Abstract
Proteins are heteropolymers of 20 amino acids with genetically determined sequences. Most proteins assume specific globular conformations under physiological conditions. Several experiments indicate that this unique native state is thermodynamically stable and encoded unambiguously by the sequence of amino acids [1].
Keywords
- Energy Function
- Root Mean Square Deviation
- Protein Data Bank Code
- Free Energy Landscape
- Conformation Space
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References
Creighton, T.E.: Proteins: structure and molecular properties. In: W.H. Freeman, (ed.), New York (1992)
Crippen, G.M.: Prediction of protein folding from amino acid sequence over discrete conformation space. Biochemistry 30, 4232–4237 (1991)
Seno, F., Maritan, A., Banavar, J.R.: Interaction potentials for protein folding. Proteins: Structures, Function and Genetics 30, 244–248 (1998)
Micheletti, C., Seno, F., Maritan, A., Banavar, J.R.: Learning effective amino acid interaction through iterative stochastic technique. Proteins: Function, Structure and Genetics (2000) (in press)
Bryngelson, J.D., Wolynes, P.G.: Spin glasses and the statistical mechanics of protein folding. Proc. Natl. Acad. Sci. USA 84, 7524–7528 (1987)
Micheletti, C., Banavar, J.R., Maritan, A., Seno, F.: Protein structures and optimal folding from a geometrical variational principle. Phys. Rev. Lett. 82, 3372–3375 (1999)
Covell, D.G., Jernigan, R.: Conformations of folded proteins in restricted spaces. Biochemistry 19, 3287 (1990)
Park, B.H., Levitt, M.: The complexity and accuracy of discrete state models of protein structure. J. Mol. Biol. 249, 493–507 (1995)
Park, B.H., Levitt, M.: Energy functions that discriminate X-ray and nearnative folds from well-constructed decoys. J. Mol. Biol. 258, 367–392 (1996)
Krauth, W., Mezard, M.: Learning algorithms with optimal stability in neural networks. J. Phys. A20, L745–L752 (1987)
Lazaridis, T., Karplus, M.: Effective energy functions for protein structure prediction. Curr. Op. in Struct. Biol. 10, 139–145 (2000)
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© 2003 Springer-Verlag Berlin Heidelberg
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Seno, F., Micheletti, C., Maritan, A., Banavar, J.R. (2003). Learning Effective Amino-Acid Interactions. In: Guerra, C., Istrail, S. (eds) Mathematical Methods for Protein Structure Analysis and Design. Lecture Notes in Computer Science(), vol 2666. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44827-3_10
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DOI: https://doi.org/10.1007/978-3-540-44827-3_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40104-9
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