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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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Potassium channel blocker crafted by ɑ-hairpinin scaffold engineering

Biophysical Journal. 2021. Vol. 120. No. 12. P. 2471–2481.
Tabakmakher V., Gigolaev A., Peigneur S., Krylov N., Tytgat J., Chugunov A., Vassilevski A., Efremov R.

ɑ-Hairpinins are a family of plant defense peptides with a common fold presenting two short ɑ-helices stabilized by two invariant S–S-bridges. We have shown previously that substitution of just two amino acid residues in a wheat ɑ-hairpinin Tk-AMP-X2 leads to Tk-hefu-2 that features specific affinity to voltage-gated potassium channels KV1.3. Here, we utilize a combined molecular modeling approach based on molecular dynamics simulations and Protein Surface Topography technique to improve the affinity of Tk-hefu-2 to KV1.3 while preserving its specificity. An important advance of this work compared to our previous studies is transition from the analysis of various physico-chemical properties of an isolated toxin molecule to its consideration in complex with its target, a membrane-bound ion channel. As a result, a panel of computationally designed Tk-hefu-2 derivatives was synthesized and tested against KV1.3. The most active mutant Tk-hefu-10 showed an IC50 of ∼150 nM being >10 times more active than Tk-hefu-2 and >200 times more active than the original Tk-hefu. We conclude that ɑ-hairpinins provide an attractive disulfide-stabilized scaffold for the rational design of ion channel inhibitors. Furthermore, success rate can be considerably increased by the proposed “target-based” iterative strategy of molecular design.

Research target: Biology Chemistry
Priority areas: IT and mathematics
Language: English
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Keywords: Molecular Dynamics SimulationsPotassium channelspotassium channel blocker
Publication based on the results of:
Supercomputer technologies for atomistic modeling of physical processes in condensed matter (2021)
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