The DOI matches its Crossref record; 80 of the 80 references it cites were found in a registry or an index.
Protein modeling and structure prediction with a reduced representation.
Open accessAbstract
Protein modeling could be done on various levels of structural details, from simplified lattice or continuous representations, through high resolution reduced models, employing the united atom representation, to all-atom models of the molecular mechanics. Here I describe a new high resolution reduced model, its force field and applications in the structural proteomics. The model uses a lattice representation with 800 possible orientations of the virtual alpha carbon-alpha carbon bonds. The sampling scheme of the conformational space employs the Replica Exchange Monte Carlo method. Knowledge-based potentials of the force field include: generic protein-like conformational biases, statistical potentials for the short-range conformational propensities, a model of the main chain hydrogen bonds and context-dependent statistical potentials describing the side group interactions. The model is more accurate than the previously designed lattice models and in many applications it is complementary and competitive in respect to the all-atom techniques. The test applications include: the ab initio structure prediction, multitemplate comparative modeling and structure prediction based on sparse experimental data. Especially, the new approach to comparative modeling could be a valuable tool of the structural proteomics. It is shown that the new approach goes beyond the range of applicability of the traditional methods of the protein comparative modeling.
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Cites
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Cited by
Coarse-Grained Protein Models and Their Applications
Kmiecik, S., Gront, D., Kolinski, M., Wieteska, L., Dawid, A. E., & Kolinski, A. (2016) · Chemical Reviews · doi:10.1021/acs.chemrev.6b00163 · Cited by 1,071
Self-citationMuller, 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
Greener, J. G., & Jones, D. T. (2021) · PLOS ONE · doi:10.1371/journal.pone.0256990 · Cited by 29
Zhang, Y., Zhang, Z., Kagaya, Y., Terashi, G., Zhao, B., Xiong, Y., & Kihara, D. (2025) · Communications Biology · doi:10.1038/s42003-025-08783-5 · Cited by 19
“Various types of methods have been developed over years, such as coarse-grained model-based methods 2 – 5 , and machine learning approaches, particularly deep learning 6 – 9 .”
Metadata
- Authors
- Kolinski, A.
- Venue
- Acta Biochimica Polonica
- Publisher
- Frontiers Media SA
- Published
- 2004-06-30
- Volume
- 51
- Issue
- 2
- Pages
- 349-371
- ISSN
- 0001-527X (ISSN-L)
- 1734-154X
- OpenAlex
- W2128577591
- Cited by
- 337
- References
- 80
- FWCI
- 4.26
- Type
- journal article
- Language
- en
- Open access
- gold
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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).
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