Open Access Research article

Unbiased descriptor and parameter selection confirms the potential of proteochemometric modelling

Eva Freyhult1*, Peteris Prusis2, Maris Lapinsh2, Jarl ES Wikberg2, Vincent Moulton1 and Mats G Gustafsson3*

Author Affiliations

1 The Linnaeus Centre for Bioinformatics, Uppsala University, Box 598, S-751 24 Uppsala, Sweden

2 Department of Pharmaceutical Biosciences, Uppsala University, Box 591, S-751 24 Uppsala, Sweden

3 Department of Engineering Sciences, Uppsala University, Box 528, S-751 20 Uppsala, Sweden

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BMC Bioinformatics 2005, 6:50  doi:10.1186/1471-2105-6-50

Published: 10 March 2005



Proteochemometrics is a new methodology that allows prediction of protein function directly from real interaction measurement data without the need of 3D structure information. Several reported proteochemometric models of ligand-receptor interactions have already yielded significant insights into various forms of bio-molecular interactions. The proteochemometric models are multivariate regression models that predict binding affinity for a particular combination of features of the ligand and protein. Although proteochemometric models have already offered interesting results in various studies, no detailed statistical evaluation of their average predictive power has been performed. In particular, variable subset selection performed to date has always relied on using all available examples, a situation also encountered in microarray gene expression data analysis.


A methodology for an unbiased evaluation of the predictive power of proteochemometric models was implemented and results from applying it to two of the largest proteochemometric data sets yet reported are presented. A double cross-validation loop procedure is used to estimate the expected performance of a given design method. The unbiased performance estimates (P2) obtained for the data sets that we consider confirm that properly designed single proteochemometric models have useful predictive power, but that a standard design based on cross validation may yield models with quite limited performance. The results also show that different commercial software packages employed for the design of proteochemometric models may yield very different and therefore misleading performance estimates. In addition, the differences in the models obtained in the double CV loop indicate that detailed chemical interpretation of a single proteochemometric model is uncertain when data sets are small.


The double CV loop employed offer unbiased performance estimates about a given proteochemometric modelling procedure, making it possible to identify cases where the proteochemometric design does not result in useful predictive models. Chemical interpretations of single proteochemometric models are uncertain and should instead be based on all the models selected in the double CV loop employed here.