Advances of deep Neural Networks (DNNs) in the development of peptide drugs

Future Medicinal Chemistry, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 15

Published: Feb. 12, 2025

Peptides are able to bind difficult disease targets with high potency and specificity, providing great opportunities meet unmet medical requirements. Nevertheless, the unique features of peptides, such as their small size, structural flexibility, scarce data availability, bring extra challenges design process. Firstly, this review sums up application peptide drugs in treating diseases. Then, probes into advantages Deep Neural Networks (DNNs) predicting designing structures. DNNs have demonstrated remarkable capabilities prediction, enabling accurate three-dimensional modeling through models like AlphaFold its successors. Finally, deliberates on coping strategies development drugs, along future research directions. Future directions focus further improving accuracy efficiency DNN-based drug design, exploring novel applications accelerating clinical translation. With continuous advancements technology accumulation, poised play an increasingly crucial role field development.

Language: Английский

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Sensory Plasticity Caused by Up-down Regulation Encodes the Information of Short-term Learning and Memory

iScience, Journal Year: 2025, Volume and Issue: 28(4), P. 112215 - 112215

Published: March 13, 2025

Language: Английский

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Neutrophil elastase binds at the central domain of extracellular Toll-like receptor 4: AI prediction, docking, and validation in disease model

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 18, 2025

The interaction between Neutrophil Elastase (NE) and Toll-like receptor 4 (TLR4) has attracted substantial scientific attention, particularly regarding its potential role in cardiovascular diseases. Employing AlphaFold2, biomolecular docking, MMGBSA calculation we aimed to predict their binding validated the results through a co-immunoprecipitation study rat model with isoproterenol (ISO) -induced cardiac hypertrophy. Our findings strongly suggest specific plausible NE TLR4, distinct from other neutrophil-derived serine proteases. Notably, AlphaFold2's precision was confirmed cross-validation known protein crystal structures, while Consurf analysis emphasized evolutionary variable conserve - TLR4 site. HADDOCK, RosettaDock, ZDOCK, MD simulation, calculations, superimposition stabilized structure complex all predicted strong TLR4. animal experiments revealed elevated expression hypertrophied myocardium following ISO infusion, data confirming physical Overall, this sheds light on intricate molecular association underlining significance pathophysiology. Furthermore, it underscores reliability as robust tool for predicting protein-protein interactions structures.

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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Successful prediction of LC8 binding to intrinsically disordered proteins sheds light on AlphaFold’s black box

Frontiers in Molecular Biosciences, Journal Year: 2025, Volume and Issue: 12

Published: April 23, 2025

Introduction LC8 is a hub protein involved in many processes from tumor suppression and cell cycle regulation to neurotransmission viral infection. Despite recent progress, prediction of binding sites for plagued by motif variability multitude weakly motifs, especially when depends on multivalency. Our site algorithm, LC8Pred has proven useful uncovering new binders, but insufficient finding all sites. Methods To address this, we probed the ability general structure predictor, AlphaFold, predict whether given sequence binds LC8. Certain combinations in-built AlphaFold scores were extracted distributions binders compared nonbinders. Results successfully places proteins at correct interface A set threshold values built-in enables differentiation between known nonbinders with minimal false positive (8%) acceptable negative rates (20%). This cutoff, along more inclusive was used elusive bind Discussion Correlations affinities provide insight into black box indicate that learned an inaccurate energy function nevertheless making inferences conclusions about physical systems. Binding predicted this method can be prioritized investigation comparing result LC8Pred, local structure, evolutionary conservation.

Language: Английский

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