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Docking With Rosetta and Deep Learning Approaches in CAPRI Rounds 47-55.

Ameya Harmalkar¹, Lee-Shin Chu¹, Samuel W Canner²

¹Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, Maryland, USA.

|September 22, 2025

Summary

This summary is machine-generated.

Recent Critical Assessment of PRediction of Interactions (CAPRI) rounds show modest accuracy gains for protein-protein interaction prediction, especially for flexible complexes. Combining Rosetta docking with deep learning methods like AlphaFold2 offers improvements but challenges remain for complex assemblies and antibody-antigen interfaces.

Keywords:

conformational changes deep learning protein docking structure prediction

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Published on: July 25, 2013

Area of Science:

Computational biology
Structural biology
Bioinformatics

Background:

The Critical Assessment of PRediction of Interactions (CAPRI) challenges the community to assess protein-protein interaction prediction methods.
Previous CAPRI rounds have driven advancements in docking and structure prediction algorithms.

Purpose of the Study:

To evaluate the performance of combined Rosetta docking and deep learning approaches in recent CAPRI rounds (47-55).
To identify and address key challenges in predicting protein-protein interactions, including conformational changes and complex assemblies.

Main Methods:

Integration of Rosetta docking tools (RosettaDock, ReplicaDock, SymDock) with deep learning predictors (AlphaFold2, IgFold, AlphaRED).
Development of enhanced sampling methods for conformational changes and a fold-and-dock approach for large hetero-multimers.
Application of Rosetta-based SymDock 2.0 for symmetric complexes and analysis of antibody-antigen interactions.

Main Results:

Modest improvements in prediction accuracy for simpler targets post-AlphaFold2, but performance for flexible complexes remains limited.
Successful prediction of binding-induced conformational changes in a bacteriophage protein (T194) using enhanced RosettaDock.
Accurate prediction of a large hetero-multimer (T160) using a fold-and-dock strategy and a symmetric complex (T230) with SymDock 2.0.
Challenges persist in modeling antibody-antigen interfaces, particularly CDR H3 loops, despite using deep learning tools.

Conclusions:

Combined docking and deep learning approaches show promise but require further refinement for complex protein interactions.
Addressing conformational flexibility and large multimeric structures remains critical for advancing protein-protein interaction prediction.
Future efforts should focus on specialized strategies for antibody-antigen interactions, including enhanced sampling and CDR-specific modeling.