SimFold energy function for de novo protein structure prediction: Consensus with Rosetta

Fujitsuka, Y.; Chikenji, George; Takada, Shoji

doi:10.1002/prot.20748

Cited by 50 publications

(52 citation statements)

References 50 publications

(69 reference statements)

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“…The SimFold simplifies an aqueous protein molecule by replacing the side-chain atoms of each amino acid with a sphere located at the center of mass of the side chain and by approximating roles of solvent water as a continuum model (17)(18)(19). The energy function is based on physical chemistry and has many empirical parameters that were optimized with use of the available structure database.…”

Section: Methodsmentioning

confidence: 99%

Folding energy landscape and network dynamics of small globular proteins

Hori

Chikenji

Berry

et al. 2009

Proc. Natl. Acad. Sci. U.S.A.

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The folding energy landscape of proteins has been suggested to be funnel-like with some degree of ruggedness on the slope. How complex the landscape, however, is still rather unclear. Many experiments for globular proteins suggested relative simplicity, whereas molecular simulations of shorter peptides implied more complexity. Here, by using complete conformational sampling of 2 globular proteins, protein G and src SH3 domain and 2 related random peptides, we investigated their energy landscapes, topological properties of folding networks, and folding dynamics. The projected energy surfaces of globular proteins were funneled in the vicinity of the native but also have other quite deep, accessible minima, whereas the randomized peptides have many local basins, including some leading to seriously misfolded forms. Dynamics in the denatured part of the network exhibited basin-hopping itinerancy among many conformations, whereas the protein reached relatively well-defined final stages that led to their native states. We also found that the folding network has the hierarchic nature characterized by the scale-free and the small-world properties.contact maps ͉ folding pathways ͉ multiple pathways ͉ principal coordinates P roteins fold on large-dimensional energy landscapes through myriads of conformations. One energy-landscape theory suggests that the global shape of the landscape is primarily funnel-like with some degree of ruggedness on the slope of the funnel (1, 2). How complex/rugged the energy landscape is and how diverse the folding-pathway ensemble is are still rather controversial. Experimentally, many small fast-folding proteins exhibit single-exponential behavior, suggesting simplicity (3). For such proteins, a perfect funnel model, Go model, has been used, as an extreme of simplicity, to model folding routes, often showing modestly good agreement with experiments (4). Conversely, there exist several clear evidences of complexity in folding. Under some conditions, proteins show strange and glassy kinetics, suggesting ruggedness of the landscape (5). Some ␤-sheet proteins, such as ␤-lactoglobulin, form nonnative ␣-helices at early stages of folding (6, 7).The computational approach has been the most direct to elucidate the complexity of folding energy landscapes. Methods developed in other areas, such as atomic clusters, have been applied to peptides and proteins, illustrating the multiple minima on the landscape (8-11). Recently, with background (10, 11), Krivov and Karplus developed the transition disconnectivity graph to visualize quantitatively the free-energy landscape and applied it for peptides finding a highly rugged non-funnel-like landscape with competing minima (12, 13). Caflisch and coworkers (14, 15) constructed a folding network for a designed peptide and uncovered a highly heterogeneous denatured ensemble. They both used network analyses without the data reduction to lower dimension and warned that the projection to low dimension, as is often done in conventional folding studies, can hide the co...

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Section: Methodsmentioning

confidence: 99%

Folding energy landscape and network dynamics of small globular proteins

Hori

Chikenji

Berry

et al. 2009

Proc. Natl. Acad. Sci. U.S.A.

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“…If achieved, a wide array of problems would be impacted. An obvious application would be the prediction of native structure from sequence using either an ab initio approach, [13][14][15][16][17][18][19][20] or homology-based prediction using threading and fold recognition. [21][22][23] With such a model, the conformational rearrangement and flexibility of native folds could be studied efficiently; these latter properties are clearly related to protein function.…”

Section: Introductionmentioning

confidence: 99%

Probing Protein Fold Space with a Simplified Model

Mináry

Levitt

2008

Journal of Molecular Biology

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We probe the stability and near-native energy landscape of protein fold space using powerful conformational sampling methods together with simple reduced models and statistical potentials. Fold space is represented by a set of 280 protein domains spanning all topological classes and having a wide range of lengths (33-300 residues) amino acid composition and number of secondary structural elements. The degrees of freedom are taken as the loop torsion angles. This choice preserves the native secondary structure but allows the tertiary structure to change. The proteins are represented by three-point per residue, three-dimensional models with statistical potentials derived from a knowledge-based study of known protein structures. When this space is sampled by a combination of parallel tempering and equi-energy Monte Carlo, we find that the three-point model captures the known stability of protein native structures with stable energy basins that are near-native (all alpha: 4.77 A, all beta: 2.93 A, alpha/beta: 3.09 A, alpha+beta: 4.89 A on average and within 6 A for 71.41%, 92.85%, 94.29% and 64.28% for all-alpha, all-beta, alpha/beta and alpha+beta, classes, respectively). Denatured structures also occur and these have interesting structural properties that shed light on the different landscape characteristics of alpha and beta folds. We find that alpha/beta proteins with alternating alpha and beta segments (such as the beta-barrel) are more stable than proteins in other fold classes.

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“…The ratio T f / T g provides a powerful metric for the optimization of this bioinformatically informed energy function, 8,9 as well as other types of function incorporating only physical information. [10][11][12] The optimization 13 of parameters using a training set of evolved proteins smooths the energy landscape from that of a random heteropolymer. However, the common problem of multiple competing minima persists, even for a reasonably accurate structure prediction potential.…”

Section: Introductionmentioning

confidence: 99%

Protein structure prediction using basin-hopping

Prentiss

Wales²,

Wolynes

2008

The Journal of Chemical Physics

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Associative memory Hamiltonian structure prediction potentials are not overly rugged, thereby suggesting their landscapes are like those of actual proteins. In the present contribution we show how basin-hopping global optimization can identify low-lying minima for the corresponding mildly frustrated energy landscapes. For small systems the basin-hopping algorithm succeeds in locating both lower minima and conformations closer to the experimental structure than does molecular dynamics with simulated annealing. For large systems the efficiency of basin-hopping decreases for our initial implementation, where the steps consist of random perturbations to the Cartesian coordinates. We implemented umbrella sampling using basin-hopping to further confirm when the global minima are reached. We have also improved the energy surface by employing bioinformatic techniques for reducing the roughness or variance of the energy surface. Finally, the basin-hopping calculations have guided improvements in the excluded volume of the Hamiltonian, producing better structures. These results suggest a novel and transferable optimization scheme for future energy function development.

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SimFold energy function for de novo protein structure prediction: Consensus with Rosetta

Cited by 50 publications

References 50 publications

Folding energy landscape and network dynamics of small globular proteins

Folding energy landscape and network dynamics of small globular proteins

Probing Protein Fold Space with a Simplified Model

Protein structure prediction using basin-hopping

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