DeepSeqPan, a novel deep convolutional neural network model for pan-specific class I HLA-peptide binding affinity prediction

Liu, Zhonghao; Cui, Yuxin; Xiong, Zheng; Nasiri, Alireza; Zhang, Ansi; Hu, Jianjun

doi:10.1101/299412

Cited by 5 publications

(4 citation statements)

References 24 publications

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“…Multiple studies have been published for predicting the peptides binding the HLA-I alleles, which are compared with the proposed HLAB framework in this study, as shown in Figure 11 . These studies are Anthem [ 9 ], MixMHCpred2.0.2 [ 33 ], NetMHCpan4.1 [ 32 ], NetMHCcons1.1 [ 34 ], NetMHCstabpan1.0 [ 35 ], ACME [ 36 ], MHCSeqNet [ 37 ] and DeepSeqPan [ 38 ]. A fair comparison is carried out on the testing dataset using the HLAB models with the best performances on the validating datasets for the specific predicting tasks.…”

Section: Resultsmentioning

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: Resultsmentioning

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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“…The first step toward developing a deep learning algorithm for PPI prediction is preprocessing the datasets to encode the categorical amino acids into a set of numerical values [95] . A common technique is one-hot encoding [95] , [96] , which may not be well-suited to problems with high cardinality, such as encoding proteins of significant length. For example, for a dataset comprising the twenty standard amino acids and an unknown amino acid (X), Alanine (A) might be represented as [10000000000000000000] using one-hot encoding, which is a simple vector with twenty zeros.…”

Section: Protein Representationmentioning

confidence: 99%

ProtInteract: A deep learning framework for predicting protein–protein interactions

Soleymani

Paquet

Viktor

et al. 2023

Computational and Structural Biotechnology Journal

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“…In the field of HLA binding, the HLA-CNN [35] , which uses three convolutional layers and two fully-connected layers with word embedding for encoding, achieves good performance and outperforms all traditional prediction methods. DeepSepPan [19] utilized the VGG-liked deep CNN to extract abstract features from HLA. Although it also achieves competitive performance compared with other current algorithms, DeepSeqPan loses flexibility and only supports the peptides which have 9 aa length.…”

Section: Feedforward Neural Network and Convolutional Neural Networkmentioning

confidence: 99%

MHCherryPan. a novel model to predict the binding affinity of pan-specific class I HLA-peptide

Xie

Han

Zhang

2019

2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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The human leukocyte antigen (HLA) system or complex plays an essential role in regulating the immune system in humans. Accurate prediction of peptide binding with HLA can efficiently help to identify those neoantigens, which potentially make a big difference in immune drug development. HLA is one of the most polymorphic genetic systems in humans, and thousands of HLA allelic versions exist[6]. Due to the high polymorphism of HLA complex, it is still pretty difficult to accurately predict the binding affinity. In this thesis, we presented a new algorithm to combine convolutional neural network and long short-term memory to solve this problem. Compared with other current popular algorithms, our model achieved the stateof-the-art results.

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DeepSeqPan, a novel deep convolutional neural network model for pan-specific class I HLA-peptide binding affinity prediction

Abstract: Interactions between human leukocyte antigens (HLAs) and peptides play a critical role 1 in the human immune system. Accurate computational prediction of HLA-binding 2 peptides can be used for peptide drug discovery. Currently, the best prediction

Cited by 5 publications

References 24 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

ProtInteract: A deep learning framework for predicting protein–protein interactions

MHCherryPan. a novel model to predict the binding affinity of pan-specific class I HLA-peptide

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