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http://lpdb.chem.lsa.umich.edu/

The Ligand Protein Database is designed to allow the selection of complexes based on various properties of receptors and ligands for the design and parametrization of new scoring functions or to assess and improve existing ones. Moreover, for each complex, a continuum of ligand positions ranging from the crystallographic position to points on the surface of the protein receptor allows an assessment of the energetic behavior of particular scoring functions. Access to the database is password protected. To obtain access to the LPDB, complete a form, available online, have it signed by your research advisor, and fax the completed form back to the attention of Professor Charles L. Brooks III, (858) 784-8688. There is no fee for academic use of the LPDB. We are currently working out details for licensing to our colleagues in industry. Please contact Professor Brooks to obtain current information on access to the LPDB.

Proper citation: LPDB: Ligand-Protein DataBase (RRID:SCR_008172) Copy   

•   Source: SciCrunch Registry

https://pharos.nih.gov/idg/index#

Database of ligands and diseases. Its goal is to develop a knowledge-base for the Druggable Genome (DG) in order to illuminate the uncharacterized and/or poorly annotated portion of the genome. DG, focusing on four of the most commonly drug-targeted protein families: G-protein-coupled receptors (GPCRs); nuclear receptors (NRs); ion channels (ICs); and kinases.

Proper citation: PHAROS (RRID:SCR_016258) Copy   

•   Source: SciCrunch Registry

http://p300db.choudharylab.org

Data collection of CBP/p300 regulated acetylome, proteome, and transcriptome in murine embryonic fibroblasts. Composed of Symbol search for quantified acetylation sites, proteins and transcripts abundance in CBP/p300, Domain search for batch query of proteins by specific domain and Conserved sites for acetylation sites that are conserved between mouse and human, and their regulation in KATi treated cells.

Proper citation: p300db (RRID:SCR_017063) Copy   

•   Source: SciCrunch Registry

http://www.broadinstitute.org/pubs/MitoCarta/

Collection of genes encoding proteins with strong support of mitochondrial localization. Inventory of genes encoding mitochondrial-localized proteins and their expression across 14 mouse tissues. Database is based on human and mouse RefSeq proteins that are mapped to NCBI Gene loci. MitoCarta 2.0 inventory provides molecular framework for system-level analysis of mammalian mitochondria.

Proper citation: MitoCarta (RRID:SCR_018165) Copy   

•   Source: SciCrunch Registry

https://www.expasy.org/resources/uniprotkb-swiss-prot

Curated component of UniProtKB (produced by the UniProt consortium). It contains hundreds of thousands of protein descriptions, including function, domain structure, subcellular location, post-translational modifications and functionally characterized variants.

Proper citation: UniProtKB/Swiss-Prot (RRID:SCR_021164) Copy   

•   Source: SciCrunch Registry

https://www.livercellatlas.org

Portal to search liver single cell RNA-sequencing datasets. Datasets for expression of genes or proteins (when CITE-seq was performed). To search for gene enter the official gene name. To search for protein please click to see specific names to use for different markers included.

Proper citation: Liver cell atlas (RRID:SCR_023627) Copy   

•   Source: SciCrunch Registry

https://alphafold.ebi.ac.uk/

Database of protein structure predictions by AlphaFold that are freely and openly available to global scientific community. Included are nearly all catalogued proteins known to science. Provides programmatic access to and interactive visualization of predicted atomic coordinates, per residue and pairwise model confidence estimates and predicted aligned errors.

Proper citation: AlphaFold Protein Structure Database (RRID:SCR_023662) Copy   

•   Source: SciCrunch Registry

http://www.ucl.ac.uk/biobank/

Two University College London (UCL) biobanks, one based at the Royal Free Hospital (RFH) Campus and the other based at Bloomsbury supporting Pathology and the Cancer Institute, will act as physical repositories for collections of biological samples and data from patients consented at UCLH, Partners Hospitals and external sources. This will incorporate collections of existing stored samples and new collections. UCL-RFH BioBank, the physical repository at the Royal Free, presents a unique opportunity to advance medical research through making access to research tissue easier, faster and much more efficient. The BioBank is both a physical repository, with capacity for up to 1 million cryogenically stored samples and a virtual repository for all tissue, cell, plasma, serum, DNA and RNA samples stored throughout UCLP. In particular, samples considered "relevant material", such as tissues and cells, that are licensed by the Human Tissue Authority, can be stored long term. Existing holdings of tissues and cells where appropriate can be transferred to the Physical BioBank at the Royal Free. UCL - Royal Free BioBank provides a flexible approach to banking, allowing the Depositor to pick and choose services that are tailored to fit their requirements. Collaborations arising from publicizing of the existence of the holdings are entirely at the discretion of the depositor, as the facility ensures that access to the deposits remains at the decision of the Depositor/User. UCL Biobank for studying Health and Disease (based at Pathology-Rockefeller building and the UCL-Cancer Institute will support projects principally involved in the study of human disease. The aim is to support primarily, research in the Pathology Department, UCLH and the UCL-Cancer Institute but it will also support other UCLH partners. The biobank will store normal and pathological specimens, surplus to diagnostic requirements, from relevant tissues and bodily fluids. Stored tissues will include; snap-frozen or cryopreserved tissue, formalin-fixed tissue, paraffin-embedded tissues, and slides prepared for histological examination. Tissues will include resection specimens obtained surgically or by needle core biopsy. Bodily fluids will include; whole blood, serum, plasma, urine, cerebrospinal fluid, milk, saliva and buccal smears and cytological specimens such as sputum and cervical smears. Fine needle aspirates obtained from tissues and bodily cavities (e.g. pleura and peritoneum) will also be collected. Where appropriate the biobank will also store separated cells, protein, DNA and RNA isolated from collected tissues and bodily fluids described above. Some of the tissue and aspirated samples will be stored in the diagnostic archive.

