stability analysis for in-silico experiments in which all possible mutant structures with 2, or pairwise, amino acid substitutions are performed. We generated two datasets of mutant structures containing exhaustive pairwise amino acid substitutions for protein structures 1HHP and 1CRN. These two exhaustive sets for the proteins contain 373,635 and 1,751,211 unique pairwise protein mutant forms respectively. Via a statistical and outlier analysis of the stability of the mutants relative to the wild type, we are able to identify those pairwise mutations that have the greatest impact on the protein’s stability. The findings of these experiment provide insight into specific amino acid sites that are most structurally sensitive to mutation.