Modeling Evolutionary Constraints and Improving Multiple Sequence Alignments using Residue Couplings

Abstract

Residue coupling in protein families has received much attention as an important indicator toward predicting protein structures and revealing functional insight into proteins. Existing coupling methods identify largely pairwise couplings and express couplings over amino acid combinations, which do not yield a mechanistic explanation. Most of these methods primarily use a multiple protein sequence alignment---most likely a resultant alignment---which better exposes couplings and is obtained through manual tweaking of an alignment constructed by a classical alignment algorithm. Classical alignment algorithms primarily focus on capturing conservations and may not fully unveil couplings in the alignment. In this dissertation, we propose methods for capturing both pairwise and higher-order couplings in protein families. Our methods provide mechanistic explanations for couplings using physicochemical properties of amino acids and discernibility between orders. We also investigate a method for mining frequent episodes---called coupled patterns---in an alignment produced by a classical algorithm for proteins and for exploiting the coupled patterns for improving the alignment quality in terms of exposition of couplings. We demonstrate the effectiveness of our proposed methods on a large collection of sequence datasets for protein families.