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http://www.jcvi.org/charprotdb/index.cgi/home

The Characterized Protein Database, CharProtDB, is designed and being developed as a resource of expertly curated, experimentally characterized proteins described in published literature. For each protein record in CharProtDB, storage of several data types is supported. It includes functional annotation (several instances of protein names and gene symbols) taxonomic classification, literature links, specific Gene Ontology (GO) terms and GO evidence codes, EC (Enzyme Commisssion) and TC (Transport Classification) numbers and protein sequence. Additionally, each protein record is associated with cross links to all public accessions in major protein databases as ��synonymous accessions��. Each of the above data types can be linked to as many literature references as possible. Every CharProtDB entry requires minimum data types to be furnished. They are protein name, GO terms and supporting reference(s) associated to GO evidence codes. Annotating using the GO system is of importance for several reasons; the GO system captures defined concepts (the GO terms) with unique ids, which can be attached to specific genes and the three controlled vocabularies of the GO allow for the capture of much more annotation information than is traditionally captured in protein common names, including, for example, not just the function of the protein, but its location as well. GO evidence codes implemented in CharProtDB directly correlate with the GO consortium definitions of experimental codes. CharProtDB tools link characterization data from multiple input streams through synonymous accessions or direct sequence identity. CharProtDB can represent multiple characterizations of the same protein, with proper attribution and links to database sources. Users can use a variety of search terms including protein name, gene symbol, EC number, organism name, accessions or any text to search the database. Following the search, a display page lists all the proteins that match the search term. Click on the protein name to view more detailed annotated information for each protein. Additionally, each protein record can be annotated.

Proper citation: CharProtDB: Characterized Protein Database (RRID:SCR_005872) Copy   

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http://www.lasige.di.fc.ul.pt/webtools/proteinon/

ProteInOn calculates semantic similarity between GO terms or proteins annotated with GO terms. It also calculates term enrichment of protein sets, by applying a term representativity score, and gives additional information on protein interactions. The query compute protein semantic similarity returns the semantic similarity scores between all proteins entered, in matrix format. The option Measure allows users to choose one of several semantic similarity measures: Resnik, Lin, or Jiang & Conrath's measures with or without the DCA approach, plus the graph-based simUI and simGIC measures. These measures are listed by order of performance as evaluated with protein sequence similarity. The option GO type allows users to choose one of the aspects of GO: molecular function, biological process and cellular component. The option Ignore IEA limits the query to non-electronic annotations, excluding evidence types: IEA, NAS, ND, NR.

Proper citation: ProteInOn (RRID:SCR_005740) Copy   

•   Source: SciCrunch Registry

http://pbildb1.univ-lyon1.fr/virhostnet/

Public knowledge base specialized in the management and analysis of integrated virus-virus, virus-host and host-host interaction networks coupled to their functional annotations. It contains high quality and up-to-date information gathered and curated from public databases (VirusMint, Intact, HIV-1 database). It allows users to search by host gene, host/viral protein, gene ontology function, KEGG pathway, Interpro domain, and publication information. It also allows users to browse viral taxonomy.

Proper citation: VirHostNet: Virus-Host Network (RRID:SCR_005978) Copy   

•   Source: SciCrunch Registry

http://bioinformatics.biol.uoa.gr/AMYLPRED/

A web tool using the consensus prediction method for identifying possible amyloidogenic regions in protein sequences. This tool uses an assortment of different methods that have been found or specifically developed to predict features related to the formation of amyloid fibrils. The consensus of these methods is defined as the the hit overlap of at least two out of five methods and it is the primary output of the program. However, the individual predictions of these methods are also made available in the form of a text file, maintained on the server for 1 (one) day. Consequently, the tool predicts probable amyloidogenic determinants for a given amino acid sequence of a peptide or protein.

