CoReGMC: Computational Resources for Genetically Modified Crops
Genetically Modified Crops

Toxic Peptide and Protein Prediction Tools

Below is a curated list of tools and research articles focused on the prediction of toxic peptides and proteins. These tools leverage advanced computational methods for toxicity prediction, enabling better understanding and identification of toxic peptides and proteins for various applications.

Name Year Description PubMed ID
ToxMVA 2022 An end-to-end multi-view deep autoencoder method for protein toxicity prediction. 36435057
ToxinPred 2.0 2022 An improved method for predicting the toxicity of proteins. 35595541
ToxinPred 3.0 2024 An improved method for predicting the toxicity of peptides. 39038391
TOXIFY 2019 A deep learning approach to classify animal venom proteins. 31293833
ATSE 2021 A peptide toxicity predictor using structural and evolutionary information based on graph neural networks and attention mechanisms. 33822870
ToxIBTL 2022 Prediction of peptide toxicity using information bottleneck and transfer learning. 34999757
ToxGIN 2024 An in silico prediction model for peptide toxicity using graph isomorphism networks integrating peptide sequence and structure information. 39530430
CSM-Toxin 2023 A web-server for predicting protein toxicity. 36839752
CAPTP 2024 Improves peptide toxicity prediction by combining convolutional layers and self-attention mechanisms. 38696758
tAMPer 2024 Uses deep learning to incorporate peptide structural information for toxicity prediction. 39196703
VISH-Pred 2024 An ensemble of fine-tuned ESM models for protein toxicity prediction. 38842509
MultiToxPred 1.0 2024 A novel comprehensive tool for predicting 27 classes of protein toxins using an ensemble machine learning approach. 38609877
ToxinMI 2022 Improves peptide toxicity prediction by fusing multimodal information based on mutual information. Link
H. Bhosale et al. 2021 Predicts pore-forming toxins using distributed representation of reduced alphabets and support vector machines. 34693886
NNTox 2019 Gene ontology-based protein toxicity prediction using neural networks. 31784686
ToxClassifer 2010 Machine learning differentiates venom toxins from other proteins with non-toxic physiological functions. Link
L. K Monroe et al. 2023 Uses new features to increase prediction accuracy for conotoxins. 37999504
ClanTox 2009 A classifier for short animal toxins. 19429697
BTXpred 2007 Predicts bacterial toxins. 18391233
ToxDL 2021 Uses deep learning with primary structure and domain embeddings for assessing protein toxicity. 32692832
Z. Zhao et al. 2022 Integrates channel attention into convolutional neural networks and gated recurrent units for protein and peptide toxicity prediction. 36385847
ProtTox 2020 Identifies toxins from protein sequences. Link
ToxTeller 2024 Predicts peptide toxicity using four different machine learning approaches. 39072096
Min-Gang Su et al. 2014 Identifies bacterial toxin proteins by incorporating amino acids composition and functional domains. 25110714
Chaohong Song 2010 Predicts bacterial toxins by improving feature extraction and fusing with the IB1 algorithm. Link
Chao Feng Lan et al. 2016 Predicts these toxins by incrementing diversity in the training set. Link
Haiyan Huo et al. 2019 Uses Chou's pseudo components and reduced amino acid compositions for animal toxins analysis and prediction. 30452961
Jianxiu Cai et al. 2023 Enhances peptide toxicity prediction using deep learning and data augmentation techniques. Link
PredCSF 2011 An integrated feature-based approach for predicting conotoxin superfamily. 20955172
CLC-Pred 2018 A web service for predicting human cell line cytotoxicity for drug-like compounds. 29370280
SpiderP 2013 SVM-based prediction identifies cleavage sites in spider toxins, highlighting toxin innovation in an Australian tarantula. 23894279

Neurotoxin Prediction Tools and Resources

Discover computational tools and research efforts focused on predicting neurotoxins, with applications ranging from ion channel studies to peptide-specific predictions.

