Introduction
				SH2PepInt has been developed to predict binding partners of 51 human 
			SH2 domains. Peptides are restrained to 7 amino acids length, 
			i.e. -2 to +4 amino acids around the pTyr position. Depending on 
			the user requirement it uses 
			PhosphoSitePlus
			and
			Gene Ontology
			databases for predicting highly reliable SH2-peptide interactions.
			
	
					
			When using SH2PepInt please cite :
- 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, Fabrizio Costa, Michael Huber, Michael Reth, and Rolf Backofen
Semi-Supervised Prediction of SH2-Peptide Interactions from Imbalanced High-Throughput Data
PLoS One, 8(5), pp. e62732, 2013. 
Results are computed with SH2PepInt version 1.0.0
			
			Overview
The following parameters are used to control the execution of SH2PepInt
Furthermore, additional information is available
Input Parameters
  Protein/Peptide FASTA
	PDZPepInt accepts input in form of a multiple FASTA file. 
				An example looks like this:
				
				
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.
>EbbB1 MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFEDHFLSLQRMFNNCEV VLGNLEITYVQRNYDLSFLKTIQEVAGYVLIALNTVERIPLENLQIIRGNMYYENSYALA >ErbB2 MELAALCRWGLLLALLPPGAASTQVCTGTDMKLRLPASPETHLDMLRHLYQGCQVVQGNL ELTYLPTNASLSFLQDIQEVQGYVLIAHNQVRQVPLQRLRIVRGTQLFEDNYALAVLDNG >ErbB3 MRANDALQVLGLLFSLARGSEVGNSQAVCPGTLNGLSVTGDAENQYQTLYKLYERCEVVM GNLEIVLTGHNADLSFLQWIREVTGYVLVAMNEFSTLPLPNLRVVRGTQVYDGKFAIFVM >ErbB4 MKPATGLWVWVSLLVAAGTVQPSDSQSVCAGTENKLSSLSDLEQQYRALRKYYENCEVVM GNLEITSIEHNRDLSFLRSVREVTGYVLVALNQFRYLPLENLRIIRGTKLYEDRYALAIF
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 ()
Defaults to ()
  Protein UniProt IDs
	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:
P00533, P04626, P21860
	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 '^[^;'"]*$'.
Defaults to ()
Defaults to ()
  SH2 Domains
	List of all SH2 protein domains available for an interaction screening.
	The parameter constraints are: The value has to match against the regular expression '^[^;'"]*$'. Only protein domains from the following list are allowed : ABL1, ABL2, APS, BCAR3, BLK, BMX, BRDG1, BTK, CRKL, CRK, CTEN, E105251, E109111, E185634, EAT2, FER, FES, FGR, FRK, GRAP2, GRB10, GRB14, GRB2, HCK, INPPL1, ITK, LCK, LCP2, LYN, MATK, MIST, NCK1, NCK2, PTK6, SH2B, SH2D1A, SH2D2A, SH2D3C, SHC1, SHC3, SOCS2, SOCS5, SRC, TEC, TENC1, TENS1, TNS, TXK, VAV1, VAV2, YES1.
Defaults to ()
Defaults to ()
Filters
  Phosphotyrosine (pY) (needs UniProt IDs)
	This filter has been implemented using the annotated information
				in the 
				PhosphoSitePlus
				database; in this way we have selected 
				only those phosphotyrosine peptides whose phosphorylation has 
				been experimentally verified. At the moment of the analysis 
				(January 2013) the PhosphoSitePlus database contained 30,228 
				phosphorylation sites from 10,688 human proteins. We have 
				ignored those peptides that were not present in the UniProtKB/ 
				Swiss-Prot database obtaining finally 27,481 phosphorylation 
				peptides out of 9621 proteins. This filter needs UniProt IDs 
				of the query proteins: 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)
Defaults to (false)
  Cellular localization (needs UniProt IDs)
	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 (e.g. GRB2 protein can be 
				found in nucleus, cytoplasm, endosome and golgi apparatus) 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 
				(such as SHD/E105251). 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)
Defaults to (false)
Output Description
			The output table summarizes all predicted protein-domain interactions.
			
			
Detailed descriptions:
			
Detailed descriptions:
- 1. Input sequence id
 - 2. Binding region in the protein
 - 3. Binding sequence
 - 4. Binding domain/s with the protein
 
Input Examples
  SH2 interactions
	This is an example for the interaction prediction of human SH2
		   domains from GRB2, CRK and CRKL proteins with the target proteins ERBB1, ERBB2 and
		   ERBB3.
The example's result can be directly accessed here
Frequently Asked Questions
If your question is not listed, please send it to us!
	Is it possible to train a SH2 prediction model with my own data?
In case you want to use your own data to make a dedicated 
		  SH2-prediction model, please
		  contact us.
List of Changes
- 3.2.0 : SH2PepInt v1.0.0 online
 
			 
			 