Over 30% of all proteins bind metal ions, including transition metals, for structural and functional purposes. Even though there are rich experimental structural and thermodynamic information on protein-ion complexes, our understanding of the physical basis for the specificity and selectivity in protein-ion recognition remains lacking. There is a great need for accurate physical models and efficient simulation software to enable computational study of protein-ion systems. We propose to develop a next generation classical force field AMOEBA+, based on the existing AMOEBA potential, to systematically model permanent electrostatics, repulsion, dispersion, charge penetration, polarization, charge transfer, and ligand field effect after quantum mechanical energy decomposition and experimental data. The new potential and high- performance molecular simulation software (Tinker for CPU & GPU systems) will allow us to comprehend the structural, physical and thermodynamic driving forces underlying protein-ion recognition using molecular dynamics simulations. Given the fundamental importance of protein interaction with transition metals including Zn, Cu, Ni, Co, Fe, and Mn, this research will have a broad impact on advancing our scientific knowledge about ions in biomolecular structure and function, and lead to new computational tools to accelerate the design of new diagnostic and therapeutic molecules targeting protein-ion interactions.

Public Health Relevance

The proposed research is important to public health because it will lead to the critical understanding of how proteins recognize specific ions for structural and functional purposes and will provide computational tools that facilitate the discovery of new drugs, protein therapeutics and diagnostics targeting protein-ion interactions.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
2R01GM114237-05
Application #
9972498
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Lyster, Peter
Project Start
2015-05-01
Project End
2024-04-30
Budget Start
2020-05-01
Budget End
2021-04-30
Support Year
5
Fiscal Year
2020
Total Cost
Indirect Cost
Name
University of Texas Austin
Department
Biomedical Engineering
Type
Biomed Engr/Col Engr/Engr Sta
DUNS #
170230239
City
Austin
State
TX
Country
United States
Zip Code
78759
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Jing, Zhifeng; Liu, Chengwen; Qi, Rui et al. (2018) Many-body effect determines the selectivity for Ca2+ and Mg2+ in proteins. Proc Natl Acad Sci U S A 115:E7495-E7501
Laury, Marie L; Wang, Zhi; Gordon, Aaron S et al. (2018) Absolute binding free energies for the SAMPL6 cucurbit[8]uril host-guest challenge via the AMOEBA polarizable force field. J Comput Aided Mol Des 32:1087-1095
Zhang, Changsheng; Lu, Chao; Jing, Zhifeng et al. (2018) AMOEBA Polarizable Atomic Multipole Force Field for Nucleic Acids. J Chem Theory Comput 14:2084-2108
Bell, David R; Cheng, Sara Y; Salazar, Heber et al. (2017) Capturing RNA Folding Free Energy with Coarse-Grained Molecular Dynamics Simulations. Sci Rep 7:45812
Obliosca, Judy M; Cheng, Sara Y; Chen, Yu-An et al. (2017) LNA Thymidine Monomer Enables Differentiation of the Four Single-Nucleotide Variants by Melting Temperature. J Am Chem Soc 139:7110-7116
Liu, Chengwen; Qi, Rui; Wang, Qiantao et al. (2017) Capturing Many-Body Interactions with Classical Dipole Induction Models. J Chem Theory Comput 13:2751-2761
Harger, Matthew; Li, Daniel; Wang, Zhi et al. (2017) Tinker-OpenMM: Absolute and relative alchemical free energies using AMOEBA on GPUs. J Comput Chem 38:2047-2055
Zhang, Changsheng; Bell, David; Harger, Matthew et al. (2017) Polarizable Multipole-Based Force Field for Aromatic Molecules and Nucleobases. J Chem Theory Comput 13:666-678
Deng, Shi; Wang, Qiantao; Ren, Pengyu (2017) Estimating and modeling charge transfer from the SAPT induction energy. J Comput Chem 38:2222-2231

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