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Fast and comprehensive structure identification in cryo-ET using deep learning and tomogram optimisation

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Fast and comprehensive structure identification in cryo-ET using deep learning and tomogram optimisation Cryo-electron tomography (cryo-ET) revolutionises cellular structure visualisation, but extracting biological insights is often hindered by limitations of subtomogram averaging (STA). With AlphaFold2's recent breakthroughs in structure prediction, identifying known structures in their 3D cellular context is often more important than structure determination by STA. The TomoFind project aims to significantly improve our ability to identify biomolecular structures within tomographic reconstructions. This goal will be pursued through three interconnected approaches: 1) Developing deep learning tools for rapid and comprehensive identification of known structures in tomograms; 2) Refining data acquisition parameters via mathematical modelling and computational optimisation; and 3) Enhancing reconstruction algorithms to fully leverage advancements from 1

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recordType

award

status

SIGNED

region

EU

value

260347.92

unit

EUR