AIMC Topic: Peptides
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Artificial intelligence in peptide-based drug design.
Drug discovery today
Jan 20, 2025
Protein-protein interactions (PPIs) are fundamental to a variety of biological processes, but targeting them with small molecules is challenging because of their large and complex interaction interfaces. However, peptides have emerged as highly promi...
From Sea Cucumbers to Soft Robots: A Photothermal-Responsive Hydrogel Actuator with Shape Memory.
ACS applied materials & interfaces
Jan 19, 2025
Soft robotics has undergone considerable progress driven by materials that can effectively transduce external stimuli into mechanical actuation. Here, we report the development of a photothermal-responsive hydrogel actuator with shape memory capabili...
AntiT2DMP-Pred: Leveraging feature fusion and optimization for superior machine learning prediction of type 2 diabetes mellitus.
Methods (San Diego, Calif.)
Jan 10, 2025
Pancreatic α-amylase breaks down starch into isomaltose and maltose, which are further hydrolyzed by α-glucosidase in the intestine into monosaccharides, rapidly raising blood sugar levels and contributing to type 2 diabetes mellitus (T2DM). Syntheti...
IL-6-Inducing Peptide Prediction Based on 3D Structure and Graph Neural Network.
Biomolecules
Jan 10, 2025
Interleukin-6 (IL-6) is a potent glycoprotein that plays a crucial role in regulating innate and adaptive immunity, as well as metabolism. The expression and release of IL-6 are closely correlated with the severity of various diseases. IL-6-inducing ...
High-Accuracy Identification and Structure-Activity Analysis of Antioxidant Peptides via Deep Learning and Quantum Chemistry.
Journal of chemical information and modeling
Jan 7, 2025
Antioxidant peptides (AOPs) hold great promise for mitigating oxidative-stress-related diseases, but their discovery is hindered by inefficient and time-consuming traditional methods. To address this, we developed an innovative framework combining ma...
Enhanced Sampling Simulations of RNA-Peptide Binding Using Deep Learning Collective Variables.
Journal of chemical information and modeling
Jan 7, 2025
Enhanced sampling (ES) simulations of biomolecular recognition, such as binding small molecules to proteins and nucleic acid targets, protein-protein association, and protein-nucleic acid interactions, have gained significant attention in the simulat...
Machine learning discovery of novel antihypertensive peptides from highland barley protein inhibiting angiotensin I-converting enzyme (ACE).
Food research international (Ottawa, Ont.)
Jan 6, 2025
Hypertension is a major global health concern, and there is a need for new antihypertensive agents derived from natural sources. This study aims to identify novel angiotensin I-converting enzyme (ACE) inhibitors from bioactive peptides derived from f...
pACP-HybDeep: predicting anticancer peptides using binary tree growth based transformer and structural feature encoding with deep-hybrid learning.
Scientific reports
Jan 2, 2025
Worldwide, Cancer remains a significant health concern due to its high mortality rates. Despite numerous traditional therapies and wet-laboratory methods for treating cancer-affected cells, these approaches often face limitations, including high cost...
π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing.
Nature communications
Jan 2, 2025
Peptide sequencing via tandem mass spectrometry (MS/MS) is essential in proteomics. Unlike traditional database searches, deep learning excels at de novo peptide sequencing, even for peptides missing from existing databases. Current deep learning mod...
NovoRank: Refinement for Peptide Sequencing Based on Spectral Clustering and Deep Learning.
Journal of proteome research
Dec 31, 2024
peptide sequencing is a valuable technique in mass-spectrometry-based proteomics, as it deduces peptide sequences directly from tandem mass spectra without relying on sequence databases. This database-independent method, however, relies solely on im...
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