A computational strategy for protein function assignment which addresses the multidomain problem

VICMpred: an SVM-based method for the prediction of functional proteins of Gram-negative bacteria using amino acid patterns and composition

In this study, an attempt has been made to predict the major functions of gram-negative bacterial proteins from their amino acid sequences. The dataset used for training and testing consists of 670 non-redundant gram-negative bacterial proteins (255 of cellular process, 60 of information molecules, 285 of metabolism, and 70 of virulence factors). First we developed an SVM-based method using amino acid and dipeptide composition and achieved the overall accuracy of 52.39% and 47.01%, respectively. We introduced a new concept for the classification of proteins based on tetrapeptides, in which we identified the unique tetrapeptides significantly found in a class of proteins. These tetrapeptides were used as the input feature for predicting the function of a protein and achieved the overall accuracy of 68.66%. We also developed a hybrid method in which the tetrapeptide information was used with amino acid composition and achieved the overall accuracy of 70.75%. A five-fold cross validation was used to evaluate the performance of these methods. The web server VICMpred has been developed for predicting the function of gram-negative bacterial proteins (http://www.imtech.res.in/raghava/vicmpred/).

Correspondence of function and phylogeny of ABC proteins based on an automated analysis of 20 model protein data sets

Using our BLAST-based procedure RiPE (Retrieval-induced Phylogeny Environment), which automates the evolutionary analysis of a protein family, we assembled a set of 1138 ABC protein components [adenosine triphosphate (ATP)-binding cassette and transmembrane domain] from the protein data sets of 20 model organisms and subjected them to phylogenetic and functional analysis. For maximum speed, we based the alignment directly on a homology search with a profile of all known human ABC proteins and used neighbor-joining tree estimation. All but 11 sequences from Homo sapiens, Arabidopsis thaliana, Drosophila melanogaster, and Saccharomyces cerevisiae were placed into the correct subtree/subfamily, reproducing published classifications of the individual organisms. By following a simple "function transfer rule", our comparative phylogenetic analysis successfully predicted the known function of human ABC proteins in 19 of 22 cases. Three functional predictions did not correspond, and 10 were novel. Predictions based on BLAST alone were inferior in five cases and superior in two. Bacterial sequences were placed close to the root of most subtrees. This placement coincides with domain architecture, suggesting an early diversification of the ABC family before the kingdoms split apart. Our approach can, in principle, be used to annotate any protein family of any organism included in the study.