Protein structure refinement via molecular‐dynamics simulations: What works and what does not?

Feig, Michael; Mirjalili, Vahid

doi:10.1002/prot.24871

Cited by 69 publications

(122 citation statements)

References 39 publications

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“…We already discussed that the new iterative protocol mostly outperformed the more conservative protocol employed in previous rounds of CASP, with additional improvements when both protocols were combined. Simply in terms of numbers, our performance during CASP13 was also better than during CASP12.…”

Section: Resultsmentioning

confidence: 91%

“…The approach has been adopted as part of many structure prediction protocols as an endgame player in structure prediction . This approach improved for a few years due to advances in force fields and benefited from an ability to carry out longer simulations . However, there was little progress in recent years even with much longer simulations .…”

Section: Introductionmentioning

confidence: 99%

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

2019

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Protein model refinement has been an essential part of successful protein structure prediction. Molecular dynamics simulation‐based refinement methods have shown consistent improvement of protein models. There had been progress in the extent of refinement for a few years since the idea of ensemble averaging of sampled conformations emerged. There was little progress in CASP12 because conformational sampling was not sufficiently diverse due to harmonic restraints. During CASP13, a new refinement method was tested that achieved significant improvements over CASP12. The new method intended to address previous bottlenecks in the refinement problem by introducing new features. Flat‐bottom harmonic restraints replaced harmonic restraints, sampling was performed iteratively, and a new scoring function and selection criteria were used. The new protocol expanded conformational sampling at reduced computational costs. In addition to overall improvements, some models were refined significantly to near‐experimental accuracy.

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Section: Resultsmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

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

2019

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“…58 In these cases, enhanced sampling, as performed in our study, is necessary. Moreover, CASP11 results demonstrated that loops tend to be more challenging to refine.…”

Section: Discussionmentioning

confidence: 89%

“…Structure prediction relying on knowledge-based approaches seems to have reached a plateau in their accuracy, 58 and different refinement strategies are now required to achieve effective improvements. The combination of knowledge-based prediction with physics-based methods looks particularly promising.…”

Section: Discussionmentioning

confidence: 99%

Using Local States To Drive the Sampling of Global Conformations in Proteins

Pandini

Fornili

2016

J. Chem. Theory Comput.

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Conformational changes associated with protein function often occur beyond the time scale currently accessible to unbiased molecular dynamics (MD) simulations, so that different approaches have been developed to accelerate their sampling. Here we investigate how the knowledge of backbone conformations preferentially adopted by protein fragments, as contained in precalculated libraries known as structural alphabets (SA), can be used to explore the landscape of protein conformations in MD simulations. We find that (a) enhancing the sampling of native local states in both metadynamics and steered MD simulations allows the recovery of global folded states in small proteins; (b) folded states can still be recovered when the amount of information on the native local states is reduced by using a low-resolution version of the SA, where states are clustered into macrostates; and (c) sequences of SA states derived from collections of structural motifs can be used to sample alternative conformations of preselected protein regions. The present findings have potential impact on several applications, ranging from protein model refinement to protein folding and design.

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“…These regions correspond predominantly to loops, insertions of any length, and non-conserved side chains. The most significant part of comparative modeling termed as model or loop refinement is done using either of techniques, fragment-guided molecular dynamic simulation (FG-MD) [19], GalaxyRefine [20,21], 3Drefine [22][23][24], protein structure refinement via molecular dynamics (PREFMD) [25], Errat-guided LM via MODELLER [7] and molecular dynamics simulations (MDS) via AMBER16 [26] However, none of them describes the effectiveness of one method over the other in terms of model validation parameters, required experimental time and tediousness of each method during refinement procedure. Identifying the most effective technique can help in selecting the best refinement method, possibly reducing the required computational time and accelerate the process for refinement of homology model.…”

Section: Mtr Homology Model Refinement Using Mdsmentioning

confidence: 99%