Accelerating<i>ab initio</i>phasing with<i>de novo</i>models

Shrestha, Rojan; Berenger, Francois; Zhang, Kam Y. J.

doi:10.1107/s090744491102779x

Cited by 14 publications

(29 citation statements)

References 16 publications

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“…Out of the 10 targets tested, EdaFold succeeded in 5 targets as compared to 3 targets succeeded by Rosetta. The success in molecular replacement is not only judged by the TFZ score but also assessed by a verification procedure [6]. For , EdaFold and Rosetta manage to find a solution in molecular replacement.…”

Section: Resultsmentioning

confidence: 99%

“…It has been shown that the coarse-grained conformation sampling plays an important role in predicting highly accurate structure of proteins [5]. In addition, when coarse-grained models are predicted near native structure, switching the models to all-atom representation subsequently gives solutions in Molecular Replacement trials without extensive optimization [6].…”

Section: Introductionmentioning

confidence: 99%

“…It has been shown recently that coarse-grained conformational sampling (where only backbone atoms are present) is the key step in ab initio protein folding in order to obtain models that pass the stringent test of molecular replacement [6]. Indeed, optimizing an all-atom model is a waste of computing power when the backbone conformation is far from the native structure.…”

Section: Introductionmentioning

confidence: 99%

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A Probabilistic Fragment-Based Protein Structure Prediction Algorithm

et al. 2012

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Conformational sampling is one of the bottlenecks in fragment-based protein structure prediction approaches. They generally start with a coarse-grained optimization where mainchain atoms and centroids of side chains are considered, followed by a fine-grained optimization with an all-atom representation of proteins. It is during this coarse-grained phase that fragment-based methods sample intensely the conformational space. If the native-like region is sampled more, the accuracy of the final all-atom predictions may be improved accordingly. In this work we present EdaFold, a new method for fragment-based protein structure prediction based on an Estimation of Distribution Algorithm. Fragment-based approaches build protein models by assembling short fragments from known protein structures. Whereas the probability mass functions over the fragment libraries are uniform in the usual case, we propose an algorithm that learns from previously generated decoys and steers the search toward native-like regions. A comparison with Rosetta AbInitio protocol shows that EdaFold is able to generate models with lower energies and to enhance the percentage of near-native coarse-grained decoys on a benchmark of proteins. The best coarse-grained models produced by both methods were refined into all-atom models and used in molecular replacement. All atom decoys produced out of EdaFold’s decoy set reach high enough accuracy to solve the crystallographic phase problem by molecular replacement for some test proteins. EdaFold showed a higher success rate in molecular replacement when compared to Rosetta. Our study suggests that improving low resolution coarse-grained decoys allows computational methods to avoid subsequent sampling issues during all-atom refinement and to produce better all-atom models. EdaFold can be downloaded from http://www.riken.jp/zhangiru/software/.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Probabilistic Fragment-Based Protein Structure Prediction Algorithm

et al. 2012

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“…Computational methods using coarse-grained models have already shown a great promise for a better description of protein structures, or their complexes, when combined with experimental data from NMR, 263,587−590 cryo-EM, 483,590−596 Xray 597,598 or SAXS. 589,599−601 In particular, recent combinations of the top performing multiscale structure prediction platforms, Rosetta and I-TASSER, with experimental measurements resulted in spectacular prediction results.…”

Section: Integrative Modelingmentioning

confidence: 99%

Coarse-Grained Protein Models and Their Applications

et al. 2016

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The traditional computational modeling of protein structure, dynamics, and interactions remains difficult for many protein systems. It is mostly due to the size of protein conformational spaces and required simulation time scales that are still too large to be studied in atomistic detail. Lowering the level of protein representation from all-atom to coarse-grained opens up new possibilities for studying protein systems. In this review we provide an overview of coarse-grained models focusing on their design, including choices of representation, models of energy functions, sampling of conformational space, and applications in the modeling of protein structure, dynamics, and interactions. A more detailed description is given for applications of coarse-grained models suitable for efficient combinations with all-atom simulations in multiscale modeling strategies.

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“…Energy-based methods using atomistic models have been used for fitting structures to low-resolution data, 22–26 refining moderate to high-resolution X-ray data, 27–29 providing low-to-moderate resolution models for use in crystal structure refinements, 30,31 folding some small proteins in MD simulations, 32 and docking macromolecules. 4 However, the success rate and accuracy of these and other applications of physics-based methods to macromolecular prediction problems remain substantially less than 100% (e.g.…”

Section: Summary and Discussionmentioning

confidence: 99%

Optimization Bias in Energy-Based Structure Prediction

Petrella

2013

J. Theor. Comput. Chem.

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Physics-based computational approaches to predicting the structure of macromolecules such as proteins are gaining increased use, but there are remaining challenges. In the current work, it is demonstrated that in energy-based prediction methods, the degree of optimization of the sampled structures can influence the prediction results. In particular, discrepancies in the degree of local sampling can bias the predictions in favor of the oversampled structures by shifting the local probability distributions of the minimum sampled energies. In simple systems, it is shown that the magnitude of the errors can be calculated from the energy surface, and for certain model systems, derived analytically. Further, it is shown that for energy wells whose forms differ only by a randomly assigned energy shift, the optimal accuracy of prediction is achieved when the sampling around each structure is equal. Energy correction terms can be used in cases of unequal sampling to reproduce the total probabilities that would occur under equal sampling, but optimal corrections only partially restore the prediction accuracy lost to unequal sampling. For multiwell systems, the determination of the correction terms is a multibody problem; it is shown that the involved cross-correlation multiple integrals can be reduced to simpler integrals. The possible implications of the current analysis for macromolecular structure prediction are discussed.

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Acceleratingab initiophasing withde novomodels

Cited by 14 publications

References 16 publications

A Probabilistic Fragment-Based Protein Structure Prediction Algorithm

A Probabilistic Fragment-Based Protein Structure Prediction Algorithm

Coarse-Grained Protein Models and Their Applications

Optimization Bias in Energy-Based Structure Prediction

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