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http://athina.biol.uoa.gr/bioinformatics/waveTM/

A web tool for the prediction of transmembrane segments in alpha-helical membrane proteins. A sliding window of 20 residues is used in order to calculate an average residue hydrophobicity profile, using a hydrophobicity scale. Discrete Wavelet Transform is applied on the average residue hydrophobicity signal and the different frequency coefficients produced are adaptively thresholded so that a denoised signal is reconstructed. A dynamic programming algorithm processes the denoised signal to provide the optimal model for the number, the length and the location of membrane-spanning segments. The end points of the predicted segments are extended to include flanking hydrophobic residues. Topology prediction can also be obtained in conjunction with OrienTM (Liakopoulos et al, 2001). Analysis of a non-redundant test set, provides a ~95% per segment accuracy and ~90% per residue accuracy. Now, you can: * Run waveTM on a sequence * Browse the results obtained with the algorithm * View additional material concerning the hydrophobicity scale

Proper citation: waveTM (RRID:SCR_006199) Copy   

•   Source: SciCrunch Registry

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

A web tool, based on a Hidden Markov Model, capable of predicting the transmembrane beta-strands of the gram-negative bacteria outer membrane proteins, and of discriminating such proteins from water-soluble ones when screening large datasets. The model is trained in a discriminative manner, aiming at maximizing the probability of the correct prediction rather than the likelihood of the sequences. The training is performed on a non-redundant database consisting of 16 outer membrane proteins (OMP''s) with their structures known at atomic resolution. We show that we can achieve predictions at least as good comparing with other existing methods, using as input only the amino-acid sequence, without the need of evolutionary information included in multiple alignments. The method is also powerful when used for discrimination purposes, as it can discriminate with a high accuracy the outer membrane proteins from water soluble in large datasets, making it a quite reliable solution for screening entire genomes. This web-server can help you run a discriminating process on any amino-acid sequence and thereafter localize the transmembrane strands and find the topology of the loops.

Proper citation: PRED-TMBB (RRID:SCR_006190) Copy   

•   Source: SciCrunch Registry

http://www.agbase.msstate.edu/cgi-bin/tools/GOanna.cgi

GOanna is used to find annotations for proteins using a similarity search. The input can be a list of IDs or it can be a list of sequences in FASTA format. GOanna will retrieve the sequences if necessary and conduct the specified BLAST search against a user-specified database of GO annotated proteins. The resulting file contains GO annotations of the top BLAST hits. The sequence alignments are also provided so the user can use these to access the quality of the match. Platform: Online tool

Proper citation: GOanna (RRID:SCR_005684) Copy   

•   Source: SciCrunch Registry

http://athina.biol.uoa.gr/PRED-TMR2/

A web server that classifies proteins into two classes from their sequences alone: the membrane protein class and the non-membrane protein class. This may be important in the functional assignment and analysis of open reading frames (ORF''s) identified in complete genomes and, especially, those ORF''s that correspond to proteins with unknown function. The network has a simple hierarchical feed-forward topology and a limited number of neurons which makes it very fast. By using only information contained in 11 protein sequences, the method was able to identify, with 100% accuracy, all membrane proteins with reliable topologies collected from several papers in the literature. Applied to a test set of 995 globular, water-soluble proteins, the neural network classified falsely 23 of them in the membrane protein class (97.7% of correct assignment). The method was also applied to the complete SWISS-PROT database with considerable success and on ORF''s of several complete genomes. The neural network developed was associated with the PRED-TMR algorithm (Pasquier,C., Promponas,V.J., Palaios,G.A., Hamodrakas,J.S. and Hamodrakas,S.J., 1999) in a new application package called PRED-TMR2.

