Energy landscapes of a hairpin peptide including NMR chemical shift restraints

Carr, Joanne M.; Whittleston, Chris S.; Wade, David; Wales, David J.

doi:10.1039/c5cp01259g

Cited by 5 publications

(6 citation statements)

References 46 publications

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“…The effect of varying mixing parameters on the hybrid energy landscape for tryptophan zipper 1 has previously been examined using the CamShift method for calculating NMR chemical shifts alongside the CHARMM22 force field . Here, we performed a similar analysis using the NapShift neural network combined with the Amber ff99SB-ILDN force field .…”

Section: Resultsmentioning

confidence: 99%

Enhancing Biomolecular Simulations with Hybrid Potentials Incorporating NMR Data

Vrettas

Biancaniello

et al. 2022

J. Chem. Theory Comput.

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Some recent advances in biomolecular simulation and global optimization have used hybrid restraint potentials, where harmonic restraints that penalize conformations inconsistent with experimental data are combined with molecular mechanics force fields. These hybrid potentials can be used to improve the performance of molecular dynamics, structure prediction, energy landscape sampling, and other computational methods that rely on the accuracy of the underlying force field. Here, we develop a hybrid restraint potential based on NapShift, an artificial neural network trained to predict protein nuclear magnetic resonance (NMR) chemical shifts from sequence and structure. In addition to providing accurate predictions of experimental chemical shifts, NapShift is fully differentiable with respect to atomic coordinates, which allows us to use it for structural refinement. By employing NapShift to predict chemical shifts from the protein conformation at each simulation step, we can compute an energy penalty and the corresponding hybrid restraint forces based on the difference between the predicted values and the experimental chemical shifts. The performance of the hybrid restraint potential was benchmarked using both basin-hopping global optimization and molecular dynamics simulations. In each case, the NapShift hybrid potential improved the accuracy, leading to better structure prediction via basin-hopping and increased local stability in molecular dynamics simulations. Our results suggest that neural network hybrid potentials based on NMR observables can enhance a broad range of molecular simulation methods, and the prediction accuracy will improve as more experimental training data become available.

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Section: Resultsmentioning

confidence: 99%

Enhancing Biomolecular Simulations with Hybrid Potentials Incorporating NMR Data

Vrettas

Biancaniello

et al. 2022

J. Chem. Theory Comput.

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“…Many variants of the technique have been developed to specifically address problems of biological interest including peptides' conformational sampling. For instance, the efficiency of basin hopping can be improved by including experimental restraints (Carr et al, 2015) or by combining the method with other approaches, such as parallel-tempering (Strodel et al, 2010;Joseph and Wales, 2018). Connected to BHGO, is the discrete path sampling approach.…”

Section: Conformational Peptide Predictionmentioning

confidence: 99%

Bioinformatics and Biosimulations as Toolbox for Peptides and Peptidomimetics Design: Where Are We?

D’Annessa

Leva

Teana

et al. 2020

Front. Mol. Biosci.

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“…This is in contrast to classical methods for structure determination, 1 which might at best produce different protein conformations. The approach is different with respect to the more recently proposed methods aiming at determining the global minimum configuration of the system 4, [55][56][57][58][59] or at determining all relative positions of monomers within a protein homo-oligomer. 60 On the contrary, the exhaustive list of conformations generated by TAiBP provides solutions for a larger range of problems.…”

Section: Introductionmentioning

confidence: 99%

Systematic Exploration of Protein Conformational Space Using a Distance Geometry Approach

Malliavin

Mucherino

Lavor

et al. 2019

J. Chem. Inf. Model.

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The optimisation approaches classically used during the determination of protein structure encounter various diffculties, specially when the size of the conformational space is large. Indeed, in such case, algorithmic convergence criteria are more difficult to set up. Moreover, the size of the search space makes it difficult to achieve a complete exploration. The interval Branch-and-Prune (iBP) approach, based on the reformulating of the Distance Geometry Problem (DGP) provides a theoretical frame for the generation of protein conformations, by systematically sampling the conformational space. When an appropriate subset of inter-atomic distances is known exactly, this worst-case exponential-time algorithm is provably complete and fixed-parameter tractable. These guarantees, however, quickly disappear as distance measurement errors are introduced. Here we propose an improvement of this approach: the threading-augmented interval Branch-and-Prune (TAiBP), where the combinatorial explosion of the original iBP approach arising from its exponential complexity is alleviated by partitioning the input instances into consecutive peptide fragments and by using Self-Organizing Maps (SOMs) to obtain clusters of similar solutions. A validation of the TAiBP approach is presented here on a set of proteins of various sizes and structures. The calculation inputs are: a uniform covalent geometry extracted from force field covalent terms, the backbone dihedral angles with error intervals, and a few long-range distances. For most of the proteins smaller than 50 residues and interval widths of 20 • , the TAiBP approach yielded solutions with RMSD values smaller than 3Å with respect to the initial protein conformation. The efficiency of TAiBP approach for proteins larger than 50 residues will require the use of non-uniform covalent geometry, and may have benefits from the recent development of residue-specific force-fields.

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Energy landscapes of a hairpin peptide including NMR chemical shift restraints

Cited by 5 publications

References 46 publications

Enhancing Biomolecular Simulations with Hybrid Potentials Incorporating NMR Data

Enhancing Biomolecular Simulations with Hybrid Potentials Incorporating NMR Data

Bioinformatics and Biosimulations as Toolbox for Peptides and Peptidomimetics Design: Where Are We?

Systematic Exploration of Protein Conformational Space Using a Distance Geometry Approach

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