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https://github.com/santeripuranen/SuperDCA

Software tool for global direct coupling analysis of input genome alignments. Implements variant of pseudolikelihood maximization direct coupling analysis, with emphasis on optimizations that enable its use on genome scale. May be used to discover co evolving pairs of loci.Used for genome wide epistasis analysis.

Proper citation: SuperDCA (RRID:SCR_018175) Copy   

•   Source: SciCrunch Registry

http://www.cbs.dtu.dk/services/BepiPred/index.php

Sequential B-Cell Epitope Predictor. Web server predicts B-cell epitopes from protein sequence. Sequence-based B-cell epitope prediction using conformational epitopes. Sequences of protein of interest should be in fasta format. BepiPred 2.0 is available as stand alone software package, with same functionality as web service., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: BepiPred-2.0 (RRID:SCR_018499) Copy   

•   Source: SciCrunch Registry

http://www.cbs.dtu.dk/services/DiscoTope/

Web server to predict discontinuous B cell epitopes from protein three dimensional structures.

Proper citation: DiscoTope (RRID:SCR_018530) Copy   

•   Source: SciCrunch Registry

https://www.ddg-pharmfac.net/AllerTOP/

Web server for in silico prediction of allergens. Alignment free server for in silico prediction of allergens based on main physicochemical properties of proteins. Used to predict the route of allergen exposure: food, inhalant or toxin.

Proper citation: AllerTop (RRID:SCR_018496) Copy   

•   Source: SciCrunch Registry

https://prosa.services.came.sbg.ac.at/prosa.php

Web service is extension of classic ProSA program used for refinement and validation of experimental protein structures and in structure prediction and modeling.

Proper citation: ProSA-web (RRID:SCR_018540) Copy   

•   Source: SciCrunch Registry

https://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi

Web tool for conserved domains searching within protein or coding nucleotide sequence.

Proper citation: Conserved Domains Search (RRID:SCR_018729) Copy   

•   Source: SciCrunch Registry

http://pinet-server.org

Web platform for downstream analysis and visualization of proteomics data. Server that facilitates integrated annotation, analysis and visualization of quantitative proteomics data, with emphasis on PTM networks and integration with LINCS library of chemical and genetic perturbation signatures in order to provide further mechanistic and functional insights. Primary input for server consists of set of peptides or proteins, optionally with PTM sites, and their corresponding abundance values.

Proper citation: piNET (RRID:SCR_018693) Copy   

•   Source: SciCrunch Registry

https://robetta.bakerlab.org/

Web tool as protein structure prediction service. Provides automated structure prediction and analysis tools that can be used to infer protein structural information from genomic data. Produces model for entire protein sequence in presence or absence of sequence homology to protein of known structure.

Proper citation: Robetta (RRID:SCR_018805) Copy   

•   Source: SciCrunch Registry

http://protein.ict.ac.cn/FALCON/

Web tool as high throughput protein structure prediction service. High throughput server for protein structure prediction.

Proper citation: FALCON (RRID:SCR_018804) Copy   

•   Source: SciCrunch Registry

https://www.bpforms.org

Software toolkit for unambiguously describing molecular structure of DNA, RNA, and proteins, including non-canonical monomeric forms, crosslinks, nicks, and circular topologies. Aims to help epigenomics, transcriptomics, proteomics, systems biology, and synthetic biology researchers share and integrate information about DNA modification, post-transcriptional modification, post-translational modification, expanded genetic codes, and synthetic parts.

Proper citation: BpForms (RRID:SCR_018653) Copy   

•   Source: SciCrunch Registry

http://tools.dice-database.org/GOnet/)

Web tool for interactive Gene Ontology analysis of any biological data sources resulting in gene or protein lists.

Proper citation: GOnet (RRID:SCR_018977) Copy   

•   Source: SciCrunch Registry

https://www.ebi.ac.uk/thornton-srv/software/PROCHECK/

Software tool to check stereochemical quality of protein structures. Its outputs comprise number of plots in PostScript format and comprehensive residue by residue listing. Includes PROCHECK-NMR for checking quality of structures solved by NMR.

Proper citation: PROCHECK (RRID:SCR_019043) Copy   

•   Source: SciCrunch Registry

http://podb.nibb.ac.jp/Organellome/

Database of images, movies, and protocols to promote a comprehensive understanding of plant organelle dynamics, including organelle function, biogenesis, differentiation, movement, and interactions with other organelles. It consists of 5 individual parts, ''Perceptive Organelles Database'', ''The Organelles Movie Database'', ''The Organellome Database'', ''The Functional Analysis Database'', and ''External Links to other databases and Web pages''. All the data and protocols in ''The Organelle Movie Database'', ''The Organellome Database'' and ''The Functional Analysis Database'' are populated by direct submission of experimentally determined data from plant researchers. Your active contributions by submission of data and protocols to our database would also be appreciated. * Perceptive Organelles Database: This database contains images and movies of organelles in various tissues during different developmental stages in response to environmental stimuli. * Organelles Movie Database: This database contains time-lapse images, Z slices and projection images of organelles in various tissues during different developmental stages, visualized using fluorescent and non-fluorescent probes. * Organellome Database: This database contains images for cellular structures that are composed of organelle images in various tissues during different developmental stages, visualized with fluorescent and non-fluorescent probes. * Functional Analysis Database: This database is a collection of protocols for plant organelle research. * External Links: Access to biological databases.