Proper citation: PRED-TMR2 (RRID:SCR_006205) Copy   

•   Source: SciCrunch Registry

http://amp.pharm.mssm.edu/l2n/upload/register.php

A web-based software system that allows users to upload lists of mammalian genes/proteins onto a server-based program for integrated analysis. The system includes web-based tools to manipulate lists with different set operations, to expand lists using existing mammalian networks of protein-protein interactions, co-expression correlation, or background knowledge co-annotation correlation, as well as to apply gene-list enrichment analyses against many gene-list libraries of prior biological knowledge such as pathways, gene ontology terms, kinase-substrate, microRNA-mRAN, and protein-protein interactions, metabolites, and protein domains. Such analyses can be applied to several lists at once against many prior knowledge libraries of gene-lists associated with specific annotations. The system also contains features that allow users to export networks and share lists with other users of the system.

Proper citation: Lists2Networks (RRID:SCR_006323) Copy   

•   Source: SciCrunch Registry

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

A web server that predicts transmembrane domains in proteins using solely information contained in the sequence itself. The algorithm refines a standard hydrophobicity analysis with a detection of potential termini (edges, starts and ends) of transmembrane regions. This allows both to discard highly hydrophobic regions not delimited by clear start and end configurations and to confirm putative transmembrane segments not distinguishable by their hydrophobic composition. The accuracy obtained on a test set of 101 non homologous transmembranes proteins with reliable topologies compares well with that of other popular existing methods. Only a slight decrease in prediction accuracy was observed when the algorithm was applied to all transmembrane proteins of the SwissProt database (release 35).

Proper citation: PRED-TMR (RRID:SCR_006203) Copy   

•   Source: SciCrunch Registry

http://xldb.fc.ul.pt/biotools/rebil/ssm/

FuSSiMeG is being discontinued, may not be working properly. Please use our new tool ProteinOn. Functional Semantic Similarity Measure between Gene Products (FuSSiMeG) provides a functional similarity measure between two proteins using the semantic similarity between the GO terms annotated with the proteins. Platform: Online tool

Proper citation: FuSSiMeG: Functional Semantic Similarity Measure between Gene-Products (RRID:SCR_005738) Copy   

•   Source: SciCrunch Registry

http://bioinf.uab.es/aggrescan/

Web-based tool for identifying hot spots of aggregation in polypeptides. Aggrescan uses an aggregation-propensity scale for natural amino acids derived from in vivo experiments and on the assumption that short and specific sequence stretches modulate protein aggregation. The algorithm is shown to identify a series of protein fragments involved in the aggregation of disease-related proteins and to predict the effect of genetic mutations on their deposition propensities. It also provides new insights into the differential aggregation properties displayed by globular proteins, natively unfolded polypeptides, amyloidogenic proteins and proteins found in bacterial inclusion bodies.

Proper citation: Aggrescan: The Hot Spot Finder (RRID:SCR_008403) Copy   

•   Source: SciCrunch Registry

http://cbl-gorilla.cs.technion.ac.il/

A tool for identifying and visualizing enriched GO terms in ranked lists of genes. It can be run in one of two modes: * Searching for enriched GO terms that appear densely at the top of a ranked list of genes or * Searching for enriched GO terms in a target list of genes compared to a background list of genes.

Proper citation: GOrilla: Gene Ontology Enrichment Analysis and Visualization Tool (RRID:SCR_006848) Copy   

•   Source: SciCrunch Registry

http://murphylab.web.cmu.edu/services/SLIF/

SLIF finds fluorescence microscope images in on-line journal articles, and indexes them according to cell line, proteins visualized, and resolution. Images can be accessed via the SLIF Web database. SLIF takes on-line papers and scans them for figures that contain fluorescence microscope images (FMIs). Figures typically contain multiple FMIs, to SLIF must segment these images into individual FMIs. When the FMI images are extracted, annotations for the images (for instance, names of proteins and cell-lines) are also extracted from the accompanying caption text. Protein annotation are also used to link to external databases, such as the Gene Ontology DB. The more detailed process includes: segmentation of images into panels; panel classification, to find FMIs; segmentation of the caption, to find which portions of the caption apply to which panels; text-based entity extraction; matching of extracted entities to database entries; extraction of panel labels from text and figures; and alignment of the text segments to the panels. Extracted FMIs are processed to find subcellular location features (SLFs), and the resulting analyzed, annotated figures are stored in a database, which is accessible via SQL queries.

