Proteochemometric Method for pIC50 Prediction of Flaviviridae.

Viruses remain an area of concern despite constant development of antiviral drugs and therapies. One of the contributors is the Flaviviridae family of viruses causing diseases that need attention. Among other anitviral methods, antiviral peptides are being studied as viable candidates. Although anti...

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Publicado en:BioMed Research International pp. 1 - 8
Autores principales: Singh, Divye, Mahadik, Avani, Surana, Shraddha, Arora, Pooja
Formato: equations & formulas research tables/charts Journal Article
Publicado: Wiley-Blackwell 9/15/2022
Acceso en línea:Ver este registro en EBSCOhost
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      dt: 9/15/2022
      pid: 480
      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        10.1155/2022/7901791
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        atl: Proteochemometric Method for pIC50 Prediction of Flaviviridae.
      aug:
        au:
          Singh, Divye
          Mahadik, Avani
          Surana, Shraddha
          Arora, Pooja
        affil: Engineering for Research, Thoughtworks Technologies, Pune, Maharashtra 411006, India
      sug:
        subj:
          Machine Learning Methods
          Flaviviridae
          Human
          Antiviral Agents
          Therapeutics
      ab: Viruses remain an area of concern despite constant development of antiviral drugs and therapies. One of the contributors is the Flaviviridae family of viruses causing diseases that need attention. Among other anitviral methods, antiviral peptides are being studied as viable candidates. Although antiviral peptides (AVPs) are emerging as potential therapeutics, it is important to assess the efficacy of a given peptide in terms of its bioactivity. Experimental identification of the bioactivity of each potential peptide is an expensive and time consuming task. Computational methods like proteochemometric modeling (PCM) is a promising method for prediction of bioactivity (pIC50) based on peptide and target sequence pair. In this study, we propose a prediction of pIC50 of AVP against the Flaviviridae family that may help make the decision to choose a peptide with desired efficacy. The peptides data was collected from a public database and target sequences were manually curated from literature. Features are calculated using peptide and target sequence PCM descriptors which consist of individual and cross-term features of peptide and respective target. The resultant R 2 and MAPE values are 0.85 and 8.44%, respectively, for prediction of pIC50 value of AVPs.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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