Comment on ‘Protein isoelectric point as a predictor for increased crystallization screening efficiency’
Author(s) -
Thomas Huber,
Boštjan Kobe
Publication year - 2004
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/bth452
Subject(s) - crystallization , isoelectric point , pi , covariance , protein crystallization , chemistry , physics , thermodynamics , crystallography , mathematics , statistics , biochemistry , enzyme
A recent article in this journal (Kantardjieff and Rupp, 2004) describes a statistical predictor to increase the efficiency of protein crystallization screens. The approach is based on the observation that a correlation exists between the calculated isoelectric point of a protein, pI, and the difference between the pI and pH of the solution in which the protein was crystallized. Kantardjjieff and Rupp specifically comment on the lack of any statistically significant correlation between a protein’s pI and pH of crystallization conditions. This has been well documented in the literature (Page et al., 2003; Wooh et al., 2003) and is also well understood in condensed matter science, where polymer model systems have been studied theoretically as well as experimentally for a long time (Belloni, 2000; Frenkel, 2002). The purpose of this comment is to point out that while there is always a correlation between pI and pH‐pI, it is of no significance when no correlation between pI and pH exists. Ignoring this fact has lead to a serious misinterpretation of crystallization data. Crystallization of (bio-)polymers is being widely applied in molecular biology and designed protein-specific crystallization screens are highly desirable to increase the efficiency of protein crystallizations. We believe, it is important to prevent the misconception that simple pI calculations can be used to design such screens. The linear correlation coefficient rx,y between two variables x and y, such as pI and pH, is conveniently defined by their variances σx and σy , and their covariance σx,y rx,y = σx,y √ σxσy , where σx = N −1 N=1 (xi −¯ x) 2 , σy = N −1 N=1 (yi −¯ y) 2 and σx,y = N −1 N=1 (xi −¯ x)(yi −¯ y); ¯ x and ¯
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