PDZPepInt is a cluster based prediction tool to predict binding peptides of PDZ domains in human, mouse, fly and worm. Total 43 built-in models that cover 226 PDZ domains across the species are available. Peptides are represented as 5 C-terminal sequences of binding proteins. Depending on the user requirement Gene Ontology database can be used for getting reliable interactions. Additionally, it will also consider the C-terminal peptides that are intrinsically unstructured for getting high confidence interactions.

• Kousik Kundu, Martin Mann, Fabrizio Costa, and Rolf Backofen
  MoDPepInt: An interactive webserver for prediction of modular domain-peptide interactions
  Bioinformatics, 2014, in press.
  
• Kousik Kundu and Rolf Backofen
  Cluster based prediction of PDZ-peptide interactions
  BMC Genomics, 15 Suppl 1 pp. S5, 2014.
  

Results are computed with PDZPepInt version 1.0.0

PDZPepInt accepts input in form of a multiple FASTA file. An example looks like this:

	
>SCN5A
MANFLLPRGTSSFRRFTRESLAAIEKRMAEKQARGSTTLQESREGLPEEEAPRPQLDLQA
SKKLPDLYGNPPQELIGEPLEDLDPFYSTQKTFIVLNKGKTIFRFSATNALYVLSPFHPI

>Sema4c
MAPHWAVWLLAAGLWGLGIGAEMWWNLVPRKTVSSGELVTVVRRFSQTGIQDFLTLTLTE
HSGLLYVGAREALFAFSVEALELQGAISWEAPAEKKIECTQKGKSNQTECFNFIRFLQPY

>Fas2
MGELPPNSVGVFLALLLCSCSLIELTRAQSPILEIYPKQEVQRKPVGKPLILTCRPTVPE
PSLVADLQWKDNRNNTILPKPNGRNQPPMYTETLPGESLALMITSLSVEMGGKYYCTASY

Input can be given either as direct text input or uploading a file.
Note: Input size is limited to restrict computation time and memory requirements.

The parameter constraints are: The input has to be in valid FASTA format. The number of sequences has to be at least 0 and at most 500. Sequence lengths have to be in the range 1-5000. The allowed sequence alphabet is 'GPAVLIMCFYWHKRQNEDST'. Either FASTA input or a UniProt ID list have to be provided. In case an enabled filter requires UniProt IDs: Each FASTA sequence header/name has to be just a valid UniProt ID or a valid UniProt FASTA header.
Defaults to ()

Instead of feeding directly protein sequences, you can provide UniProt IDs of the targeted proteins as an input. In this case, the protein sequence will be automatically downloaded from the UniProt database. Multi UniProt IDs separated by below mentioned separators are also accepted. An example looks like this:

 Q14524, Q64151, P34082

The parameter constraints are: Has to be a list of 0-500 UniProt IDs that are separated by ([,\.;: \t\n]|\r\n). Access to the UniProt database is needed. The value has to match against the regular expression '^[^;'"]*$'.
Defaults to ()

List of all PDZ protein domains available for an interaction screening.

