Therapeutic Peptide Development Revolutionized: Harnessing the Power of Artificial Intelligence for Drug Discovery

Heliyon, Journal Year: 2024, Volume and Issue: 10(22), P. e40265 - e40265

Published: Nov. 1, 2024

Due to the spread of antibiotic resistance, global attention is focused on its inhibition and expansion effective medicinal compounds. The novel functional properties peptides have opened up new horizons in personalized medicine. With artificial intelligence methods combined with therapeutic peptide products, pharmaceuticals biotechnology advance drug development rapidly reduce costs. Short-chain inhibit a wide range pathogens great potential for targeting diseases. To address challenges synthesis sustainability, methods, namely machine learning, must be integrated into their production. Learning can use complicated computations select active toxic compounds metabolic activity. Through this comprehensive review, we investigated method as tool finding peptide-based drugs providing more accurate analysis through introduction predictable databases selection development.

Language: Английский

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Harnessing AI for Enhanced Screening of Antimicrobial Bioactive Compounds in Food Safety and Preservation

Trends in Food Science & Technology, Journal Year: 2025, Volume and Issue: unknown, P. 104887 - 104887

Published: Jan. 1, 2025

Language: Английский

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Exploring the impact of bioactive peptides from fermented Milk proteins: A review with emphasis on health implications and artificial intelligence integration

Food Chemistry, Journal Year: 2025, Volume and Issue: unknown, P. 144047 - 144047

Published: March 1, 2025

Language: Английский

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Immunomodulation in Non-traditional Therapies for Methicillin-resistant Staphylococcus aureus (MRSA) Management

Current Microbiology, Journal Year: 2024, Volume and Issue: 81(10)

Published: Sept. 6, 2024

Language: Английский

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Molecular Modelling in Bioactive Peptide Discovery and Characterisation

Biomolecules, Journal Year: 2025, Volume and Issue: 15(4), P. 524 - 524

Published: April 3, 2025

Molecular modelling is a vital tool in the discovery and characterisation of bioactive peptides, providing insights into their structural properties interactions with biological targets. Many models predicting peptide function or structure rely on intrinsic properties, including influence amino acid composition, sequence, chain length, which impact stability, folding, aggregation, target interaction. Homology predicts structures based known templates. Peptide-protein can be explored using molecular docking techniques, but there are challenges related to inherent flexibility addressed by more computationally intensive approaches that consider movement over time, called dynamics (MD). Virtual screening many usually against single target, enables rapid identification potential peptides from large libraries, typically approaches. The integration artificial intelligence (AI) has transformed leveraging amounts data. AlphaFold general protein prediction deep learning greatly improved predictions conformations interactions, addition estimates model accuracy at each residue guide interpretation. Peptide being further enhanced Protein Language Models (PLMs), deep-learning-derived statistical learn computer representations useful identify fundamental patterns proteins. Recent methodological developments discussed context canonical as well those modifications cyclisations. In designing therapeutics, main outstanding challenge for these methods incorporation diverse non-canonical acids

Language: Английский

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BLIS Fingerprinting as a Tool to Investigate the Distribution and Significance of Bacteriocin Production and Immunity in Streptococcus pyogenes and Streptococcus salivarius

Applied Microbiology, Journal Year: 2025, Volume and Issue: 5(2), P. 49 - 49

Published: May 21, 2025

The study of bacteriocins has significantly enhanced our understanding microbial interactions, notably within the genus Streptococcus. Among most functionally diverse and clinically relevant are those belonging to lantibiotic class, which exhibit potent antimicrobial properties central competitive dynamics streptococcal species. This review focuses on discovery characterization produced by Streptococcus pyogenes salivarius, emphasizing their biological significance exclusive human host. A cornerstone these studies been development application pioneer agar culture-based bacteriocin detection methodology, known as fingerprinting. approach proven invaluable for initial differentiation a wide array bacteriocin-like inhibitory substances (BLIS) in populations. theme this is roles lantibiotics S. particularly relation competition, colonization dynamics, host interactions. expression determinants provides distinct advantages producing strain, including niche establishment ability suppress competing microbes. Furthermore, presence specific immunity mechanisms safeguards producer from self-inhibition potential antagonism closely related competitors. In pyogenes, production implicated virulence modulation, raising important questions about its role pathogenicity immune evasion. Conversely, prominent commensal probiotic candidate species, utilizes arsenal confer benefits mediate beneficial especially oral upper respiratory tract microbiomes. implications situ extend beyond ecology, presenting opportunities innovative therapeutic applications. harnessing bacteriocin-producing streptococci interventions, combating antibiotic-resistant pathogens, underscores translational relevance findings. integrates historical contemporary perspectives research, providing insights into future avenues leveraging bioactive peptides clinical biotechnological contexts.

Language: Английский

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Can large language models predict antimicrobial peptide activity and toxicity?

RSC Medicinal Chemistry, Journal Year: 2024, Volume and Issue: 15(6), P. 2030 - 2036

Published: Jan. 1, 2024

The large language models GPT-3 and GTP-3.5 were challenged to predict the activity hemolysis of antimicrobial peptides from their sequence compared recurrent neural networks support vector machines.

Language: Английский

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Bringing bioactive peptides into drug discovery: Challenges and opportunities for medicinal plants

Industrial Crops and Products, Journal Year: 2024, Volume and Issue: 222, P. 119855 - 119855

Published: Oct. 19, 2024

Language: Английский

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