A database of peptides based on sequence text mining and public peptide data sources. Only peptides that are 20 amino acids or shorter are stored. Only peptides with available sequences are stored. After submitting a query you can further refine the results using the new heat map retrieval tool to quickly find the entries that are most relevant to you. Text classification helps you find candidate peptides that are related to cancer, cardiovascular diseases, diabetes, apoptosis, angiogenesis and molecular imaging or peptides for which binding data exist.

PepBank is a database of peptides based on sequence text mining and public peptide data sources. Only peptides that are 20 amino acids or shorter are stored. Only peptides with available sequences are stored.

THIS RESOURCE IS NO LONGER IN SERVICE, documented on June 04, 2014. Curated database on selected from randomized pools proteins and peptides designed for accumulation of experimental data on protein functionality obtained by in vitro directed evolution methods (phage display, ribosome display, SIP etc.) ASPD is integrated by means of hyperlinks with different databases (SWISS-PROT, PDB, PROSITE, etc). The database also contains modules for pairwise correlation analysis and BLAST search.

The antimicrobial peptide database (APD) provides information on anticancer, antiviral, antifungal and antibacterial peptides.

UDAMP is a comprehensive, human database of antimicrobial and immunomodulatory peptides containing all so far known human antimicrobial and immunomodulatory peptides.

Health-enhancing potential bioactive peptide (BP) has driven an interest in food proteins as well as in the development of predictive methods. Research in this area has been especially active to use them as components in functional foods. Apparently, BPs do not have a given biological function in the containing proteins and they do not evolve under independent evolutionary constraints. In this work we performed a large-scale mapping of BPs in sequence and structural space. Using well curated BP deposited in BIOPEP database, we searched for exact matches in non-redundant sequences databases. Proteins containing BPs, were used in fold-recognition methods to predict the corresponding folds and BPs occurrences were mapped. We found that fold distribution of BP occurrences possibly reflects sequence relative abundance in databases. However, we also found that proteins with 5 or more than 5 BP in their sequences correspond to well populated protein folds, called superfolds. Also, we found that in well populated superfamilies, BPs tend to adopt similar locations in the protein fold, suggesting the existence of hotspots. We think that our results could contribute to the development of new bioinformatics pipeline to improve BP detection.

Metaproteomics has increasingly been applied to study functional changes in the human gut microbiome. And peptide identification is an important step in metaproteomics research. However, the large search space in metaproteomics studies causes significant challenges for peptide identification. Here, we constructed MetaPep, a core peptide database (including both collections of peptide sequences and tandem MS spectra) greatly accelerating the peptide identifications. Raw files from fifteen metaproteomics projects were re-analyzed and the identified peptide-spectrum matches (PSMs) were used to construct the MetaPep database. The constructed MetaPep database achieved rapid and accurate identification of peptides for human gut metaproteomics.

Norine is a database dedicated to nonribosomal peptides (NRPs). In bacteria and fungi, in addition to the traditional ribosomal proteic biosynthesis, an alternative ribosome-independent pathway called NRP synthesis allows peptide production. The molecules synthesized by NRPS contain a high proportion of nonproteogenic amino acids whose primary structure is not always linear, often being more complex and containing cycles and branchings.

NIST peptide libraries are comprehensive, annotated mass spectral reference collections from various organisms and proteins useful for the rapid matching and identification of acquired MS/MS spectra. Spectra were produced by tandem mass spectrometers using liquid chromatographic separations followed by electrospray ionization. Unlike the NIST small molecule electron ionization library which contains one spectrum per molecular structure, there are several different modes of fragmentation (ion trap and ?beam-type? collision cells are currently the most commonly used fragmentation devices) that result in spectra with different, energy dependent, patterns. These result in multiple spectral libraries, distinguished by ionization mode, each of which may contain several spectra per peptide. Different libraries have also been assembled for iTRAQ-4 derivatized peptides and for phosphorylated peptides. Separating libraries by animal species reduces search time, although investigators may elect to include several species in their searches.

Peptides are short chains of amino acids, referred to as mini-proteins, formed through dehydration.

The Peptide Sequence Database contains putative peptide sequences from human, mouse, rat, and zebrafish. Compressed to eliminate redundancy, these are about 40 fold smaller than a brute force enumeration. Current and old releases are available for download. Each species'' peptide sequence database comprises peptide sequence data from releveant species specific UniGene and IPI clusters, plus all sequences from their consituent EST, mRNA and protein sequence databases, namely RefSeq proteins and mRNAs, UniProt''s SwissProt and TrEMBL, GenBank mRNA, ESTs, and high-throughput cDNAs, HInv-DB, VEGA, EMBL, IPI protein sequences, plus the enumeration of all combinations of UniProt sequence variants, Met loss PTM, and signal peptide cleavages. The README file contains some information about the non amino-acid symbols O (digest site corresponding to a protein N- or C-terminus) and J (no digest sequence join) used in these peptide sequence databases and information about how to configure various search engines to use them. Some search engines handle (very) long sequences badly and in some cases must be patched to use these peptide sequence databases. All search engines supported by the PepArML meta-search engine can (or can be patched to) successfully search these peptide sequence databases.

The 6 Excel file correspond to the DRAMP database's general, patent, clinical, specific, stability, and expanded antimicrobial peptide datasets, each containing comprehensive peptide information such as name, sequence, source, activity, references, and more. These datasets are provided for researchers to consult.

OASis human 9-mer peptide database, generated from 118 million human antibody sequences from the Observed Antibody Space database.

