ArticleProteins2009
A coarse-grained potential for fold recognition and molecular dynamics simulations of proteins.
Article in Proteins, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Benchmarking coarse-grained models of organic semiconductors via deep backmapping.Frontiers in chemistry · 2022Article
- Finding the needle in the haystack: towards solving the protein-folding problem computationally.Critical reviews in biochemistry and molecular biology · 2018Review
- Parametrization of Backbone Flexibility in a Coarse-Grained Force Field for Proteins (COFFDROP) Derived from All-Atom Explicit-Solvent Molecular Dynamics Simulations of All Possible Two-Residue Peptides.Journal of chemical theory and computation · 2015Article
- Biallelic loss-of-function mutation in NIK causes a primary immunodeficiency with multifaceted aberrant lymphoid immunity.Nature communications · 2014Article
- COFFDROP: A Coarse-Grained Nonbonded Force Field for Proteins Derived from All-Atom Explicit-Solvent Molecular Dynamics Simulations of Amino Acids.Journal of chemical theory and computation · 2014Article
- The OPEP protein model: from single molecules, amyloid formation, crowding and hydrodynamics to DNA/RNA systems.Chemical Society reviews · 2014Review
- Recent advances in transferable coarse-grained modeling of proteins.Advances in protein chemistry and structural biology · 2014Review
- On simplified global nonlinear function for fitness landscape: a case study of inverse protein folding.PloS one · 2014Article
- Importance of the ion-pair interactions in the OPEP coarse-grained force field: parametrization and validation.Journal of chemical theory and computation · 2013Article
- PRIMO: A Transferable Coarse-grained Force Field for Proteins.Journal of chemical theory and computation · 2013Article
- Protein-protein interactions in a crowded environment: an analysis via cross-docking simulations and evolutionary information.PLoS computational biology · 2013Article
- Further optimization of a hybrid united-atom and coarse-grained force field for folding simulations: Improved backbone hydration and interactions between charged side chains.Journal of chemical theory and computation · 2012Article
- Protein side chain modeling with orientation-dependent atomic force fields derived by series expansions.Journal of computational chemistry · 2011Article
- Recovering physical potentials from a model protein databank.Proceedings of the National Academy of Sciences of the United States of America · 2010Article
- Potentials of mean force for protein structure prediction vindicated, formalized and generalized.PloS one · 2010Article
- Multiscale coarse-graining of the protein energy landscape.PLoS computational biology · 2010Article
- Computational exploration of the network of sequence flow between protein structures.Proteins · 2010Article
- Building and assessing atomic models of proteins from structural templates: learning and benchmarks.Proteins · 2009Article
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2 authors.
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Abstract
A coarse-grained potential for protein simulations and fold ranking is presented. The potential is based on a two-point model of individual amino acids and a specific implementation of hydrogen bonding. Parameters are determined for distance dependent pair interactions, pseudo bonds, angles, and torsions. A scaling factor for a hydrogen bonding term is also determined. Iterative sampling for 4867 proteins reproduces distributions of internal coordinates and distances observed in the Protein Data Bank. The adjustment of the potential and resampling are in the spirit of the generalized ensemble approach. No native structure information (e.g., secondary structure) is used in the calculation of the potential or in the simulation of a particular protein. The potential is subject to two tests as follows: (i) simulations of 956 globular proteins in the neighborhood of their native folds (these proteins were not used in the training set) and (ii) discrimination between native and decoy structures for 2470 proteins with 305,000 decoys and the "Decoys 'R' Us" dataset. In the first test, 58% of tested proteins stay within 5 A from the native fold in Molecular Dynamics simulations of more than 20 nanoseconds using the new potential. The potential is also useful in differentiating between correct and approximate folds providing significant signal for structure prediction algorithms. Sampling with the potential consistently regenerates the distribution of distances and internal coordinates it learned. Nevertheless, during Molecular Dynamics simulations structures are found that reproduce the learned distributions but are far from the native fold.
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