Proper citation: UCL Biobank (RRID:SCR_000517) Copy   

•   Source: SciCrunch Registry

http://www.biosyn.com/

A commercial supplier of custom synthetic molecules. They specialize in peptides, oligonucleotides, bioconjugation, molecular biology services, proteins and specialty chemistry.

Proper citation: Bio-Synthesis (RRID:SCR_000820) Copy   

•   Source: SciCrunch Registry

https://github.com/eduardporta/e-Driver

Software tool to identify cancer driver genes based on linear annotations of biological regions such as protein domains.Uses information on three-dimensional structures of mutated proteins to identify specific structural features. Then algorithm analyzes whether these features are enriched in cancer somatic mutations and are candidate driver genes.

Proper citation: e-Driver (RRID:SCR_002674) Copy   

•   Source: SciCrunch Registry

http://mapman.gabipd.org/web/guest/mapman

Software tool that displays large genomics datasets (e.g. gene expression data from Arabidopsis Affymetrix arrays) onto diagrams of metabolic pathways or other biological processes.

Proper citation: MapMan (RRID:SCR_003543) Copy   

•   Source: SciCrunch Registry

http://knots.mit.edu/

The knot server allows the user to check PDB entries or uploaded structures for knots and to visualize them. The size of a knot is determined by deleting amino acids from both ends. This procedure is, however, not perfect and the resulting size should only be treated as a guideline. Mathematically, knots are only well defined in closed (circular) loops. However, both the N- and C-termini of open proteins are typically located close to the surface of the protein and can be connected unambiguously: We reduce the protein to its backbone and draw two lines outward starting at the termini in the direction of the connection line between the center of mass of the backbone and the respective ends. The two lines are joined by a big loop, and the structure is topologically classified by the determination of its Alexander polynomial. To determine an estimate for the size of the knotted core, we successively delete amino acids from the N-terminus until the protein becomes unknotted. The procedure is repeated at the C-terminus starting with the last N-terminal deletion structure that contained the original knot. For each deletion, the outward-pointing line through the new termini is parallel to the respective lines computed for the full structure. Unfortunately, the size of a knot is not always precisely determined by this procedure, so reported sizes should therefore only be treated as approximate. Sponsors: Knots is funded by MIT.

Proper citation: Protein Knots (RRID:SCR_008353) Copy   

•   Source: SciCrunch Registry

http://www.peroxisomedb.org/

The aim of the PEROXISOME database (PeroxisomeDB) is to gather, organize and integrate curated information on peroxisomal genes, their encoded proteins, their molecular function and metabolic pathway they belong to, and their related disorders. PeroxisomeDB contains the complete peroxisomal proteome of Homo sapiens (encoded by 85 genes) and Saccharomyces cerevisiae (encoded by 61 genes). Now, we have included 34 new organism genomes with the acquisition of 2426 new peroxisomal homolog proteins. PeroxisomeDB 2.0 integrates the peroxisomal metabolome of whole microbody family by the new incorporation of the glycosome proteomes of trypanosomatids and the glyoxysome proteome of Arabidopsis thaliana. The site also provides a Peroxisome Metabolome of peroxisomal genes and proteins, their molecular interactions and metabolic pathways, tools for comparative genomics, predictive tools. Sponsors: Preoxisome Database is funded by Institut de Gntique et deBiologie Molculaire et Cellulaire.

