Literature DB >> 27951677

Combining Simulations and Solution Experiments as a Paradigm for RNA Force Field Refinement.

Andrea Cesari1, Alejandro Gil-Ley1, Giovanni Bussi1.   

Abstract

Recent computational efforts have shown that the current potential energy models used in molecular dynamics are not accurate enough to describe the conformational ensemble of RNA oligomers and suggest that molecular dynamics should be complemented with experimental data. We here propose a scheme based on the maximum entropy principle to combine simulations with bulk experiments. In the proposed scheme, the noise arising from both the measurements and the forward models used to back-calculate the experimental observables is explicitly taken into account. The method is tested on RNA nucleosides and is then used to construct chemically consistent corrections to the Amber RNA force field that allow a large set of experimental data on nucleosides and dinucleosides to be correctly reproduced. The transferability of these corrections is assessed against independent data on tetranucleotides and displays a previously unreported agreement with experiments. This procedure can be applied to enforce multiple experimental data on multiple systems in a self-consistent framework, thus suggesting a new paradigm for force field refinement.

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Year:  2016        PMID: 27951677     DOI: 10.1021/acs.jctc.6b00944

Source DB:  PubMed          Journal:  J Chem Theory Comput        ISSN: 1549-9618            Impact factor:   6.006


  21 in total

1.  Development and Testing of the OPLS-AA/M Force Field for RNA.

Authors:  Michael J Robertson; Yue Qian; Matthew C Robinson; Julian Tirado-Rives; William L Jorgensen
Journal:  J Chem Theory Comput       Date:  2019-03-12       Impact factor: 6.006

2.  A method of incorporating rate constants as kinetic constraints in molecular dynamics simulations.

Authors:  Z Faidon Brotzakis; Michele Vendruscolo; Peter G Bolhuis
Journal:  Proc Natl Acad Sci U S A       Date:  2021-01-12       Impact factor: 11.205

3.  Confidence Analysis of DEER Data and Its Structural Interpretation with Ensemble-Biased Metadynamics.

Authors:  Eric J Hustedt; Fabrizio Marinelli; Richard A Stein; José D Faraldo-Gómez; Hassane S Mchaourab
Journal:  Biophys J       Date:  2018-08-16       Impact factor: 4.033

4.  Coarse-Grained Directed Simulation.

Authors:  Glen M Hocky; Thomas Dannenhoffer-Lafage; Gregory A Voth
Journal:  J Chem Theory Comput       Date:  2017-08-31       Impact factor: 6.006

5.  Improving the Performance of the Amber RNA Force Field by Tuning the Hydrogen-Bonding Interactions.

Authors:  Petra Kührová; Vojtěch Mlýnský; Marie Zgarbová; Miroslav Krepl; Giovanni Bussi; Robert B Best; Michal Otyepka; Jiří Šponer; Pavel Banáš
Journal:  J Chem Theory Comput       Date:  2019-04-02       Impact factor: 6.006

6.  Predicting the Kinetics of RNA Oligonucleotides Using Markov State Models.

Authors:  Giovanni Pinamonti; Jianbo Zhao; David E Condon; Fabian Paul; Frank Noè; Douglas H Turner; Giovanni Bussi
Journal:  J Chem Theory Comput       Date:  2017-01-05       Impact factor: 6.006

Review 7.  RNA Structural Dynamics As Captured by Molecular Simulations: A Comprehensive Overview.

Authors:  Jiří Šponer; Giovanni Bussi; Miroslav Krepl; Pavel Banáš; Sandro Bottaro; Richard A Cunha; Alejandro Gil-Ley; Giovanni Pinamonti; Simón Poblete; Petr Jurečka; Nils G Walter; Michal Otyepka
Journal:  Chem Rev       Date:  2018-01-03       Impact factor: 60.622

8.  Conformational ensembles of an RNA hairpin using molecular dynamics and sparse NMR data.

Authors:  Sabine Reißer; Silvia Zucchelli; Stefano Gustincich; Giovanni Bussi
Journal:  Nucleic Acids Res       Date:  2020-02-20       Impact factor: 16.971

9.  Maximum Entropy Optimized Force Field for Intrinsically Disordered Proteins.

Authors:  Andrew P Latham; Bin Zhang
Journal:  J Chem Theory Comput       Date:  2019-12-13       Impact factor: 6.006

10.  Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems.

Authors:  John A Keith; Valentin Vassilev-Galindo; Bingqing Cheng; Stefan Chmiela; Michael Gastegger; Klaus-Robert Müller; Alexandre Tkatchenko
Journal:  Chem Rev       Date:  2021-07-07       Impact factor: 60.622

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