scholarly journals Experimental accuracy in protein structure refinement via molecular dynamics simulations

2018 ◽  
Vol 115 (52) ◽  
pp. 13276-13281 ◽  
Author(s):  
Lim Heo ◽  
Michael Feig
Keyword(s):  
Molecular Dynamics ◽  
Protein Structure ◽  
Structure Prediction ◽  
Structure Refinement ◽  
Energy Landscape ◽  
Md Simulations ◽  
Homology Model ◽  
Experimental Accuracy ◽  
Markov State Modeling ◽  
Homology Models

Refinement is the last step in protein structure prediction pipelines to convert approximate homology models to experimental accuracy. Protocols based on molecular dynamics (MD) simulations have shown promise, but current methods are limited to moderate levels of consistent refinement. To explore the energy landscape between homology models and native structures and analyze the challenges of MD-based refinement, eight test cases were studied via extensive simulations followed by Markov state modeling. In all cases, native states were found very close to the experimental structures and at the lowest free energies, but refinement was hindered by a rough energy landscape. Transitions from the homology model to the native states require the crossing of significant kinetic barriers on at least microsecond time scales. A significant energetic driving force toward the native state was lacking until its immediate vicinity, and there was significant sampling of off-pathway states competing for productive refinement. The role of recent force field improvements is discussed and transition paths are analyzed in detail to inform which key transitions have to be overcome to achieve successful refinement.

Protein-protein structure prediction by scoring molecular dynamics trajectories of putative poses

2016 ◽  
Vol 84 (9) ◽  
pp. 1312-1320 ◽  
Author(s):  
Edoardo Sarti ◽  
Ivan Gladich ◽  
Stefano Zamuner ◽  
Bruno E. Correia ◽  
Alessandro Laio
Keyword(s):  
Molecular Dynamics ◽  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction

scholarly journals Ab initio protein structure prediction via a combination of threading, lattice folding, clustering, and structure refinement

2001 ◽  
Vol 45 (S5) ◽  
pp. 149-156 ◽  
Author(s):  
Jeffrey Skolnick ◽  
Andrzej Kolinski ◽  
Daisuke Kihara ◽  
Marcos Betancourt ◽  
Piotr Rotkiewicz ◽  
...  
Keyword(s):  
Protein Structure ◽  
Ab Initio ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Structure Refinement

scholarly journals Decoy selection for protein structure prediction via extreme gradient boosting and ranking

2020 ◽  
Vol 21 (S1) ◽  
Author(s):  
Nasrin Akhter ◽  
Gopinath Chennupati ◽  
Hristo Djidjev ◽  
Amarda Shehu
Keyword(s):  
Machine Learning ◽  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Energy Landscape ◽  
Biologically Active ◽  
Test Cases ◽  
Consensus Methods ◽  
Extreme Gradient Boosting ◽  
Template Free

Abstract Background Identifying one or more biologically-active/native decoys from millions of non-native decoys is one of the major challenges in computational structural biology. The extreme lack of balance in positive and negative samples (native and non-native decoys) in a decoy set makes the problem even more complicated. Consensus methods show varied success in handling the challenge of decoy selection despite some issues associated with clustering large decoy sets and decoy sets that do not show much structural similarity. Recent investigations into energy landscape-based decoy selection approaches show promises. However, lack of generalization over varied test cases remains a bottleneck for these methods. Results We propose a novel decoy selection method, ML-Select, a machine learning framework that exploits the energy landscape associated with the structure space probed through a template-free decoy generation. The proposed method outperforms both clustering and energy ranking-based methods, all the while consistently offering better performance on varied test-cases. Moreover, ML-Select shows promising results even for the decoy sets consisting of mostly low-quality decoys. Conclusions ML-Select is a useful method for decoy selection. This work suggests further research in finding more effective ways to adopt machine learning frameworks in achieving robust performance for decoy selection in template-free protein structure prediction.

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