Proper citation: Subcellular Location Image Finder (RRID:SCR_006723) Copy   

•   Source: SciCrunch Registry

http://wwwmgs.bionet.nsc.ru/mgs/programs/panalyst/

WebProAnalyst provides web-accessible analysis for scanning the quantitative structure-activity relationships in protein families. It searches for a sequence region, whose substitutions are correlated with variations in the activities of a homologous protein set, the so-called activity modulating sites. WebProAnalyst allows users to search for the key physicochemical characteristics of the sites that affect the changes in protein activities. It enables the building of multiple linear regression and neural networks models that relate these characteristics to protein activities. WebProAnalyst implements multiple linear regression analysis, back propagation neural networks and the Structure-Activity Correlation/Determination Coefficient (SACC/SADC). A back propagation neural network is implemented as a two-layered network, one layer as input, the other as output (Rumelhart et al, 1986). WebProAnalyst uses alignment of amino acid sequences and data on protein activity (pK, Km, ED50, among others). The input data are the numerical values for the physicochemical characteristics of a site in the multiple alignment given by a slide window. The output data are the predicted activity values. The current version of WebProAnalyst handles a single activity for a single protein. The SACC/SADC may be defined as an estimate of the strongest multiple correlation between the physicochemical characteristics of a site in a multiple alignment and protein activities. The SACC/SADC coefficient makes possible the calculation of the possible highest correlation achievable for the quantitative relationship between the physicochemical properties of sites and protein activities. The SACC/SADC is a convenient means for an arrangement of positions by their functional significance. WebProAnalyst outputs a list of multiple alignment positions, the respective correlation values, also regression analysis parameters for the relationships between the amino acid physicochemical characteristics at these positions and the protein activity values.

Proper citation: Webproanalyst (RRID:SCR_008348) Copy   

•   Source: SciCrunch Registry

https://www.unr.edu/proteomics

Core offers mass spectral proteomic analysis. Assists with qualitative and quantitative characterization of proteins in biological matrices such as plasma/serum, tissue, cell lines and other biological material to gain understanding of physiological pathways, molecular interactions and regulatory signaling.

Proper citation: University of Nevada at Reno Nevada Proteomics Center Core Facility (RRID:SCR_017761) Copy   

•   Source: SciCrunch Registry

http://chemistry.vcu.edu/research/facilities/chemical-and-proteomic-mass-spectrometry-core-facility/

Core provides mass spectrometric services, from basic mass measurement to complex proteome analyses.Services include basic mass measurement, ESI-MS/MS,LC-MS,Exact mass measurement,Protein identification.

Proper citation: Virginia Commonwealth University Chemical and Proteomic Mass Spectrometry Core Facility (RRID:SCR_017806) Copy   

•   Source: SciCrunch Registry

http://nmr.uthscsa.edu/contact.shtml

Core offers high field NMR instrumentation for structural studies of biological macromolecules. Instrumentation includes four-channel Bruker Avance 500, 600, and 700 MHz NMR spectrometers, ultra high sensitivity 5mm 1H-13C-15N triple-resonance cold probe for 600 MHz spectrometers. Service include acquisition and analysis of required spectra for elucidation of small molecule structures (includes synthetic molecules, natural products, cofactors, lipids, and short peptides (30 amino acids or less)). Types of projects conducted collaboratively include determination of three-dimensional structures of biological macromolecules, including proteins and nucleic acids, both alone and as complexes with various ligands.

Proper citation: Texas University Health Science Center at San Antonio Biomolecular NMR Core Facility (RRID:SCR_017775) Copy   

•   Source: SciCrunch Registry

http://biochem.slu.edu/proteincore/coreindex.shtml

Core facility that supports expression, purification, and analysis of reagent and preclinical proteins by providing instrumentation and consultation for protein production from small to large scale. Available equipment includes shaking incubators, 5 L fermentor, high pressure cell disruptor, hollow fiber concentrator, Maxwell16 magnetic bead purification system, AKTA Purifier chromatograph, analytical ultracentrifuge, analytical HPLC, and MALDI-QIT-TOF mass spectrometer.

