Machine learning assisted screening framework for insecticidal peptides

Nambiar, Pranav; Mitra, Debirupa; Dutta, Arnab

doi:10.1016/j.matpr.2022.05.455

Cited by 3 publications

(5 citation statements)

References 17 publications

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“…AAC-based featurization results in a 20-dimensional feature matrix, where each feature represents the frequency of the 20 naturally occurring amino acids as a fraction of the peptide length. 51,52 Each descriptor in the AAC method is calculated using eqn (3).where D [i] is the descriptor value corresponding to amino acid i , n is the frequency of occurrence of amino acid i in the peptide, and L is the length of the peptide. Using these features as inputs and the labels ( i.e.…”

Section: Methodology: Antimicrobial Peptide Design (Ampd) Frameworkmentioning

confidence: 99%

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Rational design of antimicrobial peptides: an optimization approach

Sadasivam,

Nambiar,

Dutta

et al. 2024

Mol. Syst. Des. Eng.

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Section: Methodology: Antimicrobial Peptide Design (Ampd) Frameworkmentioning

confidence: 99%

Section: Optimization Frameworkmentioning

confidence: 99%

Rational design of antimicrobial peptides: an optimization approach

Sadasivam,

Nambiar,

Dutta

et al. 2024

Mol. Syst. Des. Eng.

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“…It can rapidly screen peptides with specific activities from these databases or even uncover new functions of the known ones. Accordingly, machine learning is becoming an imperative component of contemporary biological research (Nambiar et al, 2022;Shen et al, 2022). A short history of machine learning, its fundamentals, some applications of machine learning, and the maturation of machine learning methods have been discussed in a review by Lee et al (2017).…”

Section: Computer-based Screening Of Antioxidant Peptidesmentioning

confidence: 99%

“…A short history of machine learning, its fundamentals, some applications of machine learning, and the maturation of machine learning methods have been discussed in a review by Lee et al (2017). Some studies have used machine learning methods to predict the function of peptides (Hamre & Jafri, 2022;Herrera-Bravo et al, 2022;Janairo, 2021;Lee et al, 2017;Nambiar et al, 2022;Shen et al, 2022). In a recent study, Shen et al (2022) utilized this technique to design an antioxidant peptide classification model with a precision above 0.95 according to the pseudo-amino acid compositions and motifs of peptides as input features.…”

Section: Computer-based Screening Of Antioxidant Peptidesmentioning

confidence: 99%

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Antioxidant peptides: Overview of production, properties, and applications in food systems

Mardani

Badakné

Farmani

et al. 2022

Comp Rev Food Sci Food Safe

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In recent years, several studies have reported the beneficial effects of antioxidant peptides in delaying oxidation reactions. Thus, a growing number of food proteins have been investigated as suitable sources for obtaining these antioxidant peptides. In this study, some of the most critical developments in the discovery of peptidic antioxidants are discussed. Initially, the primary methods to release, purify, and identify these antioxidant peptides from various food‐derived sources are reviewed. Then, computer‐based screening methods of the available peptides are summarized, and methods to interpret their structure–activity relationship are illustrated. Finally, approaches to the large‐scale production of these bioactive peptides are described. In addition, the applications of these antioxidants in food systems are discussed, and gaps, future challenges, and opportunities in this field are highlighted. In conclusion, various food items can be considered promising sources to obtain these novel antioxidant peptides, which present various opportunities for food applications in addition to health promotion. The lack of in‐depth data on the link between the structure and activity of these antioxidants, which is critical for the prediction of possible bioactive amino acid sequences and their potency in food systems and in vivo conditions (rather than in vitro systems), requires further attention. Consequently, future collaborative research activities between the industry and academia are required to realize the commercialization objectives of these novel antioxidant peptides.

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