Understanding the recognition of protein structural classes by amino acid composition
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Updated: Oct 31, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction
Published on: November 3, 2011
I Bahar1, A R Atilgan, R L Jernigan
1Molecular Structure Section, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892-5677, USA.
Abstract:
Knowledge of amino acid composition, alone, is verified here to be sufficient for recognizing the structural class, alpha, beta, alpha + beta, or alpha/beta of a given protein with an accuracy of 81%. This is supported by results from exhaustive enumerations of all conformations for all sequences of simple, compact lattice models consisting of two types (hydrophobic and polar) of residues. Different compositions exhibit strong affinities for certain folds. Within the limits of validity of the lattice models, two factors appear to determine the choice of particular folds: 1) the coordination numbers of individual sites and 2) the size and geometry of non-bonded clusters. These two properties, collectively termed the distribution of non-bonded contacts, are quantitatively assessed by an eigenvalue analysis of the so-called Kirchhoff or adjacency matrices obtained by considering the non-bonded interactions on a lattice. The analysis permits the identification of conformations that possess the same distribution of non-bonded contacts. Furthermore, some distributions of non-bonded contacts are favored entropically, due to their high degeneracies. Thus, a competition between enthalpic and entropic effects is effective in determining the choice of a distribution for a given composition. Based on these findings, an analysis of non-bonded contacts in protein structures was made. The analysis shows that proteins belonging to the four distinct folding classes exhibit significant differences in their distributions of non-bonded contacts, which more directly explains the success in predicting structural class from amino acid composition.
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