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Connecting Peptide Physicochemical and Antimicrobial Properties by a Rational Prediction Model

https://doi.org/10.1371/journal.pone.0016968

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Mode of action of membrane active antimicrobial peptides

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QSAR modeling and computer‐aided design of antimicrobial peptides

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Methods for Building Quantitative Structure–Activity Relationship (QSAR) Descriptors and Predictive Models for Computer-Aided Design of Antimicrobial Peptides

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Antimicrobial peptides: natural templates for synthetic membrane-active compounds

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High-resolution 1H-NMR studies of self-aggregation of melittin in aqueous solution

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QSAR analysis of antimicrobial and haemolytic effects of cyclic cationic antimicrobial peptides derived from protegrin-1

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Antimicrobial peptides: General overview and clinical implications in human health and disease

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Design of bacteria-agglutinating peptides derived from parotid secretory protein, a member of the bactericidal/permeability increasing-like protein family

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Mechanism of the binding, insertion and destabilization of phospholipid bilayer membranes by α-helical antimicrobial and cell non-selective membrane-lytic peptides

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Neutron scattering in the plane of membranes: structure of alamethicin pores

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Identification of Novel Antibacterial Peptides by Chemoinformatics and Machine Learning

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Prediction of sequence-dependent and mutational effects on the aggregation of peptides and proteins

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Cationic antimicrobial peptides: update of clinical development

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Structure-Activity Relationship Study of Antimicrobial Dermaseptin S4 Showing the Consequences of Peptide Oligomerization on Selective Cytotoxicity

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Structural Requirements for Potent Versus Selective Cytotoxicity for Antimicrobial Dermaseptin S4 Derivatives

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A Synergism between Temporins toward Gram-negative Bacteria Overcomes Resistance Imposed by the Lipopolysaccharide Protective Layer

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CAMP: a useful resource for research on antimicrobial peptides

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Design of Novispirin Antimicrobial Peptides by Quantitative Structure–Activity Relationship

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Evaluating Different Descriptors for Model Design of Antimicrobial Peptides with Enhanced Activity Toward P. aeruginosa

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Degradation of Human Antimicrobial Peptide LL-37 by Staphylococcus aureus -Derived Proteinases

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De Novo Design of Potent Antimicrobial Peptides

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De Novo Generation of Cationic Antimicrobial Peptides: Influence of Length and Tryptophan Substitution on Antimicrobial Activity

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Peptide Antimicrobial Agents

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AntiBP2: improved version of antibacterial peptide prediction

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AGGRESCAN: a server for the prediction and evaluation of "hot spots" of aggregation in polypeptides

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Protein Aggregation Profile of the Bacterial Cytosol

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Serum Stabilities of Short Tryptophan- and Arginine-Rich Antimicrobial Peptide Analogs

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Melittin-Induced Bilayer Leakage Depends on Lipid Material Properties: Evidence for Toroidal Pores

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Functions of Antimicrobial Peptides in Host Defense and Immunity

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Application of ‘Inductive’ QSAR Descriptors for Quantification of Antibacterial Activity of Cationic Polypeptides

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PeptideLocator: prediction of bioactive peptides in protein sequences

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Distinct profiling of antimicrobial peptide families

inferred← citeshttps://doi.org/10.1093/bioinformatics/btv180openalex-snapshot

Overlap and diversity in antimicrobial peptide databases: compiling a non-redundant set of sequences

inferred← citeshttps://doi.org/10.1093/bioinformatics/bty179openalex-snapshot

Deep learning improves antimicrobial peptide recognition

inferred← citeshttps://doi.org/10.1093/database/baac011openalex-snapshot

A review on antimicrobial peptides databases and the computational tools

inferred← citeshttps://doi.org/10.1093/nar/gks450openalex-snapshot

AVPpred: collection and prediction of highly effective antiviral peptides

inferred← citeshttps://doi.org/10.1098/rsfs.2016.0153openalex-snapshot

What can machine learning do for antimicrobial peptides, and what can antimicrobial peptides do for machine learning?

inferred← citeshttps://doi.org/10.1098/rsta.2011.0227openalex-snapshot

A tentative taxonomy for predictive models in relation to their falsifiability

inferred← citeshttps://doi.org/10.1098/rstb.2023.0058openalex-snapshot

Novel technologies uncover novel ‘anti’-microbial peptides in Hydra shaping the species-specific microbiome

inferred← citeshttps://doi.org/10.1098/rstb.2023.0063openalex-snapshot

Innate immunity in Aedes mosquitoes: from pathogen resistance to shaping the microbiota

inferred← citeshttps://doi.org/10.1101/030619openalex-snapshot

Common and phylogenetically widespread coding for peptides by bacterial small RNAs

