Вход на сайт

Просмотр новости

Найдите то, что Вас интересует

Sampling Conformational Ensembles of Highly Dynamic Proteins via Generative Deep Learning

Дата публикации: 08-10-2026 00:00:00

Proteins are inherently dynamic, and their conformational ensembles play a crucial role in biological function. Large-scale motions may govern the protein structure-function relationship, and numerous transient but stable conformations of intrinsically disordered proteins (IDPs) can play a crucial role in biological function. Investigating conformational ensembles to understand regulations and disease-related aggregations of IDPs is challenging, both experimentally and computationally. In this paper, we first introduce a deep learning-based model, termed Internal Coordinate Net (ICoN), which learns the physical principles of conformational changes from molecular dynamics simulation data. Second, we selected data points through interpolation in the learned latent space to rapidly identify novel synthetic conformations with sophisticated and large-scale side chains and backbone arrangements. Third, with the highly dynamic amyloid-β1-42 (Aβ42) monomer, our deep learning...

Схожие новости

#Наименование новостиТональностьИнформативностьДата публикации
1Determining crystal structures through crowdsourcing and coursework07.8405-10-2026
2Revolutionizing Alzheimer’s Diagnosis: A Hybrid Deep Learning Approach for Enhanced MRI Analysis0806-10-2026
3Discrete Autoregressive Switching Processes with Cumulative Shrinkage Priors for Graphical Modeling of Time Series Data06.8307-10-2026
4Utilizing time-domain thermoreflectance (TDTR) and developing transient thermoreflectance (TTR) to investigate doping-induced changes in the thermal transport of chemically-doped semiconducting polymer systems010.8208-10-2026
5Managing misfolded proteins to prevent Parkinson’s disease012.606-10-2026
6What AI Gets Wrong About Frontier Science, and Why It Matters05.7321-08-2026
7Mapping one atom's interaction with light uncovers an unbounded network of quantum states017.5828-09-2026
8Magnetic order survives weak quantum fluctuations in gapless magnets07.728-09-2026
9Beyond origami: New algorithm designs expandable 3D lattices08.6129-08-2026
10The Role of Emerging Technologies in the Future of Energy Sector05.8502-09-2026

Классификация: Наука. Схожих патентов: 0. Схожих новостей: 10. Тональность: 0. Информативность: 8.25. Источник: www.escholarship.org.