Back-translation for discovering distant protein homologies

TL;DR

This paper introduces a novel alignment method that detects distant protein homologies by accounting for frameshift and point mutations, overcoming limitations of traditional methods in revealing evolutionary relationships.

Contribution

It presents a dynamic programming algorithm over graph representations of DNA sequences to infer homologies affected by complex mutations, a significant advancement over existing techniques.

Findings

Successfully uncovers evolutionary relationships missed by traditional methods.

Demonstrates effectiveness on biologically significant examples.

Provides a more accurate detection of distant homologies.

Abstract

Frameshift mutations in protein-coding DNA sequences produce a drastic change in the resulting protein sequence, which prevents classic protein alignment methods from revealing the proteins' common origin. Moreover, when a large number of substitutions are additionally involved in the divergence, the homology detection becomes difficult even at the DNA level. To cope with this situation, we propose a novel method to infer distant homology relations of two proteins, that accounts for frameshift and point mutations that may have affected the coding sequences. We design a dynamic programming alignment algorithm over memory-efficient graph representations of the complete set of putative DNA sequences of each protein, with the goal of determining the two putative DNA sequences which have the best scoring alignment under a powerful scoring system designed to reflect the most probable evolutionary process. This allows us to uncover evolutionary information that is not captured by traditional alignment methods, which is confirmed by biologically significant examples.