ArticleDatabase : the journal of biological databases and curation2020
LAMP2: a major update of the database linking antimicrobial peptides.
Article in Database : the journal of biological databases and curation, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 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
67 citing papers in PubMed, 121 citations in OpenAlex.
- Mechanism-Informed Machine Learning Enables Discovery of Oncolytic Peptides for Cancer Immunotherapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Harnessing generative AI for predicting and optimizing antimicrobial peptides against drug-resistant infections.npj antimicrobials and resistance · 2026Article
- The Snakin Family of Antimicrobial Peptides: Promising Alternatives to Conventional Antibiotics.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Contemporary data-driven innovations in peptide-based therapeutic design.Briefings in bioinformatics · 2026Review
- The Extreme Environment Microbiome Catalog (EEMC): a global resource for microbial diversity and antimicrobial discovery.Nature communications · 2026Article
- A generative explainable model for antimicrobial peptide prediction using bidirectional temporal convolutional neural network.Scientific reports · 2026Article
- PepGraphormer: an ESM-GAT hybrid deep learning framework for antimicrobial peptide prediction.Journal of cheminformatics · 2026Article
- tsAMP: a strain-level antimicrobial peptide identification framework based on large language models and pathogen genomic variation.Frontiers in microbiology · 2026Article
- Harnessing Machine Learning Approaches for the Identification, Characterization, and Optimization of Novel Antimicrobial Peptides.Antibiotics (Basel, Switzerland) · 2025Review
- A generative artificial intelligence approach for the discovery of antimicrobial peptides against multidrug-resistant bacteria.Nature microbiology · 2025Article
- Transfer learning on protein language models improves antimicrobial peptide classification.Scientific reports · 2025Article
- A Multi-Property Optimizing Generative Adversarial Network for de novo Antimicrobial Peptide Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- A deep reinforcement learning platform for antibiotic discovery.bioRxiv : the preprint server for biology · 2025Article
- Classifier-driven generative adversarial networks for enhanced antimicrobial peptide design.Briefings in bioinformatics · 2025Article
- SeekTP: identifying multifunctional therapeutic peptides via graph attention and deep property integration.Briefings in bioinformatics · 2025Article
- Comparative Genomic Analysis ofGenes · 2025Article
- A comprehensive dataset of therapeutic peptides on multi-function property and structure information.Scientific data · 2025Article
- Machine Learning-Assisted Prediction and Generation of Antimicrobial Peptides.Small science · 2025Article
- CPL-Diff: A Diffusion Model for De Novo Design of Functional Peptide Sequences with Fixed Length.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Multimodal geometric learning for antimicrobial peptide identification by leveraging alphafold2-predicted structures and surface features.Briefings in bioinformatics · 2025Article
7 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antimicrobial peptides (AMPs) have been regarded as a potential weapon to fight against drug-resistant bacteria, which is threating the globe. Thus, more and more AMPs had been designed or identified. There is a need to integrate them into a platform for researchers to facilitate investigation and analyze existing AMPs. The AMP database has become an important tool for the discovery and transformation of AMPs as agents. A database linking antimicrobial peptides (LAMPs), launched in 2013, serves as a comprehensive tool to supply exhaustive information of AMP on a single platform. LAMP2, an updated version of LAMP, holds 23 253 unique AMP sequences and expands to link 16 public AMP databases. In the current version, there are more than 50% (12 236) sequences only linking a single database and more than 45% of AMPs linking two or more database links. Additionally, updated categories based on primary structure, collection, composition, source and function have been integrated into LAMP2. Peptides in LAMP2 have been integrated in 8 major functional classes and 38 functional activities. More than 89% (20 909) of the peptides are experimentally validated peptides. A total of 1924 references were extracted and regarded as the evidence for supporting AMP activity and cytotoxicity. The updated version will be helpful to the scientific community.
Indexed as
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.