Modeling CAPRI Targets of Round 55 by Combining AlphaFold and Docking.

ABSTRACT: In recent years, the field of structural biology has seen remarkable advancements, particularly in modeling of protein tertiary and quaternary structures. The AlphaFold deep learning approach revolutionized protein structure prediction by achieving near-experimental accuracy on many targets. This paper presents a detailed account of structural modeling of oligomeric targets in Round 55 of CAPRI by combining deep learning-based predictions (AlphaFold2 multimer pipeline) with traditional docking techniques in a hybrid approach to protein-protein docking. To complement the AlphaFold models generated for the given oligomeric state of the targets, we built docking predictions by combining models generated for lower-oligomeric states-dimers for trimeric targets and trimers/dimers for tetrameric

SUBMITTER: Singh A 

PROVIDER: S-EPMC12354191 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature