fRMSDAlign: Protein Sequence Alignment Using Predicted Local Structure Information

Abstract

As the sequence identity between a pair of proteins decreases, alignment strategies that are based on sequence and/or sequence profiles become progressively less effective in identifying the correct structural correspondence between residue pairs. This significantly reduces the ability of comparative modeling-based approaches to build accurate structural models. Incorporating predicted information about the local structure of the protein into the alignment process holds the promise of significantly improving the alignment quality of distant proteins. This paper studies the impact on the alignment quality of a new class of predicted local structural features that measure how well fixed-length backbone fragments centered around each residue-pair align with each other. It presents a comprehensive experimental evaluation comparing these new features against existing state-of-the-art approaches utilizing profile-based and predicted secondary-structure information. It shows that for protein pairs with low sequence similarity (less than 12% sequence identity) the new structural features alone or in conjunction with profile-based information lead to alignments that are considerably better than those obtained by previous schemes.