ACP-GBDT: An improved anticancer peptide identification method with gradient boosting decision tree

Li, Yanjuan; Ma, Ding; Chen, Dong; Chen, Yu

doi:10.3389/fgene.2023.1165765

Cited by 3 publications

(4 citation statements)

References 57 publications

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“…GBDT is a popular ensemble learning method for regression and classification, which excels at generating highly accurate predictions by combining multiple decision trees [ 37 ]. GBDT has been used to classify the anticancer and non-anticancer peptides [ 38 ]. Experiments on independent test sets and 10-fold cross-validation sets demonstrated that the GBDT classifier outperformed the other methods.…”

Section: Methodsmentioning

confidence: 99%

Mining Bovine Milk Proteins for DPP-4 Inhibitory Peptides Using Machine Learning and Virtual Proteolysis

Zhang,

Zhu,

Bao

et al. 2024

Research

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Dipeptidyl peptidase-IV (DPP-4) enzyme inhibitors are a promising category of diabetes medications. Bioactive peptides, particularly those derived from bovine milk proteins, play crucial roles in inhibiting the DPP-4 enzyme. This study describes a comprehensive strategy for DPP-4 inhibitory peptide discovery and validation that combines machine learning and virtual proteolysis techniques. Five machine learning models, including GBDT, XGBoost, LightGBM, CatBoost, and RF, were trained. Notably, LightGBM demonstrated superior performance with an AUC value of 0.92 ± 0.01. Subsequently, LightGBM was employed to forecast the DPP-4 inhibitory potential of peptides generated through virtual proteolysis of milk proteins. Through a series of in silico screening process and in vitro experiments, GPVRGPF and HPHPHL were found to exhibit good DPP-4 inhibitory activity. Molecular docking and molecular dynamics simulations further confirmed the inhibitory mechanisms of these peptides. Through retracing the virtual proteolysis steps, it was found that GPVRGPF can be obtained from β-casein through enzymatic hydrolysis by chymotrypsin, while HPHPHL can be obtained from κ-casein through enzymatic hydrolysis by stem bromelain or papain. In summary, the integration of machine learning and virtual proteolysis techniques can aid in the preliminary determination of key hydrolysis parameters and facilitate the efficient screening of bioactive peptides.

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Section: Methodsmentioning

confidence: 99%

Mining Bovine Milk Proteins for DPP-4 Inhibitory Peptides Using Machine Learning and Virtual Proteolysis

Zhang,

Zhu,

Bao

et al. 2024

Research

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“…In order to better validate the capability of the model, we adopted four metrics that are most commonly used in the field of bioinformatics [22]: accuracy(ACC), specificity(SP), sensitivity (SN) and area under the curve (AUC), Matthew's correlation coefficient (MCC). These measures are defined as follows:…”

Section: Measurementmentioning

confidence: 99%

An improved predictor of thermophilic protein using protein language model

Li,

Cai

2024

Third International Conference on Biomedical and Intelligent Systems (IC-BIS 2024)

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Thermophilic proteins are proteins that can maintain good activity at high temperatures, they have important application value in the fields of enzyme engineering and biopharmaceuticals. Therefore, accurate identification of thermophilic proteins provides important information for their engineering applications. In this study, we proposed an improved predictor of thermophilic protein using ProtBert protein language model, called PB-TMP. Firstly, we used the ProtBert protein language model to extract the hidden features in protein sequences, and 1024-dimensional features are obtained. Secondly, Recursive Feature Elimination (RFE) are employed to remove the noise or redundant features, 31-dimensional features are selected as the final features set. Finally, Multi-Layer Perceptron (MLP) is trained on the final features set, then an identification model is obtained. The model yielded an accuracy of 98.53% on 10-fold cross-validation, and performed best among the existing methods. The model will accelerate the discovery of thermophilic protein and important application in protein engineering and biotechnology.

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“…The Amino Acid Index (AAindex) is a numerical index database that provides information on the physicochemical and biological properties of the 20 amino acids [30]. It provides three categories of lists for delineating amino acid properties.…”

Section: Introductionmentioning

confidence: 99%

“…These lists delve into the biological and chemical attributes of single or paired amino acids, encompassing aspects like charge, polarity, mutability, and contact potential. Indexing amino acid features based on the amino acid index list has become a common method in bioinformatics [30]. Unified representation (UniRep) is a method that transforms any protein sequence into a fixed-length vector representation, addressing the scarcity of protein informatics data by leveraging full utilization of the original sequence [31,32].…”

Section: Introductionmentioning

confidence: 99%

A Stacking Machine Learning Method for IL-10-Induced Peptide Sequence Recognition Based on Unified Deep Representation Learning

Li,

Jiang,

Pei

et al. 2023

Applied Sciences

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Interleukin-10 (IL-10) has anti-inflammatory properties and is a crucial cytokine in regulating immunity. The identification of IL-10 through wet laboratory experiments is costly and time-intensive. Therefore, a new IL-10-induced peptide recognition method, IL10-Stack, was introduced in this research, which was based on unified deep representation learning and a stacking algorithm. Two approaches were employed to extract features from peptide sequences: Amino Acid Index (AAindex) and sequence-based unified representation (UniRep). After feature fusion and optimized feature selection, we selected a 1900-dimensional UniRep feature vector and constructed the IL10-Stack model using stacking. IL10-Stack exhibited excellent performance in IL-10-induced peptide recognition (accuracy (ACC) = 0.910, Matthews correlation coefficient (MCC) = 0.820). Relative to the existing methods, IL-10Pred and ILeukin10Pred, the approach increased in ACC by 12.1% and 2.4%, respectively. The IL10-Stack method can identify IL-10-induced peptides, which aids in the development of immunosuppressive drugs.

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ACP-GBDT: An improved anticancer peptide identification method with gradient boosting decision tree

Cited by 3 publications

References 57 publications

Mining Bovine Milk Proteins for DPP-4 Inhibitory Peptides Using Machine Learning and Virtual Proteolysis

Mining Bovine Milk Proteins for DPP-4 Inhibitory Peptides Using Machine Learning and Virtual Proteolysis

An improved predictor of thermophilic protein using protein language model

A Stacking Machine Learning Method for IL-10-Induced Peptide Sequence Recognition Based on Unified Deep Representation Learning

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