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Predicting Function from Sequence

Is it possible to predict how changes in sequence will affect the biophysical properties of a protein? In this study, Lucy G. Randles and colleagues attempt to answer this question by using stable, well characterized model proteins to predict the properties of disease-associated proteins in the same structural family.

Ig (A) and fnIII (B) domains were used to analyze disease-causing mutations.

Using related model proteins in which identical or equivalent mutations had been introduced, Randles et al. analyzed 37 different disease-causing mutations located in the L1 and IL2R proteins. Their results show an extremely strong correlation between protein instability and disease. However, there are a few exceptions, which clearly indicate the limitations of the approach. The correlation is obtained by relating the sequence variability to the energies of interaction rather than to sequence entropy. This establishes the importance of protein energetics and strikes down the sequence entropy as a measure of stability. The paper leads to the strong conclusion that sequence entropy as a measure of sequence variability is not useful for relating to protein structure.

FOOTNOTES

See referenced article, J. Biol. Chem. 2006, 281, 24216-24226

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