Structure prediction for CASP7 targets using extensive all-atom refinement with Rosetta@home

Das, Rhiju; Qian, Bin; Raman, Srivatsan; Vernon, Robert; Thompson, James; Bradley, Philip; Khare, Sagar D.; Tyka, Michael D.; Bhat, Divya; Chivian, Dylan; Kim, David E.; Sheffler, William; Malmström, Lars; Wollacott, Andrew M.; Wang, Chu; André, Ingemar; Baker, David

doi:10.1002/prot.21636

Cited by 177 publications

(183 citation statements)

References 32 publications

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“…The first of two stages of automated de novo modeling alternates sets of fragment insertion moves that perturb the backbone conformation of protein monomers (11) with moves that perturb the symmetric docking arrangement of the monomers (12). The conformational energy is determined by the low-resolution energy function previously developed for monomer protein structure prediction (6,11) with coarse-grained terms for backbone hydrogen bonds and hydrophobic interactions. Cyclic or dihedral symmetry is maintained by cloning the moves to separate monomers, and each move is accepted or rejected based on the standard Metropolis criterion (12).…”

Section: Resultsmentioning

confidence: 99%

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Simultaneous prediction of protein folding and docking at high resolution

Das

André

Shen

et al. 2009

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

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149

174

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Interleaved dimers and higher order symmetric oligomers are ubiquitous in biology but present a challenge to de novo structure prediction methodology: The structure adopted by a monomer can be stabilized largely by interactions with other monomers and hence not the lowest energy state of a single chain. Building on the Rosetta framework, we present a general method to simultaneously model the folding and docking of multiple-chain interleaved homo-oligomers. For more than a third of the cases in a benchmark set of interleaved homo-oligomers, the method generates near-native models of large ␣-helical bundles, interlocking ␤ sandwiches, and interleaved ␣/␤ motifs with an accuracy high enough for molecular replacement based phasing. With the incorporation of NMR chemical shift information, accurate models can be obtained consistently for symmetric complexes with as many as 192 total amino acids; a blind prediction was within 1 Å rmsd of the traditionally determined NMR structure, and fit independently collected RDC data equally well. Together, these results show that the Rosetta ''fold-and-dock'' protocol can produce models of homo-oligomeric complexes with nearatomic-level accuracy and should be useful for crystallographic phasing and the rapid determination of the structures of multimers with limited NMR information.homo-oligomers ͉ molecular replacement ͉ NMR structure inference ͉ protein structure prediction ͉ symmetry T he majority of expressed proteins function within symmetrical homomeric complexes (1-3). Although a boon for evolving functional diversity (4), this ubiquity of oligomeric structures poses numerous challenges for modern structural biology. The phasing of crystallographic data by molecular replacement and NMR structural inference are complicated by the increasing number of degrees of freedom and spectral degeneracy, respectively, in multimeric systems. The ability to predict de novo the structures of multiple interacting molecular chains would potentially alleviate these problems, allowing unphased diffraction measurements or ambiguously assigned NMR spectra to be resurrected as constraints or as independent validation for in silico structural inference.Accurate modeling of previously unseen homomeric structures has not been demonstrated at high resolution. Significant progress has occurred in modeling the folds of individual monomeric soluble proteins (5-7) and the docking arrangements of predefined monomers (8, 9). However, as proteins interact with other proteins, nucleic acids, or smaller molecules, their lowest free-energy backbone conformations typically shift in response to their partners. These often dramatic structural changes have been a persistent and unsolved issue in blind prediction trials of recent years (8).In this report, we show how two different methods developed for de novo conformational sampling (for folding and for docking) can be melded into a more general procedure that permits the blind prediction of intertwined complexes of proteins at near-atomic resolution. Some of th...

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Section: Resultsmentioning

confidence: 99%

“…As in previous work (6), modeling runs that did not pass 50th percentile energy cuts half-way and three-quarters of the way through the simulation were terminated. A clustering threshold of 2 Å was used, and five models representing the center of the five largest clusters were selected as predictions.…”

Section: Conformational Search Protocolmentioning

confidence: 99%

Simultaneous prediction of protein folding and docking at high resolution

Das

André

Shen

et al. 2009

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

Self Cite

149

174

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“…This makes two assumptions: (i) that the native conformation should have more structural neighbors than any other conformation because of the loss in configurational entropy on folding; and (ii) that this near-native energy basin is detected by the knowledge-based scoring functions used in Rosetta in that the basin results from the long-range hydrophobic interactions associated with native globular proteins (17). In our work, we used the ''LEADER''clustering algorithm extensively tested with the Rosetta protocol in the Critical Assessment of Structure Prediction (CASP) competition (2,12,13,18). We clustered 5,000 lowest-energy structures of the 20,000 lowest-energy conformations found in independent runs by each method.…”

