ML for Protein Engineering Seminar

Deep learning guided design of protease substrates Proteases, a class of enzymes that play critical roles in health and disease, exert their function through the cleavage of peptide bonds. Identifying substrates that are efficiently and selectively cl...

Next Tues (6/3) at 4PM ET, we will have @avapamini.bsky.social
present "Deep learning guided design of protease substrates"

Paper: www.biorxiv.org/content/10.1...

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Atom level enzyme active site scaffolding using RFdiffusion2 De novo enzyme design starts from ideal active site descriptions consisting of constellations of catalytic residue functional groups around reaction transition state(s), and seeks to generate protein ...

Next Tues (5/20) at **2PM ET**, we will have Woody Ahern and Jason Yim present "Atom level enzyme active site scaffolding using RFdiffusion2"

Paper: www.biorxiv.org/content/10.1...

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Computational design of serine hydrolases The design of enzymes with complex active sites that mediate multistep reactions remains an outstanding challenge. With serine hydrolases as a model system, we combined the generative capabilities of ...

Next Tues (5/13) at 4PM ET, we will have Anna Lauko and Sam Pellock present "Computational design of serine hydrolases"

Paper: www.science.org/doi/10.1126/...

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Learning millisecond protein dynamics from what is missing in NMR spectra Many proteins’ biological functions rely on interconversions between multiple conformations occurring at micro-to millisecond (µs-ms) timescales. A lack of standardized, large-scale experimental data ...

Next Tues (4/29) at **4:30PM** ET, we will have @ginaelnesr.bsky.social @hkws.bsky.social present "Learning millisecond protein dynamics from what is missing in NMR spectra"

Paper: biorxiv.org/content/10.1...

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Targeting protein–ligand neosurfaces with a generalizable deep learning tool - Nature A computational deep learning approach is used to design synthetic proteins that target the neosurfaces formed by protein–ligand interactions, with applications in the development of new therapeutic m...

Next Tues (4/1) at **11AM** ET we will have Anthony Marchand present "Targeting protein–ligand neosurfaces with a generalizable deep learning tool"

Paper: www.nature.com/articles/s41...

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Bayesian Optimization of Antibodies Informed by a Generative Model of Evolving Sequences To build effective therapeutics, biologists iteratively mutate antibody sequences to improve binding and stability. Proposed mutations can be informed by previous measurements or by learning from larg...

Next Tues (3/18) at 4PM ET we will have Alan Amin
present "Bayesian Optimization of Antibodies Informed by a Generative Model of Evolving Sequences"

Paper: arxiv.org/abs/2412.07763

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Proteina: Scaling Flow-based Protein Structure Generative Models Recently, diffusion- and flow-based generative models of protein structures have emerged as a powerful tool for de novo protein design. Here, we develop Proteina, a new large-scale flow-based...

Next Tues (3/4) at 4PM ET we will have Karsten Kreis present "Proteina: Scaling Flow-based Protein Structure Generative Models"

Paper: openreview.net/forum?id=TVQ...

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ProtComposer: Compositional Protein Structure Generation with 3D... We develop ProtComposer to generate protein structures conditioned on spatial protein layouts that are specified via a set of 3D ellipsoids capturing substructure shapes and semantics. At inference...

Next Tues (2/18) at **5PM ET** we will have @hannes-stark.bsky.social and Bowen Jing present "ProtComposer: Compositional Protein Structure Generation with 3D Ellipsoids"

Paper: openreview.net/forum?id=0ct...

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Concept Bottleneck Language Models For protein design We introduce Concept Bottleneck Protein Language Models (CB-pLM), a generative masked language model with a layer where each neuron corresponds to an interpretable concept. Our architecture offers thr...

Next Tuesday, 2/4 @ 4 pm EST, Aya Ismail and Nathan Frey will present "Concept Bottleneck Language Models"
Paper: arxiv.org/abs/2411.06090
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InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders Protein language models (PLMs) have demonstrated remarkable success in protein modeling and design, yet their internal mechanisms for predicting structure and function remain poorly understood. Here w...

Next Tuesday, 1/21 @ 4 pm EST, Elana Simon
will present "InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders"

Paper: www.biorxiv.org/content/10.1...

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Mapping the combinatorial coding between olfactory receptors and perception with deep learning The sense of smell remains poorly understood, especially in contrast to visual and auditory coding. At the core of our sense of smell is the olfactory information flow, in which odorant molecules acti...

This Tuesday, 1/7 @ 4 pm EST, we'll be back with the 2025 seminar series!

Seyone Chithrananda will present "Mapping the combinatorial coding between olfactory receptors and perception with deep learning"

Paper: biorxiv.org/content/10.1...

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