inferred← citeshttps://doi.org/10.1101/2020.10.02.324087openalex-snapshot

A Generative Approach toward Precision Antimicrobial Peptide Design

inferred← citeshttps://doi.org/10.1101/2023.06.04.543607openalex-snapshot

sAMP-VGG16: Drude polarizable force-field assisted image-based deep neural network prediction model for short antimicrobial peptides

inferred← citeshttps://doi.org/10.1101/2024.03.03.583201openalex-snapshot

AMP-Diffusion: Integrating Latent Diffusion with Protein Language Models for Antimicrobial Peptide Generation

inferred← citeshttps://doi.org/10.1101/542548openalex-snapshot

Chemical-genetic profiling reveals cross-resistance and collateral sensitivity between antimicrobial peptides

inferred← citeshttps://doi.org/10.1109/access.2020.3015792openalex-snapshot

Prediction of Therapeutic Peptides Using Machine Learning: Computational Models, Datasets, and Feature Encodings

inferred← citeshttps://doi.org/10.1109/bibm.2014.6999187openalex-snapshot

A novel method to improve recognition of antimicrobial peptides through distal sequence-based features

inferred← citeshttps://doi.org/10.1109/bibm.2017.8217663openalex-snapshot

Feature weighting for antimicrobial peptides classification: A multi-objective evolutionary approach

inferred← citeshttps://doi.org/10.1109/bibm.2017.8217697openalex-snapshot

Long short-term memory recurrent neural networks for antibacterial peptide identification

inferred← citeshttps://doi.org/10.1109/bibm.2018.8621295openalex-snapshot

Guiding Exploration of Antimicrobial Peptide Space with a Deep Neural Network

inferred← citeshttps://doi.org/10.1109/bibm47256.2019.8983034openalex-snapshot

A Deep Neural Network for Antimicrobial Peptide Recognition

inferred← citeshttps://doi.org/10.1109/bibm49941.2020.9313565openalex-snapshot

Alphabet reduction and distributed vector representation based method for classification of antimicrobial peptides

inferred← citeshttps://doi.org/10.1109/bibmw.2012.6470203openalex-snapshot

Prediction of antimicrobial activity of peptides using relational machine learning

inferred← citeshttps://doi.org/10.1109/tcbb.2015.2462364openalex-snapshot

Improving Recognition of Antimicrobial Peptides and Target Selectivity through Machine Learning and Genetic Programming

inferred← citeshttps://doi.org/10.1109/tcbb.2019.2903800openalex-snapshot

Classification of Antibacterial Peptides Using Long Short-Term Memory Recurrent Neural Networks

inferred← citeshttps://doi.org/10.1111/1541-4337.12480openalex-snapshot

Natural Antifungal Peptides/Proteins as Model for Novel Food Preservatives

inferred← citeshttps://doi.org/10.1111/1751-7915.70072openalex-snapshot

AI Methods for Antimicrobial Peptides: Progress and Challenges

inferred← citeshttps://doi.org/10.1111/cbdd.13749openalex-snapshot

A QSAR modeling approach for predicting myeloid antimicrobial peptides with high sequence similarity

inferred← citeshttps://doi.org/10.1111/febs.12506openalex-snapshot

Towards the rational design of antimicrobial proteins

inferred← citeshttps://doi.org/10.1111/jfbc.14450openalex-snapshot

Peptidomics‐based identification of an antimicrobial peptide derived from goat milk fermented by Lactobacillus rhamnosus ( C25 )

inferred← citeshttps://doi.org/10.1117/12.2207525openalex-snapshot

Classification of antimicrobial peptides with imbalanced datasets

inferred← citeshttps://doi.org/10.1126/sciadv.aec1406openalex-snapshot

Plant-associated fungi co-opt ancient antimicrobials for host manipulation

inferred← citeshttps://doi.org/10.1128/aac.00169-13openalex-snapshot

Introduction of a Lysine Residue Promotes Aggregation of Temporin L in Lipopolysaccharides and Augmentation of Its Antiendotoxin Property

inferred← citeshttps://doi.org/10.1134/s106816201804009xopenalex-snapshot

In Silico Analysis of Peptide Potential Biological Functions

inferred← citeshttps://doi.org/10.1155/2013/263952openalex-snapshot

Prediction of IL4 Inducing Peptides

inferred← citeshttps://doi.org/10.1155/2022/7760734openalex-snapshot

[Retracted] Impact of ANN in Revealing of Viral Peptides

inferred← citeshttps://doi.org/10.1159/000336713openalex-snapshot

Lipopolysaccharide Neutralization by Antimicrobial Peptides: A Gambit in the Innate Host Defense Strategy

inferred← citeshttps://doi.org/10.11606/d.87.2021.tde-23112022-141136openalex-snapshot

Análise transcriptômica e proteômica da resposta imune inata em diplópodes (>i /i<.)

