Aligning biological sequences by exploiting residue conservation and coevolution

TL;DR

This paper introduces DCAlign, an efficient sequence alignment algorithm that incorporates coevolution signals, improving alignment accuracy for proteins and RNA without relying on structural data.

Contribution

The paper presents DCAlign, a novel alignment method that models coevolution among sequence positions using an approximate message-passing approach, surpassing traditional profile models.

Findings

DCAlign outperforms profile-based methods in simulated data.

It effectively aligns real protein and RNA sequences.

The method captures coevolution signals without structural information.

Abstract

Sequences of nucleotides (for DNA and RNA) or amino acids (for proteins) are central objects in biology. Among the most important computational problems is that of sequence alignment, i.e. arranging sequences from different organisms in such a way to identify similar regions, to detect evolutionary relationships between sequences, and to predict biomolecular structure and function. This is typically addressed through profile models, which capture position-specificities like conservation in sequences, but assume an independent evolution of different positions. Over the last years, it has been well established that coevolution of different amino-acid positions is essential for maintaining three-dimensional structure and function. Modeling approaches based on inverse statistical physics can catch the coevolution signal in sequence ensembles; and they are now widely used in predicting protein structure, protein-protein interactions, and mutational landscapes. Here, we present DCAlign, an efficient alignment algorithm based on an approximate message-passing strategy, which is able to overcome the limitations of profile models, to include coevolution among positions in a general way, and to be therefore universally applicable to protein- and RNA-sequence alignment without the need of using complementary structural information. The potential of DCAlign is carefully explored using well-controlled simulated data, as well as real protein and RNA sequences.