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Benchmarking a Fast Proton Titration Scheme in Implicit Solvent for Biomolecular Simulations

Authors: Fernando Luís Barroso da Silva; Donal MacKernan;

Benchmarking a Fast Proton Titration Scheme in Implicit Solvent for Biomolecular Simulations

Abstract

pH is a key parameter for technological and biological processes, intimately related to biomolecular charge. As such, it controls biomolecular conformation and inter molecular interactions, for example, protein/RNA stability and folding, enzyme activity, regulation through conformational switches,protein-polyelectrolyte association, and protein-RNA interactions. pH also plays an important role in technological systems in food, brewing, pharma, bioseparations and biomaterials in general. Predicting the structure of large proteins and complexes remains a great challenge, experimentally, industrially, and theoretically, despite the variety of numerical schemes available ranging from Poisson-Boltzmann approaches to explicit solvent based methods. In this work we benchmark a fast proton titration scheme against experiment and several theoretical methods on the following set of representative proteins: [HP36, BBL, HEWL (triclinic and orthorhombic), RNase, SNASE (V66K/WT, V66K/PHS, V66K/+PHS, L38D/+PHS, L38E/+PHS, L38K/+PHS), ALAC and OMTKY3] routinely used in similar tests due to the diversity of their structural features. Our scheme is rooted in the classical Tanford-Kirkwood model of impenetrable spheres, where salt is treated at the Debye-Hückel level. Treating salt implicitly dramatically reduces the computation time, thereby circumventing sampling difficulties faced by other numerical schemes. In comparison with experimental measurements, our calculated pKa values have the average, maximum absolute and root-mean-square deviations of [0.4 − 0.9], [1.0 − 5.2] and [0.5 − 1.2] pH units, respectively. These values are within the ranges commonly observed in theoretical models. They are also in the large majority of the cases studied here more accurate than the NULL model. For BBL, ALAC and OMTKY3, the predicted pKa are closer to experimental results than other analyzed theoretical data. Despite the intrinsic approximations of the fast titration scheme, its robustness and ability to properly describe the main system physics is confirmed.

Country
Brazil
Subjects by Vocabulary

Microsoft Academic Graph classification: RNA Stability Triclinic crystal system Molecular dynamics Protein structure Computational chemistry Static electricity Chemistry Intermolecular force Folding (chemistry) Crystallography Titration

Keywords

protein titration, Monte Carlo Simulations, Tanford and Kirkwood model, protein electrostatics., Protein Conformation, Static Electricity, Molecular Dynamics Simulation, Physical and Theoretical Chemistry, Ribonuclease, Pancreatic, PROTEÍNAS, Computer Science Applications, Benchmarking, Solvents, Protons

16 references, page 1 of 2

(2) Goh, G. B.; Knight, J. L.; Brooks, C. L. pH-dependent dynamics of complex RNA macromolecules. J. Chem. Theory Comput. 2013, 9, 935-943.

(3) Chen, W.; Morrow, B. H.; Shi, C.; Shen, J. K. Recent development and application of constant pH molecular dynamics. Mol. Sim. 2014, 40, 830-838.

(4) Chen, Y.; Roux, B. Constant-pH Hybrid Nonequilibrium Molecular DynamicsMonte Carlo Simulation Method. J. Chem. Theory Comput. 2015, 11, 3919-3931.

(13) Roca, M.; Messer, B.; Warshel, A. Electrostatic contributions to protein stability and folding energy. FEBS letters 2007, 581, 2065-2071.

(14) Lizatovi´c Robert,; Aurelius Oskar,; Stenstro¨m Olof,; Drakenberg Torbj¨orn,; Akke Mikael,; Logan Derek T.,; Andr´e Ingemar, A De Novo Designed Coiled-Coil Peptide with a Reversible pH-Induced Oligomerization Switch. Structure 2016, 24, 946-955.

(15) Da Silva, F. L. B.; J¨onsson, B. Polyelectrolyte-protein complexation driven by charge regulation. Soft Matter 2009, 5, 2862-2868.

(22) Chen, K.; Xu, Y.; Rana, S.; Miranda, O. R.; Dubin, P. L.; Rotello, V. M.; Sun, L.; Guo, X. Electrostatic Selectivity in Protein-Nanoparticle Interactions. Biomacromolecules 2011, 12, 2552-2561.

(39) Davies, M. N.; Toseland, C. P.; Moss, D. S.; Flower, D. R. Benchmarking pKa prediction. BMC Biochemistry 2006, 7, 1-12.

(41) Tanford, C.; Kirkwood, J. G. Theory of Protein Titration Curves I. General Equations for Impenetrable spheres. J. Am. Chem. Soc. 1957, 79, 5333-5339.

(49) Havranek, J. J.; Harbury, P. B. Tanford-Kirkwood electrostatics for protein modeling. Proc. Natl. Acad. Sci. USA 1999, 96, 11145-11150.

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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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download
citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
downloads
OpenAIRE UsageCountsDownloads provided by UsageCounts
27
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130
259
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EC| E-CAM
Project
E-CAM
An e-infrastructure for software, training and consultancy in simulation and modelling
  • Funder: European Commission (EC)
  • Project Code: 676531
  • Funding stream: H2020 | RIA
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