The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.
Monte carlo simulations of protein folding. I. Lattice model and interaction scheme
Abstract
A new hierarchical method for the simulation of the protein folding process and the de novo prediction of protein three‐dimensional structure is proposed. The reduced representation of the protein α‐carbon backbone employs lattice discretizations of increasing geometrical resolution and a single ball representation of side chain rotamers. In particular, coarser and finer lattice backbone descriptions are used. The coarser (finer) lattice represents Cα traces of native proteins with an accuracy of 1.0 (0.7) Å rms. Folding is simulated by means of very fast Monte Carlo lattice dynamics. The potential of mean force, predominantly of statistical origin, contains several novel terms that facilitate the cooperative assembly of secondary structure elements and the cooperative packing of the side chains. Particular contributions to the interaction scheme are discussed in detail. In the accompanying paper (Kolinski, A., Skolnick, J. Monte Carlo simulation of protein folding. II. Application to protein A, ROP, and crambin. Proteins 18:353–366, 1994), the method is applied to three small globular proteins. © 1994 John Wiley & Sons, Inc.
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Cites
Kabsch, W., & Sander, C. (1983) · Biopolymers · doi:10.1002/bip.360221211 · Cited by 15,320
Scaling Concepts in Polymer Physics
de Gennes, P. G., & Witten, T. A. (1980) · Physics Today · doi:10.1063/1.2914118 · Cited by 9,348
The protein data bank: A computer-based archival file for macromolecular structures
Bernstein, F. C., Koetzle, T. F., Williams, G. J. B., Meyer, E. F., Brice, M. D., Rodgers, J. R., Kennard, O., Shimanouchi, T., & Tasumi, M. (1977) · Journal of Molecular Biology · doi:10.1016/s0022-2836(77)80200-3 · Cited by 8,578
A Method to Identify Protein Sequences That Fold into a Known Three-Dimensional Structure
Bowie, J. U., Lüthy, R., & Eisenberg, D. (1991) · Science · doi:10.1126/science.1853201 · Cited by 2,851
An Introduction to Statistical Thermodynamics
Hill, T. L., & Gillis, J. (1961) · Physics Today · doi:10.1063/1.3057470 · Cited by 2,207
Creighton, T. E. (1992) · Cited by 1,583
Are there pathways for protein folding?
Levinthal, C. (1968) · Journal de Chimie Physique · doi:10.1051/jcp/1968650044 · Cited by 1,517
Kuwajima, K. (1989) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.340060202 · Cited by 1,439
Profile analysis: detection of distantly related proteins.
Gribskov, M., McLachlan, A. D., & Eisenberg, D. (1987) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.84.13.4355 · Cited by 1,325
A new approach to protein fold recognition
Jones, D. T., Taylort, W. R., & Thornton, J. M. (1992) · Nature · doi:10.1038/358086a0 · Cited by 1,221
Cited by
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
THEORY OF PROTEIN FOLDING: The Energy Landscape Perspective
Onuchic, J. N., Luthey-Schulten, Z., & Wolynes, P. G. (1997) · Annual Review of Physical Chemistry · doi:10.1146/annurev.physchem.48.1.545 · Cited by 2,210
Principles of protein folding — A perspective from simple exact models
Dill, K. A., Bromberg, S., Yue, K., Chan, H. S., Fiebig, K. M., Yee, D. P., & Thomas, P. D. (1995) · Protein Science · doi:10.1002/pro.5560040401 · Cited by 1,525
Global Optimization of Clusters, Crystals, and Biomolecules
Wales, D. J., & Scheraga, H. A. (1999) · Science · doi:10.1126/science.285.5432.1368 · Cited by 1,114
Dosztányi, Z., Csizmók, V., Tompa, P., & Simon, I. (2005) · Journal of Molecular Biology · doi:10.1016/j.jmb.2005.01.071 · Cited by 1,019
Metadata
- Authors
- Kolinski, A., & Skolnick, J.
- Venue
- Proteins: Structure, Function, and Bioinformatics
- Publisher
- Wiley
- Published
- 1994-04
- Volume
- 18
- Issue
- 4
- Pages
- 338-352
- ISSN
- 0887-3585 (ISSN-L)
- 1097-0134
- OpenAlex
- W2076488922
- Cited by
- 295
- References
- 55
- FWCI
- 8.82
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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