Section: Differences In Top Clustermentioning

confidence: 99%

Generalized ensemble methods for de novo structure prediction

Shmygelska

Levitt

2009

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

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Current methods for predicting protein structure depend on two interrelated components: (i) an energy function that should have a low value near the correct structure and (ii) a method for searching through different conformations of the polypeptide chain. Identification of the most efficient search methods is essential if we are to be able to apply such methods broadly and with confidence. In addition, efficient search methods provide a rigorous test of existing energy functions, which are generally knowl- By using a set of nonnative low-energy structures found by our extensive sampling, we discovered that the long-range and short-range backbone hydrogen-bonding energy terms of the Rosetta energy discriminate between the nonnative and native-like structures significantly better than the low-resolution score used in Rosetta.conformational search ͉ protein folding ͉ Rosetta force field P redicting the functional 3-dimensional structure (the native state) of a protein from its amino acid sequences is of central importance to structural and functional biology and has enormous applications in alleviating human disease. Even if the structures of all proteins were known, we would still not be able to answer questions related to diseases directly caused by protein misfolding, such as certain types of cancer and Alzheimer's and Parkinson disease. For this we would need to understand the physical basis of the energy terms that make the native state so special. Such understanding of the energetics of the system would also lead to more efficient and comprehensive drug design. Structure prediction depends on solving two problems: (i) describing the energy function with sufficient accuracy and (ii) searching the conformational space sufficiently well. These problems are particularly severe for proteins of biologically relevant lengths (Ͼ150 aa).In this work we focus on conformational sampling, which has been recognized as the critical step in high-resolution structure prediction (1-3). Most widely used standard methods for de novo structure prediction are based on the variants of the Monte Carlo method (4-6) and are unable to explore low-energy regions efficiently because of the ruggedness of the potential energy surface. To overcome these problems, a number of generalized ensemble Monte Carlo methods have been developed (7-10). These methods strive to search energy space better by computing the density of states, sampling expanded ranges of temperatures, or computing other physical quantities affecting transitions between the states during the search. In particular, advanced methods such as Temperature Replica Exchange Monte Carlo (TREM) (8) and Hamiltonian Replica Exchange Monte Carlo (HREM) (10), have been shown to outperform standard Monte Carlo in terms of sampling for both simplified and all-atom force fields of small proteins (8,10,11).For longer proteins, the computational cost and ruggedness of the all-atom energy function makes solving this problem particularly challenging as evidenced by the modest success of full...

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“…With the design of distributed computing infrastructures to tackle scientic research questions over the last twenty years, [30][31][32] keyboard and mouse interfaces have been widely exploited to allow crowds to participate in a range of research tasks. 33,34 For certain tasks, human creativity and judgment can outperform automated classication and search algorithms.…”

Section: Introductionmentioning

confidence: 99%

A GPU-accelerated immersive audio-visual framework for interaction with molecular dynamics using consumer depth sensors

et al. 2014

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A GPU-accelerated immersive audiovisual framework for interaction with molecular dynamics using consumer depth sensors. Faraday Discussions, 169. pp. 63-87. ISSN 1359-6640 Available from: http://eprints.uwe.ac.uk/23115We recommend you cite the published version. The publisher's URL is: http://dx.doi.org/10.1039/C4FD00008K Refereed: Yes First published online 19 Mar 2014Disclaimer UWE has obtained warranties from all depositors as to their title in the material deposited and as to their right to deposit such material. UWE makes no representation or warranties of commercial utility, title, or fitness for a particular purpose or any other warranty, express or implied in respect of any material deposited. UWE makes no representation that the use of the materials will not infringe any patent, copyright, trademark or other property or proprietary rights.UWE accepts no liability for any infringement of intellectual property rights in any material deposited but will remove such material from public view pending investigation in the event of an allegation of any such infringement. PLEASE SCROLL DOWN FOR TEXT.A GPU-accelerated immersive audio-visual framework for interaction with molecular dynamics using consumer depth sensors † With advances in computational power, the rapidly growing role of computational/ simulation methodologies in the physical sciences, and the development of new human-computer interaction technologies, the field of interactive molecular dynamics seems destined to expand. In this paper, we describe and benchmark the software algorithms and hardware setup for carrying out interactive molecular dynamics utilizing an array of consumer depth sensors. The system works by interpreting the human form as an energy landscape, and superimposing this landscape on a molecular dynamics simulation to chaperone the motion of the simulated atoms, affecting both graphics and sonified simulation data. GPU acceleration has been key to achieving our target of 60 frames per second (FPS), giving an extremely fluid interactive experience. GPU acceleration has also allowed us to scale the system for use in immersive 360 spaces with an array of up to ten depth sensors, allowing several users to simultaneously chaperone the dynamics. The flexibility of our platform for carrying out molecular dynamics simulations has been considerably enhanced by wrappers that facilitate fast communication with a portable selection of GPU-accelerated molecular force evaluation routines. In this paper, we describe a 360 atmospheric molecular dynamics simulation we have run in a chemistry/physics education context. We also describe initial tests in which users have been able to chaperone the dynamics of 10-alanine peptide embedded in an explicit water solvent. Using this system, both expert and novice users have been able to accelerate peptide rare event dynamics by 3-4 orders of magnitude.

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Structure prediction for CASP7 targets using extensive all-atom refinement with Rosetta@home

Cited by 177 publications

References 32 publications

Simultaneous prediction of protein folding and docking at high resolution

Simultaneous prediction of protein folding and docking at high resolution

Generalized ensemble methods for de novo structure prediction

A GPU-accelerated immersive audio-visual framework for interaction with molecular dynamics using consumer depth sensors

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