ArticleJournal of translational medicine2016
ProInflam: a webserver for the prediction of proinflammatory antigenicity of peptides and proteins.
Article in Journal of translational medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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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.
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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.
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Who cites it
29 citing papers in PubMed, 61 citations in OpenAlex.
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- Machine learning for antimicrobial peptide identification and design.Nature reviews bioengineering · 2024Article
- Novel Antimicrobial Peptide SAAP Mutant as a Better Adjuvant to Sulbactam-Based Treatments Against Clinical Strains of XDR Acinetobacter baumannii.Probiotics and antimicrobial proteins · 2024Article
- Protein-based nanoparticles for therapeutic nucleic acid delivery.Biomaterials · 2024Review
- Immunoreactive proteins of Capsicum-based spices as a threat to human health: mass spectrometry analysis and in silico mapping.Scientific reports · 2023Article
- Targeting Peptides: The New Generation of Targeted Drug Delivery Systems.Pharmaceutics · 2023Review
- Teleost Piscidins-In Silico Perspective of Natural Peptide Antibiotics from Marine Sources.Antibiotics (Basel, Switzerland) · 2023Review
- Application of artificial intelligence approaches to predict the metabolism of xenobiotic molecules by human gut microbiome.Frontiers in microbiology · 2023Review
- In silico designing of a recombinant multi-epitope antigen for leprosy diagnosis.Journal, genetic engineering & biotechnology · 2022Article
- MPMABP: A CNN and Bi-LSTM-Based Method for Predicting Multi-Activities of Bioactive Peptides.Pharmaceuticals (Basel, Switzerland) · 2022Article
- Comparative analysis of machine learning-based approaches for identifying therapeutic peptides targeting SARS-CoV-2.Briefings in bioinformatics · 2022Article
- Immunoinformatics Approach Toward the Introduction of a Novel Multi-Epitope Vaccine AgainstFrontiers in immunology · 2022Article
- Revisiting the Principles of Designing a Vaccine.Methods in molecular biology (Clifton, N.J.) · 2022Article
- In silico designing of vaccine candidate against Clostridium difficile.Scientific reports · 2021Article
- Unveiling Putative Functions of Mucus Proteins and Their Tryptic Peptides in Seven Gastropod Species Using Comparative Proteomics and Machine Learning-Based Bioinformatics Predictions.Molecules (Basel, Switzerland) · 2021Article
- The complete genome and comparative analysis of the phage phiC120 infecting multidrug-resistant Escherichia coli and Salmonella strains.G3 (Bethesda, Md.) · 2021Article
- Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundProinflammatory immune response involves a complex series of molecular events leading to inflammatory reaction at a site, which enables host to combat plurality of infectious agents. It can be initiated by specific stimuli such as viral, bacterial, parasitic or allergenic antigens, or by non-specific stimuli such as LPS. On counter with such antigens, the complex interaction of antigen presenting cells, T cells and inflammatory mediators like IL1α, IL1β, TNFα, IL12, IL18 and IL23 lead to proinflammatory immune response and further clearance of infection. In this study, we have tried to establish a relation between amino acid sequence of antigen and induction of proinflammatory response.
resultsA total of 729 experimentally-validated proinflammatory and 171 non-proinflammatory epitopes were obtained from IEDB database. The A, F, I, L and V amino acids and AF, FA, FF, PF, IV, IN dipeptides were observed as preferred residues in proinflammatory epitopes. Using the compositional and motif-based features of proinflammatory and non-proinflammatory epitopes, we have developed machine learning-based models for prediction of proinflammatory response of peptides. The hybrid of motifs and dipeptide-based features displayed best performance with MCC = 0.58 and an accuracy of 87.6 %.
conclusionThe amino acid sequence-based features of peptides were used to develop a machine learning-based prediction tool for the prediction of proinflammatory epitopes. This is a unique tool for the computational identification of proinflammatory peptide antigen/candidates and provides leads for experimental validations. The prediction model and tools for epitope mapping and similarity search are provided as a comprehensive web server which is freely available at http://metagenomics.iiserb.ac.in/proinflam/ and http://metabiosys.iiserb.ac.in/proinflam/ .
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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.