HIVtoolbox - for investigating HIV virology and discovery of new drug targets
Minimotif Miner - predicts new functions in proteins and causes of diesease using short linear peptide motifs
VENN - plots sequence conservation onto protein structures
SciReader - for reading complex scientific and medical documents
HIVAtlas - Helps clinicians make decisions on treating patients with HIV drugs based on geographic resistance profiles
MimoSA - a downloadable application for annotating information from the primarly literature
Minimotif Miner Query Engine - a web interface with the MnM database for building consensus minimotifs and PSSMs
Marty's Links - a collection of weblinks useful for scientists
JBOG - identifies all proteins in a proteome having the same predicted molecular mass

We ask you to cite our applications whenever they are used for research that results in a publication or if a figure is made using one of our applications. This helps us support and create new version with more capabilities.

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Relevant links
Schillerlab at SoLS webpage
MnM at Wikipedia
MnM at NAR Molecular Biology Database Collection
HIVToolbox at Wikipedia
Short Linear Motifs at Wikipedia

Approximately 40 million people are infected with HIV and the major limitation in HIV drug therapy is loss of effectiveness due to HIV acquiring resistance mutations. Here, we present HIVAtlas, a new rational-based strategy for designing patient therapy that does not require genome sequencing of the viral isolates, rather uses vast amounts of existing data to select antiretroviral therapy base don geographic location and treatment status. HIVAtlas should help design of more effective HIV therapies, minimize global drug resistance to antiretrovirals, decrease the mortality of infected patients, and reducing the economic cost of treating patients. HIVAtlas was built by Sandeep Deverasetty, with contributions from David Sargeant, Dr. Christy Strong, Viraj Rathnayake, Dorthea Maza, and Sean Tuggle.

PLEASE CITE USE OF HIVATLAS WITH THIS PAPER:
manuscript is submitted

The goal of this project is to create an integrated webtool that can be used to investigate potential drug targets in HIV (Kadaveru et al, 2008). HIVtoolbox is an analytic tool that integrates data for all HIV proteins. The application has a sequence window that maps domains, functional sites, protein-protein interactions, and potential functional minimotifs to the sequence. The sequence is interactively coupled to three structure viewers that are synchronized. These structure windows shows structures colored by domains, predicted minimotifs, functional sites, protein-protein interactions, and sequence conservation. Sequence conservation is calculated for many thousand different sequences from isolates. This is integrated with a species/group/clade selected and sequence alignment/PSSM display. HIVtoolbox was built by David Sargeant, Sandeep Deverasetty, and previous lab member Angel Villahoz Baleta. See Wikipedia article.

PLEASE CITE USE OF HIVTOOLBOX WITH THIS PAPER:
Sargeant D, Deverasetty S, Luo Y, Baleta AV, Zobrist S, Rathnayake V, Russo JC, Vyas J, Muesing MA, and Schiller MR (2011) HIVToolbox, an integrated web application for investigating HIV. PLoS One, 6, e20122. PMID: 21647445

Video on MnM. Developing protein-protein interaction theory is important for our understanding of the cell, disease mechanisms, and to facilitate drug design. The theory behind protein-protein interactions is based on first principle theory of molecular interactions and the identification of a rapidly growing number of short peptide motifs (less than 15 amino acids) that can bind to, or be acted upon by protein domains. Other than those interactions mediated through short motifs we have virtually no ability to predict protein-protein interactions.

My lab is continuing annotation of Minimotif Miner, the first bioinformatics tool that is a comprehensive database of short functional motifs currently containing ~300,000 unique motifs ( Balla et al, 2006, Rajasekaran et al, 2009). Minimotif Miner can be used by any scientist to generate new hypotheses about the function of any protein and postulate mechanisms by which mutations cause any human disease. Current projects are aimed at completing this database, enhancing the specificity of motif definitions. Minimotif Miner was built in collaboration with several scientists at the University of Connecticut and its Health Center. See more on minimotif miner at [wikipedia]

PLEASE CITE USE OF MINIMOTIF MINER WITH THESE PAPERS:
Balla S, Thapar V, Verma S, Luong T, Faghri T, Huang C-H, Rajasekaran S, del Campo JJ, Shinn JH, Mohler WA, Maciejewski MW, Gryk MR, Piccirillo B, Schiller SR, and Schiller MR. (2006). Minimotif Miner, a tool for investigating protein function.. Nat. Methods, 3, 175-177. PMID: 16489333

Rajasekaran S, Balla S, Gradie P, Gryk MR, Kadaveru K, Kundeti V, Maciejewski MW, Mi T, Rubino N, Vyas J, Schiller MR. (2009). Minimotif Miner 2nd release: a database and web system for motif search. Nucleic. Acids Res., 37, D185-190. PMID: 18978024

Mi T, Merlin JC, Deverasetty S, Gryk MR, Bill TJ, Brooks AW, Lee LY, Rathnayake V, Ross CA, Sargeant DP, Strong CL, Watts P, Rajasekaran S, Schiller MR (2012). Minimotif Miner 3.0: database expansion and significantly improved reduction of false-positive predictions from consensus sequences. Nucleic Acids Res., 40 (database issue), D252-260. PMID: 22146221

