AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Protein Folding

Showing 1 to 10 of 151 articles

Clear Filters

Rapid prediction of key residues for foldability by machine learning model enables the design of highly functional libraries with hyperstable constrained peptide scaffolds.

PLoS computational biology
Peptides are an emerging modality for developing therapeutics that can either agonize or antagonize cellular pathways associated with disease, yet peptides often suffer from poor chemical and physical stability, which limits their potential. However,...

Ab initio characterization of protein molecular dynamics with AIBMD.

Nature
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...

DPAM-AI: a domain parser for AlphaFold models powered by artificial intelligence.

Bioinformatics (Oxford, England)
MOTIVATION: Due to the breakthrough in protein structure prediction by AlphaFold, the scientific community has access to 200 million predicted protein structures with near-atomic accuracy from the AlphaFold protein structure DataBase (AFDB), covering...

Learning the language of life with AI.

Science (New York, N.Y.)
In 2021, a year before ChatGPT took the world by storm amid the excitement about generative artificial intelligence (AI), AlphaFold 2 cracked the 50-year-old protein-folding problem, predicting three-dimensional (3D) structures for more than 200 mill...

Beyond AlphaFold2: The Impact of AI for the Further Improvement of Protein Structure Prediction.

Methods in molecular biology (Clifton, N.J.)
Protein structure prediction is fundamental to molecular biology and has numerous applications in areas such as drug discovery and protein engineering. Machine learning techniques have greatly advanced protein 3D modeling in recent years, particularl...

AI protocol for retrieving protein dynamic structures from two-dimensional infrared spectra.

Proceedings of the National Academy of Sciences of the United States of America
Understanding the dynamic evolution of protein structures is crucial for uncovering their biological functions. Yet, real-time prediction of these dynamic structures remains a significant challenge. Two-dimensional infrared (2DIR) spectroscopy is a p...

Severe deviation in protein fold prediction by advanced AI: a case study.

Scientific reports
Artificial intelligence (AI) and deep learning are making groundbreaking strides in protein structure prediction. AlphaFold is remarkable in this arena for its outstanding accuracy in modelling proteins fold based solely on their amino acid sequences...

Unsupervised Learning of Progress Coordinates during Weighted Ensemble Simulations: Application to NTL9 Protein Folding.

Journal of chemical theory and computation
A major challenge for many rare-event sampling strategies is the identification of progress coordinates that capture the slowest relevant motions. Machine-learning methods that can identify progress coordinates in an unsupervised manner have therefor...

Learning transition path and membrane topological signatures in the folding pathway of bacteriorhodopsin (BR) fragment with artificial intelligence.

The Journal of chemical physics
Membrane protein folding in the viscous microenvironment of a lipid bilayer is an inherently slow process that challenges experiments and computational efforts alike. The folding kinetics is moreover associated with topological modulations of the bio...

Emerging frontiers in protein structure prediction following the AlphaFold revolution.

Journal of the Royal Society, Interface
Models of protein structures enable molecular understanding of biological processes. Current protein structure prediction tools lie at the interface of biology, chemistry and computer science. Millions of protein structure models have been generated ...