Proper citation: AMYL-PRED (RRID:SCR_006185) Copy   

•   Source: SciCrunch Registry

http://bioinformatics.biol.uoa.gr/HMM-TM/

A web tool using the Hidden Markov Model method for the topology prediction of alpha-helical membrane proteins that incorporates experimentally derived topological information. Hidden Markov Models (HMMs) have been extensively used in computational molecular biology, for modelling protein and nucleic acid sequences. In many applications, such as transmembrane protein topology prediction, the incorporation of limited amount of information regarding the topology, arising from biochemical experiments, has been proved a very useful strategy that increased remarkably the performance of even the top-scoring methods. However, no clear and formal explanation of the algorithms that retains the probabilistic interpretation of the models has been presented so far in the literature. We present here, a simple method that allows incorporation of prior topological information concerning the sequences at hand, while at the same time the HMMs retain their full probabilistic interpretation in terms of conditional probabilities. We present modifications to the standard Forward and Backward algorithms of HMMs and we also show explicitly, how reliable predictions may arise by these modifications, using all the algorithms currently available for decoding HMMs. A similar procedure may be used in the training procedure, aiming at optimizing the labels of the HMM''s classes, especially in cases such as transmembrane proteins where the labels of the membrane-spanning segments are inherently misplaced. We present an application of this approach developing a method to predict the transmembrane regions of alpha-helical membrane proteins, trained on crystallographically solved data. We show that this method compares well against already established algorithms presented in the literature, and it is extremely useful in practical applications.

Proper citation: HMM-TM (RRID:SCR_006186) Copy   

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http://bioinformatics.biol.uoa.gr/PRED-LIPO/

A web tool using the Hidden Markov Model method for the prediction of lipoprotein signal peptides of Gram-positive bacteria, trained on a set of 67 experimentally verified lipoproteins. The method outperforms LipoP and the methods based on regular expression patterns, in various data sets containing experimentally characterized lipoproteins, secretory proteins, proteins with an N-terminal TM segment and cytoplasmic proteins. The method is also very sensitive and specific in the detection of secretory signal peptides and in terms of overall accuracy outperforms even SignalP, which is the top-scoring method for the prediction of signal peptides.

Proper citation: PRED-LIPO (RRID:SCR_006187) Copy   

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http://bioinformatics.biol.uoa.gr/PRED-SIGNAL/

A web tool for prediction of signal peptides in archaea. Computational prediction of signal peptides (SPs) and their cleavage sites is of great importance in computational biology; however, currently there is no available method capable of predicting reliably the SPs of archaea, due to the limited amount of experimentally verified proteins with SPs. We performed an extensive literature search in order to identify archaeal proteins having experimentally verified SP and managed to find 69 such proteins, the largest number ever reported. A detailed analysis of these sequences revealed some unique features of the SPs of archaea, such as the unique amino acid composition of the hydrophobic region with a higher than expected occurrence of isoleucine, and a cleavage site resembling more the sequences of gram-positives with almost equal amounts of alanine and valine at the position-3 before the cleavage site and a dominant alanine at position-1, followed in abundance by serine and glycine. Using these proteins as a training set, we trained a hidden Markov model method that predicts the presence of the SPs and their cleavage sites and also discriminates such proteins from cytoplasmic and transmembrane ones.

Proper citation: PRED-SIGNAL (RRID:SCR_006181) Copy   

•   Source: SciCrunch Registry

http://tbdb.org

Database providing integrated access to genome sequence, expression data and literature curation for Tuberculosis (TB) that houses genome assemblies for numerous strains of Mycobacterium tuberculosis (MTB) as well assemblies for over 20 strains related to MTB and useful for comparative analysis. TBDB stores pre- and post-publication gene-expression data from M. tuberculosis and its close relatives, including over 3000 MTB microarrays, 95 RT-PCR datasets, 2700 microarrays for human and mouse TB related experiments, and 260 arrays for Streptomyces coelicolor. (July 2010) To enable wide use of these data, TBDB provides a suite of tools for searching, browsing, analyzing, and downloading the data.

Proper citation: Tuberculosis Database (RRID:SCR_006619) Copy   

•   Source: SciCrunch Registry

http://athina.biol.uoa.gr/PRED-CLASS/

A system of cascading neural networks that classifies any protein, given its amino acid sequence alone, into one of four possible classes: membrane, globular, fibrous, mixed.

Proper citation: PRED-CLASS (RRID:SCR_006216) Copy   

•   Source: SciCrunch Registry

http://athina.biol.uoa.gr/CoPreTHi/

A Java based web application, which combines the results of methods that predict the location of transmembrane segments in protein sequences into a joint prediction histogram. Clearly, the joint prediction algorithm, produces superior quality results than individual prediction schemes.