The parameter constraints are: The value has to match against the regular expression '^[^;'"]*$'. Only protein domains from the following list are allowed : AFAD_Human, AFAD_Mouse, APBA1-1_Human, APBA1-1_Mouse, APBA2-1_Human, APBA2-1_Mouse, APBA3-1_Human, APBA3-1_Mouse, CSKP_Drome, CSKP_Human, CSKP_Mouse, CYTIP_Human, CYTIP_Mouse, DLG1-1_Drome, DLG1-1_Human, DLG1-1_Mouse, DLG1-2_Drome, DLG1-2_Human, DLG1-2_Mouse, DLG1-3_Drome, DLG1-3_Human, DLG1-3_Mouse, DLG2-1_Human, DLG2-1_Mouse, DLG2-2_Human, DLG2-2_Mouse, DLG2-3_Human, DLG2-3_Mouse, DLG3-1_Human, DLG3-1_Mouse, DLG3-2_Human, DLG3-2_Mouse, DLG3-3_Human, DLG3-3_Mouse, DLG4-1_Human, DLG4-1_Mouse, DLG4-2_Human, DLG4-2_Mouse, DLG4-3_Human, DLG4-3_Mouse, DSH_Drome, DVL1_Human, DVL1_Mouse, DVL2_Human, DVL2_Mouse, DVL3_Human, DVL3_Mouse, DVLP1_Human, EM55_Human, EM55_Mouse, FRPD2-2_Human, GIPC1_Human, GIPC1_Mouse, GIPC2_Human, GIPC2_Mouse, GIPC3_Human, GIPC3_Mouse, Gm1582-2_Mouse, GOPC_Human, GOPC_Mouse, GRASP_Human, GRASP_Mouse, HTRA1_Human, HTRA1_Mouse, HTRA2_Human, HTRA2_Mouse, HTRA3_Human, HTRA3_Mouse, INADL-10_Human, INADL-10_Mouse, INADL-1_Human, INADL-1_Mouse, INADL-2_Human, INADL-2_Mouse, INADL-3_Human, INADL-3_Mouse, INADL-5_Human, INADL-5_Mouse, INADL-6_Human, INADL-6_Mouse, INADL-8_Human, INADL-8_Mouse, INADL-9_Human, INADL-9_Mouse, LAP2_Human, LAP2_Mouse, LAP4-1_Drome, LAP4-2_Drome, LAP4-3_Drome, LDB3_Human, LDB3_Mouse, LIN10-1_Caeel, LIN2_Caeel, LIN7A_Human, LIN7A_Mouse, LIN7B_Human, LIN7B_Mouse, LIN7C_Human, LIN7C_Mouse, LNX1-1_Human, LNX1-1_Mouse, LNX2-1_Human, LNX2-1_Mouse, LRRC7_Human, LRRC7_Mouse, MAGI1-2_Human, MAGI1-2_Mouse, MAGI1-4_Mouse, MAGI1-5_Mouse, MAGI1-6_Mouse, MAGI2-2_Human, MAGI2-2_Mouse, MAGI2-4_Human, MAGI2-4_Mouse, MAGI2-5_Human, MAGI2-5_Mouse, MAGI2-6_Human, MAGI2-6_Mouse, MAGI3-1_Human, MAGI3-1_Mouse, MAGI3-2_Human, MAGI3-2_Mouse, MAGI3-4_Human, MAGI3-4_Mouse, MAGI3-5_Human, MAGI3-5_Mouse, MAGI3-6_Human, MAGI3-6_Mouse, MPDZ-10_Human, MPDZ-10_Mouse, MPDZ-11_Human, MPDZ-11_Mouse, MPDZ-12_Human, MPDZ-12_Mouse, MPDZ-13_Human, MPDZ-13_Mouse, MPDZ-1_Human, MPDZ-1_Mouse, MPDZ-2_Human, MPDZ-2_Mouse, MPDZ-3_Human, MPDZ-3_Mouse, MPDZ-5_Human, MPDZ-5_Mouse, MPDZ-7_Human, MPDZ-7_Mouse, MPDZ-9_Human, MPDZ-9_Mouse, MPP2_Human, MPP2_Mouse, MPP6_Human, MPP6_Mouse, NHRF1-1_Human, NHRF1-1_Mouse, NHRF1-2_Human, NHRF1-2_Mouse, NHRF2-1_Human, NHRF2-1_Mouse, NHRF2-2_Human, NHRF2-2_Mouse, NHRF3-1_Human, NHRF3-1_Mouse, NHRF4-3_Human, NHRF4-3_Mouse, PATJ-1_Drome, PATJ-2_Drome, PATJ-4_Drome, PDLI1_Human, PDLI1_Mouse, PDLI2_Human, PDLI2_Mouse, PDLI3_Human, PDLI3_Mouse, PDLI4_Human, PDLI4_Mouse, PDLI5_Human, PDLI5_Mouse, PDLI7_Human, PDLI7_Mouse, PDZD2-2_Human, Pdzk3-1_Mouse, PTN13-1_Human, PTN13-1_Mouse, PTN13-2_Human, PTN13-2_Mouse, PTN13-4_Human, PTN13-4_Mouse, RGS3_Human, RGS3_Mouse, SCRIB-1_Human, SCRIB-1_Mouse, SCRIB-2_Human, SCRIB-2_Mouse, SCRIB-3_Human, SCRIB-3_Mouse, SHAN1_Human, SHAN1_Mouse, SHAN2_Human, SHAN2_Mouse, SHAN3_Human, SHAN3_Mouse, SNT1_Caeel, SNTA1_Human, SNTA1_Mouse, SNTB1_Human, SNTB1_Mouse, SNTB2_Human, SNTB2_Mouse, SNTG1_Human, SNTG1_Mouse, SNTG2_Human, SNTG2_Mouse, SYJ2B_Human, SYJ2B_Mouse, ZO1-1_Human, ZO1-1_Mouse, ZO1-3_Human, ZO1-3_Mouse, ZO2-1_Human, ZO2-1_Mouse, ZO2-3_Human, ZO2-3_Mouse, ZO3-1_Human, ZO3-1_Mouse, ZO3-3_Human, ZO3-3_Mouse.
Defaults to ()

PDZPepInt offers predictions also for domains that are newly developed and/or not comprised in the original 226 PDZ domains. The unknown domains are first aligned to the known ones, obtaining a similarity score against each known domain, finally the weighted combination of the predictions of each known model is computed. Multiple query domain sequences can also be provided. The new domain data has to be provided in multiline FASTA format. An example looks like this:

	
>New-domain
TLERGNSGLGFSIAGGTDNPHILFITKIIPGGALLQDGRLRVNDSILFVNEVE
VKEVTHSAAVEALREAGSIVRLYVM
			

The parameter constraints are: The input has to be in valid FASTA format. The number of sequences has to be at least 0 and at most 300. Sequence lengths have to be in the range 1-1000. The allowed sequence alphabet is 'GPAVLIMCFYWHKRQNEDST'.
Defaults to ()

PDZ domains have tendency to bind with intrinsically unstructured proteins (IUPs), thus this filter has been introduced to consider only those peptides that reside in a disordered protein region. We used IUPred algorithm (version 1.0) to calculate IUPred scores of last 5 residues of a protein (potential binding region). The peptides having IUPred scores more than 0.4 are considered.

The parameter constraints are: Input value has to be parsable as Boolean.
Defaults to (true)

This filter was implemented considering the terms relative to the sub-cellular localization hierarchy in the controlled vocabulary of the Gene Ontology database (January, 2013). In case of multiple cellular locations we consider a peptide viable for interaction if it shares at least one of the terms with the domain. This filter is not applicable for the domains without annotated sub-cellular localizations. UniProt Ids of the query proteins are mandatory for using this filter: within FASTA provide either ONLY the UniProt ID within the FASTA header OR use the header encoding from the UniProt database; alternatively just provide the UniProt IDs for an automated download of the query sequences.

The parameter constraints are: Input value has to be parsable as Boolean.
Defaults to (false)

This is an example for the PDZ-peptide interaction prediction. PDZ domains from different organisms bind with their respective peptides.

The example's result can be directly accessed here