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Insights on protein-DNA recognition by coarse grain modelling.

ABSTRACT: 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.

SUBMITTER: Poulain P

PROVIDER: S-EPMC2904573 | biostudies-literature | 2008 Nov

REPOSITORIES: biostudies-literature

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Insights on protein-DNA recognition by coarse grain modelling.

Poulain P P Saladin A A Hartmann B B Prévost C C

Journal of computational chemistry 20081101 15

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 for ...[more]

PMID: 18478582

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Project description:Coarse-grained (CG) methods for sampling protein conformational space have the potential to increase computational efficiency by reducing the degrees of freedom. The gain in computational efficiency of CG methods often comes at the expense of non-protein like local conformational features. This could cause problems when transitioning to full atom models in a hierarchical framework. Here, a CG potential energy function was validated by applying it to the problem of loop prediction. A novel method to sample the conformational space of backbone atoms was benchmarked using a standard test set consisting of 351 distinct loops. This method used a sequence-independent CG potential energy function representing the protein using [Formula: see text]-carbon positions only and sampling conformations with a Monte Carlo simulated annealing based protocol. Backbone atoms were added using a method previously described and then gradient minimised in the Rosetta force field. Despite the CG potential energy function being sequence-independent, the method performed similarly to methods that explicitly use either fragments of known protein backbones with similar sequences or residue-specific [Formula: see text]/[Formula: see text]-maps to restrict the search space. The method was also able to predict with sub-Angstrom accuracy two out of seven loops from recently solved crystal structures of proteins with low sequence and structure similarity to previously deposited structures in the PDB. The ability to sample realistic loop conformations directly from a potential energy function enables the incorporation of additional geometric restraints and the use of more advanced sampling methods in a way that is not possible to do easily with fragment replacement methods and also enable multi-scale simulations for protein design and protein structure prediction. These restraints could be derived from experimental data or could be design restraints in the case of computational protein design. C++ source code is available for download from http://www.sbg.bio.ic.ac.uk/phyre2/PD2/.

Dataset Information

Insights on protein-DNA recognition by coarse grain modelling.

Publications

Insights on protein-DNA recognition by coarse grain modelling.

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