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News
May 25, 2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
May 25, 2026
'The Humanities Serve as a Conscience'
Maria Mizernaia studies Soviet literature and the history of book publishing. In this interview for the HSE Young Scientists project, she discusses plans to publish a novel about besieged Leningrad, AI-provoked reflections on what it means to be human, and how novels can help satisfy our dopamine hunger.
May 25, 2026
Is It Possible to Predict a Citys Life Based on the Shape of Its Neighbourhoods?
Is it possible to predict, based on the configuration of streets and buildings, where a café will open or where traffic congestion will occur? Participants in the Spatial Analysis and Modelling of Urban Processes research and study group use open data and machine learning to identify universal patterns. Alexander Sheludkov and Eduard Somov discuss the purpose of comparing cities, the need for new forms of urban statistics, and how open data is transforming approaches to urban studies.

 

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Structure-based rational design of an enhanced fluorogen-activating protein for fluorogens based on GFP chromophore

Communications Biology. 2022. Vol. 5. Article 706.
Goncharuk M., Baleeva N., Nolde Dmitry E., Gavrikov A., Mishin A., Mishin A., Sosorev A., Arseniev A., Goncharuk S., Borshchevskiy V., Efremov Roman G., Mineev K., Baranov M.

“Fluorescence-Activating and absorption-Shifting Tag” (FAST) is a well-studied fluorogenactivating
protein with high brightness and low size, able to activate a wide range of
fluorogens. This makes FAST a promising target for both protein and fluorogen optimization.
Here, we describe the structure-based rational design of the enhanced FAST mutants,
optimized for the N871b fluorogen. Using the spatial structure of the FAST/N871b complex,
NMR relaxation analysis, and computer simulations, we identify the mobile regions in the
complex and suggest mutations that could stabilize both the protein and the ligand. Two of
our mutants appear brighter than the wild-type FAST, and these mutants provide up to 35%
enhancement for several other fluorogens of similar structure, both in vitro and in vivo.
Analysis of the mutants by NMR reveals that brighter mutants demonstrate the highest
stability and lowest length of intermolecular H-bonds. Computer simulations provide the
structural basis for such stabilization.

Research target: Chemistry Physics Biology
Language: English
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Keywords: молекулярная динамикаchemical synthesisхимический синтезNMR molecular dynamicsЯМРfluorescent proteinsфлуоресцентные белки
Publication based on the results of:
Supercomputer technologies, theory and atomistic multiscale modeling in materials physics and life sciences (2022)
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