Integrated structure prediction of protein–protein docking with experimental restraints using ColabDock

Corso, G., Stärk, H., Jing, B., Barzilay, R. & Jaakkola, T. Diffdock: diffusion steps, twists, and turns for molecular docking. In Proc. 2023 International Conference on Learning Representations (ICLR, 2023). https://doi.org/10.48550/arXiv.2210.01776Tsaban, T. et al. Harnessing protein folding neural networks for peptide–protein docking. Nat. Commun. 13, 176 (2022).Article 

Google Scholar 
Masters, M., Mahmoud, A. H., Wei, Y. & Lill, M. A. Deep learning model for efficient protein–ligand docking with implicit side-chain flexibility. J. Chem. Inf. Model. 63, 1695–1707 (2023).Article 

Google Scholar 
Zheng, W., Wuyun, Q., Freddolino, P. L. & Zhang, Y. Proteins: Structure, Function, and Bioinformatics (Wiley, 2023).Peng, Z., Wang, W., Wei, H., Li, X. & Yang, J. Improved protein structure prediction with trRosettaX2, AlphaFold2, and optimized MSAs in CASP15. Proteins Struct. Funct. Bioinf. 91, 1704–1711 (2023).Article 

Google Scholar 
Wallner, B. Improved multimer prediction using massive sampling with AlphaFold in CASP15. Proteins 91, 1734–1746 (2023).Article 

Google Scholar 
Pierce, B. G. et al. ZDOCK server: interactive docking prediction of protein–protein complexes and symmetric multimers. Bioinformatics 30, 1771–1773 (2014).Article 

Google Scholar 
Cheng, T. M.-K., Blundell, T. L. & Fernandez-Recio, J. pyDock: electrostatics and desolvation for effective scoring of rigid-body protein–protein docking. Proteins 68, 503–515 (2007).Article 

Google Scholar 
Torchala, M., Moal, I. H., Chaleil, R. A. G., Fernandez-Recio, J. & Bates, P. A. SwarmDock: a server for flexible protein–protein docking. Bioinformatics 29, 807–809 (2013).Article 

Google Scholar 
de Vries, S. J., van Dijk, M. & Bonvin, A. M. J. J. The HADDOCK web server for data-driven biomolecular docking. Nat. Protoc. 5, 883–897 (2010).Article 

Google Scholar 
Dominguez, C., Boelens, R. & Bonvin, A. M. J. J. HADDOCK: a protein–protein docking approach based on biochemical or biophysical information. J. Am. Chem. Soc. 125, 1731–1737 (2003).Article 

Google Scholar 
Comeau, S. R., Gatchell, D. W., Vajda, S. & Camacho, C. J. ClusPro: a fully automated algorithm for protein-protein docking. Nucleic Acids Res. 32, W96–W99 (2004).Article 

Google Scholar 
Comeau, S. R., Gatchell, D. W., Vajda, S. & Camacho, C. J. ClusPro: an automated docking and discrimination method for the prediction of protein complexes. Bioinformatics 20, 45–50 (2004).Article 

Google Scholar 
Kozakov, D. et al. The ClusPro web server for protein–protein docking. Nat. Protoc. 12, 255–278 (2017).Article 

Google Scholar 
Vajda, S., Hall, D. R. & Kozakov, D. Sampling and scoring: a marriage made in heaven: sampling and scoring. Proteins 81, 1874–1884 (2013).Article 

Google Scholar 
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).Article 

Google Scholar 
Evans, R. et al. Protein complex prediction with AlphaFold-Multimer. Preprint at bioRxiv https://doi.org/10.1101/2021.10.04.463034 (2022).Baek, M. et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science 373, 871–876 (2021).Article 

Google Scholar 
Roney, J. P. & Ovchinnikov, S. State-of-the-art estimation of protein model accuracy using AlphaFold. Phys. Rev. Lett. 129, 238101 (2022).Article 

