The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Theory of protein secondary structure and algorithm of its prediction
Abstract
A molecular theory of protein secondary structure is presented that takes account of both local interactions inside each chain region and long‐range interactions between different regions, incorporating all these interactions in a single Ising‐like model. Local interactions are evaluated from the stereochemical theory describing the relative stabilities of α‐ and β‐structures for different residues in synthetic polypeptides, while long‐range effects are approximated by the interaction of each chain region with the averaged hydrophobic template. Based on this theory, an algorithm of protein secondary structure prediction is proposed and examples are given of “blind” predictions made before the x‐ray structural data became available.
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Cites
Comparison of the predicted and observed secondary structure of T4 phage lysozyme
Matthews, B. W. (1975) · Biochimica et Biophysica Acta (BBA) - Protein Structure · doi:10.1016/0005-2795(75)90109-9 · Cited by 5,399
Prediction of protein conformation
Chou, P. Y., & Fasman, G. D. (1974) · Biochemistry · doi:10.1021/bi00699a002 · Cited by 3,309
Chou, P. Y., & Fasman, G. D. (1974) · Biochemistry · doi:10.1021/bi00699a001 · Cited by 2,172
Cited by
Combining evolutionary information and neural networks to predict protein secondary structure
Rost, B., & Sander, C. (1994) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.340190108 · Cited by 1,452
Preformed structural elements feature in partner recognition by intrinsically unstructured proteins
Fuxreiter, M., Simon, I., Friedrich, P., & Tompa, P. (2004) · Journal of Molecular Biology · doi:10.1016/j.jmb.2004.03.017 · Cited by 518
Spurlino, J. C., Lu, G.-Y., & Quiocho, F. A. (1992) · Worldwide Protein Data Bank · doi:10.2210/pdb1mbp/pdb · Cited by 458
Spurlino, J. C., Lu, G. Y., & Quiocho, F. A. (1991) · Journal of Biological Chemistry · doi:10.1016/s0021-9258(19)67774-4 · Cited by 455
Gorbalenya, A. E., Donchenko, A. P., Blinov, V. M., & Koonin, E. V. (1989) · FEBS Letters · doi:10.1016/0014-5793(89)80109-7 · Cited by 440
Serrano, L., Sancho, J., Hirshberg, M., & Fersht, A. R. (1992) · Journal of Molecular Biology · doi:10.1016/0022-2836(92)90906-z · Cited by 368
Geley, S., Jöhrer, K., Peter, M., Denner, K., Bernhardt, R., Sippell, W. G., & Kofler, R. (1995) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jcem.80.2.7852500 · Cited by 48
AAontology: An Ontology of Amino Acid Scales for Interpretable Machine Learning
Breimann, S., Kamp, F., Steiner, H., & Frishman, D. (2024) · Journal of Molecular Biology · doi:10.1016/j.jmb.2024.168717 · Cited by 15
Metadata
- Authors
- Ptitsyn, O. B., & Finkelstein, A. V.
- Venue
- Biopolymers
- Publisher
- Wiley
- Published
- 1983-01
- Volume
- 22
- Issue
- 1
- Pages
- 15-25
- ISSN
- 0006-3525 (ISSN-L)
- 1097-0282
- OpenAlex
- W1963885440
- Cited by
- 234
- References
- 3
- FWCI
- 3.37
- 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).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