Proper citation: Saint Louis University School of Medicine Protein Core Facility (RRID:SCR_017811) Copy   

•   Source: SciCrunch Registry

http://www.kumc.edu/mspc.html/

Core provides access to mass spectrometry based proteomics applications, customizes mass spectrometry designs that fit individual needs. Used for general proteomics evaluations and protein identification designs, quantitation techniques, hydrogen/deuterium exchange, and oxidative footprinting. Equipment includes Orbitrap Fusion Lumos.

Proper citation: Kansas University Medical Center Mass Spectrometry/Proteomics Core Facility (RRID:SCR_017818) Copy   

•   Source: SciCrunch Registry

http://www.protein.iastate.edu

Core offers protein/peptide sequencing, large and small scale peptide synthesis (Fmoc), matrix assisted laser desorption/ionization (MALDI) mass spectrometry, SDS-PAGE/electroblotting, 2-D gel electrophoresis, isoelectric focusing (IEF), in-gel and solution digestion, tandem mass spectrometry (LC-MS/MS), ion mobility mass spectrometry (IM-MS), digital image acquisition and analysis using Typhoon imaging system and 2D gel documentation/analysis system, and semi-preparative, analytical and micro-analytical high performance liquid chromatography (HPLC). MALDI, HPLC, SDS-PAGE/blotting, IEF, 2D gel electrophoresis, 2D gel documentation/analysis and Typhoon imaging system are all available for user operation after appropriate training.

Proper citation: Iowa State University Office of Biotechnology Protein Core Facility (RRID:SCR_017780) Copy   

•   Source: SciCrunch Registry

http://proteomics.rockefeller.edu/

Core provides analysis of proteins, peptides, lipids, polar metabolites and small molecules by high resolution/high mass spectrometry. Targeted and hypothesis free analysis are offered combined with relative quantitation based on either label free, tandem mass tags or metabolic labelling. Acquisition tools include Data Dependent (DDA) and Data Independent (DIA).

Proper citation: The Rockefeller University Proteomics Resource Center Core Facility (RRID:SCR_017797) Copy   

•   Source: SciCrunch Registry

https://biophysics.fsu.edu/facilities/x-ray-crystallography-facility

Shared macromolecular x-ray crystallography facility provides instruments and expertise for screening, optimizing, imaging, growing, and storing crystals of biological macromolecules. The X-Ray Facility coordinates single crystal x-ray diffraction data collection at third generation synchrotron x-ray source using FSU's membership at the National Synchrotron Light Source II at Brookhaven National Lab, Upton, NY. XRF also offers custom buffer preparation, optimization, and crystal set-up using multi-well format crystallization blocks and plates.XRF has ARI Crystal Gryphon robot, Formulatrix Rock Imager, Formulator 16, Rock Maker software, RUMED incubator, Cryo storage and shipping dewars, Leica S8 AP0 Zoom microscope and other amenities.

Proper citation: Florida State University X-Ray Crystallography Core Facility (RRID:SCR_017922) Copy   

•   Source: SciCrunch Registry

http://proteomics.northwestern.edu/collaborate

Core offers multiple types of experiments from simple protein identification to protein quantitation. Performs traditional bottom-up proteomics, where proteins are digested with enzyme prior to analysis and intact, top-down proteomics analyses. Services include proteins identification after in-gel or in-solution digestion, top-down mass spectrometry to preserve post-translationally modified forms of proteins present in vivo by measuring them intact, IP-MS Pulldown,BioID service to identify target of biotin ligase that has been tagged onto their protein via traditional cloning methods,Untargeted Quantitative Peptide Proteomics,Targeted Quantitative Peptide Proteomics,Epiproteomic Histone Modification Panel A,Epiproteomic Histone Modification Panel B,Untargeted Metabolomics,Phosphoproteomics,PTM Scan,ChIP-MS.

Proper citation: Northwestern University Proteomics Core Facility (RRID:SCR_017945) Copy   

•   Source: SciCrunch Registry