Flexibility-aware graph model for accurate epitope identification

Wang, Yiqi; Tang, Haomiao; Gao, Chuang; Ge, Meixin; Li, Ziteng; Dong, Zhiqiang; Zhao, Liang

doi:10.1016/j.compbiomed.2022.106064

Cited by 3 publications

(3 citation statements)

References 39 publications

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“…In recent years, the high rate of deep learning research resulted in a variety of deep learning based methods proposed by researchers [12,13,18,19]. Many approaches combine graph theory principles with deep learning or deep learning on graphs [20][21][22][23] to detect interactive propensities embedded in HLA-peptide pairs.…”

Section: Related Workmentioning

confidence: 99%

In silico antibody-peptide epitope prediction for personalized cancer therapy

Jacobs¹,

Ming

Mong

et al. 2023

Front. Appl. Math. Stat.

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The human leukocyte antigen (HLA) system is a complex of genes on chromosome 6 in humans that encodes cell surface proteins responsible for regulating the immune system. Viral peptides presented to cancer cell surfaces by the HLA trigger the immune system to kill the cells, creating antibody–peptide epitopes (APE). This study proposes an in silico approach to identify patient-specific APEs by applying complex networks diagnostics on a novel multiplex data structure as an input for a deep learning model. The proposed analytical model identifies patient- and tumor-specific APEs with as few as 20 labeled data points. In addition, the proposed data structure employs complex network theory and other statistical approaches that can better explain and reduce the black box effect of deep learning. The proposed approach achieves an F1-score of 80% and 93% on patients one and two, respectively, and above 90% on tumor-specific tasks. In addition, it minimizes the required training time and the number of parameters.

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Section: Related Workmentioning

confidence: 99%

In silico antibody-peptide epitope prediction for personalized cancer therapy

Jacobs¹,

Ming

Mong

et al. 2023

Front. Appl. Math. Stat.

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“…In the recent years, the high rate of deep learning research resulted in a variety of deep learning based methods proposed by researchers (12,13,18,19,20). A number of approaches combine graph theory principles with deep learning or deep learning on graphs (21,22,23,24) in order to detect interactive propensities embedded in HLA-peptide pairs. Even though high affinity in an MHC-peptide complex tends to be associated with immune responsiveness, it is not sufficient to define immunogenicity.…”

Section: Introductionmentioning

confidence: 99%

In silico Antibody-Peptide Epitope prediction for Personalized cancer therapy

Jacobs¹,

Lim

Emmanoil

et al. 2023

Preprint

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The human leukocyte antigen (HLA) system is a complex of genes on chromosome 6 in humans that encodes cell-surface proteins responsible for regulating the immune system. Viral peptides presented to cancer cell surfaces by the HLA trigger the immune system to kill the cells, creating Antibody-peptide epitopes (APE). This study proposes an in-silico approach to identify patient-specific APEs by applying complex networks diagnostics on a novel multiplex data structure as input for a deep learning model. The proposed analytical model identifies patient and tumor-specific APEs with as few as 20 labeled data points. Additionally, the proposed data structure employs complex network theory, and other statistical approaches that can better explain and reduce the black box effect of deep learning. The proposed approach achieves an F1-score above 80% on patient and tumor specific tasks and minimizes training time and the number of parameters.

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“…The other model applies graph partitioning to obtain a subgraph of the residual graph surface and continues with seed expansion to obtain a candidate epitope [14]. Wang et al introduced conformations in graph formation [15]. Zhao applied a graph kernel for classification, but Wang used the Graph Convolution network.…”

Section: Introductionmentioning

confidence: 99%

A Novel Epitope Dataset: Performance of the MCL-Based Algorithms to Generate Dataset for Graph Learning Model

Solihah

Musdholifah

Azhari

2023

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Naturally, the epitope dataset can be presented as a graph. Dataset preparation in the previous methods is part of model development. There are many graph-based classification and regression methods. Still, it is difficult to identify their performance on the conformational epitope prediction model because datasets in a suitable format are unavailable. This research aims to build a dataset in a suitable format to evaluate kernel graph and graph convolution network. This dataset, which results from graph clustering on graph antigens, can be used to identify the performance of many graph neural network-based algorithms for conformational epitope prediction. The Ag-Ab complexes that meet the criteria for forming a conformational epitope prediction dataset from previous studies were downloaded from the Protein Data Bank. Raw datasets in the form of specific exposed antigen chain residues are labeled as epitope or non-epitope based on their proximity to the paratope. The engineering features in the raw dataset are derived from the structure of the antigen-antibody complex and the propensity score. Aggregating atomic-level interactions into residual levels create an initial graph of the antigen chain. The MCL, MLR-MCL, and PS-MCL are graph clustering algorithms to obtain labeled sub-clusters from the initial graph. A balance factor parameter is set to several values to identify the optimal dataset formation based on minimal fragmentation. The output of the MCL algorithm is used as a baseline. As a result of the fragmentation analysis that occurs, the MLR-MCL algorithm gives the best model performance at a balance factor equal to 2. PS-MCL gives the best performance at a value of 0.9. Based on the minimum fragmentation, the MLR-MCL algorithm provides the best model performance compared to MCL and PS-MCL. The dataset in a format according to benchmarking dataset can be used to identify the characteristics of antigen subgraphs formed from the graph clustering process and to explore the performance of graph-based learning conformational epitope prediction models such as graph convolution networks.

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Flexibility-aware graph model for accurate epitope identification

Cited by 3 publications

References 39 publications

In silico antibody-peptide epitope prediction for personalized cancer therapy

In silico antibody-peptide epitope prediction for personalized cancer therapy

In silico Antibody-Peptide Epitope prediction for Personalized cancer therapy

A Novel Epitope Dataset: Performance of the MCL-Based Algorithms to Generate Dataset for Graph Learning Model

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