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Evolution-like selection of fast-folding model proteins.
Author(s) -
A. M. Gutin,
Victor Abkevich,
Eugene I. Shakhnovich
Publication year - 1995
Publication title -
proceedings of the national academy of sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.011
H-Index - 771
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.92.5.1282
Subject(s) - protein folding , sequence (biology) , folding (dsp implementation) , stability (learning theory) , selection (genetic algorithm) , random sequence , crystallography , biological system , chemistry , computational biology , chemical physics , biophysics , computer science , biology , mathematics , biochemistry , artificial intelligence , engineering , mathematical analysis , distribution (mathematics) , machine learning , electrical engineering
We propose an algorithm providing sequences of model proteins with rapid folding into a given target (native) conformation. This algorithm is applied to a chain of 27 residues on a cubic lattice. It generates sequences with folding 2 orders of magnitude faster than that of the practically random starting sequence. Thermodynamic analysis shows that the increase in speed is matched by an increase in stability: the evolved sequences are much more stable in their native conformation than the initial random sequence. The unfolding temperature for evolved sequences is slightly higher than the simulation temperature, bearing direct correspondence to the relatively low stability of real proteins.

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