The HIVToolbox 2 web system integrates sequence, structure, function and mutation analysis.

There is enormous interest in studying HIV pathogenesis for improving the treatment of patients with HIV infection. HIV infection has become one of the best-studied systems for understanding how a virus can hijack a cell. To help facilitate discovery, we previously built HIVToolbox, a web system for visual data mining. The original HIVToolbox integrated information for HIV protein sequence, structure, functional sites, and sequence conservation. This web system has been used for almost 40,000 searches. We report improvements to HIVToolbox including new functions and workflows, data updates, and updates for ease of use. HIVToolbox2, is an improvement over HIVToolbox with new functions. HIVToolbox2 has new functionalities focused on HIV pathogenesis including drug-binding sites, drug-resistance mutations, and immune epitopes. The integrated, interactive view enables visual mining to generate hypotheses that are not readily revealed by other approaches. Most HIV proteins form multimers, and there are posttranslational modification and protein-protein interaction sites at many of these multimerization interfaces. Analysis of protease drug binding sites reveals an anatomy of drug resistance with different types of drug-resistance mutations regionally localized on the surface of protease. Some of these drug-resistance mutations have a high prevalence in specific HIV-1 M subtypes. Finally, consolidation of Tat functional sites reveals a hotspot region where there appear to be 30 interactions or posttranslational modifications. A cursory analysis with HIVToolbox2 has helped to identify several global patterns for HIV proteins. An initial analysis with this tool identifies homomultimerization of almost all HIV proteins, functional sites that overlap with multimerization sites, a global drug resistance anatomy for HIV protease, and specific distributions of some DRMs in specific HIV M subtypes. HIVToolbox2 is an open-access web application available at [http://hivtoolbox2.bio-toolkit.com].

The Geogenomic Mutational Atlas of Pathogens (GoMAP) web system.

We present a new approach for pathogen surveillance we call Geogenomics. Geogenomics examines the geographic distribution of the genomes of pathogens, with a particular emphasis on those mutations that give rise to drug resistance. We engineered a new web system called Geogenomic Mutational Atlas of Pathogens (GoMAP) that enables investigation of the global distribution of individual drug resistance mutations. As a test case we examined mutations associated with HIV resistance to FDA-approved antiretroviral drugs. GoMAP-HIV makes use of existing public drug resistance and HIV protein sequence data to examine the distribution of 872 drug resistance mutations in ∼ 502,000 sequences for many countries in the world. We also implemented a broadened classification scheme for HIV drug resistance mutations. Several patterns for geographic distributions of resistance mutations were identified by visual mining using this web tool. GoMAP-HIV is an open access web application available at http://www.bio-toolkit.com/GoMap/project/

A computational tool for identifying minimotifs in protein-protein interactions and improving the accuracy of minimotif predictions.

Protein-protein interactions are important to understanding cell functions; however, our theoretical understanding is limited. There is a general discontinuity between the well-accepted physical and chemical forces that drive protein-protein interactions and the large collections of identified protein-protein interactions in various databases. Minimotifs are short functional peptide sequences that provide a basis to bridge this gap in knowledge. However, there is no systematic way to study minimotifs in the context of protein-protein interactions or vice versa. Here we have engineered a set of algorithms that can be used to identify minimotifs in known protein-protein interactions and implemented this for use by scientists in Minimotif Miner. By globally testing these algorithms on verified data and on 100 individual proteins as test cases, we demonstrate the utility of these new computation tools. This tool also can be used to reduce false-positive predictions in the discovery of novel minimotifs. The statistical significance of these algorithms is demonstrated by an ROC analysis (P = 0.001).