The DELPHI Web Server
Department of Computer Science
University of Western Ontario
Contact information:
Yiwei Li: yli922uwo.ca
Lucian Ilie: ilieuwo.ca

Run DELPHI

Welcome to the DELPHI web server. DELPHI is a sequence-based deep learning suite for PPI binding sites prediction.

Please enter the protein sequences and your email address then click predict.
The server allows maximum 10 input sequences at a time with minimum 31 amino acid residues.
The sequence should be in FASTA format. Each protein should consist of two lines: >[protein_id] and [protein_sequence].
The results will be emailed to you if email address is provided. You will also be able to download the results through a link

Example input:

>Q53WI4
MVVLKVTLLEGRPPEKKRELVRRLTEMASRLLGEPYEEVRVILYEVRRDQWAAGGVLFSDKEGT
>Q0TCE9
MKAKELREKSVEELNTELLNLLREQFNLRMQAASGQLQQSHLLKQVRRDVARVKTLLNEKAGA

Sequences:
E-mail (optional):

Downloads

Training and testing datasets used in DELPHI can be downloaded here.
The prediction results of DELPHI and other programs on the Dset_448, Dset_355, Dset_72, Dset_186, and Dset_164 are available here.
The datasets and DELPHI predictions for sites conservation are available here.
The human proteins in fasta format are available here.
The DELPHI predicted human proteome is available here.

Source code

The DELPHI web server allows up to 10 sequences per submission. It is recommended to use the source code for high-throughput predictions. The DELPHI source code is freely available on github.

Citation

- Li, Yiwei, Golding, Brian, and Ilie, Lucian, DELPHI: accurate deep ensemble model for protein interaction sites prediction. Bioinformatics, 2020, btaa750
- Li, Y. and Golding, G. Brian and Ilie, L., DELPHI: accurate deep ensemble model forprotein interaction sites prediction (ISMB’20), 3DSIG, Montreal, 2020. (abstract and poster )

Acknowledgment

The DELPHI pipe line uses the following programs:
SPRINT
pisblast
hhblits
ASAquick
ANCHOR

The backend cloud server of DELPHI is provided by:
Computer Canada
Last updated: Sep 2020. Release notes
Logo credit: K. Qi