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Integrates complementary analyses to produce information about proteins in one online automated package. ProteINSIDE is able to extract information for a list of genes or proteins ID from myriad data sources with a single input. It has been used to propose new hypotheses of research that should help to better understand the growth of adipose tissues (AT) and muscle in bovine. This tool predicts proteins secreted by cellular processes that do not involve a signal peptide.

C-HPP / Chromosome-Centric Human Proteome Project

Maps the human protein subset or parts list coded by genes on each chromosome. C-HPP aims to define the full set of proteins encoded in each chromosome through development of a standardized approach for analyzing the massive proteomic data sets currently being generated from dedicated efforts of national and international teams. The initial goal of the C-HPP is to identify at least one representative protein encoded by each of the approximately 20,300 human genes.


Facilitates the interactive navigation through kinase knowledge by linking biochemical, structural, and disease association data to the human kinome tree. KinMap represents a new generation of kinome tree viewers which facilitates interactive exploration of the human kinome. KinMap enables generation of high-quality annotated images of the human kinome tree as well as exchange of kinome-related data in scientific communications. To this end, preprocessed data from freely-available sources, such as ChEMBL, the Protein Data Bank (PDB), and the Center for Therapeutic Target Validation platform are integrated into KinMap and can easily be complemented by proprietary data. Furthermore, KinMap supports multiple input and output formats and recognizes alternative kinase names and links them to a unified naming scheme, which makes it a useful tool across different disciplines and applications.

SDIndex / Structural Difficulty Index

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Reflects the capability of predicting accurate structures and helps to assess the potential for developing proteome level structural databases for various organisms with some of the best methodologies available currently. SDIndex is derived from secondary structures, homology and physicochemical features of protein sequences. For 77 human pathogenic viruses comprising 2365 globular viral proteins out of which only 162 structures are solved experimentally, SD index scores 1336 proteins in the modelable zone. Availability of reliable protein structures may prove a crucial aid in developing species-wise structural proteomic databases for accelerating function annotation and for drug development endeavors.


Provides many FDR estimation and visualization features for several popular search algorithms. ProteoStats also provides accurate q-values, which can be easily integrated in any proteomics pipeline to provide automated, accurate, high-throughput statistical validation and minimize manual errors. It is a highly versatile, platform independent, open source, extensible and easy-to-use framework for FDR estimation and statistical control of results from shotgun proteomics database search. ProteoStats is a part of MSSuite.


Consists of an implementation of object-oriented mapping of PDB files. MolTalk is a programming library implemented in Objective-C, that maps PDB structure files to object space, as well as of a scripting language based on Smalltalk. The software provides methods enabling users to develop software towards their individual needs, and to allow for novel insights from protein structure analyses. It can be useful for benchmark analysis and can serve as a database front end to extract information encoded in PDB files.