Quantitative sequence-activity modeling of ACE peptide originated from milk using ACC–QTMS amino acid indices

Bahadori, Maryam; Hemmateenejad, Bahram; Yousefinejad, Saeed

doi:10.1007/s00726-019-02761-y

Cited by 11 publications

(3 citation statements)

References 48 publications

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“…In addition, previous studies have explored the importance of these QSAR models for in silico prediction of novel compounds that can be used to predict the activity of a target or receptor when this is a computationally intensive process that requires learning from bioactivity data. [53][54][55][56]…”

Section: Predictions Of Dds Set Of Compoundsmentioning

confidence: 99%

Insight into TLR4 receptor inhibitory activity via QSAR for the treatment of Mycoplasma pneumonia disease

et al. 2023

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Section: Predictions Of Dds Set Of Compoundsmentioning

confidence: 99%

Insight into TLR4 receptor inhibitory activity via QSAR for the treatment of Mycoplasma pneumonia disease

et al. 2023

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“…Bioinformatic studies have become more and more popular in peptide’s design, particularly the quantitative structure–activity relationship (QSAR) study. QSAR models utilize a mathematical function to summarize the relationship between biological activities of a set of compounds and their structural characteristics. − So far, QSAR models have been successfully established for angiotensin-converting enzyme (ACE)-inhibitory peptides, antioxidant peptides, antimicrobial peptides, bitter peptides, antitumor peptides, etc. − To develop a QSAR model, a set of numerical descriptors is generated to characterize the structure of interest, e.g., amino acids, which serves as independent variables, while the biological activities are the dependent variables. Since the activities of peptides are determined by the amino acid compositions, sequences, and structures, a proper encoding technique should be employed for representing the sequence of amino acids.…”

Section: Introductionmentioning

confidence: 99%

In Silico Rational Design and Virtual Screening of Bioactive Peptides Based on QSAR Modeling

2020

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Predicting the bioactivity of peptides is an important challenge in drug development and peptide research. In this study, numerical descriptive vectors (NDVs) for peptide sequences were calculated based on the physicochemical properties of amino acids (AAs) and principal component analysis (PCA). The resulted NDV had the same length as the peptide sequence, so that each entry of NDV corresponded to one AA in the sequence. They were then applied to quantitative structure−activity relationship (QSAR) analysis of angiotensin-converting enzyme (ACE) inhibitor dipeptides, bitter-tasting dipeptides, and nonameric binding peptides of the human leukocyte antigens (HLA-A*0201). Multiple linear regression was used to construct the QSAR models. For each peptide set, a proper subset of physicochemical properties was chosen by the ant colony optimization algorithm. The leave-one-out cross-validation (q loo 2 ) values were 0.855, 0.936, and 0.642 and the root-mean-square errors (RMSEs) were 0.450, 0.149, and 0.461. Our results revealed that the new numerical descriptive vector can afford extensive characterization of peptide sequence so that it can be easily employed in peptide QSAR studies. Moreover, the proposed numerical descriptive vectors were able to determine hot spot residues in the peptides under study.

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“…QSAR has been previously applied in studying ACE inhibitory peptides based on known structure matching from developed databases and has shown certain ability to predict new peptides . It also should be noted that many previous studies on ACE inhibitory peptides focused on one food source such as milk or soy. , Systematic studies of the peptides obtained from various foods and the rational selection of peptides with potential anti-ACE activities based on structure–activity relationships are urgently required. The objectives of the study were to establish a peptide database with ACE inhibitory activity from existing publications, develop QSAR models using various machine learning approaches, and apply the resulted models to screen all available dipeptides to select new ACE inhibitory candidates.…”

Section: Introductionmentioning

confidence: 99%

Predictive Modeling of Angiotensin I-Converting Enzyme Inhibitory Peptides Using Various Machine Learning Approaches

Wang

Russo

Liu

et al. 2020

J. Agric. Food Chem.

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Food-derived angiotensin I-converting enzyme (ACE) inhibitory peptides could potentially be used as safe supportive therapeutic products for high blood pressure. Theoretical approaches are promising methods with the advantage through exploring the relationships between peptide structures and their bioactivities. In this study, peptides with ACE inhibitory activity were collected and curated. Quantitative structure−activity relationship (QSAR) models were developed by using the combination of various machine learning approaches and chemical descriptors. The resultant models have revealed several structure features accounting for the ACE inhibitions. 14 new dipeptides predicted to lower blood pressure by inhibiting ACE were selected. Molecular docking indicated that these dipeptides formed hydrogen bonds with ACE. Five of these dipeptides were synthesized for experimental testing. The QSAR models developed were proofed to design and propose novel ACE inhibitory peptides. Machine learning algorithms and properly selected chemical descriptors can be promising modeling approaches for rational design of natural functional food components.

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Quantitative sequence-activity modeling of ACE peptide originated from milk using ACC–QTMS amino acid indices

Cited by 11 publications

References 48 publications

Insight into TLR4 receptor inhibitory activity via QSAR for the treatment of Mycoplasma pneumonia disease

Insight into TLR4 receptor inhibitory activity via QSAR for the treatment of Mycoplasma pneumonia disease

In Silico Rational Design and Virtual Screening of Bioactive Peptides Based on QSAR Modeling

Predictive Modeling of Angiotensin I-Converting Enzyme Inhibitory Peptides Using Various Machine Learning Approaches

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