Deep-AFPpred: identifying novel antifungal peptides using pretrained embeddings from seq2vec with 1DCNN-BiLSTM

Sharma, Ritesh Kumar; Shrivastava, Sameer; Singh, Sanjay Kumar; Kumar, Abhinav; Saxena, Sonal; Singh, Raj Kumar

doi:10.1093/bib/bbab422

Cited by 39 publications

(25 citation statements)

References 38 publications

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“…The bi-directional version LSTM (BiLSTM) shows a better capability in capturing the text patterns by a combination of forward and backward LSTMs [ 23 ]. BiLSTM has been successfully utilized for the predictions of antibacterial and antifungal peptides [ 24 , 25 ].…”

Section: Methodsmentioning

confidence: 99%

HLAB: learning the BiLSTM features from the ProtBert-encoded proteins for the class I HLA-peptide binding prediction

Zhang

Zhu

et al. 2022

Briefings in Bioinformatics

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Human Leukocyte Antigen (HLA) is a type of molecule residing on the surfaces of most human cells and exerts an essential role in the immune system responding to the invasive items. The T cell antigen receptors may recognize the HLA-peptide complexes on the surfaces of cancer cells and destroy these cancer cells through toxic T lymphocytes. The computational determination of HLA-binding peptides will facilitate the rapid development of cancer immunotherapies. This study hypothesized that the natural language processing-encoded peptide features may be further enriched by another deep neural network. The hypothesis was tested with the Bi-directional Long Short-Term Memory-extracted features from the pretrained Protein Bidirectional Encoder Representations from Transformers-encoded features of the class I HLA (HLA-I)-binding peptides. The experimental data showed that our proposed HLAB feature engineering algorithm outperformed the existing ones in detecting the HLA-I-binding peptides. The extensive evaluation data show that the proposed HLAB algorithm outperforms all the seven existing studies on predicting the peptides binding to the HLA-A*01:01 allele in AUC and achieves the best average AUC values on the six out of the seven k-mers (k=8,9,...,14, respectively represent the prediction task of a polypeptide consisting of k amino acids) except for the 9-mer prediction tasks. The source code and the fine-tuned feature extraction models are available at http://www.healthinformaticslab.org/supp/resources.php.

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Section: Methodsmentioning

confidence: 99%

HLAB: learning the BiLSTM features from the ProtBert-encoded proteins for the class I HLA-peptide binding prediction

Zhang

Zhu

et al. 2022

Briefings in Bioinformatics

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“…Construction of meta-predictor: A similar CNN network architecture was adopted based on recent studies [40] , [41] . However, we optimized four filters, epochs, and batch size using 10-fold cross-validation.…”

Section: Methodsmentioning

confidence: 99%

MLACP 2.0: An updated machine learning tool for anticancer peptide prediction

Phan

Park²,

Pitti³

et al. 2022

Computational and Structural Biotechnology Journal

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“…Sharma et al [ 149 ] proposed a DL method called Deep-AFPpred using transfer learning and one-dimensional CNN and Bi-LSTM to identify novel AFPs. Transfer learning was completed by using pre-trained embeddings from seq2vec (PESTV) [ 150 ], which trained embeddings from the ELMo language models on millions of protein sequences from UniRef50.…”

Section: Amp Prediction By Deep Learningmentioning

confidence: 99%

Recent Progress in the Discovery and Design of Antimicrobial Peptides Using Traditional Machine Learning and Deep Learning

et al. 2022

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Antimicrobial resistance has become a critical global health problem due to the abuse of conventional antibiotics and the rise of multi-drug-resistant microbes. Antimicrobial peptides (AMPs) are a group of natural peptides that show promise as next-generation antibiotics due to their low toxicity to the host, broad spectrum of biological activity, including antibacterial, antifungal, antiviral, and anti-parasitic activities, and great therapeutic potential, such as anticancer, anti-inflammatory, etc. Most importantly, AMPs kill bacteria by damaging cell membranes using multiple mechanisms of action rather than targeting a single molecule or pathway, making it difficult for bacterial drug resistance to develop. However, experimental approaches used to discover and design new AMPs are very expensive and time-consuming. In recent years, there has been considerable interest in using in silico methods, including traditional machine learning (ML) and deep learning (DL) approaches, to drug discovery. While there are a few papers summarizing computational AMP prediction methods, none of them focused on DL methods. In this review, we aim to survey the latest AMP prediction methods achieved by DL approaches. First, the biology background of AMP is introduced, then various feature encoding methods used to represent the features of peptide sequences are presented. We explain the most popular DL techniques and highlight the recent works based on them to classify AMPs and design novel peptide sequences. Finally, we discuss the limitations and challenges of AMP prediction.

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Deep-AFPpred: identifying novel antifungal peptides using pretrained embeddings from seq2vec with 1DCNN-BiLSTM

Cited by 39 publications

References 38 publications

HLAB: learning the BiLSTM features from the ProtBert-encoded proteins for the class I HLA-peptide binding prediction

HLAB: learning the BiLSTM features from the ProtBert-encoded proteins for the class I HLA-peptide binding prediction

MLACP 2.0: An updated machine learning tool for anticancer peptide prediction

Recent Progress in the Discovery and Design of Antimicrobial Peptides Using Traditional Machine Learning and Deep Learning

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