A reversible fragment assembly method for <i>de novo</i> protein structure prediction

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

doi:10.1063/1.1597474

Cited by 48 publications

(38 citation statements)

References 29 publications

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“…Accordingly, fragment assembly is the most effective current method for protein structure prediction (47), and it has been used with impressive success in the ROSETTA program, developed by Baker and colleagues (48)(49)(50)(51).…”

Section: Discussionmentioning

confidence: 99%

Building native protein conformation from highly approximate backbone torsion angles

Gong

Fleming

Rose

2005

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

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Reconstructing a protein in three dimensions from its backbone torsion angles is an ongoing challenge because minor inaccuracies in these angles produce major errors in the structure. As a familiar example, a small change in an elbow angle causes a large displacement at the end of your arm, the longer the arm, the larger the displacement. Even accurate knowledge of the backbone torsions and is insufficient, owing to the small, but cumulative, deviations from ideality in backbone planarity, which, if ignored, also lead to major errors in the structure. Against this background, we conducted a computational experiment to assess whether protein conformation can be determined from highly approximate backbone torsion angles, the kind of information that is now obtained readily from NMR. Specifically, backbone torsion angles were taken from proteins of known structure and mapped into 60°؋ 60°grid squares, called mesostates. Side-chain atoms beyond the ␤-carbon were discarded. A mesostate representation of the protein backbone was then used to extract likely candidates from a fragment library of mesostate pentamers, followed by Monte Carlo-based fragment-assembly simulations to identify stable conformations compatible with the given mesostate sequence. Only three simple energy terms were used to gauge stability: molecular compaction, soft-sphere repulsion, and hydrogen bonding. For the six representative proteins described here, stable conformers can be partitioned into a remarkably small number of topologically distinct clusters. Among these, the native topology is found with high frequency and can be identified as the cluster with the most favorable energy.protein structure ͉ protein secondary structure ͉ protein fragment assembly ͉ Monte Carlo simulation P rotein molecules are known to undergo a reversible disorder order transition (1). In the classical view, the unfolded state is thought to be a structurally featureless ensemble that adopts its native structure spontaneously and uniquely under conditions that favor folding. For many small proteins of biophysical interest, this well studied folding reaction is apparently a two-state process. Accordingly, it can be represented by the equation: U(nfolded)^N(ative), with equilibrium constant, K eq ϭ N͞U, and free energy, ⌬G 0 ϭ ϪRT ln K eq , the free energy difference between the two populations. Central to this view, U is thought to be largely comprised of randomly coiled molecules, and N is thought to be largely comprised of uniquely structured molecules.Lately, we have been exploring the doubly divergent alternative view that the unfolded state is more organized (2) and the folded state is less homogeneous (3) than previously thought. If so, then both states can be treated productively as constrained thermodynamic ensembles. Numerous recent papers suggest that the unfolded population is not featureless, despite the fact that it does indeed exhibit random-coil statistics (4). Based on residual dipolar couplings from NMR, Shortle (5) and Shortle and Ackerman (6) have a...

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Section: Discussionmentioning

confidence: 99%

Building native protein conformation from highly approximate backbone torsion angles

Gong

Fleming

Rose

2005

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

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“…Recent work has also shown that FA can reliably produce native-like structures of smaller proteins using even a relatively simple energy function (7). Although much progress has been made in improving the FA methods, whether a well-funneled energy function is necessary for its success has been unclear.…”

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confidence: 99%

“…To properly quantify how the restricted conformational space via FA changes the energy landscape, one is required to calculate free energies which can be done with the reversible FA approach used here (see Methods) (7). However, we take a cruder approach by simply comparing structures sampled during simulated annealing runs (or low energy structures).…”

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confidence: 99%

“…Except for short peptides, fully atomistic molecular mechanics methods are therefore presently limited in carrying out de novo structure prediction. Hybrid approaches combining bioinformatic information with physical energy functions have been designed to overcome this computational difficulty.Presently, there are two reasonably successful hybrid approaches: the fragment assembly (FA) methods (6,7,8) and knowledge-based energy function methods using specific protein database input (9, 10) such as the associative memory Hamiltonians (AMH) (specifically we will study one with water-mediated interactions (AMW) (11)). Both hybrid approaches use knowledge from the database to either restrict directly the conformational search space (FA) or to design a better guided coarse-grained energy function with most of the physico-chemically relevant features, by using local sequence matching (AMW).…”

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confidence: 99%

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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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“…In homology modelling, FB5-HMM could guide the construction of variable loops [52,53]. Because of the probabilistic nature of the model, it can be used to propose local conformational changes that respect the detailed balance condition [16,21], making it possible to estimate thermodynamic averages using MCMC simulations [54]. In experimental methods such as NMR, X-ray crystallography, or Small Angle X-ray Scattering, the model could be used to generate conformations that take the local structural bias and the experimental data into account [55][56][57].…”

Section: Discussionmentioning

confidence: 99%

Sampling realistic protein conformations using local structural bias

Hamelryck¹,

Kent²,

Krogh³

2005

PLoS Comp Biol

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The prediction of protein structure from sequence remains a major unsolved problem in biology. The most successful protein structure prediction methods make use of a divide-and-conquer strategy to attack the problem: a conformational sampling method generates plausible candidate structures, which are subsequently accepted or rejected using an energy function. Conceptually, this often corresponds to separating local structural bias from the long-range interactions that stabilize the compact, native state. However, sampling protein conformations that are compatible with the local structural bias encoded in a given protein sequence is a long-standing open problem, especially in continuous space. We describe an elegant and mathematically rigorous method to do this, and show that it readily generates native-like protein conformations simply by enforcing compactness. Our results have far-reaching implications for protein structure prediction, determination, simulation, and design.Citation: Hamelryck T, Kent JT, Krogh A (2006) Sampling realistic protein conformations using local structural bias. PLoS Comput Biol 2(9): e131.

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A reversible fragment assembly method for de novo protein structure prediction

Cited by 48 publications

References 29 publications

Building native protein conformation from highly approximate backbone torsion angles

Building native protein conformation from highly approximate backbone torsion angles

Restriction versus guidance in protein structure prediction

Sampling realistic protein conformations using local structural bias

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