scholarly journals Protein structure refinement using a quantum mechanics-based chemical shielding predictor

2017 ◽  
Vol 8 (3) ◽  
pp. 2061-2072 ◽  
Author(s):  
Lars A. Bratholm ◽  
Jan H. Jensen
Keyword(s):  
Quantum Mechanics ◽  
Protein Structure ◽  
Chemical Shift ◽  
Chemical Shifts ◽  
Structure Refinement ◽  
Structural Refinement ◽  
Chemical Shielding ◽  
Protein Backbone ◽  
Protein Structure Refinement ◽  
Comparable Accuracy

We show that a QM-based predictor of a protein backbone and CB chemical shifts is of comparable accuracy to empirical chemical shift predictors after chemical shift-based structural refinement that removes small structural errors (errors in chemical shifts shown in red).

scholarly journals Protein structure refinement using a quantum mechanics-based chemical shielding predictor

2016 ◽  
Author(s):  
Lars A. Bratholm ◽  
Jan H. Jensen
Keyword(s):  
Quantum Mechanics ◽  
Simulated Annealing ◽  
Chemical Shift ◽  
Force Field ◽  
Chemical Shifts ◽  
Protein Structures ◽  
Empirical Methods ◽  
Structural Refinement ◽  
Chemical Shielding ◽  
Chemical Shift Prediction

The accurate prediction of protein chemical shifts using quantum mechanics (QM)-based method has been the subject of intense research for more than 20 years but so far empirical methods for chemical shift prediction have proven more accurate. In this paper we show that a QM-based predictor of protein backbone and CB chemical shifts (ProCS15, PeerJ 2016, 3:e1344) is of comparable accuracy to empirical chemical shift predictors after chemical shift-based structural refinement that removes small structural errors. We present a method by which quantum chemistry based predictions of isotropic chemical shielding values (ProCS15) can be used to refine protein structures using Markov Chain Monte Carlo (MCMC) simulations, relating the chemical shielding values to the experimental chemical shifts probabilistically. Two kinds of MCMC structural refinement simulations were performed using force field geometry optimized X-ray structures as starting points: Simulated annealing of the starting structure and constant temperature MCMC simulation followed by simulated annealing of a representative ensemble structure. Annealing of the CHARMM structure changes the CA-RMSD by an average of 0.4 Å but lowers the chemical shift RMSD by 1.0 and 0.7 ppm for CA and N. Conformational averaging has a relatively small effect (0.1 - 0.2 ppm) on the overall agreement with carbon chemical shifts but lowers the error for nitrogen chemical shifts by 0.4 ppm. If a residue-specific offset is included the ProCS15 predicted chemical shifts have RMSD values relative to experiment that are comparable to popular empirical chemical shift predictors. The annealed representative ensemble structures differs in CA-RMSD relative to the initial structures by an average of 2.0 Å, with >2.0 Å difference for six proteins. In four of the cases, the largest structural differences arise in structurally flexible regions of the protein as determined by NMR, and in the remaining two cases, the large structural change may be due to force field deficiencies. The overall accuracy of the empirical methods are slightly improved by annealing the CHARMM structure with ProCS15, which may suggest that the minor structural changes introduced by ProCS15-based annealing improves the accuracy of the protein structures. Having established that QM-based chemical shift prediction can deliver the same accuracy as empirical shift predictors we hope this can help increase the accuracy of related approaches such as QM/MM or linear scaling approaches or interpreting protein structural dynamics from QM-derived chemical shift.

scholarly journals Protein structure refinement using a quantum mechanics-based chemical shielding predictor

2016 ◽  
Author(s):  
Lars A. Bratholm ◽  
Jan H. Jensen
Keyword(s):  
Quantum Mechanics ◽  
Simulated Annealing ◽  
Chemical Shift ◽  
Force Field ◽  
Chemical Shifts ◽  
Protein Structures ◽  
Empirical Methods ◽  
Structural Refinement ◽  
Chemical Shielding ◽  
Chemical Shift Prediction

