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

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.

Automated summary

Sequence-based protein homology detection is a sensitive and accurate approach to protein structure prediction but remains challenging for weakly homologous proteins with divergent evolutionary profiles. Deep neural network architectures have shown promise in mining coevolutionary signal from multiple sequence alignments for improved homology detection. This article summarizes recent developments in protein homology detection driven by threading inter-residue interaction maps, highlighting trends in distant-homology protein threading through alignment of predicted interaction maps at various granularities. The authors also discuss current limitations and future avenues for enhancing the sensitivity of protein homology detection.