Driven to near‐experimental accuracy by refinement via molecular dynamics simulations

Heo, Lim; Arbour, Collin F.; Feig, Michael

doi:10.1002/prot.25759

Cited by 43 publications

(80 citation statements)

References 63 publications

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“…Our prediction for target R0974s1 is among the most accurate in all CASP refinement challenges ( Figure 3A). with restraints, 23 high-accuracy refined models were achieved comparable to ours. Based on our results and those of the Feig group on R0974s1, even for relatively easy refinement problems, it is critical to allow significant energy barrier hopping during search.…”

Section: High-accuracy Refinement Through Regularized Conformationasupporting

confidence: 68%

“…Our MD refinement alone on the input model did not result in this level of accuracy (GDT‐HA of 83 and RMSD 1.6 å). However, when more aggressive sampling with MD was tried by the Feig group involving multiple rounds of Markov state simulations with restraints, high‐accuracy refined models were achieved comparable to ours. Based on our results and those of the Feig group on R0974s1, even for relatively easy refinement problems, it is critical to allow significant energy barrier hopping during search.…”

Section: Resultssupporting

confidence: 52%

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High‐accuracy refinement using Rosetta in CASP13

et al. 2019

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Because proteins generally fold to their lowest free energy states, energy‐guided refinement in principle should be able to systematically improve the quality of protein structure models generated using homologous structure or co‐evolution derived information. However, because of the high dimensionality of the search space, there are far more ways to degrade the quality of a near native model than to improve it, and hence, refinement methods are very sensitive to energy function errors. In the 13th Critial Assessment of techniques for protein Structure Prediction (CASP13), we sought to carry out a thorough search for low energy states in the neighborhood of a starting model using restraints to avoid straying too far. The approach was reasonably successful in improving both regions largely incorrect in the starting models as well as core regions that started out closer to the correct structure. Models with GDT‐HA over 70 were obtained for five targets and for one of those, an accuracy of 0.5 å backbone root‐mean‐square deviation (RMSD) was achieved. An important current challenge is to improve performance in refining oligomers and larger proteins, for which the search problem remains extremely difficult.

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Section: High-accuracy Refinement Through Regularized Conformationasupporting

confidence: 68%

Section: Resultssupporting

confidence: 52%

High‐accuracy refinement using Rosetta in CASP13

et al. 2019

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“…For example, the Xu group (4) used CNS (8) and the AlphaFold group (7) used gradient-descent following conversion of the predicted distances into smooth restraints. Progress was also evident in protein structure refinement at CASP13 using energy guided refinement (9)(10)(11).…”

Section: Introductionmentioning

confidence: 99%

Improved protein structure prediction using predicted inter-residue orientations

Anishchenko

Park

Peng

et al. 2019

Preprint

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The prediction of inter-residue contacts and distances from co-evolutionary data using deep learning has considerably advanced protein structure prediction. Here we build on these advances by developing a deep residual network for predicting inter-residue orientations in addition to distances, and a Rosetta constrained energy minimization protocol for rapidly and accurately generating structure models guided by these restraints. In benchmark tests on CASP13 and CAMEO derived sets, the method outperforms all previously described structure prediction methods. Although trained entirely on native proteins, the network consistently assigns higher probability to de novo designed proteins, identifying the key fold determining residues and providing an independent quantitative measure of the "ideality" of a protein structure. The method promises to be useful for a broad range of protein structure prediction and design problems.

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“…Physics-based methods based on molecular dynamics (MD) simulations have been most successful in improving both global and local structure (Feig, 2016;Feig & Mirjalili, 2016;Heo & Feig, 2018a, 2018bMirjalili & Feig, 2013;Mirjalili, Noyes, & Feig, 2014;Read, Sammito, Kryshtafovych, & Croll, 2019). Current methods can provide consistent refinement for most structures while it is becoming increasingly possible to approach experimental accuracy based on root mean square deviations (RMSDs) of 1 Å or better as demonstrated during CASP13 (Heo, Arbour, & Feig, 2019).…”

Section: Introductionmentioning

confidence: 99%

High-Accuracy Protein Structures by Combining Machine-Learning with Physics-Based Refinement

Heo

Feig

2019

Preprint

Self Cite

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Protein structure prediction has long been available as an alternative to experimental structure determination, especially via homology modeling based on templates from related sequences. Recently, models based on distance restraints from co-evoluttionary analysis via machine learning have significantly expanded the ability to predict structures for sequences without templates. One such method, AlphaFold, also performs well on sequences were templates are available but without using such information directly. Here we show that combining machine-learning based models from AlphaFold with state-of-the-art physics-based refinement via molecular dynamics simulations further improves predictions to outperform any other prediction method tested during the latest round of CASP. The resulting models have highly accurate global and local structure, including high accuracy at functionally important interface residues, and they are highly suitable as initial models for crystal structure determination via molecular replacement.

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Driven to near‐experimental accuracy by refinement via molecular dynamics simulations

Cited by 43 publications

References 63 publications

High‐accuracy refinement using Rosetta in CASP13

High‐accuracy refinement using Rosetta in CASP13

Improved protein structure prediction using predicted inter-residue orientations

High-Accuracy Protein Structures by Combining Machine-Learning with Physics-Based Refinement

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