Proper citation: Peroxisome Database (RRID:SCR_008352) Copy   

•   Source: SciCrunch Registry

http://cssb.biology.gatech.edu/skolnick/files/gpcr/gpcr.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented on August 19,2019.Database of tertiary structural modeling results of threading assembly refinement (TASSER) method for all 907 G protein-coupled receptors (GPCRs) in human genome. All sequences were collected from GPCR database http://www.gpcr.org/7tm/ and http://www.expasy.org/cgi-bin/lists?7tmrlist.txt. Unlike traditional homology modeling approaches, TASSER modeling does not require solved homologous template structures; moreover, it often refines the structures closer to native. G protein-coupled receptors (GPCRs), encoded by about 5% of human genes, comprise the largest family of integral membrane proteins and act as cell surface receptors responsible for the transduction of endogenous signal into a cellular response. Although tertiary structural information is crucial for function annotation and drug design, there are few experimentally determined GPCR structures. To address this issue, we employ the recently developed threading assembly refinement (TASSER) method to generate structure predictions for all 907 putative GPCRs in the human genome. Unlike traditional homology modeling approaches, TASSER modeling does not require solved homologous template structures; moreover, it often refines the structures closer to native. These features are essential for the comprehensive modeling of all human GPCRs when close homologous templates are absent. Based on a benchmarked confidence score, approximately 820 predicted models should have the correct folds. The majority of GPCR models share the characteristic seven-transmembrane helix topology, but 45 ORFs are predicted to have different structures. This is due to GPCR fragments that are predominantly from extracellular or intracellular domains as well as database annotation errors. Our preliminary validation includes the automated modeling of bovine rhodopsin, the only solved GPCR in the Protein Data Bank. With homologous templates excluded, the final model built by TASSER has a global C(alpha) root-mean-squared deviation from native of 4.6 angstroms, with a root-mean-squared deviation in the transmembrane helix region of 2.1 angstroms. Models of several representative GPCRs are compared with mutagenesis and affinity labeling data, and consistent agreement is demonstrated. Structure clustering of the predicted models shows that GPCRs with similar structures tend to belong to a similar functional class even when their sequences are diverse. These results demonstrate the usefulness and robustness of the in silico models for GPCR functional analysis. Sponsors: GPCR is funded by the University at Buffalo, Buffalo, New York.

Proper citation: Structure modeling of 907 G protein coupled receptors in the human genome (RRID:SCR_008351) Copy   

•   Source: SciCrunch Registry

http://www.imtech.res.in/raghava/bhairpred/

Bhairpred server is based on machine learning technique SVM using single sequence information, evolutionary profile, predicted and observed secondary structure (as obtained using Psipred and DSSP), predicted and observed accessibility values (as obtainned from Netasa and DSSP). The methods were trained and tested on dataset of 2880 proteins and their performance was evaluated on dataset of 534 proteins used by Thornton (PNAS, 2002). Best prediction results were obtained with hybrid approach that combined prediction results from evolutionary profile, predicted secondary structure and accessibility.

Proper citation: SVM based method for predicting beta hairpin structures in proteins (RRID:SCR_008349) Copy   

•   Source: SciCrunch Registry

http://mitointeractome.kobic.kr/

Database that gathers data on interactions in the mitochondrial proteome that has been used to construct a network for the aging process in humans and to identify interactions that influence this process, since mitochondria is a major source of cellular reactive oxygen species that accumulate during aging. It will: # aid in increasing our understanding of the molecular functions and interaction networks of mitochondrial proteins, # help in identifying new target proteins for experimental research using predicted protein-protein interaction information, and # help in identifying biomarkers for diagnosis and new molecular targets for drug development related to mitochondria. How is MitoInteractome different? * Provides protein-protein interaction information with graphical display. * Applies newly added new mitochondrial protein information by using BLAST incorporated in Mitointeractome * Shows correlation of mutation with their impact * Provides specific pathway information to aid study of their impact * Contains SNP Information

Proper citation: MitoInteractome (RRID:SCR_010225) Copy   

•   Source: SciCrunch Registry

http://www.grenoble.prabi.fr/obiwarehouse/unipathway

A manually curated database of enzyme-catalyzed and spontaneous chemical reactions. It provides a hierarchical representation of metabolic pathways and a controlled vocabulary for pathway annotation in UniProtKB. UniPathway data are cross-linked to existing metabolic resources such as ChEBI/Rhea, KEGG and MetaCyc. Users may do a quick search, browse pathway, browse compound, or browse organism.

Proper citation: UniPathway (RRID:SCR_010513) Copy   

•   Source: SciCrunch Registry

http://bleoberis.bioc.cam.ac.uk/mcsm

Data analysis service to the study of missense mutations which relies on graph-based signatures.

Proper citation: mCSM (RRID:SCR_010776) Copy   

•   Source: SciCrunch Registry

http://blast.ncbi.nlm.nih.gov/Blast.cgi?PROGRAM=tblastn&PAGE_TYPE=BlastSearch&LINK_LOC=blasthome

Tool to search translated nucleotide databases using a protein query.

Proper citation: TBLASTN (RRID:SCR_011822) Copy   

•   Source: SciCrunch Registry

http://www.imtech.res.in/raghava/hslpred/

A support vector machine (SVM)-based method for the prediction of 4 major subcellular localization (cytoplasm, mitochondrial, nuclear and plasma membrane) of human proteins using various features such as i) amino acid composition, ii) dipeptide composition and iii) evolutionary information of proteins.

Proper citation: HSLPred (RRID:SCR_011972) Copy   

•   Source: SciCrunch Registry