alexa An intriguing controversy over protein structural class prediction.
Bioinformatics & Systems Biology

Bioinformatics & Systems Biology

Journal of Proteomics & Bioinformatics

Author(s): Zhou GP

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Abstract A recent report by Bahar et al. [(1997), Proteins 29, 172-185] indicates that the coupling effects among different amino acid components as originally formulated by K. C. Chou [(1995), Proteins 21, 319-344] are important for improving the prediction of protein structural classes. These authors have further proposed a compact lattice model to illuminate the physical insight contained in the component-coupled algorithm. However, a completely opposite result was concluded by Eisenhaber et al. [(1996), Proteins 25, 169 179], using a different dataset constructed according to their definition. To address such an intriguing controversy, tests were conducted by various approaches for the datasets from an objective database, the SCOP database [Murzin et al. (1995), J. Mol. Biol. 247, 536-540]. The results obtained by both self-consistency and jackknife tests indicate that the overall rates of correct prediction by the algorithm incorporating the coupling effect among different amino acid components are significantly higher than those by the algorithms without counting such an effect. This is fully consistent with the physical reality that the folding of a protein is the result of a collective interaction among its constituent amino acid residues, and hence the coupling effects of different amino acid components must be incorporated in order to improve the prediction quality. It was found by a revisiting the calculation procedures by Eisenhaber et al. that there was a conceptual mistake in constructing the structural class datasets and a systematic mistake in applying the component-coupled algorithm. These findings are informative for understanding and utilizing the component-coupled algorithm to study the structural classes of proteins.
This article was published in J Protein Chem and referenced in Journal of Proteomics & Bioinformatics

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