The PepShop database can be searched by UniProt accession number, gene symbol, organism name (7 species), exact amino acid sequence, and peptide monoisotopic mass with adjustable mass tolerance level.
The primary accession numbers in one of the following databases: UniProt (Examples: P22005, P01193), NCBI-RefSeq (Examples: NP_079142.2, NP_891558.1), UniGene (Examples: Mm.210541, Rn.141298, Hs.1897), Mouse genome index database (Examples: MGI:108058, MGI:2675256), Entrez Gene (Examples: 223780, 4879, 24602).
The prohormone symbol (Examples: "ADML", "COLI").
PepShop supports the prohormone and peptide identification based on the availability of sequenced genomes. Currently information is available for the Human, Mouse, Rat, Rhesus monkey, Cattle, Dog and Pig species. User can select the organism name from the pull down menu.
The monoisotpic mass of peptide with adjustable mass tolerance (±).
The full or partial exact prohormone or peptide amino acid sequence. There are two supported sequence formats: FASTA format or only sequence without FASTA header (Examples: "YGGFM", "KYVMGHFRWD"). Multiple sequence submissions are not supported at the same time.
Description of parameters:
|Da||absolute units of Da|
|ppm||fraction expressed as parts per million|
|mass||m+h value is converted to mass|
|mz||tolerance as mass-to-charge ratio|
|Precursor charge state|
|Mass type||monoisotopic average|
|Ion types||a ions b ions c ions x ions y ions z ions|
Neutral mass losses
|Ammonia loss (-17Da) Water loss (-18Da)|
Description of parameters:
Bioinformatics tools for prediction of cleavage sites in prohromone sequences, single sequence search (BLASTP) and Multiple Sequence Alignment (MUSCLE) have been provided in PepShop.
The basic use of these tools requires:
The following cleavage prediction models are available, the first model is an empirical model, whereas all other models are binary logistic regression models.
The following models are available:
Prohormone precursors undergo extensive modification before active neuropeptides and hormones are obtained. In addition to cleavages at basic sites and immediate removal of C-terminal basic residues (Trim C-terminal K and R), several other modifications may be present. The most common PTMs are amidation (of C-terminal glycine) and pyroglutamylation (cyclization of N-terminal glutamate or glutamine). Sulfation of tyrosine (Tyr-Sulfation) and acetylation are also common, albeit occurring somewhat less frequently; thus these four common PTMs are grouped together while even less common PTMs are grouped separately. Disulfide bond formation between two cysteine residues resulting in a mass loss of 2 Da is a common PTM in neuropeptides, but it is difficult to predict whether the disulfide bond is formed between two peptides or within a peptide containing two or more cysteines. In addition, it is difficult to identify the cysteine pairs involved in bond formation. For these reasons, disulfide bond formation is not modeled in NeuroPred. The user may consult web-based tools such as Cyspred to determine potentially disulfide bonding cysteines. A table of the available PTMs can be found here.
Post-Translational Modifications (PTMs) Trim C-terminal K and R Most Common PTMs Less Common PTMs Amidation O-linked Glycosylation of S N-linked Glycosylation of S Bromination of W Pyroglutamination O-linked Glycosylation of T N-linked Glycosylation of T Methylation of E Acetylation Dipeptidase Hydroxylation of P Methylation of H Sulfation of Y Carboxylation of E Phosphorylation of S Methylation of K DiAcetylation Phosphorylation of T Methylation of R
Standard protein-protein BLAST (blastp) will attempt to identify both submitted amino acid sequences and other similar sequences in selected protein database. The blastp find local regions of similarity using pairwise sequence alignment. When sequence similarity spans the whole sequence, blastp will also report a global alignment, which is the preferred result for prohormone identification purposes. The following databases are available in PepShop for blastp: RefSeq-mammalian, RefSeq-invertebrate, RefSeq-othervertebrates, UniProt and PepShop prohormone databases.
Multiple Sequence Alignment (MSA) of two or more than two submitted sequences can be performed using Muscle. To get alignment, click on
Amare, A., Hummon, A.B., Southey, B.R., Zimmerman, T.A., Rodriguez-Zas, S.L., Sweedler, J.V., Bridging neuropeptidomics and genomics with bioinformatics: prediction of mammalian neuropeptide prohormone processing. J. Proteome Res. 2006, 5, 1162-1167. Abstract.
Hummon, A.B., Hummon, N.P., Corbin, R.W., Li, L.J., Vilim, F.S., Weiss, K.R., Sweedler, J.V., From precursor to final peptides: a statistical sequence-based approach to predicting prohormone processing. J. Proteome Res. 2003, 2, 650-656. Abstract.
Hummon, A.B. Richmond, T.A. Verleyen, P. Baggerman, G. Huybrechts, J. Ewing, M A. Vierstraete, E. Rodriguez-Zas, S.L. Schoofs, L. Robinson, G.E. Sweedler, J.V. , From the Genome to the Proteome: Uncovering Peptides in the Apis Brain, Science 2006, 314, 647-649. Abstract.
Southey, B.R., Rodriguez-Zas, S.L., Sweedler, J.V., Prediction of neuropeptide prohormone cleavages with application to RFamides. Peptides 2006a, 27, 1087-1098. Abstract.
Southey B.R., Amare A., Zimmerman T.A., Rodriguez-Zas S.L., Sweedler J.V., NeuroPred: a tool to predict cleavage sites in neuropeptide precursors and provide the masses of the resulting peptides. Nucleic Acids Res. 2006b, 34 (Web Server issue), W267-272. Abstract.
Tegge, A.N. Southey, B.R. Sweedler, J.V. Rodriguez-Zas, S.L., Enhanced Prediction of Cleavage in Bovine Precursor Sequences. Lecture Notes in Computer Science, Bioinformatics Research and Applications, Vol. 4463, pp. 350-360, 2007, Springer. Abstract.
Southey, B.R., Hummon, A.B., Richmond, T.A., Sweedler, J.V., Rodriguez-Zas, S.L., Prediction of neuropeptide cleavage sites in insects. Bioinformatics, 2008, 24, 815-825. Full Text
Tegge, A.N., Southey, B.R., Sweedler, J.V., Rodriguez-Zas, S.L., Comparative Analysis of Neuropeptide Cleavage Sites in Human, Mouse, Rat, and Cattle. Mamm. Genome, 2008 , 19(2), 106-120. Abstract.
Questions or comments: Sandra Rodriguez Zas (email@example.com)