The accurate prediction of protein chemical shifts using quantum mechanics (QM)-based method has been the subject of intense research for more than 20 years but so far empirical methods for chemical shift prediction have proven more accurate. In this paper we show that a QM-based predictor of protein backbone and CB chemical shifts (ProCS15, PeerJ 2016, 3:e1344) is of comparable accuracy to empirical chemical shift predictors after chemical shift-based structural refinement that removes small structural errors. We present a method by which quantum chemistry based predictions of isotropic chemical shielding values (ProCS15) can be used to refine protein structures using Markov Chain Monte Carlo (MCMC) simulations, relating the chemical shielding values to the experimental chemical shifts probabilistically. Two kinds of MCMC structural refinement simulations were performed using force field geometry optimized X-ray structures as starting points: Simulated annealing of the starting structure and constant temperature MCMC simulation followed by simulated annealing of a representative ensemble structure. Annealing of the CHARMM structure changes the CA-RMSD by an average of 0.4 Å but lowers the chemical shift RMSD by 1.0 and 0.7 ppm for CA and N. Conformational averaging has a relatively small effect (0.1 - 0.2 ppm) on the overall agreement with carbon chemical shifts but lowers the error for nitrogen chemical shifts by 0.4 ppm. If a residue-specific offset is included the ProCS15 predicted chemical shifts have RMSD values relative to experiment that are comparable to popular empirical chemical shift predictors. The annealed representative ensemble structures differs in CA-RMSD relative to the initial structures by an average of 2.0 Å, with >2.0 Å difference for six proteins. In four of the cases, the largest structural differences arise in structurally flexible regions of the protein as determined by NMR, and in the remaining two cases, the large structural change may be due to force field deficiencies. The overall accuracy of the empirical methods are slightly improved by annealing the CHARMM structure with ProCS15, which may suggest that the minor structural changes introduced by ProCS15-based annealing improves the accuracy of the protein structures. Having established that QM-based chemical shift prediction can deliver the same accuracy as empirical shift predictors we hope this can help increase the accuracy of related approaches such as QM/MM or linear scaling approaches or interpreting protein structural dynamics from QM-derived chemical shift.

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.

Rapid calculation of protein chemical shifts using bond polarization theory and its application to protein structure refinement

2012 ◽  
Vol 14 (35) ◽  
pp. 12263 ◽  
Author(s):  
Igor Jakovkin ◽  
Marco Klipfel ◽  
Claudia Muhle-Goll ◽  
Anne S. Ulrich ◽  
Burkhard Luy ◽  
...  
Keyword(s):  
Protein Structure ◽  
Chemical Shifts ◽  
Structure Refinement ◽  
Protein Structure Refinement ◽  
Rapid Calculation ◽  
Polarization Theory ◽  
Bond Polarization

scholarly journals A new default restraint library for the protein backbone inPhenix: a conformation-dependent geometry goes mainstream

2016 ◽  
Vol 72 (1) ◽  
pp. 176-179 ◽  
Author(s):  
Nigel W. Moriarty ◽  
Dale E. Tronrud ◽  
Paul D. Adams ◽  
P. Andrew Karplus
Keyword(s):  
Protein Structure ◽  
Protein Data Bank ◽  
Transition Period ◽  
Structure Refinement ◽  
Data Bank ◽  
Protein Backbone ◽  
Protein Structure Refinement ◽  
Fundamental Part ◽  
Backbone Conformation ◽  
Protein Backbone Conformation

Chemical restraints are a fundamental part of crystallographic protein structure refinement. In response to mounting evidence that conventional restraints have shortcomings, it has previously been documented that using backbone restraints that depend on the protein backbone conformation helps to address these shortcomings and improves the performance of refinements [Moriartyet al.(2014),FEBS J.281, 4061–4071]. It is important that these improvements be made available to all in the protein crystallography community. Toward this end, a change in the default geometry library used byPhenixis described here. Tests are presented showing that this change will not generate increased numbers of outliers during validation, or deposition in the Protein Data Bank, during the transition period in which some validation tools still use the conventional restraint libraries.

scholarly journals Protein Structure Refinement Using13Cα Chemical Shift Tensors

2009 ◽  
Vol 131 (3) ◽  
pp. 985-992 ◽  
Author(s):  
Benjamin J. Wylie ◽  
Charles D. Schwieters ◽  
Eric Oldfield ◽  
Chad M. Rienstra
Keyword(s):  
Protein Structure ◽  
Chemical Shift ◽  
Structure Refinement ◽  
Chemical Shift Tensors ◽  
Protein Structure Refinement
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