Name Year Description PubMed ID / Link
NTXpred 2007 Predicts neurotoxins based on their function and source. 18391230
PEP-PREDNa+ 2022 A web server for predicting highly specific peptides targeting voltage-gated Na+ channels using machine learning techniques. 35358751
NaBPred-Fuse 2024 Leverages a meta-learning approach to improve the accuracy of Nav-blocking peptides prediction. 38396246
NaII-Pred 2024 An ensemble-learning framework for identifying and interpreting sodium ion inhibitors by fusing multiple feature representations. 38879934
iCTX-type 2014 A sequence-based predictor for identifying the types of conotoxins targeting ion channels. 24991545
Yuan et al. 2013 Predicts the types of ion channel-targeted conotoxins using a radial basis function network. 23280100
ICTC-RAAC 2020 An improved web predictor for identifying the types of ion channel-targeted conotoxins using reduced amino acid cluster descriptors. 32950852
Lei Yang et al. 2009 Predicts presynaptic and postsynaptic neurotoxins by incrementing the diversity in the dataset. 19138734
Yao Yu et al. 2020 Analyzes and predicts animal neurotoxin proteins using reduced amino acid alphabet and biological properties. 32433000
Lee et al. 2022 Prediction models identify peptides modulating ion channels via knowledge transfer approaches. 36070258
Lee et al. 2021 Uses a deep learning approach with data augmentation to predict novel spider neurotoxic peptides. 34830173
Lei Yang et al. 2016 Predicts presynaptic and postsynaptic neurotoxins using a hybrid approach and pseudo amino acid composition. Link
Xuan-Min Guang et al. 2010 Uses support vector machine and multiple feature vectors for neurotoxin prediction. 20658336
Haiyan Huo et al. 2017 Combines various Chou’s pseudo components to predict presynaptic and postsynaptic neurotoxins. 28724993
Zhu et al. 2021 Predicts presynaptic and postsynaptic neurotoxins by extracting relevant features. 34517517
Li et al. 2020 A random forest-based method for identifying presynaptic and postsynaptic neurotoxins. 32372714

Hemotoxic Peptide and Protein Prediction Tools

Explore computational tools and resources for predicting hemotoxic peptides and proteins, aiding research in therapeutic peptide design and toxicity analysis.

Name Year Description PubMed ID / Link
PeptideBERT 2023 A language model based on transformers for predicting peptide properties. 37956397
Ansari et al. (2023) 2023 Predicts peptide properties using recurrent neural networks with a serverless architecture. 37010950
AMPDeep 2022 Uses transfer learning for hemolytic activity prediction of antimicrobial peptides. 36163001
HAPPENN 2020 A neural network-based tool for hemolytic activity prediction of therapeutic peptides. 32616760
HemoPred 2017 A web server for predicting the hemolytic activity of peptides. 28211294
HemoPImod 2020 Predicts the hemolytic potency of chemically modified peptides based on their structure. 32153395
HemoPI2 2025 Predicts hemolytic peptides and their hemolytic concentration. 39905233
Abdelbaky et al. 2024 Enhances prediction of hemolytic activity in antimicrobial peptides through deep learning-based sequence analysis. 39604856
HemoDL 2024 Uses double ensemble engines with rich sequence-derived and transformer-enhanced information for hemolytic peptides prediction. 38552762
HemoFuse 2024 Identifies hemolytic peptides using multi-feature fusion based on multi-head cross-attention. 39342017
HemoNet 2021 Predicts hemolytic activity of peptides using integrated feature learning. 34353244
Almotairi et al. 2024 Combines transformers and convolutional neural networks to accurately predict peptide hemolytic potential. 38902287
HLPpred-Fuse 2020 Improves and robustly predicts hemolytic peptides and their activity by fusing multiple feature representations. 32145017
Plisson et al. 2020 Uses machine learning to discover and design non-hemolytic peptides. 33024236
Hemolytic-Pred 2023 Machine learning-based predictor for hemolytic proteins using position and composition-based features. 37434723
Capecchi et al. 2021 Designs non-hemolytic antimicrobial peptides using machine learning. 34349895