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CpACpP: In Silico Cell-Penetrating Anticancer Peptide Prediction Using a Novel Bioinformatics Framework

https://doi.org/10.1021/acsomega.1c02569

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What this work says

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

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Cell‐Penetrating Peptides Targeting and Distorting Biological Membranes

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Selective Cytotoxicity of a Novel Trp‐Rich Peptide against Lung Tumor Spheroids Encapsulated inside a 3D Microfluidic Device

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Arginine/Tryptophan‐Rich Cyclic α/β‐Antimicrobial Peptides: The Roles of Hydrogen Bonding and Hydrophobic/Hydrophilic Solvent‐Accessible Surface Areas upon Activity and Membrane Selectivity

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The cationic cell‐penetrating KT2 peptide promotes cell membrane defects and apoptosis with autophagy inhibition in human HCT 116 colon cancer cells

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Machine intelligence in peptide therapeutics: A next‐generation tool for rapid disease screening

inferredcites →https://doi.org/10.1007/s00726-015-2029-7openalex-snapshot

Antimicrobial activity and membrane-active mechanism of tryptophan zipper-like β-hairpin antimicrobial peptides

inferredcites →https://doi.org/10.1007/s00726-016-2350-9openalex-snapshot

Isoleucine/leucine residues at “a” and “d” positions of a heptad repeat sequence are crucial for the cytolytic activity of a short anticancer lytic peptide

inferredcites →https://doi.org/10.1007/s10822-020-00307-zopenalex-snapshot

TargetCPP: accurate prediction of cell-penetrating peptides from optimized multi-scale features using gradient boost decision tree

inferredcites →https://doi.org/10.1016/j.artmed.2017.06.008openalex-snapshot

iACP-GAEnsC: Evolutionary genetic algorithm based ensemble classification of anticancer peptides by utilizing hybrid feature space

inferredcites →https://doi.org/10.1016/j.canlet.2014.05.002openalex-snapshot

Peptide-based cancer therapy: Opportunity and challenge

inferredcites →https://doi.org/10.1016/j.chembiol.2016.12.009openalex-snapshot

Glycan Alteration Imparts Cellular Resistance to a Membrane-Lytic Anticancer Peptide

inferredcites →https://doi.org/10.1016/j.dnarep.2019.102673openalex-snapshot

Inflammation-induced DNA damage, mutations and cancer

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Cell-penetrating peptides: classes, origin, and current landscape

inferredcites →https://doi.org/10.1016/j.jtbi.2013.08.037openalex-snapshot

Predicting anticancer peptides with Chou′s pseudo amino acid composition and investigating their mutagenicity via Ames test

inferredcites →https://doi.org/10.1016/j.neucom.2018.02.097openalex-snapshot

Protein–protein interactions prediction based on ensemble deep neural networks

inferredcites →https://doi.org/10.1016/j.omtn.2019.04.025openalex-snapshot

ACP-DL: A Deep Learning Long Short-Term Memory Model to Predict Anticancer Peptides Using High-Efficiency Feature Representation

inferredcites →https://doi.org/10.1016/j.omtn.2020.10.005openalex-snapshot

DeepACP: A Novel Computational Approach for Accurate Identification of Anticancer Peptides by Deep Learning Algorithm

inferredcites →https://doi.org/10.1021/acs.biochem.0c00565openalex-snapshot

Morphing of Amphipathic Helices to Explore the Activity and Selectivity of Membranolytic Antimicrobial Peptides

inferredcites →https://doi.org/10.1021/acs.jcim.0c00841openalex-snapshot

IAMPE: NMR-Assisted Computational Prediction of Antimicrobial Peptides

inferredcites →https://doi.org/10.1021/acs.jmedchem.5b02016openalex-snapshot

Amphipathicity Determines Different Cytotoxic Mechanisms of Lysine- or Arginine-Rich Cationic Hydrophobic Peptides in Cancer Cells

inferredcites →https://doi.org/10.1021/acs.jproteome.7b00019openalex-snapshot

CPPred-RF: A Sequence-based Predictor for Identifying Cell-Penetrating Peptides and Their Uptake Efficiency

inferredcites →https://doi.org/10.1021/acs.jproteome.8b00148openalex-snapshot

Machine-Learning-Based Prediction of Cell-Penetrating Peptides and Their Uptake Efficiency with Improved Accuracy

inferredcites →https://doi.org/10.1021/acs.jproteome.8b00322openalex-snapshot

KELM-CPPpred: Kernel Extreme Learning Machine Based Prediction Model for Cell-Penetrating Peptides