inferred← citeshttps://doi.org/10.1186/s12864-017-3932-yopenalex-snapshot

Common and phylogenetically widespread coding for peptides by bacterial small RNAs

inferred← citeshttps://doi.org/10.1186/s12864-018-5030-1openalex-snapshot

Optimal selection of molecular descriptors for antimicrobial peptides classification: an evolutionary feature weighting approach

inferred← citeshttps://doi.org/10.1186/s12864-020-06978-0openalex-snapshot

ACEP: improving antimicrobial peptides recognition through automatic feature fusion and amino acid embedding

inferred← citeshttps://doi.org/10.1186/s12864-024-10153-0openalex-snapshot

The transcriptional response in mosquitoes distinguishes between fungi and bacteria but not Gram types

inferred← citeshttps://doi.org/10.1186/s13040-019-0196-xopenalex-snapshot

Encodings and models for antimicrobial peptide classification for multi-resistant pathogens

inferred← citeshttps://doi.org/10.1186/s42490-024-00075-zopenalex-snapshot

Prediction of the synergistic effect of antimicrobial peptides and antimicrobial agents via supervised machine learning

inferred← citeshttps://doi.org/10.1186/s43141-021-00175-6openalex-snapshot

Molecular characterization of a novel β-defensin isoform from the red-toothed trigger fish, Odonus niger (Ruppel, 1836)

inferred← citeshttps://doi.org/10.12688/f1000research.136310.1openalex-snapshot

Bactericidal activity of newly synthesized antimicrobial peptides against methicillin-resistant staphylococcus aureus and biofilm-forming methicillin-resistant staphylococcus aureus

inferred← citeshttps://doi.org/10.1360/tb-2020-0456openalex-snapshot

Machine learning for synthetic biology: Methods and applications

inferred← citeshttps://doi.org/10.1371/journal.pone.0045848openalex-snapshot

Probing Protein Sequences as Sources for Encrypted Antimicrobial Peptides

inferred← citeshttps://doi.org/10.1371/journal.pone.0051444openalex-snapshot

CS-AMPPred: An Updated SVM Model for Antimicrobial Activity Prediction in Cysteine-Stabilized Peptides

inferred← citeshttps://doi.org/10.1371/journal.pone.0070166openalex-snapshot

Analysis and Prediction of Highly Effective Antiviral Peptides Based on Random Forests

inferred← citeshttps://doi.org/10.1371/journal.pone.0079728openalex-snapshot

C-PAmP: Large Scale Analysis and Database Construction Containing High Scoring Computationally Predicted Antimicrobial Peptides for All the Available Plant Species

inferred← citeshttps://doi.org/10.1371/journal.pone.0119490openalex-snapshot

Analysis and Prediction of the Critical Regions of Antimicrobial Peptides Based on Conditional Random Fields

inferred← citeshttps://doi.org/10.1371/journal.pone.0120066openalex-snapshot

Prediction and Analysis of Quorum Sensing Peptides Based on Sequence Features

inferred← citeshttps://doi.org/10.1371/journal.ppat.1003005openalex-snapshot

Exploring New Biological Functions of Amyloids: Bacteria Cell Agglutination Mediated by Host Protein Aggregation

inferred← citeshttps://doi.org/10.14232/phd.10231openalex-snapshot

Chemical-genetic profiling to investigate cross-resistance and collateral sensitivity between antimicrobial peptides

inferred← citeshttps://doi.org/10.17762/ijritcc.v11i9.8824openalex-snapshot

Artificial Intelligence in Battle against Antimicrobial Resistance: Opportunities and Challenges

inferred← citeshttps://doi.org/10.19053/01211129.v26.n44.2017.5834openalex-snapshot

Assessing the behavior of machine learning methods to predict the activity of antimicrobial peptides

inferred← citeshttps://doi.org/10.21203/rs.3.rs-1549550/v1openalex-snapshot

Novel antimicrobial peptides with bactericidal effect against Methicillin-resistant Staphylococcus aureus & biofilm forming Methicillin-resistant Staphylococcus aureus

inferred← citeshttps://doi.org/10.21203/rs.3.rs-2166720/v1openalex-snapshot

Prediction of the Synergistic Effect of Antimicrobial Peptides and Antibiotics via Supervised Machine Learning Technique

inferred← citeshttps://doi.org/10.21203/rs.3.rs-3938402/v1openalex-snapshot

Structure-aware machine learning strategies for antimicrobial peptide discovery

inferred← citeshttps://doi.org/10.21203/rs.3.rs-5430498/v1openalex-snapshot

Prediction and Characterization of Antimicrobial Peptides from Sea Cucumbers (Holothuria sp.) in Papuan Waters, Indonesia

inferred← citeshttps://doi.org/10.2139/ssrn.6832340openalex-snapshot

Comparative properties of antimicrobial peptides from human (host), bacterial, and milk (food) origins

inferred← citeshttps://doi.org/10.2174/0929866526666190925152957openalex-snapshot

