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Protein structure modelling and evaluation based on a 4-distance description of side-chain interactions.

ABSTRACT: BACKGROUND: Accurate evaluation and modelling of residue-residue interactions within and between proteins is a key aspect of computational structure prediction including homology modelling, protein-protein docking, refinement of low-resolution structures, and computational protein design. RESULTS: Here we introduce a method for accurate protein structure modelling and evaluation based on a novel 4-distance description of residue-residue interaction geometry. Statistical 4-distance preferences were extracted from high-resolution protein structures and were used as a basis for a knowledge-based potential, called Hunter. We demonstrate that 4-distance description of side chain interactions can be used reliably to discriminate the native structure from a set of decoys. Hunter ranked the native structure as the top one in 217 out of 220 high-resolution decoy sets, in 25 out of 28 "Decoys 'R' Us" decoy sets and in 24 out of 27 high-resolution CASP7/8 decoy sets. The same concept was applied to side chain modelling in protein structures. On a set of very high-resolution protein structures the average RMSD was 1.47 A for all residues and 0.73 A for buried residues, which is in the range of attainable accuracy for a model. Finally, we show that Hunter performs as good or better than other top methods in homology modelling based on results from the CASP7 experiment. The supporting web site http://bioinfo.weizmann.ac.il/hunter/ was developed to enable the use of Hunter and for visualization and interactive exploration of 4-distance distributions. CONCLUSIONS: Our results suggest that Hunter can be used as a tool for evaluation and for accurate modelling of residue-residue interactions in protein structures. The same methodology is applicable to other areas involving high-resolution modelling of biomolecules.

SUBMITTER: Potapov V

PROVIDER: S-EPMC2912888 | biostudies-literature | 2010

REPOSITORIES: biostudies-literature

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Protein structure modelling and evaluation based on a 4-distance description of side-chain interactions.

Potapov Vladimir V Cohen Mati M Inbar Yuval Y Schreiber Gideon G

BMC bioinformatics 20100712

<h4>Background</h4>Accurate evaluation and modelling of residue-residue interactions within and between proteins is a key aspect of computational structure prediction including homology modelling, protein-protein docking, refinement of low-resolution structures, and computational protein design.<h4>Results</h4>Here we introduce a method for accurate protein structure modelling and evaluation based on a novel 4-distance description of residue-residue interaction geometry. Statistical 4-distance p ...[more]

PMID: 20624289

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Project description:Significant advancements in the field of protein structure prediction have necessitated the need for objective and robust evaluation of protein structural models by comparing predicted models against the experimentally determined native structures to quantitate their structural similarities. Existing protein model versus native similarity metrics either consider the distances between alpha carbon (C?) or side-chain atoms for computing the similarity. However, side-chain orientation of a protein plays a critical role in defining its conformation at the atomic-level. Despite its importance, inclusion of side-chain orientation in structural similarity evaluation has not yet been addressed. Here, we present SPECS, a side-chain-orientation-included protein model-native similarity metric for improved evaluation of protein structural models. SPECS combines side-chain orientation and global distance based measures in an integrated framework using the united-residue model of polypeptide conformation for computing model-native similarity. Experimental results demonstrate that SPECS is a reliable measure for evaluating structural similarity at the global level including and beyond the accuracy of C? positioning. Moreover, SPECS delivers superior performance in capturing local quality aspect compared to popular global C? positioning-based metrics ranging from models at near-experimental accuracies to models with correct overall folds-making it a robust measure suitable for both high- and moderate-resolution models. Finally, SPECS is sensitive to minute variations in side-chain ? angles even for models with perfect C? trace, revealing the power of including side-chain orientation. Collectively, SPECS is a versatile evaluation metric covering a wide spectrum of protein modeling scenarios and simultaneously captures complementary aspects of structural similarities at multiple levels of granularities. SPECS is freely available at http://watson.cse.eng.auburn.edu/SPECS/.

Project description:Prediction of protein secondary structure from FTIR spectra usually relies on the absorbance in the amide I-amide II region of the spectrum. It assumes that the absorbance in this spectral region, i.e., roughly 1700-1500 cm-1 is solely arising from amide contributions. Yet, it is accepted that, on the average, about 20% of the absorbance is due to amino acid side chains. The present paper evaluates the contribution of amino acid side chains in this spectral region and the potential to improve secondary structure prediction after correcting for their contribution. We show that the β-sheet content prediction is improved upon subtraction of amino acid side chain contributions in the amide I-amide II spectral range. Improvement is relatively important, for instance, the error of prediction of β-sheet content decreases from 5.42 to 4.97% when evaluated by ascending stepwise regression. Other methods tested such as partial least square regression and support vector machine have also improved accuracy for β-sheet content evaluation. The other structures such as α-helix do not significantly benefit from side chain contribution subtraction, in some cases prediction is even degraded. We show that co-linearity between secondary structure content and amino acid composition is not a main limitation for improving secondary structure prediction. We also show that, even though based on different criteria, secondary structures defined by DSSP and XTLSSTR both arrive at the same conclusion: only the β-sheet structure clearly benefits from side chain subtraction. It must be concluded that side chain contribution subtraction benefit for the evaluation of other secondary structure contents is limited by the very rough description of side chain absorbance which does not take into account the variations related to their environment. The study was performed on a large protein set. To deal with the large number of proteins present, we worked on protein microarrays deposited on BaF2 slides and FTIR spectra were acquired with an imaging system.

Dataset Information

Protein structure modelling and evaluation based on a 4-distance description of side-chain interactions.

Publications

Protein structure modelling and evaluation based on a 4-distance description of side-chain interactions.

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