AIMC Topic: Peptides
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Ab initio characterization of protein molecular dynamics with AIBMD.
Nature
Nov 6, 2024
Biomolecular dynamics simulation is a fundamental technology for life sciences research, and its usefulness depends on its accuracy and efficiency. Classical molecular dynamics simulation is fast but lacks chemical accuracy. Quantum chemistry methods...
Antiviral Peptide-Generative Pre-Trained Transformer (AVP-GPT): A Deep Learning-Powered Model for Antiviral Peptide Design with High-Throughput Discovery and Exceptional Potency.
Viruses
Oct 25, 2024
Traditional antiviral peptide (AVP) discovery is a time-consuming and expensive process. This study introduces AVP-GPT, a novel deep learning method utilizing transformer-based language models and multimodal architectures specifically designed for AV...
LineageFilter: Improved Proteotyping of Complex Samples Using Metaproteomics and Machine Learning.
Journal of proteome research
Oct 19, 2024
Metaproteomics is a powerful tool to characterize how microbiota function by analyzing their proteic content by tandem mass spectrometry. Given the complexity of these samples, accurately assessing their taxonomical composition without prior informat...
Development of Peptide Identification System for ToF-SIMS Spectra Using Supervised Machine Learning.
Journal of the American Society for Mass Spectrometry
Oct 12, 2024
Time-of-flight secondary ion mass spectrometry (ToF-SIMS) data interpretation for organic materials is complicated because of various fragment ions produced from each molecule and the overlapping of certain mass peaks from different molecules. Fragme...
Artificial Intelligence-Enhanced Analysis of Genomic DNA Visualized with Nanoparticle-Tagged Peptides under Electron Microscopy.
Small (Weinheim an der Bergstrasse, Germany)
Oct 9, 2024
DNA visualization has advanced across multiple microscopy platforms, albeit with limited progress in the identification of novel staining agents for electron microscopy (EM), notwithstanding its ability to furnish a broad magnification range and high...
HybAVPnet: A Novel Hybrid Network Architecture for Antiviral Peptides Prediction.
IEEE/ACM transactions on computational biology and bioinformatics
Oct 9, 2024
Viruses pose a great threat to human production and life, thus the research and development of antiviral drugs is urgently needed. Antiviral peptides play an important role in drug design and development. Compared with the time-consuming and laboriou...
iACP-DFSRA: Identification of Anticancer Peptides Based on a Dual-channel Fusion Strategy of ResCNN and Attention.
Journal of molecular biology
Oct 1, 2024
Anticancer peptides (ACPs) have been widely applied in the treatment of cancer owing to good safety, rational side effects, and high selectivity. However, the number of ACPs that have been experimentally validated is limited as identification of ACPs...
DeepAIP: Deep learning for anti-inflammatory peptide prediction using pre-trained protein language model features based on contextual self-attention network.
International journal of biological macromolecules
Sep 30, 2024
Non-steroidal anti-inflammatory drugs (NSAIDs), glucocorticoids, and other immunosuppressants are commonly used medications for treating inflammation. However, these drugs often come with numerous side effects. Therefore, finding more effective metho...
Decoding the impact of neighboring amino acids on ESI-MS intensity output through deep learning.
Journal of proteomics
Sep 26, 2024
Peptide-level quantification using mass spectrometry (MS) is no trivial task as the physicochemical properties affect both response and detectability. The specific amino acid (AA) sequence affects these properties, however the connection between sequ...
Predicting Antidiabetic Peptide Activity: A Machine Learning Perspective on Type 1 and Type 2 Diabetes.
International journal of molecular sciences
Sep 18, 2024
Diabetes mellitus (DM) presents a critical global health challenge, characterized by persistent hyperglycemia and associated with substantial economic and health-related burdens. This study employs advanced machine-learning techniques to improve the ...
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