Towards Improving Cryo-Electron Tomography Through Software Feedback
ORAL
Abstract
Cryo-electron tomography can be used to solve the structure of proteins in their natural configuration in cells. These structure solutions can form the basis of drug discovery using machine learning tools. However, the decades-old stage design necessary for tilting the thinly cut cells during tomographic data acquisition introduces large lateral and vertical shifts that make tracking difficult. The reproducibility specifications in these stage positions are a few hundreds of nanometers in scale, far from the atomic size desired in tomographic reconstructions. In moving towards better tracking, better stages need to be developed through hardware and software. A software solution is discussed and experimental results are shown indicating a path forward that may improve current stage position reproducibility by an order of magnitude.
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Presenters
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Nathan Joshua
University of California, Los Angeles
Authors
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Nathan Joshua
University of California, Los Angeles
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Khang Tran
University of California, Los Angeles
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Tristan P O'Neill
University of California, Los Angeles (UCLA), University of California, Los Angeles
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Judy Su
University of California, Los Angeles