MutHTP: mutations in human transmembrane proteins.

Motivation: Existing sources of experimental mutation data do not consider the structural environment of amino acid substitutions and distinguish between soluble and membrane proteins. They also suffer from a number of further limitations, including data redundancy, lack of disease classification, incompatible information content, and ambiguous annotations (e.g. the same mutation being annotated as disease and benign). Results: We have developed a novel database, MutHTP, which contains information on 183 395 disease-associated and 17 827 neutral mutations in human transmembrane proteins. For each mutation site MutHTP provides a description of its location with respect to the membrane protein topology, structural environment (if available) and functional features. Comprehensive visualization, search, display and download options are available. Availability and implementation: The database is publicly available at http://www.iitm.ac.in/bioinfo/MutHTP/. The website is implemented using HTML, PHP and javascript and supports recent versions of all major browsers, such as Firefox, Chrome and Opera. Supplementary information: Supplementary data are available at Bioinformatics online.

A matrix based algorithm for Protein-Protein Interaction prediction using Domain-Domain Associations.

Protein-Protein Interactions (PPI) are vital to many cellular processes. The availability of high-throughput protein interaction data has provided us with an opportunity to assess domain associations in interacting proteins using computational approaches. High throughput PPI data, wherein the interaction status of every protein in the dataset has been experimentally tested against all the other proteins in the dataset contains information not only on protein interactions but also on proteins which do not interact with each other. We call such datasets "all against all" datasets. In the current study, using these datasets and the Pfam domain composition of the proteins in the sets, we have developed a matrix based method for predicting PPI. We infer positive and negative Domain-Domain Associations (DDA) by our method. We have generated more than a million domain association values which can be utilized for predicting new PPI. The performance of the algorithm was evaluated against a test set and the sensitivity and specificity was found to be 68.1% and 65.3%, respectively. The overall prediction accuracy of the algorithm with individual test sets from IntAct, DIP, 3did, iPfam databases and a literature curated set from Saccharomyces cerevisiae was found to be around 70%. The insights gained in the study have a potential application in providing leads for experimental interaction studies and understanding host pathogen interactions amongst others.

HuVarBase: A human variant database with comprehensive information at gene and protein levels.

Human variant databases could be better exploited if the variant data available in multiple resources is integrated in a single comprehensive resource along with sequence and structural features. Such integration would improve the analyses of variants for disease prediction, prevention or treatment. The HuVarBase (HUmanVARiantdataBASE) assimilates publicly available human variant data at protein level and gene level into a comprehensive resource. Protein level data such as amino acid sequence, secondary structure of the mutant residue, domain, function, subcellular location and post-translational modification are integrated with gene level data such as gene name, chromosome number & genome position, DNA mutation, mutation type origin and rs ID number. Disease class has been added for the disease causing variants. The database is publicly available at https://www.iitm.ac.in/bioinfo/huvarbase. A total of 774,863 variant records, integrated in the HuVarBase, can be searched with options to display, visualize and download the results.