Predicting Protein Structures using Deep Learning with Jonathan King

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Jonathan King is is currently a PhD student in Computational Biology at Carnegie Mellon. As part of our Virtual Deep Learning Salon he talks about how he used neural machine translation inspired methods to predict the shape and structure of proteins. He’ll share his research progress as well as his arguments as to why this problem is critical to the development of new medicines and understanding the molecular basis of life.

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Автор

Great work.... One question, how many data points do you have available? Because a transformer model probably overfits if you have let's say less than 1 million structures available.... Transformers work great on NLP tasks because bilions of sentences are available

osteinh
Автор

I thought Alphafold won the CASP competition in December 2018. Moreover, the alpha helix is always local. If there is a beta turn, the beta sheet is local. Otherwise, hydrogen bonds are global. More than everything else combined, global bonds of the beta sheet are the reason the protein folding problem hasn't been solved.

InfiniteUniverse