PrMFTP: Multi-functional therapeutic peptides prediction based on multi-head self-attention mechanism and class weight optimization

Yan, Wenhui; Tang, Wending; Wang, Lihua; Bin, Yannan; Xia, Junfeng

doi:10.1371/journal.pcbi.1010511

Cited by 25 publications

(22 citation statements)

References 48 publications

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“…We compared our proposed method iMFP-LG with several state-of-the-art methods, including four conventional machine learning-based methods (CLR [12], RAKEL [34], RBRL [40] and MLDF [43]) and three deep learning-based methods (MPMABP [19], MFBP [31] and PrMFTP [42]). MPMABP and MFBP employed CNNs and RNNs for identifying multi-functional peptides.…”

Section: Imfp-lg Outperforms the State-of-the-art Methodsmentioning

confidence: 99%

“…We evaluated our proposed method on two multi-functional peptide datasets, multifunctional bioactive peptide (MFBP) [31] and multi-functional therapeutic peptides (MFTP) [42]. Both of them are collected from the literature by searching specific keywords in Google Scholar.…”

Section: Benchmark Datasetsmentioning

confidence: 99%

“…Several metrics have been proposed to evaluate the performance of multi-functional peptide prediction in previous studies [14,31,42,48]. In order to make a full and fair comparison with the state-of-the-art methods, we adopt 5 evaluation metrics, including precision, coverage, accuracy, absolute true, and absolute false.…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…The norm constraint ϵ of adversarial training is set to 0.5. In order to reduce the effects caused by the random initialisation of the deep learning framework and to maintain a consistent setting with the compared methods [31,42], all model results were trained 10 times repeatedly and the prediction results were averaged as the final prediction for testing samples.…”

Section: Implementation Detailsmentioning

confidence: 99%

“…Tang et al [31] and Li et al [19] identified multi-functional peptides by using a multi-label deep learning method, which combines CNN and RNN to extract peptide features and assigns function labels separately. PrMFTP [42] improved the performance of multi-functional therapeutic peptide identification by employing multi-scale CNN, attention-based biLSTM. Since the number of multi-functional peptides is fewer than mono-functional peptides and is extremely imbalanced, these approaches can't generate a comprehensive peptide representation and have high false positive rate prediction.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Discovery of novel multi-functional peptides by using protein language models and graph-based deep learning

Chen

Luo

Yang

et al. 2023

Preprint

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Functional peptides are one kind of short protein fragments that have a wide range of beneficial functions for living organisms. The majority of previous research focused on mono-functional peptides, but a growing number of multi-functional peptides have been discovered. Although enormous experimental efforts endeavor to assay multi-functional peptides, only a small fraction of millions of known peptides have been explored. Effective and precise techniques for identifying multi-functional peptides can facilitate their discovery and mechanistic understanding. In this article, we presented a novel method, called iMFP-LG, for identifying multi-functional peptides based on protein language models (pLMs) and graph attention networks (GATs). iMFP-LG converts multi-function identification into graph node classification, where graph node representations are extracted from peptide sequences by pLMs and their relationships are captured by GATs. Comparison results showed iMFP-LG significantly outperforms state-of-the-art methods on both multi-functional bioactive peptides and multi-functional therapeutic peptides datasets. The interpretability of iMFP-LG was also illustrated by visualizing patterns learned by attention mechanism. More importantly, we employed iMFP-LG to discover 13 candidate peptides with both anticancer and antimicrobial functions from UniRef90. We anticipate iMFP-LG can assist in the discovery of multi-functional peptides and contribute to the advancement of peptide drug design.

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Section: Imfp-lg Outperforms the State-of-the-art Methodsmentioning

confidence: 99%

Section: Benchmark Datasetsmentioning

confidence: 99%

Section: Evaluation Metricsmentioning

confidence: 99%

Section: Implementation Detailsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Discovery of novel multi-functional peptides by using protein language models and graph-based deep learning

Chen

Luo

Yang

et al. 2023

Preprint

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Prediction of peptide hormones using an ensemble of machine learning and similarity‐based methods

Kaur,

Arora,

Vigneshwar

et al. 2024

Proteomics

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Peptide hormones serve as genome‐encoded signal transduction molecules that play essential roles in multicellular organisms, and their dysregulation can lead to various health problems. In this study, we propose a method for predicting hormonal peptides with high accuracy. The dataset used for training, testing, and evaluating our models consisted of 1174 hormonal and 1174 non‐hormonal peptide sequences. Initially, we developed similarity‐based methods utilizing BLAST and MERCI software. Although these similarity‐based methods provided a high probability of correct prediction, they had limitations, such as no hits or prediction of limited sequences. To overcome these limitations, we further developed machine and deep learning‐based models. Our logistic regression‐based model achieved a maximum AUROC of 0.93 with an accuracy of 86% on an independent/validation dataset. To harness the power of similarity‐based and machine learning‐based models, we developed an ensemble method that achieved an AUROC of 0.96 with an accuracy of 89.79% and a Matthews correlation coefficient (MCC) of 0.8 on the validation set. To facilitate researchers in predicting and designing hormone peptides, we developed a web‐based server called HOPPred. This server offers a unique feature that allows the identification of hormone‐associated motifs within hormone peptides. The server can be accessed at: https://webs.iiitd.edu.in/raghava/hoppred/.

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AMHF‐TP: Multifunctional therapeutic peptides prediction based on multi‐granularity hierarchical features

Tuo,

Zhu,

Lin

et al. 2024

Quant. Biol.

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Multifunctional therapeutic peptides (MFTP) hold immense potential in diverse therapeutic contexts, yet their prediction and identification remain challenging due to the limitations of traditional methodologies, such as extensive training durations, limited sample sizes, and inadequate generalization capabilities. To address these issues, we present AMHF‐TP, an advanced method for MFTP recognition that utilizes attention mechanisms and multi‐granularity hierarchical features to enhance performance. The AMHF‐TP is composed of four key components: a migration learning module that leverages pretrained models to extract atomic compositional features of MFTP sequences; a convolutional neural network and self‐attention module that refine feature extraction from amino acid sequences and their secondary structures; a hypergraph module that constructs a hypergraph for complex similarity representation between MFTP sequences; and a hierarchical feature extraction module that integrates multimodal peptide sequence features. Compared with leading methods, the proposed AMHF‐TP demonstrates superior precision, accuracy, and coverage, underscoring its effectiveness and robustness in MFTP recognition. The comparative analysis of separate hierarchical models and the combined model, as well as with five contemporary models, reveals AMHF‐TP’s exceptional performance and stability in recognition tasks.

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PrMFTP: Multi-functional therapeutic peptides prediction based on multi-head self-attention mechanism and class weight optimization

Cited by 25 publications

References 48 publications

Discovery of novel multi-functional peptides by using protein language models and graph-based deep learning

Discovery of novel multi-functional peptides by using protein language models and graph-based deep learning

Prediction of peptide hormones using an ensemble of machine learning and similarity‐based methods

AMHF‐TP: Multifunctional therapeutic peptides prediction based on multi‐granularity hierarchical features

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