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.
Highlights
The development of computational approaches for accurately predicting the protein threedimensional (3D) structure directly from the sequence information is of central importance in structural biology (Jones et al, 1992; Baker and Sali, 2001; Dill and MacCallum, 2012)
The most widely used distant-homology modeling technique, aims to address the challenge by leveraging multiple sources of information by mining the evolutionary profile of the query and templates to reveal potential distant homology and perform distant-homology modeling to predict the 3D structure of the query protein
The recent advancement in predicting the interresidue interaction maps using sequence coevolution and deep learning (Morcos et al, 2011; He et al, 2017; Wang et al, 2017; Adhikari et al, 2018; Hanson et al, 2018; Kandathil et al, 2019; Yang et al, 2020) has opened new possibilities to further improve the sensitivity of distant-homology protein threading by incorporating the predicted inter-residue interaction information
Summary
The development of computational approaches for accurately predicting the protein threedimensional (3D) structure directly from the sequence information is of central importance in structural biology (Jones et al, 1992; Baker and Sali, 2001; Dill and MacCallum, 2012). The recent advancement in predicting the interresidue interaction maps using sequence coevolution and deep learning (Morcos et al, 2011; He et al, 2017; Wang et al, 2017; Adhikari et al, 2018; Hanson et al, 2018; Kandathil et al, 2019; Yang et al, 2020) has opened new possibilities to further improve the sensitivity of distant-homology protein threading by incorporating the predicted inter-residue interaction information.
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