Proper citation: CoPreTHi (RRID:SCR_006217) Copy   

•   Source: SciCrunch Registry

http://prorepeat.bioinformatics.nl/

ProRepeat is an integrated curated repository and analysis platform for in-depth research on the biological characteristics of amino acid tandem repeats. ProRepeat collects repeats from all proteins included in the UniProt knowledgebase, together with 85 completely sequenced eukaryotic proteomes contained within the RefSeq collection. It contains non-redundant perfect tandem repeats, approximate tandem repeats and simple, low-complexity sequences, covering the majority of the amino acid tandem repeat patterns found in proteins. The ProRepeat web interface allows querying the repeat database using repeat characteristics like repeat unit and length, number of repetitions of the repeat unit and position of the repeat in the protein. Users can also search for repeats by the characteristics of repeat containing proteins, such as entry ID, protein description, sequence length, gene name and taxon. ProRepeat offers powerful analysis tools for finding biological interesting properties of repeats, such as the strong position bias of leucine repeats in the N-terminus of eukaryotic protein sequences, the differences of repeat abundance among proteomes, the functional classification of repeat containing proteins and GC content constrains of repeats' corresponding codons.

Proper citation: ProRepeat (RRID:SCR_006113) Copy   

•   Source: SciCrunch Registry

http://pcidb.russelllab.org/

The database of protein-chemical structural interactions includes all existing 3D structures of complexes of proteins with low molecular weight ligands. When one considers the proteins and chemical vertices of a graph, all these interactions form a network. Biological networks are powerful tools for predicting undocumented relationships between molecules. The underlying principle is that existing interactions between molecules can be used to predict new interactions. For pairs of proteins sharing a common ligand, we use protein and chemical superimpositions combined with fast structural compatibility screens to predict whether additional compounds bound by one protein would bind the other. The current version includes data from the Protein Data Bank as of August 2011. The database is updated monthly.

Proper citation: ProtChemSI (RRID:SCR_006115) Copy   

•   Source: SciCrunch Registry

http://bioinformatics.biol.uoa.gr/ConBBPRED/

A web tool for the Consensus Prediction of TransMembrane Beta-Barrel Proteins. Prediction of the transmembrane strands and topology of beta-barrel outer membrane proteins is of interest in current bioinformatics research. Several methods have been applied so far for this task, utilizing different algorithmic techniques and a number of freely available predictors exist. The methods can be grossly divided to those based on Hidden Markov Models (HMMs), on Neural Networks (NNs) and on Support Vector Machines (SVMs). In this work, we compare the different available methods for topology prediction of beta-barrel outer membrane proteins. We evaluate their performance on a non-redundant dataset of 20 beta-barrel outer membrane proteins of gram-negative bacteria, with structures known at atomic resolution. Also, we describe, for the first time, an effective way to combine the individual predictors, at will, to a single consensus prediction method. We assess the statistical significance of the performance of each prediction scheme and conclude that Hidden Markov Model based methods, HMM-B2TMR, ProfTMB and PRED-TMBB, are currently the best predictors, according to either the per-residue accuracy, the segments overlap measure (SOV) or the total number of proteins with correctly predicted topologies in the test set. Furthermore, we show that the available predictors perform better when only transmembrane beta-barrel domains are used for prediction, rather than the precursor full-length sequences, even though the HMM-based predictors are not influenced significantly. The consensus prediction method performs significantly better than each individual available predictor, since it increases the accuracy up to 4% regarding SOV and up to 15% in correctly predicted topologies.

Proper citation: ConBBPRED (RRID:SCR_006194) Copy   

•   Source: SciCrunch Registry

http://biogps.org/

An extensible and customizable gene annotation portal that emphasizes community extensibility and user customizability. It is a complete resource for learning about gene and protein function. Community extensibility reflects a belief that any BioGPS user should be able to add new content to BioGPS using the simple plugin interface, completely independently of the core developer team. User customizability recognizes that not all users are interested in the same set of gene annotation data, so the gene report layouts enable each user to define the information that is most relevant to them. Currently, BioGPS supports eight species: Human (Homo sapiens), Mouse (Mus musculus), Rat (Rattus norvegicus), Fruitfly (Drosophila melanogaster), Nematode (Caenorhabditis elegans), Zebrafish (Danio rerio), Thale-cress (Arabidopsis thaliana), Frog (Xenopus tropicalis), and Pig (Sus scrofa). BioGPS presents data in an ortholog-centric format, which allows users to display mouse plugins next to human ones. Our data for defining orthologs comes from NCBI's HomoloGene database.