Google Scholar 
Jendrusch, M., Korbel, J. O. & Sadiq, S. K. AlphaDesign: a de novo protein design framework based on AlphaFold. Preprint at bioRxiv https://doi.org/10.1101/2021.10.11.463937 (2021).Moffat, L., Kandathil, S. M. & Jones, D. T. Design in the DARK: learning deep generative models for de novo protein design. Preprint at bioRxiv https://doi.org/10.1101/2022.01.27.478087 (2022).Wicky, B. I. M. et al. Hallucinating symmetric protein assemblies. Science 378, 56–61 (2022).Article 

Google Scholar 
Frank, C. et al. Efficient and scalable de novo protein design using a relaxed sequence space. Preprint at bioRxiv https://doi.org/10.1101/2023.02.24.529906 (2023).Jiang, W. & Zheng, S. Structural insights into galanin receptor signaling. Proc. Natl Acad. Sci. USA 119, e2121465119 (2022).Article 

Google Scholar 
Jin, Z. et al. Structure of a TOC–TIC supercomplex spanning two chloroplast envelope membranes. Cell 185, 4788–4800.e13 (2022).Article 

Google Scholar 
Drake, Z. C., Seffernick, J. T. & Lindert, S. Protein complex prediction using rosetta, alphafold, and mass spectrometry covalent labeling. Nat. Commun. 13, 7846 (2022).Article 

Google Scholar 
Mitternacht, S. FreeSASA: an open source C library for solvent accessible surface area calculations. F1000Res 5, 189 (2016).Article 

Google Scholar 
Almagro, J. C. et al. Second antibody modeling assessment (AMA-II): 3D antibody modeling. Proteins 82, 1553–1562 (2014).Article 

Google Scholar 
Anishchenko, I., Kundrotas, P. J. & Vakser, I. A. Modeling complexes of modeled proteins: modeling complexes of modeled proteins. Proteins 85, 470–478 (2017).Article 

Google Scholar 
Ganea, O.-E. et al. Independent SE(3)-equivariant models for end-to-end rigid protein docking. In Proc. 2022 International Conference on Learning Representations (ICLR, 2022). https://doi.org/10.48550/arXiv.2111.07786Yan, Y., Tao, H., He, J. & Huang, S.-Y. The HDOCK server for integrated protein–protein docking. Nat. Protoc. 15, 1829–1852 (2020).Article 

Google Scholar 
Yin, R., Feng, B. Y., Varshney, A. & Pierce, B. G. Benchmarking AlphaFold for protein complex modeling reveals accuracy determinants. Protein Sci. 31, e4379 (2022).Article 

Google Scholar 
Huang, M. et al. The mechanism of an inhibitory antibody on TF-initiated blood coagulation revealed by the crystal structures of human tissue factor, Fab 5G9 and TF·5G9 complex 1. J. Mol. Biol. 275, 873–894 (1998).Article 

Google Scholar 
Bryant, P., Kelkar, A., Guljas, A., Clementi, C. & Noé, F. Structure prediction of protein–ligand complexes from sequence information with Umol. Nat. Commun. 15, 4536 (2024).Article 

Google Scholar 
Baek, M. et al. Accurate prediction of protein–nucleic acid complexes using RoseTTAFoldNA. Nat. Methods 21, 117–121 (2024).Article 

Google Scholar 
Krishna, R. et al. Generalized biomolecular modeling and design with RoseTTAFold All-Atom. Science 384, eadl2528 (2024).Article 

Google Scholar 
Vreven, T. et al. Updates to the integrated protein–protein interaction benchmarks: docking benchmark version 5 and affinity benchmark version 2. J. Mol. Biol. 427, 3031–3041 (2015).Article 

Google Scholar 
Mirdita, M. et al. ColabFold: making protein folding accessible to all. Nat. Methods 19, 679–682 (2022).Article 

Google Scholar 
Joachims, T. Optimizing search engines using clickthrough data. In Proc. Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 133–142 (ACM, 2002).Basu, S. & Wallner, B. DockQ: a quality measure for protein–protein docking models. PLoS ONE 11, e0161879 (2016).Article 

Google Scholar 
Feng, S., et al. ColabDock (data). OSF https://doi.org/10.17605/OSF.IO/N6R48 (2024).Feng, S., et al. ColabDock (source code). OSF https://doi.org/10.5281/ZENODO.10467048 (2024).