Industrial Crops and Products, Journal Year: 2024, Volume and Issue: 222, P. 119855 - 119855
Published: Oct. 19, 2024
Language: Английский
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
Citations
8
Trends in Food Science & Technology, Journal Year: 2025, Volume and Issue: unknown, P. 104887 - 104887
Published: Jan. 1, 2025
Language: Английский
Citations
1
Food Chemistry, Journal Year: 2025, Volume and Issue: unknown, P. 144047 - 144047
Published: March 1, 2025
Language: Английский
Citations
0
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
Citations
0
Current Microbiology, Journal Year: 2024, Volume and Issue: 81(10)
Published: Sept. 6, 2024
Language: Английский
Citations
2
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
Citations
1
Industrial Crops and Products, Journal Year: 2024, Volume and Issue: 222, P. 119855 - 119855
Published: Oct. 19, 2024
Language: Английский
Citations
0