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Upcoming Seminars
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Aug 7: Machine Learning-based Beam Size Stabilization at ALS
Date: August 7, 2020 1:00pm (remote)
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In state-of-the-art synchrotron light sources the overall source stability is limited by the achievable level of electron beam size stability. This source size stability is presently on the few- percent level, which is still 1-2 orders of magnitude larger than already demonstrated stability of source position/angle (slow/fast orbit feedbacks) and current (top-off injection). Until now, source size stabilization has been achieved through corrections based on a combination of static predetermined physics models and lengthy calibration measurements (feed-forward tables), periodically repeated to counteract drift in the accelerator and instrumentation. We now demonstrate for the first time [PRL 123 194801 (2019)], how application of machine learning allows for a physics- and model-independent stabilization of source size relying only on previously existing instrumentation in ALS. Such feed-forward correction based on neural networks that can be continuously online-retrained achieves source size stability as low as 0.2 microns rms (0.4%) which results in overall source stability approaching the sub-percent noise floor of the most sensitive experiments.
Aug 14: Sequence-guided protein structure determination using graph convolutional and recurrent networks
Date: August 14, 2020 1:00pm (remote)
Speaker: Po-Nan Li (Stanford)
Single particle imaging performed at cryogenic electron microscopy (cryo-EM) facilities, including the S2C2 at SLAC, now routinely outputs high-resolution data for large proteins and their complexes. Building an atomic model into a cryo-EM density map, however, remains challenging, particularly when no structure for the target protein is known a priori. Existing protocols for this type of task often rely on significant human intervention and can take hours to days to produce an output. Here, we present a fully automated, template-free model building approach that is based entirely on neural networks. We use a graph convolutional network (GCN) to generate an embedding from a set of rotamer-based amino acid identities and candidate 3-dimensional C-alpha locations. Starting from this embedding, we use a bidirectional long short-term memory (LSTM) module to order and label the candidate identities and atomic locations consistent with the input protein sequence to obtain a protein structural model. Our approach paves the way for determining protein structures from cryo-EM densities at a fraction of the time of existing approaches and without the need for human intervention.
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