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Molecular Modelling in Bioactive Peptide Discovery and Characterisation.

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  • معلومة اضافية
    • المصدر:
      Publisher: MDPI Country of Publication: Switzerland NLM ID: 101596414 Publication Model: Electronic Cited Medium: Internet ISSN: 2218-273X (Electronic) Linking ISSN: 2218273X NLM ISO Abbreviation: Biomolecules Subsets: MEDLINE
    • بيانات النشر:
      Original Publication: Basel, Switzerland : MDPI, 2011-
    • الموضوع:
    • نبذة مختصرة :
      Molecular modelling is a vital tool in the discovery and characterisation of bioactive peptides, providing insights into their structural properties and interactions with biological targets. Many models predicting bioactive peptide function or structure rely on their intrinsic properties, including the influence of amino acid composition, sequence, and chain length, which impact stability, folding, aggregation, and target interaction. Homology modelling predicts peptide structures based on known templates. Peptide-protein interactions can be explored using molecular docking techniques, but there are challenges related to the inherent flexibility of peptides, which can be addressed by more computationally intensive approaches that consider their movement over time, called molecular dynamics (MD). Virtual screening of many peptides, usually against a single target, enables rapid identification of potential bioactive peptides from large libraries, typically using docking approaches. The integration of artificial intelligence (AI) has transformed peptide discovery by leveraging large amounts of data. AlphaFold is a general protein structure prediction tool based on deep learning that has greatly improved the predictions of peptide conformations and interactions, in addition to providing estimates of model accuracy at each residue which greatly guide interpretation. Peptide function and structure prediction are being further enhanced using Protein Language Models (PLMs), which are large deep-learning-derived statistical models that learn computer representations useful to identify fundamental patterns of proteins. Recent methodological developments are discussed in the context of canonical peptides, as well as those with modifications and cyclisations. In designing potential peptide therapeutics, the main outstanding challenge for these methods is the incorporation of diverse non-canonical amino acids and cyclisations.
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    • Grant Information:
      847402 Supported by Enterprise Ireland and received funding from the European Union Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie
    • Contributed Indexing:
      Keywords: AlphaFold; Protein Language Models; bioactive peptides; homology modelling; molecular docking; molecular dynamics simulation; molecular modelling; virtual screening
    • الرقم المعرف:
      0 (Peptides)
    • الموضوع:
      Date Created: 20250430 Date Completed: 20250430 Latest Revision: 20250514
    • الموضوع:
      20250514
    • الرقم المعرف:
      PMC12025251
    • الرقم المعرف:
      10.3390/biom15040524
    • الرقم المعرف:
      40305228