scholarly journals 3Drefine: Consistent protein structure refinement by optimizing hydrogen bonding network and atomic-level energy minimization

2012 ◽  
Vol 81 (1) ◽  
pp. 119-131 ◽  
Author(s):  
Debswapna Bhattacharya ◽  
Jianlin Cheng
Keyword(s):  
Hydrogen Bonding ◽  
Protein Structure ◽  
Energy Minimization ◽  
Structure Refinement ◽  
Atomic Level ◽  
Level Energy ◽  
Protein Structure Refinement ◽  
Hydrogen Bonding Network

refineD: improved protein structure refinement using machine learning based restrained relaxation

2019 ◽  
Vol 35 (18) ◽  
pp. 3320-3328 ◽  
Author(s):  
Debswapna Bhattacharya
Keyword(s):  
Machine Learning ◽  
Protein Structure ◽  
Structure Refinement ◽  
Supplementary Information ◽  
Conformational Sampling ◽  
Level Energy ◽  
Structural Refinement ◽  
Native State ◽  
Protein Structure Refinement ◽  
Protein Models

AbstractMotivationProtein structure refinement aims to bring moderately accurate template-based protein models closer to the native state through conformational sampling. However, guiding the sampling towards the native state by effectively using restraints remains a major issue in structure refinement.ResultsHere, we develop a machine learning based restrained relaxation protocol that uses deep discriminative learning based binary classifiers to predict multi-resolution probabilistic restraints from the starting structure and subsequently converts these restraints to be integrated into Rosetta all-atom energy function as additional scoring terms during structure refinement. We use four restraint resolutions as adopted in GDT-HA (0.5, 1, 2 and 4 Å), centered on the Cα atom of each residue that are predicted by ensemble of four deep discriminative classifiers trained using combinations of sequence and structure-derived features as well as several energy terms from Rosetta centroid scoring function. The proposed method, refineD, has been found to produce consistent and substantial structural refinement through the use of cumulative and non-cumulative restraints on 150 benchmarking targets. refineD outperforms unrestrained relaxation strategy or relaxation that is restrained to starting structures using the FastRelax application of Rosetta or atomic-level energy minimization based ModRefiner method as well as molecular dynamics (MD) simulation based FG-MD protocol. Furthermore, by adjusting restraint resolutions, the method addresses the tradeoff that exists between degree and consistency of refinement. These results demonstrate a promising new avenue for improving accuracy of template-based protein models by effectively guiding conformational sampling during structure refinement through the use of machine learning based restraints.Availability and implementationhttp://watson.cse.eng.auburn.edu/refineD/.Supplementary informationSupplementary data are available at Bioinformatics online.

scholarly journals A branch and bound algorithm for protein structure refinement from sparse NMR data sets 1 1Edited by F. Cohen

1999 ◽  
Vol 285 (4) ◽  
pp. 1691-1710 ◽  
Author(s):  
Daron M. Standley ◽  
Volker A. Eyrich ◽  
Anthony K. Felts ◽  
Richard A. Friesner ◽  
Ann E. McDermott
Keyword(s):  
Protein Structure ◽  
Branch And Bound ◽  
Structure Refinement ◽  
Branch And Bound Algorithm ◽  
Data Sets ◽  
Protein Structure Refinement ◽  
Nmr Data

scholarly journals KoBaMIN: a knowledge-based minimization web server for protein structure refinement

2012 ◽  
Vol 40 (W1) ◽  
pp. W323-W328 ◽  
Author(s):  
J. P. G. L. M. Rodrigues ◽  
M. Levitt ◽  
G. Chopra
Keyword(s):  
Protein Structure ◽  
Structure Refinement ◽  
Web Server ◽  
Protein Structure Refinement ◽  
Knowledge Based
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