ArticleProteins2019
Decrypting protein surfaces by combining evolution, geometry, and molecular docking.
Article in Proteins, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Quantitative Characterization of the Impact of Protein-Protein Interactions on Ligand-Protein Binding: A Multi-Chain Dynamics Perturbation Analysis Method.International journal of molecular sciences · 2024Article
- Deep Local Analysis evaluates protein docking conformations with locally oriented cubes.Bioinformatics (Oxford, England) · 2022Article
- Article
- From complete cross-docking to partners identification and binding sites predictions.PLoS computational biology · 2022Article
- Challenges in protein docking.Current opinion in structural biology · 2020Review
- Docking approaches for modeling multi-molecular assemblies.Current opinion in structural biology · 2020Review
- Multiple protein-DNA interfaces unravelled by evolutionary information, physico-chemical and geometrical properties.PLoS computational biology · 2020Article
- Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The growing body of experimental and computational data describing how proteins interact with each other has emphasized the multiplicity of protein interactions and the complexity underlying protein surface usage and deformability. In this work, we propose new concepts and methods toward deciphering such complexity. We introduce the notion of interacting region to account for the multiple usage of a protein's surface residues by several partners and for the variability of protein interfaces coming from molecular flexibility. We predict interacting patches by crossing evolutionary, physicochemical and geometrical properties of the protein surface with information coming from complete cross-docking (CC-D) simulations. We show that our predictions match well interacting regions and that the different sources of information are complementary. We further propose an indicator of whether a protein has a few or many partners. Our prediction strategies are implemented in the dynJET
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.