MOTIVATION: The need for new drugs and new targets is particularly compelling in an era that is witnessing an alarming increase of drug resistance in human pathogens. The identification of new targets of known drugs is a promising approach, which has proven successful in several cases. Here, we describe a database that includes information on 5153 putative drug-target pairs for 150 human pathogens derived from available drug-target crystallographic complexes. AVAILABILITY AND IMPLEMENTATION: The TiPs database is freely available at http://biocomputing.it/tips. CONTACT: firstname.lastname@example.org or email@example.com.
MOTIVATION: Antibodies or immunoglobulins are proteins of paramount importance in the immune system. They are extremely relevant as diagnostic, biotechnological and therapeutic tools. Their modular structure makes it easy to re-engineer them for specific purposes. Short of undergoing a trial and error process, these experiments, as well as others, need to rely on an understanding of the specific determinants of the antibody binding mode. RESULTS: In this article, we present a method to identify, on the basis of the antibody sequence alone, which residues of an antibody directly interact with its cognate antigen. The method, based on the random forest automatic learning techniques, reaches a recall and specificity as high as 80% and is implemented as a free and easy-to-use server, named prediction of Antibody Contacts. We believe that it can be of great help in re-design experiments as well as a guide for molecular docking experiments. The results that we obtained also allowed us to dissect which features of the antibody sequence contribute most to the involvement of specific residues in binding to the antigen. AVAILABILITY: http://www.biocomputing.it/proABC. CONTACT: firstname.lastname@example.org or email@example.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
The results of differential expression analyses provide scientists with hundreds to thousands of differentially expressed genes that need to be interpreted in light of the biology of the specific system under study. This requires mapping the genes to functional classifications that can be, for example, the KEGG pathways or InterPro families they belong to, their GO Molecular Function, Biological Process or Cellular Component. A statistically significant overrepresentation of one or more category terms in the set of differentially expressed genes is an essential step for the interpretation of the biological significance of the results. Ideally, the analysis should be performed by scientists who are well acquainted with the biological problem, as they have a wealth of knowledge about the system and can, more easily than a bioinformatician, discover less obvious and, therefore, more interesting relationships. To allow experimentalists to explore their data in an easy and at the same time exhaustive fashion within a single tool and to test their hypothesis quickly and effortlessly, we developed FIDEA. The FIDEA server is located at http://www.biocomputing.it/fidea; it is free and open to all users, and there is no login requirement.
Export search results
The export option will allow you to export the current search results of the entered query to a file. Different
formats are available for download. To export the items, click on the button corresponding with the preferred download format.
By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.
For anonymous users the allowed maximum amount is 50 search results.
To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export.
The amount of items that can be exported at once is similarly restricted as the full export.
After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.