inferredcites →https://doi.org/10.1021/acsami.0c17648openalex-snapshot

Palmitoylation of Membrane-Penetrating Magainin Derivatives Reinforces Necroptosis in A549 Cells Dependent on Peptide Conformational Propensities

inferredcites →https://doi.org/10.1021/acsomega.9b04119openalex-snapshot

BIPEP: Sequence-based Prediction of Biofilm Inhibitory Peptides Using a Combination of NMR and Physicochemical Descriptors

inferredcites →https://doi.org/10.1021/ja308459jopenalex-snapshot

Molecular Basis for Nanoscopic Membrane Curvature Generation from Quantum Mechanical Models and Synthetic Transporter Sequences

inferredcites →https://doi.org/10.1021/ja409082wopenalex-snapshot

Evidence for Phenylalanine Zipper-Mediated Dimerization in the X-ray Crystal Structure of a Magainin 2 Analogue

inferredcites →https://doi.org/10.1021/ja507790zopenalex-snapshot

Fundamental Molecular Mechanism for the Cellular Uptake of Guanidinium-Rich Molecules

inferredcites →https://doi.org/10.1021/jacs.0c07921openalex-snapshot

Uncoupling the Folding-Function Paradigm of Lytic Peptides to Deliver Impermeable Inhibitors of Intracellular Protein–Protein Interactions

inferredcites →https://doi.org/10.1021/mp400221ropenalex-snapshot

p28, an Anionic Cell-Penetrating Peptide, Increases the Activity of Wild Type and Mutated p53 without Altering Its Conformation

inferredcites →https://doi.org/10.1038/cddiscovery.2017.37openalex-snapshot

The development of activatable lytic peptides for targeting triple negative breast cancer

inferredcites →https://doi.org/10.1038/s41467-017-01554-9openalex-snapshot

Membrane curvature induced by proximity of anionic phospholipids can initiate endocytosis

inferredcites →https://doi.org/10.1038/s41586-019-1730-1openalex-snapshot

A view on drug resistance in cancer

inferredcites →https://doi.org/10.1038/s41598-017-08963-2openalex-snapshot

Cell surface binding, uptaking and anticancer activity of L-K6, a lysine/leucine-rich peptide, on human breast cancer MCF-7 cells

inferredcites →https://doi.org/10.1038/s41598-018-19752-wopenalex-snapshot

AmPEP: Sequence-based prediction of antimicrobial peptides using distribution patterns of amino acid properties and random forest

inferredcites →https://doi.org/10.1038/s41598-018-32443-wopenalex-snapshot

Using a Classifier Fusion Strategy to Identify Anti-angiogenic Peptides

inferredcites →https://doi.org/10.1038/s41598-021-82513-9openalex-snapshot

Improved prediction and characterization of anticancer activities of peptides using a novel flexible scoring card method

inferredcites →https://doi.org/10.1038/srep02984openalex-snapshot

In Silico Models for Designing and Discovering Novel Anticancer Peptides

inferredcites →https://doi.org/10.1038/srep24952openalex-snapshot

Pronounced peptide selectivity for melanoma through tryptophan end-tagging

inferredcites →https://doi.org/10.1039/c6md00376aopenalex-snapshot

Membranolytic anticancer peptides

inferredcites →https://doi.org/10.1039/d1sc01713fopenalex-snapshot

Machine learning designs non-hemolytic antimicrobial peptides

inferredcites →https://doi.org/10.1073/pnas.1812939115openalex-snapshot

Peptide design by optimization on a data-parameterized protein interaction landscape

inferredcites →https://doi.org/10.1093/bib/bbaa312openalex-snapshot

Large-scale comparative review and assessment of computational methods for anti-cancer peptide identification

inferredcites →https://doi.org/10.1093/bib/bby124openalex-snapshot

Empirical comparison and analysis of web-based cell-penetrating peptide prediction tools

inferredcites →https://doi.org/10.1093/bib/bbz088openalex-snapshot

ACPred-Fuse: fusing multi-view information improves the prediction of anticancer peptides

inferredcites →https://doi.org/10.1093/bioinformatics/btaa131openalex-snapshot

StackCPPred: a stacking and pairwise energy content-based prediction of cell-penetrating peptides and their uptake efficiency

inferredcites →https://doi.org/10.1093/bioinformatics/btq003openalex-snapshot

CD-HIT Suite: a web server for clustering and comparing biological sequences

inferredcites →https://doi.org/10.1093/bioinformatics/bts141openalex-snapshot

DADP: the database of anuran defense peptides

inferredcites →https://doi.org/10.1093/bioinformatics/btx285openalex-snapshot

modlAMP: Python for antimicrobial peptides

inferredcites →https://doi.org/10.1093/bioinformatics/bty140openalex-snapshot

iFeature : a Python package and web server for features extraction and selection from protein and peptide sequences

