Pushing the limits of what is achievable in protein–DNA docking: benchmarking HADDOCK’s performance
By M. V. van Dijk, Alexandre M. J. J. Bonvin. 2010
55 citations recorded by the source corpus.
The intrinsic flexibility of DNA and the difficulty of identifying its interaction surface have long been challenges that prevented the development of efficient protein–DNA docking methods. We have demonstrated the ability our flexible data-driven docking method HADDOCK to deal with these before, by using custom-built DNA structural models. Here we put our method to the test on a set of 47 complexes from the protein–DNA docking benchmark. We show that HADDOCK is able to predict many of the specific DNA conformational changes required to assemble the interface(s). Our DNA analysis and modelling procedure captures the bend and twist motions occurring upon complex formation and uses these to generate custom-built DNA structural models, more closely resembling the bound form, for use in a second docking round. We achieve throughout the benchmark an overall success rate of 94% of one-star solutions or higher (interface root mean square deviation ≤4 Å and fraction of native contacts >10%) according to CAPRI criteria. Our improved protocol successfully predicts even the challenging protein–DNA complexes in the benchmark. Finally, our method is the first to readily dock multiple molecules (N > 2) simultaneously, pushing the limits of what is currently achievable in the field of protein–DNA docking.
Subfields: Molecular Biology, Computational Biology & Bioinformatics
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In Molecular Biology
- Zeynep Kurkcuoglu
- Pushmeet Kohli
- Alexandre M. J. J. Bonvin
- Alexander Pritzel
- Eirini Arvaniti
- Michael Figurnov
- Ellen D. Zhong
- M. V. van Dijk
In Computational Biology & Bioinformatics
- Zeynep Kurkcuoglu
- Pushmeet Kohli
- Alexandre M. J. J. Bonvin
- Kathryn Tunyasuvunakool
- Eirini Arvaniti
- Ellen D. Zhong
Citations in the corpus
- Accurate structure prediction of biomolecular interactions with AlphaFold 3 (2024, methodology): “A wide range of predictors for various specific interaction types has been developed 16–28 , as well as one generalist method developed concurrently with the present work 29 , but the accuracy of such deep-learning attempts has been mixed and often below that of physics-inspired methods 30,31 .”
- Pre‐ and post‐docking sampling of conformational changes using ClustENM and HADDOCK for protein‐protein and protein‐DNA systems (2019, methodology): “For protein-DNA docking, the experimental restraints described in the study by van Dijk and Bonvin, 2010 21 were used to drive the docking.”