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Due to the rising number of solved protein structures, computer-based techniques for automatic protein functional annotation and classification into families are of high scientific interest. DoGSiteScorer automatically calculates global descriptors for self-predicted pockets based on the 3D structure of a protein. Protein function predictors on three levels with increasing granularity are built by use of a support vector machine (SVM), based on descriptors of 26632 pockets from enzymes with known structure and enzyme classification. The SVM models represent a generalization of the available descriptor space for each enzyme class, subclass, and substrate-specific sub-subclass. Cross-validation studies show accuracies of 68.2% for predicting the correct main class and accuracies between 62.8% and 80.9% for the six subclasses. Substrate-specific recall rates for a kinase subset are 53.8%. Furthermore, application studies show the ability of the method for predicting the function of unknown proteins and gaining valuable information for the function prediction field. Proteins 2013. © 2012 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

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Previously, (Hähnke et al., J Comput Chem 2009, 30, 761) we presented the Pharmacophore Alignment Search Tool (PhAST), a ligand-based virtual screening technique representing molecules as strings coding pharmacophoric features and comparing them by global pairwise sequence alignment. To guarantee unambiguity during the reduction of two-dimensional molecular graphs to one-dimensional strings, PhAST employs a graph canonization step. Here, we present the results of the comparison of 11 different algorithms for graph canonization with respect to their impact on virtual screening. Retrospective screenings of a drug-like data set were evaluated using the BEDROC metric, which yielded averaged values between 0.4 and 0.14 for the best-performing and worst-performing canonization technique. We compared five scoring schemes for the alignments and found preferred combinations of canonization algorithms and scoring functions. Finally, we introduce a performance index that helps prioritize canonization approaches without the need for extensive retrospective evaluation. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2010 [ABSTRACT FROM AUTHOR]

Given the three-dimensional structure of a protein, how can one find the sites where other molecules might bind to it? Do these sites have the properties necessary for high affinity binding? Is this protein a suitable target for drug design? Here, we discuss recent developments in computational methods to address these and related questions. Geometric methods to identify pockets on protein surfaces have been developed over many years but, with new algorithms, their performance is still improving. Simulation methods show promise in accounting for protein conformational variability to identify transient pockets but lack the ease of use of many of the (rigid) shape-based tools. Sequence and structure comparison approaches are benefiting from the constantly increasing size of sequence and structure databases. Energetic methods can aid identification and characterization of binding pockets, and have undergone recent improvements in the treatment of solvation and hydrophobicity. The 'druggability' of a binding site is still difficult to predict with an automated procedure. The methodologies available for this purpose range from simple shape and hydrophobicity scores to computationally demanding free energy simulations. Copyright © 2009 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This article presents information on an integrated bioinformatics application that can be used for automated target discovery in DNA sequence analysis. The term "target" is used here to describe a protein against which it could be useful to develop a drug relevant for a specific disease. The authors have conducted a research towards a solution using database management and rigorously minimized direct operations at the level of the flat file. The task involved several dynamic data sources, predicted to grow in complexity as the project developed. They chose a relational platform as the basis of our data warehouse. The schema that the authors have implemented to handle the data sources was matched as closely as possible to the specific source, exploiting the highest normal form possible to reduce possible internal inconsistencies. The original data consisted of a few million expressed sequence tags, assembled into half a million or so assemblies. The first step to reduce the number of computations performed was to decrease the number of assemblies to annotate. The integrated silicon system, developed here, was used to provide "complementary entry points" to the proprietary assemblies. In this context, therefore, the system was used either to get additional information on a specific assembly or to find assemblies potentially related to a query.