Integrative modeling in the age of machine learning: a summary of HADDOCK strategies in CAPRI rounds 47-55
Reys, V.; Giulini, M.; Cojocaru, V.; Engel, A. L.; Xu, X.; Roel-Touris, J.; Geng, C.; Ambrosetti, F.; Jimenez-Garcia, B.; Jandova, Z.; Koukos, P. I.; van Noort, C. W.; Teixeira, J. M. C.; van Keulen, S. C.; Reau, M.; Honorato, R. V.; Bonvin, A. M. J. J.
Show abstract
The HADDOCK team participated in CAPRI rounds 47-55 as both server, manual predictor, and scorers. Throughout these CAPRI rounds, we used a plethora of computational strategies to predict the structure of protein complexes. Of the 10 targets comprising 24 interfaces, we achieved acceptable or better models for 3 targets in the human category and 1 in the server category. Our performance in the scoring challenge was slightly better, with our simple scoring protocol being the only one capable of identifying an acceptable model for Target 234. This result highlights the robustness of the simple, fully physics-based HADDOCK scoring function, especially when applied to highly flexible antibody-antigen complexes. Inspired by the significant advances in machine learning for structural biology and the dramatic improvement in our success rates after the public release of Alphafold2, we identify the integration of classical approaches like HADDOCK with AI-driven structure prediction methods as a key strategy for improving the accuracy of model generation and scoring.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- DrugForm-DTA: Towards real-world drug-target binding Affinity Model 94%
- Predicting stable binding modes from simulated dimers of the D76N mutant of β2-microglobulin 94%
- Evolutionary progression of collective mutations in Omicron sub-lineages towards efficient RBD-hACE2: allosteric communications between and within viral and human proteins 94%
Similar papers in this journal
Similar papers in this journal
- Improved protein complex prediction with AlphaFold-multimer by denoising the MSA profile 96%
- Computer-guided Binding Mode Identification and Affinity Improvement of an LRR Protein Binder without Structure Determination 95%
- Exploring the Potential of Structure-Based Deep Learning Approaches for T cell Receptor Design 95%
Similar papers in this journal
- Protein-protein docking with large-scale backbone flexibility using coarse-grained Monte-Carlo simulations 96%
- Comprehensive collection and prediction of ABC transmembrane protein structures in the AI era of structural biology 95%
- Evaluation of Deep Neural Network ProSPr for Accurate Protein Distance Predictions on CASP14 Targets 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.