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

Applications of Deep Learning on Protein Mutations

Recent developments in Deep Learning have enabled new approaches to important prediction problems in biology. In particular, models can approximate experimental laboratory investigations at a scale that would otherwise be prohibitive in time and cost. In this work, we report on three research threads adapting deep learning methods for applications involving proteins. Our longest running thread focuses on the use of rigidity analysis to assess protein stability, most recently using multiclass classification to predict the stability change caused by a mutation in a protein, with explicit modeling of experimental uncertainty. This work was recently published at BICOB 2018. The second thread involves accelerating or approximating an exhaustive analysis of in-silico protein mutations. While an exhaustive analysis is possible using parallel computing for pairwise mutations, it is infeasible to analyze higher level of protein mutation. We are using low rank matrix factorization techniques to approximate the exhaustive results with dramatically less computation. Our newest thread involves training variational autoencoders on protein sequences to learn a fixed-size latent representation of proteins, which can then be leveraged for a variety of applications (e.g. optimizing protein properties). We are also analyzing the biological significance of our model\u27s errors when translating from the latent space back into a sequence of amino acids