An Atomistic Statistically Effective Energy Function for Computational Protein Design
| Autor: | Isabelle André, Christopher M. Topham, Sophie Barbe |
|---|---|
| Přispěvatelé: | Laboratoire d'Ingénierie des Systèmes Biologiques et des Procédés (LISBP), Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de la Recherche Agronomique (INRA), We gratefully acknowledge access granted to the HPC resources of the Midi-Pyrenees Regional Computing Centre (CALMIP, Toulouse, France)., ANR-12-MONU-0015,ProtiCAD,Modèles multi-physiques et algorithmes robotiques pour la conception assistée par ordinateur de protéines(2012), French National Research Agency (ANR Project PROTICAD) [ANR-12-MONU-0015-03] Funding Text : This work was supported by the French National Research Agency (ANR Project PROTICAD, ANR-12-MONU-0015-03). We gratefully acknowledge access granted to the HPC resources of the Midi-Pyrenees Regional Computing Centre (CALMIP, Toulouse, France)., Institut National de la Recherche Agronomique (INRA)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS) |
| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
Protein Folding Protein Conformation Globular protein [SDV]Life Sciences [q-bio] Protein domain Protein design Ligands globular-proteins 010402 general chemistry Polymorphism Single Nucleotide 01 natural sciences Accessible surface area Viral Proteins 03 medical and health sciences Superoxide Dismutase-1 Protein structure Atom scoring function data-bank Bacteriophage T4 Humans Statistical physics Physical and Theoretical Chemistry Databases Protein knowledge-based potentials Protein Unfolding ligand-binding affinities chemistry.chemical_classification Quantitative Biology::Biomolecules reference state improves Function (mathematics) structure prediction 0104 chemical sciences Computer Science Applications 030104 developmental biology chemistry Mutagenesis mean force Thermodynamics Muramidase accessible surface-area Protein folding Atomic physics quasi-chemical approximation |
| Zdroj: | Journal of Chemical Theory and Computation Journal of Chemical Theory and Computation, American Chemical Society, 2016, 12 (8), pp.4146-4168. ⟨10.1021/acs.jctc.6b00090⟩ Journal of Chemical Theory and Computation, 2016, 12 (8), pp.4146-4168. ⟨10.1021/acs.jctc.6b00090⟩ |
| ISSN: | 1549-9626 1549-9618 |
| Popis: | Shortcomings in the definition of effective free-energy surfaces of proteins are recognized to be a major contributory factor responsible for the low success rates of existing automated methods for computational protein design (CPD). The formulation of an atomistic statistically effective energy function (SEEF) suitable for a wide range of CPD applications and its derivation from structural data extracted from protein domains and protein-ligand complexes are described here. The proposed energy function comprises nonlocal atom-based and local residue based SEEFs, which are coupled using a novel atom connectivity number factor to scale short-range, pairwise, nonbonded atomic interaction energies and a surface-area-dependent cavity energy term. This energy function was used to derive additional SEEFs describing the unfolded-state ensemble of any given residue sequence based on computed average energies for partially or fully solvent-exposed fragments in regions of irregular structure in native proteins. Relative thermal stabilities of 97 T4 bacteriophage lysozyme mutants were predicted from calculated energy differences for folded and unfolded states with an average unsigned error (AUE) of 0.84 kcal mol(-1) when compared to experiment. To demonstrate the utility of the energy function for CPD, further validation was carried out in tests of its capacity to recover cognate protein sequences and to discriminate native and near-native protein folds, loop conformers, and small-molecule ligand binding poses from non-native benchmark decoys. Experimental ligand binding free energies for a diverse set of 80 protein complexes could be predicted with an AUE of 2.4 kcal mol(-1) using an additional energy term to account for the loss in ligand configurational entropy upon binding. The atomistic SEEF is expected to improve the accuracy of residue-based coarse-grained SEEFs currently used in CPD and to extend the range of applications of extant atom-based protein statistical potentials. |
| Databáze: | OpenAIRE |
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