About Anti-Biofilm Peptide Predictor

This predictor helps the scientific community working in the fields of Quorum Sening (QS) and biofilm to get appropiate efficiency of a peptide to inhibit biofilm. Our predictor gives the percentage reduction in four ranges viz. not active, less than 50 %, between 51-90% and greater than 90%.

Not Active

Peptides that are unable to inhibit biofilm

Less than 50%

Peptides that can inhibit biofilm with percentage inhibition less than 50%

51 - 90%

Peptides having efficiency to inhibit biofilm with efficieny 51-90%

greater than 90%

Highly efficient anti-biofilm peptide with efficiency to inhibit biofilm greater than 90%


  Enter peptide sequence(s) (fasta format) below:  


  SVMmulticlass based prediction model used:
  Amino Acid+Dipeptide+Binary 5N/5C+Physicochemical Properties


How to Interpret Results?

Result can be interpret by the "ABPpred prediction result page" as shown in the image below. The input sequences categorized in four categories: 1 (Not active), 2 (<50%), 3 (51-90%) and 4 (>90%).

Frequently Asked Questions

Q 1

What is anti-biofilm peptides (ABPs)?

These peptides are distinct group of antimicrobial or host defence peptides which are experimentally tested to be active against biofilms.

Q 2

Is there any computational tool to pedict inhibitory percentage of an ABP ?

There is no in silico tool available to predict inhibitory percentage of ABPs.

Q 3

How the data for developing the algorithm was collected ?

Total data of 179 non-reundant ABPs was collected from BaAMPs database.

Q 4

Which machine learning technique (MLT) used in developing this algorithm?

Support Vector Machine (SVM) multiclass classification technique with "one v/s one" approach used in this algorithm.

Meet Our Team

Akanksha Rajput Senior Research Fellow

Bioinformatics Centre (BIC), CSIR-Institute of Microbial Technology.


Dr. Manoj Kumar Senior Scientist

Bioinformatics Centre (BIC), CSIR-Institute of Microbial Technology.


Dr. Manoj Kumar

Senior Scientist,
Bioinformatics Centre (BIC),CSIR-Institute of Microbial Technology
Sector 39A, Chandigarh, INDIA

Copyright © 2016 CSIR-Institute of Microbial Technology , Chandigarh 160036, INDIA.