Illuminating protein space with a programmable generative model

Three billion years of evolution have produced a tremendous diversity of protein molecules, but it is yet unknown how thoroughly evolution has sampled the space of possible protein folds and functions. Here, by introducing a new, scalable generative prior for proteins and protein complexes, we provide further evidence that earth's extant molecular biodiversity represents only a small fraction of what is possible for polypeptides. To enable this, we introduce customized neural networks that enable long-range reasoning, that respect the statistical structures of polymer ensembles, and that can efficiently realize 3D structures of proteins from predicted geometries. We show how this framework broadly enables protein design under auxiliary constraints, which can be any composition of semantics, substructure, symmetries, shape, and even natural language prompts.

Bio: John Ingraham is the Head of Machine Learning at Generate Biomedicines, Inc, where he leads a team of scientists and engineers developing new kinds of machine learning systems for protein design. He has spent most of his career developing structured statistical models of the rich diversity found in protein sequences and structures, including as a postdoc at MIT CSAIL with Tommi Jaakkola and Regina Barzilay working on some of the first generative models for structure-based sequence design and before that in his PhD with Debora Marks at Harvard Medical School developing deep learning and statistical-physics inspired models of deep evolutionary sequence variation and protein folding.

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