ArticleACS omega2020
BIPEP: Sequence-based Prediction of Biofilm Inhibitory Peptides Using a Combination of NMR and Physicochemical Descriptors.
Article in ACS omega, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed.
- 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
- Prediction of the synergistic effect of antimicrobial peptides and antimicrobial agents via supervised machine learning.BMC biomedical engineering · 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
- MFPPDB: a comprehensive multi-functional plant peptide database.Frontiers in plant science · 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
- PrMFTP: Multi-functional therapeutic peptides prediction based on multi-head self-attention mechanism and class weight optimization.PLoS computational biology · 2022Article
- Biofilm-Molecules (Basel, Switzerland) · 2022Article
- Machine Learning Prediction of Antimicrobial Peptides.Methods in molecular biology (Clifton, N.J.) · 2022Article
- CpACpP:ACS omega · 2021Article
- Therapeutic Potential of Antimicrobial Peptides in Polymicrobial Biofilm-Associated Infections.International journal of molecular sciences · 2021Review
- Identification of Distinct Characteristics of Antibiofilm Peptides and Prospection of Diverse Sources for Efficacious Sequences.Frontiers in microbiology · 2021Article
- AMPing Up the Search: A Structural and Functional Repository of Antimicrobial Peptides for Biofilm Studies, and a Case Study of Its Application toFrontiers in cellular and infection microbiology · 2021Article
- Integration of Machine Learning and Structural Analysis for Predicting Peptide Antibiofilm Effects: Advancements in Drug Discovery for Biofilm-Related Infections.Iranian journal of pharmaceutical research : IJPRArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Biofilms are biological systems that are formed by a community of microorganisms in which microbial cells are connected on a surface within a self-produced matrix of an extracellular polymeric substance. On some occasions, microorganisms use biofilms to protect themselves against the harmful effects of the host body immune system and the surrounding environment, hence increasing their chances of survival against the various anti-microbial agents. Biofilms play a crucial role in medicine and industry because of the problems they cause. Designing agents that inhibit bacterial biofilm formation is very costly and takes too much time in the laboratory to be discovered and validated. Therefore, developing computational tools for the prediction of biofilm inhibitor peptides is inevitable and important. Here, we present a computational prediction tool to screen the vast number of peptide sequences and select potential candidate peptides for further lab experiments and validation. In this learning model, different feature vectors, extracted from the peptide primary structure, are exploited to learn patterns from the sequence of biofilm inhibitory peptides. Various classification algorithms including SVM, random forest, and k-nearest neighbor have been examined to evaluate their performance. Overall, our approach showed better prediction in comparison with other prediction methods. In this study, for the first time, we applied features extracted from NMR spectra of amino acids along with physicochemical features. Although each group of features showed good discrimination potential alone, we used a combination of features to enhance the performance of our method. Our prediction tool is freely available.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.