ArticleFrontiers in microbiology2016
Prediction of Biofilm Inhibiting Peptides: An In silico Approach.
Article in Frontiers in microbiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Identification of Opportunistic Pathogens on the Skin of Salamanders for Use as Molecular Targets of a De Novo Design of Multitarget Anti-International journal of microbiology · 2026Article
- Mapping the Antibiofilm Peptide Space with Similarity Networks and Curated Negative Sets.ACS omega · 2025Article
- Prediction and validation of nanowire proteins inComputational and structural biotechnology journal · 2025Article
- Article
- AntiBP3: A Method for Predicting Antibacterial Peptides against Gram-Positive/Negative/Variable Bacteria.Antibiotics (Basel, Switzerland) · 2024Article
- Advancing understanding of microbial biofilms through machine learning-powered studies.Food science and biotechnology · 2023Review
- iAMPCN: a deep-learning approach for identifying antimicrobial peptides and their functional activities.Briefings in bioinformatics · 2023Article
- Complex Networks Analyses of Antibiofilm Peptides: An Emerging Tool for Next-Generation Antimicrobials' Discovery.Antibiotics (Basel, Switzerland) · 2023Article
- Computational tools for exploring peptide-membrane interactions in gram-positive bacteria.Computational and structural biotechnology journal · 2023Review
- Polymicrobial Infections and Biofilms: Clinical Significance and Eradication Strategies.Antibiotics (Basel, Switzerland) · 2022Review
- Responses of Salmonella biofilms to oxidizing biocides: Evidence of spatial clustering.Environmental microbiology · 2022Article
- Antibiofilm, AntiAdhesive and Anti-Invasive Activities of Bacterial Lysates Extracted fromFoods (Basel, Switzerland) · 2022Article
- Biofilm-Molecules (Basel, Switzerland) · 2022Article
- Machine Learning Prediction of Antimicrobial Peptides.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Molecular regulation of conditioning film formation and quorum quenching in sulfate reducing bacteria.Frontiers in microbiology · 2022Review
- The Two Weapons against Bacterial Biofilms: Detection and Treatment.Antibiotics (Basel, Switzerland) · 2021Review
- K-Nearest Neighbor and Random Forest-Based Prediction of Putative Tyrosinase Inhibitory Peptides of AbaloneMolecules (Basel, Switzerland) · 2021Article
- Therapeutic Potential of Antimicrobial Peptides in Polymicrobial Biofilm-Associated Infections.International journal of molecular sciences · 2021Review
- Antimicrobial Peptides Derived From Insects Offer a Novel Therapeutic Option to Combat Biofilm: A Review.Frontiers in microbiology · 2021Review
- Identification of Distinct Characteristics of Antibiofilm Peptides and Prospection of Diverse Sources for Efficacious Sequences.Frontiers in microbiology · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Approximately 75% of microbial infections found in humans are caused by microbial biofilms. These biofilms are resistant to host immune system and most of the currently available antibiotics. Small peptides are extensively studied for their role as anti-microbial peptides, however, only a limited studies have shown their potential as inhibitors of biofilm. Therefore, to develop a unique computational method aimed at the prediction of biofilm inhibiting peptides, the experimentally validated biofilm inhibiting peptides sequences were used to extract sequence based features and to identify unique sequence motifs. Biofilm inhibiting peptides were observed to be abundant in positively charged and aromatic amino acids, and also showed selective abundance of some dipeptides and sequence motifs. These individual sequence based features were utilized to construct Support Vector Machine-based prediction models and additionally by including sequence motifs information, the hybrid models were constructed. Using 10-fold cross validation, the hybrid model displayed the accuracy and Matthews Correlation Coefficient (MCC) of 97.83% and 0.87, respectively. On the validation dataset, the hybrid model showed the accuracy and MCC value of 97.19% and 0.84, respectively. The validated model and other tools developed for the prediction of biofilm inhibiting peptides are available freely as web server at http://metagenomics.iiserb.ac.in/biofin/ and http://metabiosys.iiserb.ac.in/biofin/.
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Registered trials
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