AIMC Topic: Peptides

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Peptide-based drug design using generative AI.

Chemical communications (Cambridge, England)
Peptide-based therapeutics have emerged as a significant treatment strategy, offering high specificity and tunable pharmacokinetics. Recent advances in Artificial Intelligence (AI) have shifted the focus towards structure prediction, generative desig...

AI-driven peptide discovery for endometrial cancer: deep generative modeling and molecular simulation in the big data era.

Journal of computer-aided molecular design
The integration of artificial intelligence (AI) with molecular modeling offers new opportunities to accelerate therapeutic discovery. In this study, we developed an AI-driven generative pipeline combining deep reinforcement learning (DRL), generative...

Deep learning guided design of protease substrates.

Nature communications
Proteases, enzymes that play critical roles in health and disease, exert their function through the cleavage of peptide bonds. Identifying substrates that are efficiently and selectively cleaved by target proteases is essential for studying protease ...

Prediction and analysis of anti-aging peptides using data augmentation and machine learning algorithms.

BMC biology
BACKGROUND: For most species, Aging is an inevitable biological process that poses significant challenges to global healthcare due to age-related diseases. Recent advances in peptide therapy have highlighted anti-aging peptides (AAPs) as a promising ...

Large Data Set Analysis Reveals Structural Origin of Peptide Collisional Cross Section Bimodal Behavior.

Journal of the American Society for Mass Spectrometry
Recent advances in ion mobility spectrometry have enabled the measurement of rotationally averaged collisional cross-sectional area (CCS) for millions of peptides as part of routine proteomic mass spectrometry workflows. One of the most striking find...

Machine Learning-Driven Extracellular Vesicles Peptidomics Powers Precision Classification of Endometrial Cancer.

Analytical chemistry
Endometrial cancer (EC) molecular subtyping is critical for prognosis and treatment but remains hindered by reliance on invasive tissue biopsies and time-consuming genomic sequencing. Here, we present a minimally invasive approach integrating MALDI-T...

SpecQuality: A Tool for Reliable Spectral Quality Assessment in Proteomics and Proteogenomics.

Journal of the American Society for Mass Spectrometry
Proteogenomics integrates genomics and mass spectrometry (MS) data to understand complex biological systems, disease mechanisms, and potential biomarkers. However, the high volume and noise in MS data present computational and interpretational challe...

Next-generation antifungal peptide discovery: the synergy of artificial intelligence and omics technologies.

World journal of microbiology & biotechnology
There is a growing concern about fungal infections and antifungal resistance among fungal species, underscoring the need for finding alternative treatments. Antifungal peptides (AFPs) are interesting and promising candidates for developing novel anti...

AI-designed PNA-peptide chimera overcomes suboptimal binding for dual inhibition of viral RdRp.

European journal of medicinal chemistry
The chimera combining the peptide nucleic acids (PNAs) and peptides represent a promising bifunctional strategy by concurrently binding with protein catalytic pocket and its associated RNA template, effectively disrupting protein's function. Conventi...

Carafe enables high quality in silico spectral library generation for data-independent acquisition proteomics.

Nature communications
Data-independent acquisition (DIA)-based mass spectrometry is becoming an increasingly popular mass spectrometry acquisition strategy for carrying out quantitative proteomics experiments. Most of the popular DIA search engines make use of in silico g...