Paper Info
Title
Limits and potential of combined folding and docking
Abstract
Motivation: In the last decade, de novo protein structure prediction accuracy for individual proteins has improved significantly by utilising deep learning (DL) methods for harvesting the co-evolution information from large multiple sequence alignments (MSAs). The same approach can, in principle, also be used to extract information about evolutionary-based contacts across protein-protein interfaces. However, most earlier studies have not used the latest DL methods for inter-chain contact distance prediction. This article introduces a fold-and-dock method based on predicted residue-residue distances with trRosetta. Results: The method can simultaneously predict the tertiary and quaternary structure of a protein pair, even when the structures of the monomers are not known. The straightforward application of this method to a standard dataset for protein-protein docking yielded limited success. However, using alternative methods for generating MSAs allowed us to dock accurately significantly more proteins. We also introduced a novel scoring function, PconsDock, that accurately separates 98% of correctly and incorrectly folded and docked proteins. The average performance of the method is comparable to the use of traditional, template-based or ab initio shape-complementarity-only docking methods. Moreover, the results of conventional and fold-and-dock approaches are complementary, and thus a combined docking pipeline could increase overall docking success significantly. This methodology contributed to the best model for one of the CASP14 oligomeric targets, H1065.
Year
DOI
Venue
2022
10.1093/bioinformatics/btab760
BIOINFORMATICS
DocType
Volume
Issue
Journal
38
4
ISSN
Citations 
PageRank 
1367-4803
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Gabriele Pozzati100.34
Wensi Zhu200.34
Claudio Bassot300.34
John Lamb401.01
Petras Kundrotas500.34
Arne Elofsson600.34