Proper citation: Plant Organelles Database (RRID:SCR_006520) Copy   

•   Source: SciCrunch Registry

http://kobas.cbi.pku.edu.cn/

Web server to identify statistically enriched pathways, diseases, and GO terms for a set of genes or proteins, using pathway, disease, and GO knowledge from multiple famous databases. It allows for both ID mapping and cross-species sequence similarity mapping. It then performs statistical tests to identify statistically significantly enriched pathways and diseases. KOBAS 2.0 incorporates knowledge across 1327 species from 5 pathway databases (KEGG PATHWAY, PID, BioCyc, Reactome and Panther) and 5 human disease databases (OMIM, KEGG DISEASE, FunDO, GAD and NHGRI GWAS Catalog). A standalone command line version is also available, THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: KOBAS (RRID:SCR_006350) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/pdbe/emdb/

Repository for electron microscopy density maps of macromolecular complexes and subcellular structures at Protein Data Bank in Europe. Covers techniques, including single-particle analysis, electron tomography, and electron (2D) crystallography.

Proper citation: Electron Microscopy Data Bank at PDBe (MSD-EBI) (RRID:SCR_006506) Copy   

•   Source: SciCrunch Registry

http://gmd.mpimp-golm.mpg.de/

It facilitates the search for and dissemination of mass spectra from biologically active metabolites quantified using Gas chromatography (GC) coupled to mass spectrometry (MS). Use the Search Page to search for a compound of your interest, using the name, mass, formula, InChI etc. as query input. Additionally, a Library Search service enables the search of user submitted mass spectra within the GMD. In parallel to the library search, a prediction of chemical sub-groups is performed. This approach has reached beta level and a publication is currently under review. Using several sub-group specific Decision Trees (DTs), mass spectra are classified with respect to the presence of the chemical moieties within the linked (unknown) compound. Prediction of functional groups (ms analysis) facilitates the search of metabolites within the GMD by means of user submitted GC-MS spectra consisting of retention index (n-alkanes, if vailable) and mass intensities ratios. In addition, a functional group prediction will help to characterize those metabolites without available reference mass spectra included in the GMD so far. Instead, the unknown metabolite is characterized by predicted presence or absence of functional groups. For power users this functionality presented here is exposed as soap based web services. Functional group prediction of compounds by means of GC-EI-MS spectra using Microsoft analysis service decision trees All currently available trained decision trees and sub-structure predictions provided by the GMD interface. Table describes the functional group, optional use of an RI system, record date of the trained decision tree, number of MSTs with proportion of MSTs linked to metabolites with the functional group present for each tree. Average and standard deviation of the 50-fold CV error, namely the ratio false over correctly sorted MSTs in the trained DT, are listed. The GMD website offers a range of mass spectral reference libraries to academic users which can be downloaded free of charge in various electronic formats. The libraries are constituted by base peak normalized consensus spectra of single analytes and contain masses in the range 70 to 600 amu, while the ubiquitous mass fragments typically generated from compounds carrying a trimethylsilyl-moiety, namely the fragments at m/z 73, 74, 75, 147, 148, and 149, were excluded.