Clinical Applications of Antimicrobial Peptides (AMPs): Where do we Stand Now?

inferred← citeshttps://doi.org/10.2174/1389203721666200117163802openalex-snapshot

Progress in the Development of Antimicrobial Peptide Prediction Tools

inferred← citeshttps://doi.org/10.2174/138945012802002311openalex-snapshot

Discovering New In Silico Tools for Antimicrobial Peptide Prediction

inferred← citeshttps://doi.org/10.2174/1568026616666160713125555openalex-snapshot

Use of Peptide Libraries for Identification and Optimization of Novel Antimicrobial Peptides

inferred← citeshttps://doi.org/10.2174/1568026620666200303122626openalex-snapshot

Synergism between Host Defence Peptides and Antibiotics Against Bacterial Infections

inferred← citeshttps://doi.org/10.2174/1875036201307010049openalex-snapshot

Artificial Neural Network in Drug Delivery and Pharmaceutical Research

inferred← citeshttps://doi.org/10.22159/ijap.2018v10i5.28300openalex-snapshot

ARTIFICIAL NEURAL NETWORKS: FUNCTIONINGANDAPPLICATIONS IN PHARMACEUTICAL INDUSTRY

inferred← citeshttps://doi.org/10.26512/2016.11.t.22933openalex-snapshot

Explorando genomas : a busca por peptídeos antimicrobianos intragênicos

inferred← citeshttps://doi.org/10.29007/rbxjopenalex-snapshot

Exploring Deep Neural Network Architectures: A Case Study on Improving Antimicrobial Peptide Recognition

inferred← citeshttps://doi.org/10.2983/035.043.0213openalex-snapshot

Molecular Characterization of the Anti-Lipopolysaccharide Factor 5 (Lvalf5) in the Whiteleg Shrimp (Litopenaeus vannamei, Boone 1931)

inferred← citeshttps://doi.org/10.3109/07388551.2011.594423openalex-snapshot

Antimicrobial peptides: key components of the innate immune system

inferred← citeshttps://doi.org/10.31390/gradschool_dissertations.694openalex-snapshot

Proteomic Analysis of the Blood of Alligator mississippiensis

inferred← citeshttps://doi.org/10.32614/rj-2015-001openalex-snapshot

Peptides: A Package for Data Mining of Antimicrobial Peptides

inferred← citeshttps://doi.org/10.32657/10356/147784openalex-snapshot

Polycarbonate : antimicrobial polymer delivery, monomer synthesis, rapid polymerization and functionalization

inferred← citeshttps://doi.org/10.3389/fchem.2017.00025openalex-snapshot

Screening and Optimizing Antimicrobial Peptides by Using SPOT-Synthesis

inferred← citeshttps://doi.org/10.3389/fchem.2019.00043openalex-snapshot

Reassessing the Host Defense Peptide Landscape

inferred← citeshttps://doi.org/10.3389/fcimb.2020.00326openalex-snapshot

Antimicrobial Susceptibility Testing of Antimicrobial Peptides to Better Predict Efficacy

inferred← citeshttps://doi.org/10.3389/fcimb.2020.526459openalex-snapshot

The Reversible Non-covalent Aggregation Into Fibers of PGLa and Magainin 2 Preserves Their Antimicrobial Activity and Synergism

inferred← citeshttps://doi.org/10.3389/fimmu.2017.01499openalex-snapshot

Host Antimicrobial Peptides: The Promise of New Treatment Strategies against Tuberculosis

inferred← citeshttps://doi.org/10.3389/fmicb.2019.01357openalex-snapshot

Testing a Human Antimicrobial RNase Chimera Against Bacterial Resistance

inferred← citeshttps://doi.org/10.3389/fmicb.2024.1304044openalex-snapshot

AGRAMP: machine learning models for predicting antimicrobial peptides against phytopathogenic bacteria

inferred← citeshttps://doi.org/10.3389/fphar.2026.1732644openalex-snapshot

Peptide-based antimicrobial effect against carbapenem-resistant Acinetobacter baumannii: preclinical drug assessment and translational potential

inferred← citeshttps://doi.org/10.3389/frabi.2026.1769369openalex-snapshot

Antimycobacterial peptides as natural therapeutics for tuberculosis: mechanisms and structural features

inferred← citeshttps://doi.org/10.3389/fvets.2025.1681083openalex-snapshot

Data-driven discovery of antiviral peptides against PRRSV using multiple machine learning models

inferred← citeshttps://doi.org/10.3390/antibiotics11030363openalex-snapshot

Performance of Novel Antimicrobial Protein Bg_9562 and In Silico Predictions on Its Properties with Reference to Its Antimicrobial Efficiency against Rhizoctonia solani

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