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.

AlphaFold, Artificial Intelligence (AI), and Allostery

“…We outline its strengths and some weaknesses. We emphasize what it has accomplished, and what it has not, and the magnitude of the challenges, underscoring the difference between the theoretical folding problem , which was not solved, − and practical predictions by incorporating additional evolution information that generally have been. − We proceed to AI approaches to the complementary problem of protein–protein interactions (PPIs) by these methods and others, − with the human–microbiome PPI as a relevant and topical example . AI-powered prediction of human–microbe PPIs can accelerate research into questions such as how microbiota hijack cell signaling and provide drug targets. − We discuss how AI can reshape drug discovery, for example by amplifying repurposing of FDA-approved drugs, − an area which is already thriving.…”

Evolutionary analyses of genes in Echinodermata offer insights towards the origin of metazoan phyla

“…The performance of different orthology prediction tools in describing the gene content of the last eukaryotic common ancestor has previously been investigated [ 14 ], and the associated study noted that (by design) such tools do not return consistent orthogroups when calling distant homologies. That said, emerging methods that are capable of considering protein structure and interactions when calling remote protein homologies seem promising [ 7 , 37 , 62 , 63 ], and could prove well suited to predicting distant homologous relationships.…”

Understanding structural variability in proteins using protein structural networks

“…The alteration of protein structure due to dynamics is characterized by variation of inter-residue interactions. The information of inter-residue interactions is vital to understanding protein function and is used in studying protein folding and stability ( Gromiha and Selvaraj, 2004 ; Baker, 2000 ), homology detection ( Bhattacharya et al., 2021 ), prediction of protein structures ( Yang et al., 2020 ), and several other aspects. From a topological perspective, intra-protein interactions between spatially proximal residues can be represented on a graph using edges where the residues are represented as nodes.…”