Proper citation: BioGPS: The Gene Portal Hub (RRID:SCR_006433) Copy   

•   Source: SciCrunch Registry

https://www.mcgill.ca/cian/

Core facility at Biology Department in McGill Faculty of Science. Expertise in Light Microscopy and Image Analysis. Provides light microscopes, ranging from Point Scanning and Spinning Disc Confocals to Multi-Photon, TIRF, Light Sheet and Super-Resolution microscopes. Provides services in Automation/High throughput screening (liquid handler, pinning robot), Protein expression and antibody production. Users get training.

Proper citation: McGill Cell Imaging and Analysis Network Core Facility (RRID:SCR_012623) Copy   

•   Source: SciCrunch Registry

http://blanco.biomol.uci.edu/membrane_proteins_xtal.html

Table providing information about integral membrane proteins whose crystallographic, or sometimes NMR, structures have been determined to a resolution sufficient to identify TM helices of helix-bundle membrane proteins (typically 4 - 4.5 angstroms). It is based upon Preusch et al. (1998) as revised by White & Wimley (1999). Reference is made to all of the protein types whose structures have been determined. They have attempted to make the database as inclusive as possible.

Proper citation: Mpstruct (RRID:SCR_013284) Copy   

•   Source: SciCrunch Registry

http://www.jhugicc.org/GIConteCenter/pages/cores/proteomicsCore.html

Core facility that uses mass spectrometry coupled to one (1D) and two (2D) dimensional separations by column chromatography or gel electrophoresis to identify, quantify or characterize proteins and their post-translational modifications, that are expressed in well characterized protein fractions from the small intestine, colon, kidney, liver and pancreas. Techniques such as difference gel electrophoresis (DIGE), isobaric tag for relative and absolute quantitation (iTRAQ), tandem mass tags (TMT) and stable isotope labeling of amino acids in cell culture (SILAC) as well as non-labeling methods (MudPIT, multi-dimensional protein identification technology) are available for quantifying relative differences in protein expression and post-translational modifications, such as acetylation, glycosylation, phosphorylation, nitrosation, ubiquitination and novel cleavage sites.

Proper citation: Hopkins Conte Digestive Diseases Basic and Translational Research Core Center Proteomics Core (RRID:SCR_015597) Copy   

•   Source: SciCrunch Registry

https://www.cimr.cam.ac.uk/about/facilities/bioinformatics

Core provides biological data handling and analysis in differential expression analysis, next generation sequencing, networks, protein architecture, and motif searching for in house researchers.

Proper citation: Cambridge Institute for Medical Research Bioinformatics Core Facility (RRID:SCR_017186) Copy   

•   Source: SciCrunch Registry

http://www.uvm.edu/sbb/cxx/cxx.html

Core for high resolution structural biology at the University of Vermont. X-ray crystallography allows biological and biomedical researchers to visualize proteins, RNA, DNA and their complexes at atomic resolution. The molecular details of specimens as small as DNA binding domains and as large as the ribosome have been elucidated via this powerful method. The CXX provides resources for all stages of macromolecular structure determination.

Proper citation: Vermont University Center for X-Ray Crystallography Core Facility (RRID:SCR_017688) Copy   

•   Source: SciCrunch Registry

http://biophysics.fsu.edu/facilities/protein-expression-facility/

Core to facilitate the large scale expression of recombinant proteins in bacterial, insects, and mammalian cells. Serves primarily faculty and students from the laboratories in the Kasha Laboratory Building and laboratories in Biology, Biochemistry, Chemistry and Nutrition at Florida State University, Tallahassee, FL.

Proper citation: Florida State University Protein Expression Core Facility (RRID:SCR_016757) Copy   

•   Source: SciCrunch Registry