ArticleCurrent opinion in colloid & interface science2018
Machine learning antimicrobial peptide sequences: Some surprising variations on the theme of amphiphilic assembly.
Article in Current opinion in colloid & interface science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- A unified model of transient poration induced by antimicrobial peptides.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Advances in Antimicrobial Peptide Discovery via Machine Learning and Delivery via Nanotechnology.Microorganisms · 2023Review
- Therapeutic Effects of Synthetic Triblock Amphiphilic Short Antimicrobial Peptides on Human Lung Adenocarcinoma.Pharmaceutics · 2022Article
- Antimicrobial Peptides: An Update on Classifications and Databases.International journal of molecular sciences · 2021Review
- Accelerating antibiotic discovery through artificial intelligence.Communications biology · 2021Review
- Review
- Molecular engineering of antimicrobial peptides: microbial targets, peptide motifs and translation opportunities.Biophysical reviews · 2021Review
- PACAP is a pathogen-inducible resident antimicrobial neuropeptide affording rapid and contextual molecular host defense of the brain.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Functional Reciprocity of Amyloids and Antimicrobial Peptides: Rethinking the Role of Supramolecular Assembly in Host Defense, Immune Activation, and Inflammation.Frontiers in immunology · 2020Review
- How Melittin Inserts into Cell Membrane: Conformational Changes, Inter-Peptide Cooperation, and Disturbance on the Membrane.Molecules (Basel, Switzerland) · 2019Review
- Helical antimicrobial peptides assemble into protofibril scaffolds that present ordered dsDNA to TLR9.Nature communications · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Antimicrobial peptides (AMPs) collectively constitute a key component of the host innate immune system. They span a diverse space of sequences and can be α-helical, β-sheet, or unfolded in structure. Despite a wealth of knowledge about them from decades of experiments, it remains difficult to articulate general principles governing such peptides. How are they different from other molecules that are also cationic and amphiphilic? What other functions, in immunity and otherwise, are enabled by these simple sequences? In this short review, we present some recent work that engages these questions using methods not usually applied to AMP studies, such as machine learning. We find that not only do AMP-like sequences confer membrane remodeling activity to an unexpectedly broad range of protein classes, their cationic and amphiphilic signature also allows them to act as meta-antigens and self-assemble with immune ligands into nanocrystalline complexes for multivalent presentation to Toll-like receptors.
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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.