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Homology modeling in the time of collective and artificial intelligence.

Abstract：

:Homology modeling is a method for building protein 3D structures using protein primary sequence and utilizing prior knowledge gained from structural similarities with other proteins. The homology modeling process is done in sequential steps where sequence/structure alignment is optimized, then a backbone is built and later, side-chains are added. Once the low-homology loops are modeled, the whole 3D structure is optimized and validated. In the past three decades, a few collective and collaborative initiatives allowed for continuous progress in both homology and ab initio modeling. Critical Assessment of protein Structure Prediction (CASP) is a worldwide community experiment that has historically recorded the progress in this field. Folding@Home and Rosetta@Home are examples of crowd-sourcing initiatives where the community is sharing computational resources, whereas RosettaCommons is an example of an initiative where a community is sharing a codebase for the development of computational algorithms. Foldit is another initiative where participants compete with each other in a protein folding video game to predict 3D structure. In the past few years, contact maps deep machine learning was introduced to the 3D structure prediction process, adding more information and increasing the accuracy of models significantly. In this review, we will take the reader in a journey of exploration from the beginnings to the most recent turnabouts, which have revolutionized the field of homology modeling. Moreover, we discuss the new trends emerging in this rapidly growing field.

journal_name

Comput Struct Biotechnol J

journal_title

Computational and structural biotechnology journal

authors

Hameduh T,Haddad Y,Adam V,Heger Z

doi

10.1016/j.csbj.2020.11.007

subject

Has Abstract

pub_date

2020-11-14 00:00:00

pages

3494-3506

issn

2001-0370

pii

S2001-0370(20)30474-8

journal_volume

18

pub_type

杂志文章,评审

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