A Probabilistic Fragment-Based Protein Structure Prediction Algorithm

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

doi:10.1371/journal.pone.0038799

Cited by 42 publications

(51 citation statements)

References 23 publications

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“…In our previous communication, we presented which focused on enrichment of the near-native structures at a coarse-grained level. We have demonstrated that the improved coarse-grained models can lead to better all-atom models by refining the top quality coarse-grained models into all-atom models [18]. However, the question of how top quality coarse-grained models are identified was not addressed.…”

Section: Discussionmentioning

confidence: 99%

“…Models generated at a given iteration are stored in the final set and are not reused for subsequent iterations. inherits its sampling engine from [18]. The sampling is performed using an alternation of simulated annealing [21] and iterated hill climbing [22].…”

Section: Methodsmentioning

confidence: 99%

“…The rugged nature of the energy landscape, due to the inaccuracy of the energy functions and the size of the search space, necessitates the use of stochastic sampling methods. Recently, we have proposed a method () that takes advantage of the large size of the data-set by enabling communications between predictions during the sampling [18]. The idea was that if some fragments occur more often in low energy models, then these fragments are more likely to resemble the native structure.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Sampling in Fragment-Based Protein Structure Prediction Using an Estimation of Distribution Algorithm

Simoncini

Zhang

2013

PLoS ONE

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Fragment assembly is a powerful method of protein structure prediction that builds protein models from a pool of candidate fragments taken from known structures. Stochastic sampling is subsequently used to refine the models. The structures are first represented as coarse-grained models and then as all-atom models for computational efficiency. Many models have to be generated independently due to the stochastic nature of the sampling methods used to search for the global minimum in a complex energy landscape. In this paper we present , a fragment-based approach which shares information between the generated models and steers the search towards native-like regions. A distribution over fragments is estimated from a pool of low energy all-atom models. This iteratively-refined distribution is used to guide the selection of fragments during the building of models for subsequent rounds of structure prediction. The use of an estimation of distribution algorithm enabled to reach lower energy levels and to generate a higher percentage of near-native models. uses an all-atom energy function and produces models with atomic resolution. We observed an improvement in energy-driven blind selection of models on a benchmark of in comparison with the AbInitioRelax protocol.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Efficient Sampling in Fragment-Based Protein Structure Prediction Using an Estimation of Distribution Algorithm

Simoncini

Zhang

2013

PLoS ONE

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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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“…[4] Since the empirically derived energies used in these methods contain significant errors, the model with the lowest energy may not be the closest to the native structure. Typically, a small pool of models with the lowest energies are retained and analyzed for the identification of the best model.…”

Section: Introductionmentioning

confidence: 99%

Quality Assessment of Predicted Protein Models Using Energies Calculated by the Fragment Molecular Orbital Method

Simoncini

Nakata

Ogata

et al. 2015

Molecular Informatics

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Protein structure prediction directly from sequences is a very challenging problem in computational biology. One of the most successful approaches employs stochastic conformational sampling to search an empirically derived energy function landscape for the global energy minimum state. Due to the errors in the empirically derived energy function, the lowest energy conformation may not be the best model. We have evaluated the use of energy calculated by the fragment molecular orbital method (FMO energy) to assess the quality of predicted models and its ability to identify the best model among an ensemble of predicted models. The fragment molecular orbital method implemented in GAMESS was used to calculate the FMO energy of predicted models. When tested on eight protein targets, we found that the model ranking based on FMO energies is better than that based on empirically derived energies when there is sufficient diversity among these models. This model diversity can be estimated prior to the FMO energy calculations. Our result demonstrates that the FMO energy calculated by the fragment molecular orbital method is a practical and promising measure for the assessment of protein model quality and the selection of the best protein model among many generated.

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A Probabilistic Fragment-Based Protein Structure Prediction Algorithm

Cited by 42 publications

References 23 publications

Efficient Sampling in Fragment-Based Protein Structure Prediction Using an Estimation of Distribution Algorithm

Efficient Sampling in Fragment-Based Protein Structure Prediction Using an Estimation of Distribution Algorithm

Optimization Bias in Energy-Based Structure Prediction

Quality Assessment of Predicted Protein Models Using Energies Calculated by the Fragment Molecular Orbital Method

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