The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
CafeMol: A Coarse-Grained Biomolecular Simulator for Simulating Proteins at Work
Abstract
For simulating proteins at work in millisecond time scale or longer, we develop a coarse-grained (CG) molecular dynamics (MD) method and software, CafeMol. At the resolution of one-particle-per-residue, CafeMol equips four structure-based protein models: (1) the off-lattice Go model, (2) the atomic interaction based CG model for native state and folding dynamics, (3) the multiple-basin model for conformational change dynamics, and (4) the elastic network model for quasiharmonic fluctuations around the native structure. Ligands can be treated either explicitly or implicitly. For mimicking functional motions of proteins driven by some external force, CafeMol has various and flexible means to "switch" the energy functions that induce active motions of the proteins. CafeMol can do parallel computation with modest sized PC clusters. We describe CafeMol methods and illustrate it with several examples, such as rotary motions of F1-ATPase and drug exports from a transporter. The CafeMol source code is available at www.cafemol.org .
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
VMD: visual molecular dynamics
Humphrey, W., Dalke, A., & Schulten, K. (1996) · Journal of Molecular Graphics · doi:10.1016/0263-7855(96)00018-5 · Cited by 66,952
Fast Parallel Algorithms for Short-Range Molecular Dynamics
Plimpton, S. (1995) · Journal of Computational Physics · doi:10.1006/jcph.1995.1039 · Cited by 44,721
Scalable molecular dynamics with NAMD
Phillips, J. C., Braun, R., Wang, W., Gumbart, J., Tajkhorshid, E., Villa, E., Chipot, C., Skeel, R. D., Kalé, L., & Schulten, K. (2005) · Journal of Computational Chemistry · doi:10.1002/jcc.20289 · Cited by 16,914
Kabsch, W., & Sander, C. (1983) · Biopolymers · doi:10.1002/bip.360221211 · Cited by 15,320
Funnels, pathways, and the energy landscape of protein folding: A synthesis
Bryngelson, J. D., Onuchic, J. N., Socci, N. D., & Wolynes, P. G. (1995) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.340210302 · Cited by 2,648
Coarse Grained Model for Semiquantitative Lipid Simulations
Marrink, S. J., de Vries, A. H., & Mark, A. E. (2004) · The Journal of Physical Chemistry B · doi:10.1021/jp036508g · Cited by 2,257
The nature of folded states of globular proteins
Honeycutt, J. D., & Thirumalai, D. (1992) · Biopolymers · doi:10.1002/bip.360320610 · Cited by 360
Li, W., Wolynes, P. G., & Takada, S. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1018983108 · Cited by 159
Self-citationInternal strain regulates the nucleotide binding site of the kinesin leading head
Hyeon, C., & Onuchic, J. N. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0610939104 · Cited by 141
Cited by
Kubo, S., Black, C. S., Joachimiak, E., Yang, S. K., Legal, T., Peri, K., Khalifa, A. A. Z., Ghanaeian, A., McCafferty, C. L., Valente-Paterno, M., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-37868-0 · Cited by 93
Determining structures of RNA conformers using AFM and deep neural networks
Degenhardt, M. F. S., Degenhardt, H. F., Bhandari, Y. R., Lee, Y.-T., Ding, J., Yu, P., Heinz, W. F., Stagno, J. R., Schwieters, C. D., Watts, N. R., et al. (2025) · Nature · doi:10.1038/s41586-024-07559-x · Cited by 56
(2024) · doi:10.1038/s41467-024-47654-1 · Cited by 39
Terakawa, T., Higo, J., & Takada, S. (2014) · Biophysical Journal · doi:10.1016/j.bpj.2014.06.026 · Cited by 32
Self-citationChromosomal domain formation by archaeal SMC, a roadblock protein, and DNA structure
(2025) · doi:10.1038/s41467-025-56197-y · Cited by 10
Metadata
- Authors
- Kenzaki, H., Koga, N., Hori, N., Kanada, R., Li, W., Okazaki, K.-I., Yao, X.-Q., & Takada, S.
- Venue
- Journal of Chemical Theory and Computation
- Publisher
- American Chemical Society (ACS)
- Published
- 2011-05-19
- Volume
- 7
- Issue
- 6
- Pages
- 1979-1989
- ISSN
- 1549-9618 (ISSN-L)
- 1549-9626
- OpenAlex
- W2010713749
- Cited by
- 238
- References
- 9
- FWCI
- 3.17
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: CafeMol: A Coarse-Grained Biomolecular Simulator for Simulating Proteins at Work. https://app.cochat.ai/papers/10.1021/ct2001045
- The paper
- Kenzaki, H., Koga, N., Hori, N., Kanada, R., Li, W., Okazaki, K.-I., Yao, X.-Q., & Takada, S. (2011). CafeMol: A Coarse-Grained Biomolecular Simulator for Simulating Proteins at Work. Journal of Chemical Theory and Computation. https://doi.org/10.1021/ct2001045
Data sources
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.
