Evaluating protein structure-prediction schemes using energy landscape theory

Eastwood, Michael P.; Hardin, Corey; Luthey‐Schulten, Zaida; Wolynes, Peter G.

doi:10.1147/rd.453.0475

Cited by 77 publications

(106 citation statements)

References 26 publications

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“…We employ a structural homology measure which is based on the structural similarity measure, Q, developed by Wolynes, Luthey-Schulten, and coworkers (18) in the field of protein folding. Our adaptation of Q is referred to as Q H , and the measure is designed to include the effects of the gaps on the aligned portion: Q H ϭ Ꭽ (q aln ϩ q gap ), where Ꭽ is the normalization, specifically given below.…”

Section: Homology Measurementioning

confidence: 99%

On the Evolution of Structure in Aminoacyl-tRNA Synthetases

O’Donoghue

Luthey‐Schulten

2003

Microbiol Mol Biol Rev

250

315

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The aminoacyl-tRNA synthetases are one of the major protein components in the translation machinery. These essential proteins are found in all forms of life and are responsible for charging their cognate tRNAs with the correct amino acid. The evolution of the tRNA synthetases is of fundamental importance with respect to the nature of the biological cell and the transition from an RNA world to the modern world dominated by protein-enzymes. We present a structure-based phylogeny of the aminoacyl-tRNA synthetases. By using structural alignments of all of the aminoacyl-tRNA synthetases of known structure in combination with a new measure of structural homology, we have reconstructed the evolutionary history of these proteins. In order to derive unbiased statistics from the structural alignments, we introduce a multidimensional QR factorization which produces a nonredundant set of structures. Since protein structure is more highly conserved than protein sequence, this study has allowed us to glimpse the evolution of protein structure that predates the root of the universal phylogenetic tree. The extensive sequence-based phylogenetic analysis of the tRNA synthetases (Woese et al., Microbiol. Mol. Biol. Rev. 64:202-236, 2000) has further enabled us to reconstruct the complete evolutionary profile of these proteins and to make connections between major evolutionary events and the resulting changes in protein shape. We also discuss the effect of functional specificity on protein shape over the complex evolutionary course of the tRNA synthetases

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Section: Homology Measurementioning

confidence: 99%

On the Evolution of Structure in Aminoacyl-tRNA Synthetases

O’Donoghue

Luthey‐Schulten

2003

Microbiol Mol Biol Rev

250

315

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“…25,26 This model is sometimes termed the associative memory contact ͑AMC͒ model to distinguish it from the associative memory water ͑AMW͒ model, which uses nonadditive water mediated interactions. 14,27 Since this model has been described in detail before, 15,28 we will only summarize its form here. We employ a version of the coarse-grained model where the 20 letter amino acid code has been reduced to four, and the number of atoms per residue is limited to three ͑C ␣ , C ␤ , and O͒, except for glycine.…”

Section: Theory and Computational Detailsmentioning

confidence: 99%

“…Many studies have employed additional constraining potentials to characterize unsampled regions of coordinate space while using molecular dynamics. 15,34 To characterize the landscape sampled with basin-hopping, we also used a structure constraining potential to identify ensembles with fixed but varying fractions of native structure. Using such a potential allows the analysis of interesting configurations that are unlikely to be thermally sampled.…”

Section: ͑4͒mentioning

confidence: 99%

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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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“…The premise of energy landscape design strategy is to learn the parameters by requiring the potential to produce a low energy native state while, according to landscape theory, also creating a gap between the energies of the molten globule states and the native state. Mathematically, the learning procedure involves maximizing over the possible energy parameter values, the energy gap divided by the variance of decoy energies for training proteins (10,14,15). The associative memory (AM) terms of the potential are obtained from a sequence-structure threading procedure (1) which, while based on a global alignment, applies only to interactions relatively close in sequence distance, i.e., 12 residues or less.…”

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confidence: 99%

Restriction versus guidance in protein structure prediction

Hegler

Lätzer

Shehu

et al. 2009

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

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Conformational restriction by fragment assembly and guidance in molecular dynamics are alternate conformational search strategies in protein structure prediction. We examine both approaches using a version of the associative memory Hamiltonian that incorporates the influence of water-mediated interactions (AMW). For short proteins (<70 residues), fragment assembly, while searching a restricted space, compares well to molecular dynamics and is often sufficient to fold such proteins to near-native conformations (4Å) via simulated annealing. Longer proteins encounter kinetic sampling limitations in fragment assembly not seen in molecular dynamics which generally samples more native-like conformations. We also present a fragment enriched version of the standard AMW energy function, AMW-FME, which incorporates the local sequence alignment derived fragment libraries from fragment assembly directly into the energy function. This energy function, in which fragment information acts as a guide not a restriction, is found by molecular dynamics to improve on both previous approaches.fragment assembly ͉ associative memory Hamiltonian ͉ protein folding ͉ annealing ͉ molecular dynamics I t is useful to categorize protein structure prediction schemes into two classes: template-based modeling and de novo prediction. Template-based modeling depends on the existence, and identification of, at least one experimentally-solved structure with significant global structural similarity to the target to be predicted, usually a sequence homolog. The identification can be made either by a global sequence-sequence alignment or a global sequencestructure alignment (1). Finding the proper template is a search problem but unlike folding, a search highly restricted to a relatively modest number of possibilities. After finding a template, the homolog structure acts as a global constraint which again severely restricts the remainder of the relevant conformational space to be searched. This leads overall to a much simpler optimization problem to solve. Various energy functions can be used which often lead to successful predictions defined by significant improvement relative to input homolog information (2).However, modeling a protein structure when no experimentallydetermined homologs exist to match the structures globally (or none are recognized to exist) is quite challenging. Such de novo structure prediction can employ all-atom molecular mechanics or hybrid models. Molecular mechanics methods are based on physico-chemical interactions such as van der Waals, electrostatics, hydrogen bonding, solvation energy, and basic backbone steric constraints (covalent bond lengths and angles and torsion angle preferences). Model parameters are generally inferred from experimental measurements and/or quantum chemical calculations on small organic molecules (3, 4). Based on such data, one can generate a transferable energy function (5). The resulting energy function can be used in a variety of search procedures, including template-based modeling. Ultimately, a...

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Evaluating protein structure-prediction schemes using energy landscape theory

Cited by 77 publications

References 26 publications

On the Evolution of Structure in Aminoacyl-tRNA Synthetases

On the Evolution of Structure in Aminoacyl-tRNA Synthetases

Protein structure prediction using basin-hopping

Restriction versus guidance in protein structure prediction

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