AIMC Topic: Protein Folding

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Artificial intelligence and first-principle methods in protein redesign: A marriage of convenience?

Protein science : a publication of the Protein Society
Since AlphaFold2's rise, many deep learning methods for protein design have emerged. Here, we validate widely used and recognized tools, compare them with first-principle methods, and explore their combinations, focusing on their effectiveness in pro...

Characterization of conformational flexibility in protein structures by applying artificial intelligence to molecular modeling.

Journal of structural biology
Recent AI applications have revolutionized the modeling of structurally unresolved protein regions, thereby complementing traditional computational methods. These state-of-the-art techniques can generate numerous candidate structures, significantly e...

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...

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...

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...

ThermoLink: Bridging disulfide bonds and enzyme thermostability through database construction and machine learning prediction.

Protein science : a publication of the Protein Society
Disulfide bonds, covalently formed by sulfur atoms in cysteine residues, play a crucial role in protein folding and structure stability. Considering their significance, artificial disulfide bonds are often introduced to enhance protein thermostabilit...

Protein sequence design on given backbones with deep learning.

Protein engineering, design & selection : PEDS
Deep learning methods for protein sequence design focus on modeling and sampling the many- dimensional distribution of amino acid sequences conditioned on the backbone structure. To produce physically foldable sequences, inter-residue couplings need ...

Recent Advances in Protein Folding Pathway Prediction through Computational Methods.

Current medicinal chemistry
The protein folding mechanisms are crucial to understanding the fundamental processes of life and solving many biological and medical problems. By studying the folding process, we can reveal how proteins achieve their biological functions through spe...