Research group of Prof. Doeke Hekstra | Harvard Department of Molecular & Cellular Biology
Cambridge, MA
Joined January 2021
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Hekstra Lab retweeted
1/5
Ensembles are hot in structural biology!
But here’s a question we don’t ask enough:
What information survives measurement and processing?
In our latest blog, @PilarCossio2 @EmilThomasen and @mhli41 have a think 🤔:
rs-station.github.io/2026/08…
🤖 Made with AI
Hekstra Lab retweeted
You shouldn’t have to have access to a GPU in-house to run ROCKET 💸 Phenix now hosts it on a server (free for academics rocket-9.gitbook.io/rocket-d…). Try phenix.rocket and reach out if you need help – this is brand new! 15/15
We're a big step closer to automated determination of protein structures. The key? Having AlphaFold listen to experimental data. Great work, led by @alisiafadini and @mhli41 in an inspiring collaboration with @MoAlQuraishi and Randy Read.
Here's a detailed thread by Alisia:
ROCKET 🚀 inference-time optimization of AlphaFold to fit structural data is published! rdcu.be/fa9YH
Since our preprint, we’ve pushed it to regimes where other methods break: low resolution, weak signal, real experimental edge cases. Here’s what we learned: 1/15
Hekstra Lab retweeted
ROCKET 🚀 inference-time optimization of AlphaFold to fit structural data is published! rdcu.be/fa9YH
Since our preprint, we’ve pushed it to regimes where other methods break: low resolution, weak signal, real experimental edge cases. Here’s what we learned: 1/15
Can AlphaFold refine structures for X-ray & Cryo-EM/ET data? Now it can! Great collab with @MoAlQuraishi and Randy Read, led by Alisia @FadiniAli and @mhli41. Preprint: biorxiv.org/content/10.1101/… Summary 🧵below. Code to follow...
Structural biology is in an era of dynamics & assemblies but turning raw experimental data into atomic models at scale remains challenging.
@mhli41 and I present ROCKET🚀: an AlphaFold augmentation that integrates crystallographic and cryoEM/ET data with room for more! 1/14.
Now in Cell: K+ ion channels enable electric currents consisting of potassium ions to generate action potentials in heart, brain, and muscle. For the first time, we can now see these currents atom-by-atom. cell.com/cell/abstract/S0092… a thread🧵 1/8
X-ray diffraction can access protein dynamics. Why so few datasets? One reason? Lack of powerful pulsed X-ray sources. XFELs are limited. Synchrotrons throw away >99% of photons. Working with Aaron Bewster (DIALS/LBNL) and BioCARS (biocars.uchicago.edu/) to change this! 1/2
Led by Rick Hewitt and Kevin Dalton, the result is Laue-DIALS, hosted by @rs-station.bsky.social at rs-station.github.io/laue-di…. Wanna try this out but need advice? Please get in touch. Further upgrades coming soon! Read more at pubs.aip.org/aca/sdy/article… 2/2
Crystal structures are *not* God-given truth. They approximate, w/ flaws & errors, X-ray diffraction data. AlphaFold etc. have been trained on structures, not data. SFCalculator now differentiably connects structures to diffraction data. What does this enable? 🧵 1/4
With SFCalculator you can also *train* structure prediction / generative models directly on X-ray data. As @JHoltonMADSci has shown, the data are much more accurate than the models. 3/4
Enabling better crystal structures & better structure prediction. Can’t wait to see where folks take that! SFCalculator is implemented in PyTorch, TensorFlow, and JAX. Open Source. See biorxiv.org/content/10.1101/… for more info & github.com/Hekstra-Lab/SFcal… to get started. 4/4
In evolution, members of a protein family are often variations on a theme. The dynamic states resemble stable states of other channels: The sequence of steps of ion permeation is conserved from channel to channel with variations in relative energy. 7/8
Fruit of a close collaboration with Rama Ranganathan's group, where Doeke, Ian (@kiwhite), and Rama first developed EFX, and BoRam worked tirelessly to grow crystals and analyze data. New hardware and software from @HekstraLab were critical for success! 8/8