Video on VENN VENN is an integrative computer program, written by Jay Vyas, for mapping protein sequence conservation among homologues onto a three dimensional structure of a protein (Vyas et al., 2009). VENN was named after John VENN, the inventor of VENN diagrams, because VENN can be used to explore the interaction of sequence and structure to identify important functional regions in proteins, The only input needed in a VENN analysis is an experimental or model PDB structure identifier (e.g. 1EX4). An alternative site for structure models can be found here . VENN retrieves and interprets the PDB file to identify different structure chains. Protein sequences with sequence similarity to each chain are automatically retrieved from the EBI and a list of up to 500 such sequences is presented to the user. The user selects sequences using canned strategies or by their own ad hoc selections. VENN then aligns the protein sequences, calculates a heatmap for conservation of each amino acid position and plots the heatmap onto the protein structure. Different strategies for selecting groups of sequences can be used to identify functional regions and specificity determinants in gene families.

Do to changes in the data access at EBI, the stand alone version of VENN no longer functions and has been deprecated. This has been replaced by a web version of VENN that contains all of the critical functions reported in the publication. VENN was built by Jay Vyas.

PLEASE CITE USE OF VENN WITH THIS PAPER:
Vyas J, Gryk MR, and Schiller. (2009). Venn, a tool for titrating sequence conservation onto protein structures. Nucleic Acids Res., 37, e124. PMID: 19656955

Video on SciReader SciReader is a web application where a document can be uploaded and mapped to nearly 1,000,000 biological and english word definitions. A small frame in the top of the window displays any word selected from an uploaded document. No more changing pages to figure out the meaning of that biological or medical word. SciReader was built by Patrick Gradie.

PLEASE CITE USE OF SCIREADER: Gradie PR, Litster M, Thomas R, Vyas J, Schiller MR. (2011). SciReader enables reading of medical content with instantaneous definitions. BMC Med Inform Decis Mak, 11, 4. PMID: 21266060

MimoSA is a downloadable software application that can be used to manage Bioinformatics projects that annotate information from the scientific literature (Vyas et al, 2010). MimoSA (Minimotif System for Annotation) was built to annotate minimotifs but is readily adapted to other domains. It interfaces with a MySQL database and has many features such as form based entry, read, write, edit, and delete functions, a database viewer, a paper browser, paper status manager, and a SPAM-like algorithm that scores papers for desired content. MimoSA was built by Jay Vyas, RJ Nowling, Thomas Meusburger, and David Sargeant.

PLEASE CITE USE OF MIMOSA OR TEXTMINE: Jay Vyas, Ronald J.Nowling, Thomas Meusberger, David Sargeant, Krishna Kadaveru. Michael R. Gryk, Vamsi Kundeti Sanguthevar Rajasekaran and Martin R. Schiller. (2010). MimoSA: a system for minimotif annotation. BMC Bioinformatics, 11, 328. PMID: 19656955

Minimotif Miner Query Engine was build as a more advanced interface with the Minimotif Miner Database (Vyas et al., 2009). The users can select different minimotif instances or consensus sequence to build a super-consensus for a set of instances. Position specific scoring matrices can also be created from selected instances. This allows the data from multiple studies to be used to arrive at a consensus sequences. An example application to SH3 domains showed that there are 10 distinct types of SH3 domain binding motifs which are shown in the figure.

PLEASE CITE USE OF MNM QUERY ENGINE:
Vyas J, Nowling RJ, Maciejewski MW, Rajasekaran S, Gryk MR, and Schiller MR. (2009). A Proposed Syntax for Minimotif Semantics, Version 1. BMC Genomics., 10, 360. PMID: 19656396

JBOG (Java-based Band On Gel) can be used to identify all proteins in a proteome in a small range of molecular masses. Did you ever run a gel and wonder what might be in that band? JBOG can provide you with a list of potential answers. JBOG was written by Jay Vyas.

This is a collection of weblinks useful for biologists. This include weblinks to NCBI databases, medical dictionary, statistical analysis packages, genetic code, rodent atlases, funding sources, links for analysis of proteins and nucleic acids, etc.

Understanding how neurons initiate axon outgrowth is important, not just for our basic understanding of neuronal connectivity, but also for treating neurodegenerative diseases, spinal cord injury, and head trauma. Axonal outgrowth requires the coordination of many cellular processes. As the axon navigates the nervous system to find targets, it must make complicated decisions that require a higher level of interpretation. Very little is known how the axon is capable of interpreting the many inputs it receives. to address this question, we are continuing to study how a multidomain protein called Kalirin is involved in coordination of axonal signal processing (May et al., 2001, Penzes et al, 2003, Schiller et al. 2005, Schiller et al. 2006, Chakrabarti et al. 2006, Schiller, 2007, Schiller et al. 2008). This project has involved the determination of structures for two domains of Kalirin (2KR9, 1U3O), which has given insight into its mechanism of action Schiller et al. 2006.