Recent Advances in Protein Homology Detection Propelled by Inter-Residue Interaction Map Threading

Abstract

Sequence-based protein homology detection has emerged as one of the most sensitive and accurate approaches to protein structure prediction. Despite the success, homology detection remains very challenging for weakly homologous proteins with divergent evolutionary profile. Very recently, deep neural network architectures have shown promising progress in mining the coevolutionary signal encoded in multiple sequence alignments, leading to reasonably accurate estimation of inter-residue interaction maps, which serve as a rich source of additional information for improved homology detection. Here, we summarize the latest developments in protein homology detection driven by inter-residue interaction map threading. We highlight the emerging trends in distant-homology protein threading through the alignment of predicted interaction maps at various granularities ranging from binary contact maps to finer-grained distance and orientation maps as well as their combination. We also discuss some of the current limitations and possible future avenues to further enhance the sensitivity of protein homology detection.

Keywords: homology modeling; inter-residue interaction map; protein homology; protein structure prediction; protein threading.

FIGURE 1

Illustration of protein interaction map threading.

FIGURE 2

Structural superposition between predicted models using various threading methods (in violet) and the corresponding experimental structures (in gray) for representative CAMEO targets 6D2S_A of length 289 residues and 6CP8_D of length 164 residues.