UniDL4BioPep: A universal deep learning architecture for bioactive peptide prediction
The webserver is the implementation of the paper "Du, Z., Ding, X., Xu, Y., & Li, Y. (2023). UniDL4BioPep: a universal deep learning architecture for binary classification in peptide bioactivity. Briefings in Bioinformatics, bbad135."
Notice: For very large dataset processing: please download our model locally or contact us at zhenjiao@ksu.edu or yonghui@ksu.edu for more assistant.
Quick output version: 1. Choose a model → 2. Input a peptide sequence
Large-scale output version: 1. Prepare your files (xls, xlsx, fasta, or txt) and click “Choose File” for uploading → 2. Choose one or multiple models → 3. Download the results.
Usage of the webserver:
Example for “Quick output version” :
1. Select “Antihypertensive” model for antihypertensive activity prediction. → → → 2. Insert a peptide or protein sequence, “VPP” → → → 3. Click “Run”→ → → 4. The result will be returned in seconds below the “Run” button
Notice: it also support multiple sequence at the same time. Just input as “VPP,IPP,CCL,AGR” (sequences are separated by comma, no space)
Example for “Large-scale output version:” :
1. Prepare your xls, xlsx, txt or fasta files → → → 2. Upload the file through “Choose File” botton → → → 3. Select one or several models → → → 4. Click “Run” → → → 5. It will automatically download your results.
Notice: File preparation should follow the examples under this repository https://github.com/dzjxzyd/UniDL4BioPep_webserver/tree/main/Example%20uploading%20files
Detailed explaination of the activity abbreviation
Antihypertensive: Angiotensin-converting enzyme inhibitory activity (main target in for hypertension); DPPIV: dipeptidyl peptidase IV (DPPIV) inhibitory activity (main target for diabetes); AMP: antimicrobial activity; AMAP: antimalarial activity (main and alternative is corresponding to two datasets; QS: quorum-sensing activity; ACP: anticancer activity: MRSA: anti-methicillin-resistant S. aureus strains activity); TTCA: tumor T cell antigens; BBP: blood-brain barrier peptide; APP: anti-parasitic activity; FL: is just an indicator of the Focal loss as loss function version, typically, we recommend the FL-version if available (for balanced datassets, we do not using FL for model generation, but you can try it based our our template tutorial in github)
The whole model architecture
Dataset for the those models
Bioactivity
Training dataset
Test dataset
ACE inhibitory activity
913 positives and 913 negatives
386 positives and 386 negatives
DPP IV inhibitory activity
532 positives and 532 negatives
133 positives and 133 negatives
Bitter
256 positives and 256 negatives
64 positives and 64 negatives
Umami
112 positives and 241 negatives
28 positives and 61 negatives
Antimicrobial activity
3876 positives and 9552 negatives
2584 positives and 6369 negatives
Antimalarial activity
Main dataset (111 positives and 1708 negatives); alternative dataset (111 positives and 542 negatives)
Main dataset (28 positives and 427 negatives); alternative dataset (28 positives and 135 negatives)
Quorum sensing activity
200 positives and 200 negatives
20 positives and 20 negatives
Anticancer activity
Main dataset (689 positives and 689 negatives); alternative dataset (776 positives and 776 negatives)
Main dataset (172 positives and 172 negatives); alternative dataset (194 positives and 194 negatives)
Anti-MRSA strains activity
118 positives and 678 negatives
30 positives and 169 negatives
Tumor T cell antigens
470 positives and 318 negatives
122 positives and 75 negatives
Blood-Brain Barrier
100 positives and 100 negatives
19 positives and 19 negatives
Anti-parasitic activity
255 positives and 255 negatives
46 positives and 46 negatives
Neuropeptide
1940 positives and 1940 negatives
485 positives and 485 negatives
Antibacterial activity
6583 positives and 6583 negatives
1695 positives and 1695 negatives
Antifungal activity
778 positives and 778 negatives
215 positives and 215 negatives
Antiviral activity
2321 positives and 2321 negatives
623 positives and 623 negatives
Toxicity
1642 positives and 1642 negatives
290 positives and 290 negatives
Antioxidant activity
582 positives and 541 negatives
146 positives and 135 negatives