Dataset Information

SIDEpro: a novel machine learning approach for the fast and accurate prediction of side-chain conformations.

ABSTRACT: Accurate protein side-chain conformation prediction is crucial for protein modeling and existing methods for the task are widely used; however, faster and more accurate methods are still required. Here we present a new machine learning approach to the problem where an energy function for each rotamer in a structure is computed additively over pairs of contacting atoms. A family of 156 neural networks indexed by amino acid and contacting atom types is used to compute these rotamer energies as a function of atomic contact distances. Although direct energy targets are not available for training, the neural networks can still be optimized by converting the energies to probabilities and optimizing these probabilities using Markov Chain Monte Carlo methods. The resulting predictor SIDEpro makes predictions by initially setting the rotamer probabilities for each residue from a backbone-dependent rotamer library, then iteratively updating these probabilities using the trained neural networks. After convergences of the probabilities, the side-chains are set to the highest probability rotamer. Finally, a post processing clash reduction step is applied to the models. SIDEpro represents a significant improvement in speed and a modest, but statistically significant, improvement in accuracy when compared with the state-of-the-art for rapid side-chain prediction method SCWRL4 on the following datasets: (1) 379 protein test set of SCWRL4; (2) 94 proteins from CASP9; (3) a set of seven large protein-only complexes; and (4) a ribosome with and without the RNA. Using the SCWRL4 test set, SIDEpro's accuracy (?(1) 86.14%, ?(1+2) 74.15%) is slightly better than SCWRL4-FRM (?(1) 85.43%, ?(1+2) 73.47%) and it is 7.0 times faster. On the same test set SIDEpro is clearly more accurate than SCWRL4-rigid rotamer model (RRM) (?(1) 84.15%, ?(1+2) 71.24%) and 2.4 times faster. Evaluation on the additional test sets yield similar accuracy results with SIDEpro being slightly more accurate than SCWRL4-flexible rotamer model (FRM) and clearly more accurate than SCWRL4-RRM; however, the gap in CPU time is much more significant when the methods are applied to large protein complexes. SIDEpro is part of the SCRATCH suite of predictors and available from: http://scratch.proteomics.ics.uci.edu/.

SUBMITTER: Nagata K

PROVIDER: S-EPMC3240718 | biostudies-literature | 2012 Jan

REPOSITORIES: biostudies-literature

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SIDEpro: a novel machine learning approach for the fast and accurate prediction of side-chain conformations.

Nagata Ken K Randall Arlo A Baldi Pierre P

Proteins 20111109 1

Accurate protein side-chain conformation prediction is crucial for protein modeling and existing methods for the task are widely used; however, faster and more accurate methods are still required. Here we present a new machine learning approach to the problem where an energy function for each rotamer in a structure is computed additively over pairs of contacting atoms. A family of 156 neural networks indexed by amino acid and contacting atom types is used to compute these rotamer energies as a f ...[more]

PMID: 22072531

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Project description:An approach is described that improves the prediction of the conformations of surface side chains in crystal structures, given the main-chain conformation of a protein. A key element of the methodology involves the use of the colony energy. This phenomenological term favors conformations found in frequently sampled regions, thereby approximating entropic effects and serving to smooth the potential energy surface. Use of the colony energy significantly improves prediction accuracy for surface side chains with little additional computational cost. Prediction accuracy was quantified as the percentage of side-chain dihedral angles predicted to be within 40 degrees of the angles measured by X-ray diffraction. Use of the colony energy in predictions for single side chains improved the prediction accuracy for chi(1) and chi(1+2) from 65 and 40% to 74 and 59%, respectively. Several other factors that affect prediction of surface side-chain conformations were also analyzed, including the extent of conformational sampling, details of the rotamer library employed, and accounting for the crystallographic environment. The prediction of conformations for polar residues on the surface was generally found to be more difficult than those for hydrophobic residues, except for polar residues participating in hydrogen bonds with other protein groups. For surface residues with hydrogen-bonded side chains, the prediction accuracy of chi(1) and chi(1+2) was 79 and 63%, respectively. For surface polar residues, in general (all side-chain prediction), the accuracy of chi(1) and chi(1+2) was only 73 and 56%, respectively. The most accurate results were obtained using the colony energy and an all-atom description that includes neighboring molecules in the crystal (protein chains and hetero atoms). Here, the accuracy of chi(1) and chi(1+2) predictions for surface side chains was 82 and 73%, respectively. The root mean square deviations obtained for hydrogen-bonding surface side chains were 1.64 and 1.81 A, with and without consideration of crystal packing effects, respectively.

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SIDEpro: a novel machine learning approach for the fast and accurate prediction of side-chain conformations.

Publications

SIDEpro: a novel machine learning approach for the fast and accurate prediction of side-chain conformations.

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