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How can we use natural evolution and genetic experiments to design protein functions?
Ada Shaw · June Shin · Debora Marks
Event URL: https://openreview.net/forum?id=XG8EoZm4HG »

A major goal in biotechnology is to be able to design/generate proteins while optimizing specific properties. Previous work has used probabilistic models of natural sequences sometimes together with labeled data to generate novel functional examples. These methods typically depend on identifying and aligning a set of proteins believed to have a similar function but the challenge is to know how narrow or broad to make the alignment. Furthermore, it is necessary to quantify how evolutionary information alone predicts and/or the number and types of labels are needed for designing functional and diverse sequences. We explore different model architectures using evolutionary sequences and sets of experimental labels to assess where labels are the most powerful; results are validated on existing published experimental data.

Author Information

Ada Shaw (Harvard University)
June Shin (Seismic Therapeutic)
Debora Marks (Harvard University)

Debora is a mathematician and computational biologist with a track record of using novel algorithms and statistics to successfully address unsolved biological problems. She has a passion for interpreting genetic variation in a way that impacts biomedical applications. During her PhD, she quantified the pan-genomic scope of microRNA targeting - the combinatorial regulation of protein expression and co-discovered the first microRNA in a virus.  As a postdoc she made a breakthrough in the classic, unsolved problem of ab initio 3D structure prediction of proteins using undirected graphical probability models for evolutionary sequences. She has developed this approach to determine functional interactions, biomolecular structures, including the 3D structure of RNA and RNA-protein complexes and the conformational ensembles of apparently disordered proteins. Her new lab at Harvard is interested in developing methods in deep learning to address a wide range of biological challenges including designing drug affinity libraries for large numbers of human genes, predicting epistasis in antibiotic resistance, the effects of genetic variation on human disease etiology and drug response and sequence design for biosynthetic applications.

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