GalaxyRefine: protein structure refinement driven by side-chain repacking

Heo, Lim; Park, Hahnbeom; Seok, Chaok

doi:10.1093/nar/gkt458

Cited by 862 publications

(629 citation statements)

References 31 publications

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“…Only very few methods could consistently refine the targets. Noteworthy examples are FEIG 20,61 (positive ΔGDT_HA for 24 targets), Seok 62 (positive ΔGDT_HA for 16 targets) and KnowMIN (positive ΔGDT_HA for 15 targets) 6 . As the assessors pointed out 6 , wfFUIK improved GDT-HA significantly less frequently than the FEIG and Seok groups (i.e.…”

Section: Resultsmentioning

confidence: 99%

WeFold: A coopetition for protein structure prediction

et al. 2014

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The protein structure prediction problem continues to elude scientists. Despite the introduction of many methods, only modest gains were made over the last decade for certain classes of prediction targets. To address this challenge, a social-media based worldwide collaborative effort, named WeFold, was undertaken by thirteen labs. During the collaboration, the labs were simultaneously competing with each other. Here, we present the first attempt at “coopetition” in scientific research applied to the protein structure prediction and refinement problems. The coopetition was possible by allowing the participating labs to contribute different components of their protein structure prediction pipelines and create new hybrid pipelines that they tested during CASP10. This manuscript describes both successes and areas needing improvement as identified throughout the first WeFold experiment and discusses the efforts that are underway to advance this initiative. A footprint of all contributions and structures are publicly accessible at http://www.wefold.org.

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Section: Resultsmentioning

confidence: 99%

WeFold: A coopetition for protein structure prediction

et al. 2014

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“…The energy functions are the standard Rosetta energy as well as FACTS energy: standard Rosetta energy employs an effective solvation term (Lazaridis & Karpuls, 1999) while FACTS energy describes the solvation effect by Generalized Born / Surface Area (GB/SA) approach using FACTS model (Haberthur & Caflisch, 2008). Side-chains were initially optimized using the Rosetta packer (Leaver-Fay, et al, 2014) with either standard energy weights, or “softened” energy weights inspired from other refinement methods (Heo, Park, & Seok, 2013) where van der Waals interactions are dampened to reduce the sensitivity to inaccurate initial backbone placement. For each MD method 12 replicas of 20 ps simulations are performed from which structures are collected every 1 ps.…”

Section: Methodsmentioning

confidence: 99%

The Origin of Consistent Protein Structure Refinement from Structural Averaging

2015

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Recent studies have shown that explicit solvent molecular dynamics (MD) simulation followed by structural averaging can consistently improve protein structure models. In this study, we investigate the origin of improvements from averaging. We first show that improvement upon averaging is not limited to explicit water MD simulation, as consistent improvements are also observed for more efficient implicit solvent MD or Monte Carlo minimization simulations. We next examine the changes in model accuracy brought about by averaging at the individual residue level, and find that these changes are correlated with the extent of residue fluctuation. Residues undergoing large fluctuations tend to diverge, and these deviations are dampened by averaging. Residues undergoing medium sized fluctuations often improve significantly, and averaging amplifies these improvements. These observations are consistent with an energy landscape model in which the magnitude of the energy gradient towards the native structure decreases with increasing distance from the native state.

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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%

“…GalaxyRefine executes repetitive structure trepidation and subsequent overall structural moderation by molecular dynamics simulation to produce five refined models [20,21]. The structure trepidation is applied only to clusters of side-chains for first model.…”

Section: Introductionmentioning

confidence: 99%