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Organizers: Joakim Andén, Dorit Hanein, Roy R. Lederman and Steven J. Ludtke

All Talks – Past talks – Upcoming Talks

Next Talk

Minhuan Li
Flatiron Research Fellow
Flatiron Institute

Date and time: October 7th, 2026 Wednesday at 8am PST / 11am EST / 4pm GMT / 5pm CET / 12am China

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From Prediction to Scientific Inference: Grounding Generative Models in Biological Experiments

Biological measurements and predictive models are both scaling rapidly, yet the interface between them remains a bottleneck. In structural biology, deposited structures have enabled major advances in AI but capture only part of the underlying experimental evidence. Connecting models more directly to measurements could better exploit information about conformational populations, experimental context, and uncertainty.

This talk presents experiment-grounded generative inference: combining learned priors with measurements to infer sample-specific biological states. I will develop these ideas primarily in structural biology, where the states of interest are biomolecular conformations. I will focus on ROCKET, which uses AlphaFold as a learned prior for inference from X-ray crystallography and cryo-EM measurements, and EmbedOpt, which steers conditional protein diffusion models by optimizing their conditioning embeddings at inference time. Both approaches use pretrained models as structured search spaces, allowing experimental evidence to guide inference beyond a model’s default prediction. I will also briefly discuss GOTO, an optimal-transport-based objective that provides informative optimization signals for joint inference of molecular states and cryo-EM imaging parameters.

I will close with an outlook on MIMIC and possible routes for making measurements first-class inputs and training signals in multimodal biomolecular models. The broader goal is to use learned priors to interpret experimental evidence—and ultimately use that evidence to improve the priors themselves.

References:
Golkar, Li, et al. — MIMIC (arXiv, 2026)
Fadini, Li et al. — AlphaFold as a prior (Nature Methods, 2026)
Li et al. — EmbedOpt (arXiv, 2026)
Woollard, Li et al. — GOTO (bioRxiv, 2025)

Last Talk

Joey Davis
Associate Professor of Biology
Massachusetts Institute of Technology

Date and time: May 6th, 2026 Wednesday at 8am PST / 11am EST / 4pm GMT / 5pm CET / 12am China

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Structural Ensembles as Molecular Phenotypes of Translation

Structural studies of ribosomes by single particle cryogenic electron microscopy (cryoEM) have traditionally relied on purified or reconstituted samples, with particles often trapped in desired states using genetic, pharmacological, or biochemical perturbations. While informative, such approaches can fail to capture the full diversity of structural states and associated factors present in cells. In this talk, I will describe our development of cryoDRGN and cryoPRISM, a rapid ex vivo workflow for high-resolution analysis of ribosomes directly from cell lysates, and show how this approach enables visualization and quantitative analysis of ribosomal ensembles spanning assembly, translation, and hibernation under near-native conditions. I will focus on the idea that these structural ensembles can be interpreted as molecular phenotypes, and that perturbations such as small molecules, protein factors, or environmental stresses reshape the structural landscape by shifting the distribution of states. Importantly, I will discuss how such molecular phenotypes can reveal the mechanisms by which these perturbations modulate translation. Examples will be drawn from our recent work in bacterial and archaeal systems, illustrating how we are using cryoPRISM to understand how translation is modulated across diverse biological contexts.