The multiple dissociation constants of glutathione disulfide: interpreting experimental pH-titration curves with ab initio MD simulations

文献信息

发布日期 2019-04-09
DOI 10.1039/C9CP00761J
影响因子 3.676
作者

Vaishali Arunachalam, Anil Kumar Tummanapelli, Sukumaran Vasudevan


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摘要

The hexapeptide glutathione disulfide (GSSG) has six ionizable groups with six associated dissociation constants. The experimentally measured pH-titration curve, however, does not exhibit the six corresponding equivalence points and bears little resemblance to standard textbook examples of acid–base pH-titration curves. The curve highlights the difficulties in determining multiple pKa values of polyprotic acids – typically proteins and peptides – from experiment. The six pKa values of GSSG can, however, be estimated using Car–Parrinello molecular dynamics (CPMD) simulations in conjunction with metadynamics sampling of the underlying free energy landscape of the dissociation reactions. Ab initio MD simulations were performed on a GSSG molecule solvated by 200 water molecules. Using the estimated pKa values the theoretical titration curve was calculated and found to be in good agreement with experiment. The results clearly highlight how dissociation constants estimated from ab initio MD simulations can facilitate the interpretation of the pH-titration curves of complex chemical and biological systems.

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来源期刊

Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics
CiteScore: 5.5
自引率: 10.3%
年发文量: 3036

Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.

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