Explainable Deep Hypergraph Learning Modeling the Peptide Secondary Structure Prediction

Jiang, Yi; Wang, Ruheng; Feng, Jiuxin; Jin, Junru; Liang, Sirui; Li, Zhongshen; Yu, Yinhui; Ma, Anjun; Su, Ran; Zou, Quan; Ma, Qin; Wei, Leyi

doi:10.1002/advs.202206151

Cited by 38 publications

(13 citation statements)

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“…In addition, the PHAT interface was used to generate the secondary structure data of SSPs. 47 The modified interface uses the hypergraph attention mechanism to fine-tune the pretrained language model and completes the knowledge transfer well. These secondary structure data were then integrated into the SSP data.…”

Section: Resultsmentioning

confidence: 99%

An Effective Plant Small Secretory Peptide Recognition Model Based on Feature Correction Strategy

Wang,

Zhou,

et al. 2023

J. Chem. Inf. Model.

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Plant small secretory peptides (SSPs) play an important role in the regulation of biological processes in plants. Accurately predicting SSPs enables efficient exploration of their functions. Traditional experimental verification methods are very reliable and accurate, but they require expensive equipment and a lot of time. The method of machine learning speeds up the prediction process of SSPs, but the instability of feature extraction will also lead to further limitations of this type of method. Therefore, this paper proposes a new feature-correction-based model for SSP recognition in plants, abbreviated as SE-SSP. The model mainly includes the following three advantages: First, the use of transformer encoders can better reveal implicit features. Second, design a feature correction module suitable for sequences, named 2-D SENET, to adaptively adjust the features to obtain a more robust feature representation. Third, stack multiple linear modules to further dig out the deep information on the sample. At the same time, the training based on a contrastive learning strategy can alleviate the problem of sparse samples. We construct experiments on publicly available data sets, and the results verify that our model shows an excellent performance. The proposed model can be used as a convenient and effective SSP prediction tool in the future. Our data and code are publicly available at https://github.com/wrab12/SE-SSP/.

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Section: Resultsmentioning

confidence: 99%

An Effective Plant Small Secretory Peptide Recognition Model Based on Feature Correction Strategy

Wang,

Zhou,

et al. 2023

J. Chem. Inf. Model.

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“…Finally, we obtained the generated peptide samples with different types of attributes. To verify the generation rate for each type of our generated peptide samples, we selected three published well-known methods (i.e., ToxIBTL for toxicity, PHAT for secondary structure, and CAMP for antimicrobial properties) for validation.…”

Section: Resultsmentioning

confidence: 99%

Multi-CGAN: Deep Generative Model-Based Multiproperty Antimicrobial Peptide Design

Yu,

Wang,

Qiao

et al. 2023

J. Chem. Inf. Model.

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Antimicrobial peptides are peptides that are effective against bacteria and viruses, and the discovery of new antimicrobial peptides is of great importance to human life and health. Although the design of antimicrobial peptides using machine learning methods has achieved good results in recent years, it remains a challenge to learn and design novel antimicrobial peptides with multiple properties of interest from peptide data with certain property labels. To this end, we propose Multi-CGAN, a deep generative model-based architecture that can learn from single-attribute peptide data and generate antimicrobial peptide sequences with multiple attributes that we need, which may have a potentially wide range of uses in drug discovery. In particular, we verified that our Multi-CGAN generated peptides with the desired properties have good performance in terms of generation rate. Moreover, a comprehensive statistical analysis demonstrated that our generated peptides are diverse and have a low probability of being homologous to the training data. Interestingly, we found that the performance of many popular deep learning methods on the antimicrobial peptide prediction task can be improved by using Multi-CGAN to expand the data on the training set of the original task, indicating the high quality of our generated peptides and the robust ability of our method. In addition, we also investigated whether it is possible to directionally generate peptide sequences with specified properties by controlling the input noise sampling for our model.

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“…Peptide secondary structure : In this study, we use the PHAT web interface to generate peptide secondary structure. PHAT was proposed by Jiang et al (2023) . PHAT is a novel deep learning framework for predicting peptide secondary structures.…”

Section: Methodsmentioning

confidence: 99%

ExamPle: explainable deep learning framework for the prediction of plant small secreted peptides

Jin

Wang

et al. 2023

Bioinformatics

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Motivation Plant Small Secreted Peptides (SSPs) play an important role in plant growth, development, and plant-microbe interactions. Therefore, the identification of SSPs is essential for revealing the functional mechanisms. Over the last few decades, machine learning-based methods have been developed, accelerating the discovery of SSPs to some extent. However, existing methods highly depend on handcrafted feature engineering, which easily ignores the latent feature representations and impacts the predictive performance. Results Here, we propose ExamPle, a novel deep learning model using Siamese network and multi-view representation for the explainable prediction of the plant SSPs. Benchmarking comparison results show that our ExamPle performs significantly better than existing methods in the prediction of plant SSPs. Also, our model shows excellent feature extraction ability by using dimension reduction tools. Importantly, by utilizing in silico mutagenesis (ISM) experiments, ExamPle can discover sequence characteristics and identify the contribution of each amino acid. The key novel principle learned by our model is that the head region of the peptide and some specific sequential patterns are strongly associated with the SSPs’ functions. Thus, ExamPle is a competitive model and tool for predicting plant SSPs and designing effective plant SSPs. Availability Our codes and datasets are available at https://github.com/Johnsunnn/ExamPle. Supplementary information Supplementary data are available at Bioinformatics online.

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Explainable Deep Hypergraph Learning Modeling the Peptide Secondary Structure Prediction

Cited by 38 publications

References 50 publications

An Effective Plant Small Secretory Peptide Recognition Model Based on Feature Correction Strategy

An Effective Plant Small Secretory Peptide Recognition Model Based on Feature Correction Strategy

Multi-CGAN: Deep Generative Model-Based Multiproperty Antimicrobial Peptide Design

ExamPle: explainable deep learning framework for the prediction of plant small secreted peptides

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