Proper citation: GMD (RRID:SCR_006625) Copy   

•   Source: SciCrunch Registry

http://espript.ibcp.fr/ESPript/ESPript/

A utility, whose output is a PostScript file of aligned sequences with graphical enhancements. Its main input is an ascii file of pre-aligned sequences. Optional files allow further rendering. The program calculates a similarity score for each residue of the aligned sequences. The output shows: * Secondary Structures * Aligned sequences * Similarities * Accessibility * Hydropathy * User-supplied markers * Intermolecular contacts In addition, similarity score can be written in the bfactor column of a pdb file, to enable direct display of highly conserved areas. You can run ESPript from this server with the HTML interface. It is configured for a maximum of 1,000 sequences. Links to webESPript * ENDscript: you can upload a PDB file or enter a PDB code such as 1M85. The programs DSSP and CNS are executed via the interface, so as to obtain an ESPript figure with a lot of structural information (secondary structure elements, intermolecular contacts). You can also find homologous sequences with a BLAST search, perform multiple sequence alignments with MULTALIN or CLUSTALW and create an image with BOBSCRIPT or MOLSCRIPT to show similarities on your 3D structure. * ProDom: you can enter a sequence identifier to find homologous domains, perform multiple sequence alignments with MULTALIN and click on the link to ESPript. * Predict Protein: you can receive a mail in text (do not use the HTML option when you submit your request in Predict Protein) with aligned sequences and numerous information including secondary structure prediction. Click on a special html link to upload your mail in ESPript. * NPS(at): you can execute the programs BLAST and CLUSTALW to obtain multiple alignments. You can predict secondary structure elements and click on the link to ESPript. This program started in the laboratory of Dr Richard Wade at the Institut de Biologie Structurale, Grenoble. It moved later to the Laboratory of Molecular Biophysics in Oxford, then to the Institut de Pharmacologie et de Biologie Structurale in Toulouse. It is now developed in the Laboratoire de BioCristallographie of Dr Richard Haser, Institut de Biologie et de Chimie des Prot��������ines, Lyon and in the Laboratoire de Biologie Mol��������culaire et de Relations Plantes-Organismes, group of Dr Daniel Kahn, Institut National de la Recherche Agronomique de Toulouse.

Proper citation: ESPript 2.2 (RRID:SCR_006587) Copy   

•   Source: SciCrunch Registry

http://www.thegpm.org/

The Global Proteome Machine Organization was set up so that scientists involved in proteomics using tandem mass spectrometry could use that data to analyze proteomes. The projects supported by the GPMO have been selected to improve the quality of analysis, make the results portable and to provide a common platform for testing and validating proteomics results. The Global Proteome Machine Database was constructed to utilize the information obtained by GPM servers to aid in the difficult process of validating peptide MS/MS spectra as well as protein coverage patterns. This database has been integrated into GPM server pages, allowing users to quickly compare their experimental results with the best results that have been previously observed by other scientists.

Proper citation: Global Proteome Machine Database (GPM DB) (RRID:SCR_006617) Copy   

•   Source: SciCrunch Registry

http://commonfund.nih.gov/Proteincapture/

Program that is developing new resources and tools to understand the critical role the multitude of cellular proteins play in normal development and health as well as in disease. These resources will support a wide-range of research and clinical applications that will enable the isolation and tracking of proteins of interest and permit their use as diagnostic biomarkers of disease onset and progression. The program is being implemented in phases, with three Funding Opportunity Announcements (FOAs): * FOA 1: Antigen Production (RFA-RM-10-007) To produce human transcription factor antigens for making monoclonal antibodies or other affinity capture reagents; this effort is already underway. * FOA 2: Anti-Transcription Factor Antibodies Production (RFA-RM-10-017) To optimize and scale anti-transcription factor capture reagent production to develop a community antibody resource. * FOA 3: New Reagent Technology Development and Piloting (RFA-RM-10-018) To develop improvements in the reagent production pipeline with regard to quality, utility, cost, and production scalability. To understand what makes a cell function normally and what may go awry in disease, we need better tools and resources, such as renewable protein capture reagents and probes, to study how proteins work in isolation and how they interact with other proteins, carbohydrates, or DNA regions within a cell. Ideally, this resource would allow us to identify and isolate all proteins within cells, in their various forms the so called proteome to ensure broad application in research and clinical studies aimed at understanding, preventing, detecting and treating disease. Existing protein capture reagents, such monoclonal antibodies, have been developed for a number of protein targets, although these represent only a subset of all proteins comprising the human proteome. In addition, many monoclonal antibodies lack the desired level of specificity and do not reliably target only the protein of interest. This is particularly problematic given the multiple forms of any one protein and the broad range of protein types in the body. The Protein Capture Reagents Program is organized as a pilot program using transcription factors as a test case to examine the feasibility and value of generating a community resource of low cost, renewable affinity reagents for all human proteins. The reagents must be specifically designed for high quality and broad experimental utility in order to meet the growing demands of biomedical researchers. Based on what is learned from these funding initiatives, the program may expand to a larger production effort to provide a broad community resource of human protein capture reagents.

Proper citation: Common Fund Protein Capture Reagents (RRID:SCR_006570) Copy   

•   Source: SciCrunch Registry

http://ligand-expo.rutgers.edu/

An integrated data resource for finding chemical and structural information about small molecules bound to proteins and nucleic acids within the structure entries of the Protein Data Bank. Tools are provided to search the PDB dictionary for chemical components, to identify structure entries containing particular small molecules, and to download the 3D structures of the small molecule components in the PDB entry. A sketch tool is also provided for building new chemical definitions from reported PDB chemical components.

Proper citation: Ligand Expo (RRID:SCR_006636) Copy   

•   Source: SciCrunch Registry