Molecular dynamics simulations of peptide carboxylate hydration
Literature Information
T. Liang, T. R. Walsh
Aqueous solvation of carboxylate groups, as present in the glycine zwitterion and the dipeptide aspartylalanine, is studied employing a force-field that includes distributed multipole electrostatics and induction contributions (Amoebapro: P. Ren and J. W. Ponder, J. Comput. Chem., 2002, 23, 1497; P. Ren and J. W. Ponder, J. Phys. Chem. B, 2003, 107, 5933; J. W. Ponder and D. A. Case, Adv. Protein Chem., 2003, 66, 27). Radial and orientation distribution functions, as well as hydration numbers, are calculated and compared with existing simulation data derived from Car-Parrinello molecular dynamics (CPMD), and also distributed-charge force-fields. Connections are also made with experimental data for solvation of carboxylates in water. Our findings show that Amoebapro yields carboxylate solvation properties in very good agreement with CPMD results, significantly closer agreement than can be obtained from traditional force-fields. We also demonstrate that the influence of solvation on the conformation of the dipeptide is markedly different using Amoebapro compared with the other force-fields.
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Physical Chemistry Chemical Physics

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.