inferredcites →https://doi.org/10.1093/bioinformatics/bty451openalex-snapshot

ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides

inferredcites →https://doi.org/10.1093/nar/gku892openalex-snapshot

CancerPPD: a database of anticancer peptides and proteins

inferredcites →https://doi.org/10.1093/nar/gkv1174openalex-snapshot

DBAASP v.2: an enhanced database of structure and antimicrobial/cytotoxic activity of natural and synthetic peptides

inferredcites →https://doi.org/10.1093/nar/gkv1266openalex-snapshot

CPPsite 2.0: a repository of experimentally validated cell-penetrating peptides

inferredcites →https://doi.org/10.1093/nar/gkv1278openalex-snapshot

APD3: the antimicrobial peptide database as a tool for research and education

inferredcites →https://doi.org/10.1096/fj.12-216176openalex-snapshot

Tryptophan within basic peptide sequences triggers glycosaminoglycan‐dependent endocytosis

inferredcites →https://doi.org/10.1152/ajpcell.2000.278.6.c1063openalex-snapshot

Properties of cytotoxic peptide-formed ion channels

inferredcites →https://doi.org/10.1155/2014/236717openalex-snapshot

An Empirical Study of Different Approaches for Protein Classification

inferredcites →https://doi.org/10.1158/1535-7163.mct-10-0811openalex-snapshot

Studies on Mechanism of Action of Anticancer Peptides by Modulation of Hydrophobicity Within a Defined Structural Framework

inferredcites →https://doi.org/10.1186/1479-5876-11-74openalex-snapshot

In silico approaches for designing highly effective cell penetrating peptides

inferredcites →https://doi.org/10.1186/s12859-019-2754-0openalex-snapshot

FeatureSelect: a software for feature selection based on machine learning approaches

inferredcites →https://doi.org/10.1186/s12864-017-4128-1openalex-snapshot

SkipCPP-Pred: an improved and promising sequence-based predictor for predicting cell-penetrating peptides

inferredcites →https://doi.org/10.1186/s13040-016-0114-4openalex-snapshot

Compensation of feature selection biases accompanied with improved predictive performance for binary classification by using a novel ensemble feature selection approach

inferredcites →https://doi.org/10.1371/journal.pone.0016968openalex-snapshot

Connecting Peptide Physicochemical and Antimicrobial Properties by a Rational Prediction Model

inferredcites →https://doi.org/10.18632/oncotarget.20365openalex-snapshot

MLACP: machine-learning-based prediction of anticancer peptides

inferredcites →https://doi.org/10.18632/oncotarget.7815openalex-snapshot

iACP: a sequence-based tool for identifying anticancer peptides

inferredcites →https://doi.org/10.2174/138620712799361807openalex-snapshot

QSAR Modeling and Design of Cationic Antimicrobial Peptides Based on Structural Properties of Amino Acids

inferredcites →https://doi.org/10.3322/caac.21590openalex-snapshot

Cancer statistics, 2020

inferredcites →https://doi.org/10.3389/fmicb.2013.00294openalex-snapshot

From antimicrobial to anticancer peptides. A review

inferredcites →https://doi.org/10.3390/ijms20081964openalex-snapshot

mACPpred: A Support Vector Machine-Based Meta-Predictor for Identification of Anticancer Peptides

inferredcites →https://doi.org/10.3390/ijms20143511openalex-snapshot

An Ensemble Classifier to Predict Protein–Protein Interactions by Combining PSSM-based Evolutionary Information with Local Binary Pattern Model

inferredcites →https://doi.org/10.3390/molecules24101973openalex-snapshot

ACPred: A Computational Tool for the Prediction and Analysis of Anticancer Peptides

inferredcites →https://doi.org/10.48550/arxiv.1201.0490openalex-snapshot

Scikit-learn: Machine Learning in Python

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AI-Driven Revolution in Peptide Classification Landscape

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Bridging machine learning and peptide design for cancer treatment: a comprehensive review

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Design and Characterization of Anticancer Peptides Derived from Snake Venom Metalloproteinase Library

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ToxinPred 3.0: An improved method for predicting the toxicity of peptides

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mACPpred 2.0: Stacked Deep Learning for Anticancer Peptide Prediction with Integrated Spatial and Probabilistic Feature Representations

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Designing a novel fusion protein from Streptococcus agalactiae with apoptosis induction effects on cervical cancer cells

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