Using Evolutionarily-Based Correlation Measures and Machine Learning to Improve Protein Structure Prediction in BCL::Fold

Abstract

De novo protein structure prediction is a challenge due to the sheer size of the search space. One can limit the set of potential models with long-range contact restraints (positions distant in the primary sequence but known to be in close proximity within the tertiary structure). Most available contact prediction methods achieve accuracies insufficient for de novo protein folding. Direct Information (DI), which finds the minimal set of correlations that explains all global correlation, is a notable exception. DI has been used to determine the structures of some membrane and soluble proteins with large numbers of homologous sequences compiled into large sequence alignments. However, DI has many limitations. I have leveraged machine learning methods to predict contacts more accurately by combining DI with sequence information thereby improving protein structure prediction accuracy in the Biochemical Library (BCL). The BCL is a C++ library developed in the Meiler lab. This innovative resource will augment the elucidation of traditionally challenging membrane protein structures – specifically larger proteins, which are computationally difficult to address.