Peptide–protein interactions are important to many processes of life, particularly for signal transmission or regulatory mechanisms. When no information is known about the interaction between a protein and a peptide, it is of interest to propose candidate sites of interaction at the protein surface, to assist the design of biological experiments to probe the interaction, or to serve as a starting point for more focused in silico approaches. PEP-SiteFinder is a tool that will, given the structure of a protein and the sequence of a peptide, identify protein residues predicted to be at peptide–protein interface. PEP-SiteFinder relies on the 3D de novo generation of peptide conformations given its sequence. These conformations then undergo a fast blind rigid docking on the complete protein surface, and we have found, as the result of a benchmark over 41 complexes, that the best poses overlap to some extent the experimental patch of interaction for close to 90% complexes. In addition, PEP-SiteFinder also returns a propensity index we have found informative about the confidence of the prediction. The PEP-SiteFinder web server is available at http://bioserv.rpbs.univ-paris-diderot.fr/PEP-SiteFinder.
Coarse grain modelling of macromolecules is a new approach, potentially well adapted to answer numerous issues, ranging from physics to biology. We propose here an original DNA coarse grain model specifically dedicated to protein-DNA docking, a crucial, but still largely unresolved, question in molecular biology. Using a representative set of protein-DNA complexes, we first show that our model is able to predict the interaction surface between the macromolecular partners taken in their bound form. In a second part, the impact of the DNA sequence and electrostatics, together with the DNA and protein conformations on docking is investigated. Our results strongly suggest that the overall DNA structure mainly contributes in discriminating the interaction site on cognate proteins. Direct electrostatic interactions between phosphate groups and amino acid side chains strengthen the binding. Overall, this work demonstrates that coarse grain modeling can reveal itself a precious auxiliary for a general and complete description and understanding of protein-DNA association mechanisms.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.