The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Polarizable Atomic Multipole-Based AMOEBA Force Field for Proteins
Open accessAbstract
Development of the AMOEBA (atomic multipole optimized energetics for biomolecular simulation) force field for proteins is presented. The current version (AMOEBA-2013) utilizes permanent electrostatic multipole moments through the quadrupole at each atom, and explicitly treats polarization effects in various chemical and physical environments. The atomic multipole electrostatic parameters for each amino acid residue type are derived from high-level gas phase quantum mechanical calculations via a consistent and extensible protocol. Molecular polarizability is modeled via a Thole-style damped interactive induction model based upon distributed atomic polarizabilities. Inter- and intramolecular polarization is treated in a consistent fashion via the Thole model. The intramolecular polarization model ensures transferability of electrostatic parameters among different conformations, as demonstrated by the agreement between QM and AMOEBA electrostatic potentials, and dipole moments of dipeptides. The backbone and side chain torsional parameters were determined by comparing to gas-phase QM (RI-TRIM MP2/CBS) conformational energies of dipeptides and to statistical distributions from the Protein Data Bank. Molecular dynamics simulations are reported for short peptides in explicit water to examine their conformational properties in solution. Overall the calculated conformational free energies and J -coupling constants are consistent with PDB statistics and experimental NMR results, respectively. In addition, the experimental crystal structures of a number of proteins are well maintained during molecular dynamics (MD) simulation. While further calculations are necessary to fully validate the force field, initial results suggest the AMOEBA polarizable multipole force field is able to describe the structure and energetics of peptides and proteins, in both gas-phase and solution environments.
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Cites
Lipari, G., & Szabo, A. (1982) · Journal of the American Chemical Society · doi:10.1021/ja00381a009 · Cited by 3,777
Current Status of the AMOEBA Polarizable Force Field
Ponder, J. W., Wu, C., Ren, P., Pande, V. S., Chodera, J. D., Schnieders, M. J., Haque, I., Mobley, D. L., Lambrecht, D. S., DiStasio, R. A., et al. (2010) · The Journal of Physical Chemistry B · doi:10.1021/jp910674d · Cited by 1,440
Self-citationDesigning a 20-residue protein
Neidigh, J. W., Fesinmeyer, R. M., & Andersen, N. H. (2002) · Nature Structural Biology · doi:10.1038/nsb798 · Cited by 793
Simulation of Ca2+ and Mg2+ Solvation Using Polarizable Atomic Multipole Potential
Jiao, D., King, C., Grossfield, A., Darden, T. A., & Ren, P. (2006) · The Journal of Physical Chemistry B · doi:10.1021/jp062230r · Cited by 278
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Thompson, A. P., Aktulga, H. M., Berger, R., Bolintineanu, D. S., Brown, W. M., Crozier, P. S., in 't Veld, P. J., Kohlmeyer, A., Moore, S. G., Nguyen, T. D., et al. (2022) · Computer Physics Communications · doi:10.1016/j.cpc.2021.108171 · Cited by 12,598
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Eastman, P., Swails, J., Chodera, J. D., McGibbon, R. T., Zhao, Y., Beauchamp, K. A., Wang, L.-P., Simmonett, A. C., Harrigan, M. P., Stern, C. D., et al. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005659 · Cited by 2,748
Unke, O. T., Chmiela, S., Sauceda, H. E., Gastegger, M., Poltavsky, I., Schütt, K. T., Tkatchenko, A., & Müller, K.-R. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c01111 · Cited by 1,647
Improved Peptide and Protein Torsional Energetics with the OPLS-AA Force Field
Robertson, M. J., Tirado-Rives, J., & Jorgensen, W. L. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00356 · Cited by 854
Polarizable Force Field for Peptides and Proteins Based on the Classical Drude Oscillator
Lopes, P. E. M., Huang, J., Shim, J., Luo, Y., Li, H., Roux, B., & MacKerell, A. D. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct400781b · Cited by 386
Muller, M. P., Jiang, T., Sun, C., Lihan, M., Pant, S., Mahinthichaichan, P., Trifan, A., & Tajkhorshid, E. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00608 · Cited by 282
The many-body expansion combined with neural networks
Yao, K., Herr, J. E., & Parkhill, J. (2017) · The Journal of Chemical Physics · doi:10.1063/1.4973380 · Cited by 120
Tollefson, M. R., Gogal, R. A., Weaver, A. M., Schaefer, A. M., Marini, R. J., Azaiez, H., Kolbe, D. L., Wang, D., Weaver, A. E., Casavant, T. L., et al. (2023) · Human Genetics · doi:10.1007/s00439-023-02559-9 · Cited by 20
Villard, J., Kılıç, M., & Rothlisberger, U. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c01078 · Cited by 19
Dynamic pre-structuration of lipid nanodomain-segregating remorin proteins
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Metadata
- Authors
- Shi, Y., Xia, Z., Zhang, J., Best, R., Wu, C., Ponder, J. W., & Ren, P.
- Venue
- Journal of Chemical Theory and Computation
- Publisher
- American Chemical Society (ACS)
- Published
- 2013-08-14
- Volume
- 9
- Issue
- 9
- Pages
- 4046-4063
- ISSN
- 1549-9618 (ISSN-L)
- 1549-9626
- OpenAlex
- W2171047927
- Cited by
- 691
- References
- 4
- FWCI
- 18.56
- Type
- journal article
- Language
- en
- Open access
- green
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