The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Improving the Physical Realism and Structural Accuracy of Protein Models by a Two-Step Atomic-Level Energy Minimization
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Equation of State Calculations by Fast Computing Machines
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953) · The Journal of Chemical Physics · doi:10.1063/1.1699114 · Cited by 37,192
MolProbity : all-atom structure validation for macromolecular crystallography
Chen, V. B., Arendall, W. B., Headd, J. J., Keedy, D. A., Immormino, R. M., Kapral, G. J., Murray, L. W., Richardson, J. S., & Richardson, D. C. (2010) · Acta Crystallographica Section D Biological Crystallography · doi:10.1107/s0907444909042073 · Cited by 14,115
LGA: a method for finding 3D similarities in protein structures
Zemla, A. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkg571 · Cited by 1,069
Zhou, H., & Zhou, Y. (2002) · Protein Science · doi:10.1110/ps.0217002 · Cited by 902 · cited 2×
“Edfire is the pairwise statistical potential from DFIRE (21), and ELJ is the pairwise physicsbased Lennard-Jones 12-6 potential (22).”
BackgroundProtein structure prediction: when is it useful?
Zhang, Y. (2009) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2009.02.005 · Cited by 277
Self-citation
Cited by
Single-sequence protein structure prediction using language models from deep learning
Chowdhury, R., Bouatta, N., Biswas, S., Rochereau, C., Church, G. M., Sorger, P. K., & AlQuraishi, M. (2021) · doi:10.1101/2021.08.02.454840 · Cited by 455
Single-sequence protein structure prediction using a language model and deep learning
Chowdhury, R., Bouatta, N., Biswas, S., Floristean, C., Kharkar, A., Roy, K., Rochereau, C., Ahdritz, G., Zhang, J., Church, G. M., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01432-w · Cited by 455
Elephantid Genomes Reveal the Molecular Bases of Woolly Mammoth Adaptations to the Arctic
Lynch, V. J., Bedoya-Reina, O. C., Ratan, A., Sulak, M., Drautz-Moses, D. I., Perry, G. H., Miller, W., & Schuster, S. C. (2015) · Cell Reports · doi:10.1016/j.celrep.2015.06.027 · Cited by 130
Singh, K. S., Kumar, S., Mohanty, A. K., Grover, S., & Kaushik, J. K. (2018) · Scientific Reports · doi:10.1038/s41598-018-32417-y · Cited by 52
Recent Advances in Studying Toll-like Receptors with the Use of Computational Methods
Bzówka, M., Bagrowska, W., & Góra, A. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00419 · Cited by 34
Metadata
- Authors
- Xu, D., & Zhang, Y.
- Venue
- Biophysical Journal
- Publisher
- Elsevier BV
- Published
- 2011-11
- Volume
- 101
- Issue
- 10
- Pages
- 2525-2534
- ISSN
- 0006-3495 (ISSN-L)
- OpenAlex
- W2139915497
- Cited by
- 1,049
- References
- 5
- FWCI
- 5.64
- Type
- journal article
- Language
- en
- Open access
- bronze
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- The paper
- Xu, D., & Zhang, Y. (2011). Improving the Physical Realism and Structural Accuracy of Protein Models by a Two-Step Atomic-Level Energy Minimization. Biophysical Journal. https://doi.org/10.1016/j.bpj.2011.10.024
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
