A knowledge-based scoring function based on residue triplets for protein structure prediction

Ngan, Shing-Chung; Inouye, Michael T.; Samudrala, Ram

doi:10.1093/protein/gzj018

Cited by 17 publications

(12 citation statements)

References 42 publications

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“…Further studies of plausible functional forms of potentials, building on innovative work on modeling many-body potentials [24, 26, 71], with comprehensive sampling and iterative refinement of potential parameters are of considerable interest.…”

Section: Final Remarksmentioning

confidence: 99%

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A coarse‐grained potential for fold recognition and molecular dynamics simulations of proteins

Marynen

Elber

2009

Proteins

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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 re-sampling 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: (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 Å from the native fold in Molecular Dynamics simulations of more than twenty 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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Section: Final Remarksmentioning

confidence: 99%

“…It suggests lack of correlations between the y i 's. The use of multiple internal coordinate probabilities [24-26] p ( y i y j ) addresses some of the concerns. However the choice of correlations to focus on is not trivial and acquiring appropriate statistics for these higher order interaction terms is another challenge.…”

Section: Introductionmentioning

confidence: 99%

A coarse‐grained potential for fold recognition and molecular dynamics simulations of proteins

Marynen

Elber

2009

Proteins

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“…GmRLK18-1 was predicted to be both PRI-like and PGIP-like. Several knowledge-based scoring functions have been developed with varying degrees of success [40,41]. These functions usually compile statistics from databases that contain experimentally determined structures, and use such statistics to test the probability of a given conformation to be native-like.…”

Section: Discussionmentioning

confidence: 99%

Homo-dimerization and ligand binding by the leucine-rich repeat domain at RHG1/RFS2 underlying resistance to two soybean pathogens

et al. 2013

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BackgroundThe protein encoded by GmRLK18-1 (Glyma_18_02680 on chromosome 18) was a receptor like kinase (RLK) encoded within the soybean (Glycine max L. Merr.) Rhg1/Rfs2 locus. The locus underlies resistance to the soybean cyst nematode (SCN) Heterodera glycines (I.) and causal agent of sudden death syndrome (SDS) Fusarium virguliforme (Aoki). Previously the leucine rich repeat (LRR) domain was expressed in Escherichia coli.ResultsThe aims here were to evaluate the LRRs ability to; homo-dimerize; bind larger proteins; and bind to small peptides. Western analysis suggested homo-dimers could form after protein extraction from roots. The purified LRR domain, from residue 131–485, was seen to form a mixture of monomers and homo-dimers in vitro. Cross-linking experiments in vitro showed the H274N region was close (<11.1 A) to the highly conserved cysteine residue C196 on the second homo-dimer subunit. Binding constants of 20–142 nM for peptides found in plant and nematode secretions were found. Effects on plant phenotypes including wilting, stem bending and resistance to infection by SCN were observed when roots were treated with 50 pM of the peptides. Far-Western analyses followed by MS showed methionine synthase and cyclophilin bound strongly to the LRR domain. A second LRR from GmRLK08-1 (Glyma_08_g11350) did not show these strong interactions.ConclusionsThe LRR domain of the GmRLK18-1 protein formed both a monomer and a homo-dimer. The LRR domain bound avidly to 4 different CLE peptides, a cyclophilin and a methionine synthase. The CLE peptides GmTGIF, GmCLE34, GmCLE3 and HgCLE were previously reported to be involved in root growth inhibition but here GmTGIF and HgCLE were shown to alter stem morphology and resistance to SCN. One of several models from homology and ab-initio modeling was partially validated by cross-linking. The effect of the 3 amino acid replacements present among RLK allotypes, A87V, Q115K and H274N were predicted to alter domain stability and function. Therefore, the LRR domain of GmRLK18-1 might underlie both root development and disease resistance in soybean and provide an avenue to develop new variants and ligands that might promote reduced losses to SCN.

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“…Singh (Singh et al 1996) was the first to derive the potential for four-body interactions, in which C a from four nearest-neighbor residues were considered. These so-called multi-body statistical potentials were developed by several groups (Cohen et al 2009;Krishnamoorthy and Tropsha 2003;Masso and Vaisman 2007;Ngan et al 2006) and the performances were all good. The only problem with these multi-body statistical potentials was the high cost in the computation compared with the original pairwise interaction potential.…”

Section: Discussionmentioning

confidence: 99%

Statistical energy potential: reduced representation of Dehouck–Gilis–Rooman function by selecting against decoy datasets

Huang

Wei

et al. 2011

Amino Acids

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Statistical effective energy function (SEEF) is derived from the statistical analysis of the database of known protein structures. Dehouck-Gilis-Rooman (DGR) group has recently created a new generation of SEEF in which the additivity of the energy terms was manifested by decomposing the total folding free energy into a sum of lower order terms. We have tried to optimize the potential function based on their work. By using decoy datasets as screening filter, and through modification of algorithms in calculation of accessible surface area and residue-residue interaction cutoff, four new combinations of the energy terms were found to be comparable to DGR potential in performance test. Most importantly, the term number was reduced from the original 30 terms to only 5 in our results, thereby substantially decreasing the computation time while the performance was not sacrificed. Our results further proved the additivity and manipulability of the DGR original energy function, and our new combination of the energy could be used in prediction of protein structures.

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A knowledge-based scoring function based on residue triplets for protein structure prediction

Cited by 17 publications

References 42 publications

A coarse‐grained potential for fold recognition and molecular dynamics simulations of proteins

A coarse‐grained potential for fold recognition and molecular dynamics simulations of proteins

Homo-dimerization and ligand binding by the leucine-rich repeat domain at RHG1/RFS2 underlying resistance to two soybean pathogens

Statistical energy potential: reduced representation of Dehouck–Gilis–Rooman function by selecting against decoy datasets

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