Attached is a gzipped SQLite database containing two tables: "peptides" and "subjects".

Links:

• BioPhi codebase and documentation: https://github.com/Merck/BioPhi
• Public BioPhi server: https://biophi.dichlab.org
• OAS Database: http://opig.stats.ox.ac.uk/webapps/oas/

The MHC-Peptide Interaction Database version T (MPID-T) is a new generation database for sequence-structure-function information on T cell receptor/peptide/MHC interactions. It contains all structures of TcR/pMHC and pMHC complexes, with emphasis on the structural characterization of these complexes. MPID-T will facilitate the development of algorithms to predict whether a peptide sequence will bind to a specific MHC allele. The database has been populated with the data from the Protein Data Bank(PDB). The data from the PDB is manually verified and classified, after which each structure is analysed for atomic interactions relevant to MHC-Peptide complex.

Antimicrobial peptides (AMPs) are naturally produced by pro- and eukaryotes and are promising alternatives to antibiotics to fight multidrug-resistant microorganisms. However, despite thousands of AMP entries in respective databases, predictions about their structure–activity relationships are still limited. Similarly, common or dissimilar properties of AMPs that have evolved in different taxonomic groups are nearly unknown. We leveraged data entries for 10,987 peptides currently listed in the three antimicrobial peptide databases APD, DRAMP and DBAASP to aid structure–activity predictions. However, this number reduced to 3,828 AMPs that we could use for computational analyses, due to our stringent quality control criteria. The analysis uncovered a strong bias towards AMPs isolated from amphibians (1,391), whereas only 35 AMPs originate from fungi (0.9%), hindering evolutionary analyses on the origin and phylogenetic relationship of AMPs. The majority (62%) of the 3,828 AMPs consists of less than 40 amino acids but with a molecular weight higher than 2.5 kDa, has a net positive charge and shares a hydrophobic character. They are enriched in glycine, lysine and cysteine but are depleted in glutamate, aspartate and methionine when compared with a peptide set of the same size randomly selected from the UniProt database. The AMPs that deviate from this pattern (38%) can be found in different taxonomic groups, in particular in Gram-negative bacteria. Remarkably, the γ-core motif claimed so far as a unifying structural signature in cysteine-stabilised AMPs is absent in nearly 90% of the peptides, questioning its relevance as a prerequisite for antimicrobial activity. The disclosure of AMPs pattern and their variation in producing organism groups extends our knowledge of the structural diversity of AMPs and will assist future peptide screens in unexplored microorganisms. Structural design of peptide antibiotic drugs will benefit using natural AMPs as lead compounds. However, a reliable and statistically balanced database is missing which leads to a large knowledge gap in the AMP field. Thus, thorough evaluation of the available data, mitigation of biases and standardised experimental setups need to be implemented to leverage the full potential of AMPs for drug development programmes in the clinics and agriculture.

Predicted and Experimental Peptide Binding Information (PEPBI) Database

Access this dataset on Dryad

This dataset is the publicly available data associated with A Paired Database of Predicted and Experimental Protein Peptide Binding Information, submitted to Scientific Data and hereafter referred to as the PEPBI manuscript. The Predicted and Experimental Peptide Binding Information (PEPBI) dataset provides curated thermodynamic and structural data for protein–peptide complexes. It includes:

• Experimental Data: Isothermal titration calorimetry (ITC) data collected through an extensive literature review, compiled in the PEPBI.xlsx Excel spreadsheet.
• Computational Data: Computationally predicted protein–peptide structures in .pdb format, each derived from a shared reference structure (template). These templates are experimentally determined structures from the Protein Data Bank (PDB) and are used as the foundation for ...

Proteomics crowdsourced "Big Data". The GPM is an experimental project to create knowledge from proteomics data and reuse it to solve biomedical research problems. 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 the GPM server pages, allowing users to quickly compare their experimental results with the best results that have been previously observed by users of the machine.

Chemical cross-linking combined with mass spectrometric analysis has become an important technique for probing protein three-dimensional structure and protein–protein interactions. A key step in this process is the accurate identification and validation of cross-linked peptides from tandem mass spectra. The identification of cross-linked peptides, however, presents challenges related to the expanded nature of the search space (all pairs of peptides in a sequence database) and the fact that some peptide-spectrum matches (PSMs) contain one correct and one incorrect peptide but often receive scores that are comparable to those in which both peptides are correctly identified. To address these problems and improve detection of cross-linked peptides, we propose a new database search algorithm, XLSearch, for identifying cross-linked peptides. Our approach is based on a data-driven scoring scheme that independently estimates the probability of correctly identifying each individual peptide in the cross-link given knowledge of the correct or incorrect identification of the other peptide. These conditional probabilities are subsequently used to estimate the joint posterior probability that both peptides are correctly identified. Using the data from two previous cross-link studies, we show the effectiveness of this scoring scheme, particularly in distinguishing between true identifications and those containing one incorrect peptide. We also provide evidence that XLSearch achieves more identifications than two alternative methods at the same false discovery rate (availability: https://github.com/COL-IU/XLSearch).

'DRAMP_clinical_amps.xlsx. is the antimicrobial peptide dataset involved in clinical research. Data in this dataset were organized as sequence, name, description, activity, medical use, stage of development, comments, company and reference. It is worthy to note that some clinical peptides whose amino acid sequences are absent were also included in this dataset as we don’t want to lose any clinical information.

Database used for peptide identification from MS spectra.