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Improving hybrid statistical and physical forcefields through local structure enumeration.

ABSTRACT: Forcefields used in biomolecular simulations are comprised of energetic terms that are physical in nature, based on parameter fitting to quantum mechanical simulation or experimental data, or statistical, drawing off high-resolution structural data to describe distributions of molecular features. Combining the two in a single forcefield is challenging, since physical terms describe some, but not all, of the observed statistics, leading to double counting. In this manuscript, we develop a general scheme for correcting statistical potentials used in combination with physical terms. We apply these corrections to the sidechain torsional potential used in the Rosetta all-atom forcefield. We show the approach identifies instances of double-counted interactions, including electrostatic interactions between sidechain and nearby backbone, and steric interactions between neighboring C? atoms within secondary structural elements. Moreover, this scheme allows for the inclusion of intraresidue physical terms, previously turned off to avoid overlap with the statistical potential. Combined, these corrections lead to a forcefield with improved performance on several structure prediction tasks, including rotamer prediction and native structure discrimination.

SUBMITTER: Conway P

PROVIDER: S-EPMC4972208 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

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Improving hybrid statistical and physical forcefields through local structure enumeration.

Conway Patrick P DiMaio Frank F

Protein science : a publication of the Protein Society 20160616 8

Forcefields used in biomolecular simulations are comprised of energetic terms that are physical in nature, based on parameter fitting to quantum mechanical simulation or experimental data, or statistical, drawing off high-resolution structural data to describe distributions of molecular features. Combining the two in a single forcefield is challenging, since physical terms describe some, but not all, of the observed statistics, leading to double counting. In this manuscript, we develop a general ...[more]

PMID: 27239808

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Project description:BackgroundWhen characterizing the structural topology of proteins, protein secondary structure (PSS) plays an important role in analyzing and modeling protein structures because it represents the local conformation of amino acids into regular structures. Although PSS prediction has been studied for decades, the prediction accuracy reaches a bottleneck at around 80%, and further improvement is very difficult.ResultsIn this paper, we present an improved dictionary-based PSS prediction method called SymPred, and a meta-predictor called SymPsiPred. We adopt the concept behind natural language processing techniques and propose synonymous words to capture local sequence similarities in a group of similar proteins. A synonymous word is an n-gram pattern of amino acids that reflects the sequence variation in a protein's evolution. We generate a protein-dependent synonymous dictionary from a set of protein sequences for PSS prediction.On a large non-redundant dataset of 8,297 protein chains (DsspNr-25), the average Q3 of SymPred and SymPsiPred are 81.0% and 83.9% respectively. On the two latest independent test sets (EVA Set_1 and EVA_Set2), the average Q3 of SymPred is 78.8% and 79.2% respectively. SymPred outperforms other existing methods by 1.4% to 5.4%. We study two factors that may affect the performance of SymPred and find that it is very sensitive to the number of proteins of both known and unknown structures. This finding implies that SymPred and SymPsiPred have the potential to achieve higher accuracy as the number of protein sequences in the NCBInr and PDB databases increases.ConclusionsOur experiment results show that local similarities in protein sequences typically exhibit conserved structures, which can be used to improve the accuracy of secondary structure prediction. For the application of synonymous words, we demonstrate an example of a sequence alignment which is generated by the distribution of shared synonymous words of a pair of protein sequences. We can align the two sequences nearly perfectly which are very dissimilar at the sequence level but very similar at the structural level. The SymPred and SymPsiPred prediction servers are available at http://bio-cluster.iis.sinica.edu.tw/SymPred/.

Dataset Information

Improving hybrid statistical and physical forcefields through local structure enumeration.

Publications

Improving hybrid statistical and physical forcefields through local structure enumeration.

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