lindsey guan
@lindseyguan.bsky.social
680 followers 140 following 10 posts
she/her | PhD student in the Keating Lab @MIT | Bio & CS @UCBerkeley
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Reposted by lindsey guan
eli.lipsitz.net
The Game Bub Crowd Supply campaign is live! And it's been redesigned with a new horizontal design.

You can now back the campaign to get your very own pre-assembled Game Bub!

www.crowdsupply.com/second-bedro...
Game Bub
An open-source FPGA retro-emulation handheld
www.crowdsupply.com
lindseyguan.bsky.social
we hope that this analysis is useful for folks interested in predicting protein-peptide or domain-SLiM interactions (like us!). and we’re big fans of making rigorous test sets, which gets harder and harder as modeling tasks get more complex.

www.biorxiv.org/content/10.1...
www.biorxiv.org
lindseyguan.bsky.social
and in the few cases of truly novel complexes, success was often explained by pairing common secondary structure elements together. e.g., AF3 prediction of PDB 8HDJ (5/5)
lindseyguan.bsky.social
while the target MSA was almost always necessary, we were able to rule out inter-chain covariation as important for good performance by shuffling sequence pairing. e.g., AF2 prediction of PDB 3TRS (4/5)
lindseyguan.bsky.social
we even found that this bias might even mislead predictions to use wrong but common binding sites. e.g., Boltz-1 prediction of PDB 8V8E (3/5)
lindseyguan.bsky.social
we found some concerning evidence of memorization for both pre-cutoff structures and structures homologous to pre-cutoff structures 🤔 (2/5)
lindseyguan.bsky.social
preprint is out for the first project of my phd! we investigated how structure prediction models work for protein-peptide complexes, where the peptide often doesn’t have a deep MSA / there’s no good paired MSA 🧵 (1/5)

www.biorxiv.org/content/10.1...
www.biorxiv.org
Reposted by lindsey guan
biorxiv-bioinfo.bsky.social
How AlphaFold and related models predict protein-peptide complex structures https://www.biorxiv.org/content/10.1101/2025.06.18.660495v1
Reposted by lindsey guan
davidpfau.com
The war on science in the US is already having an effect on private sector research like AlphaFold. Bears repeating but the private sector builds on top of things created by academic research for the public good. This hurts everyone.
Reposted by lindsey guan
eli.lipsitz.net
There’s been a lot of interest in Game Bub, so I’m going to be launching a crowdfunding campaign through @crowdsupply.bsky.social!

Sign up for updates and to hear when the campaign goes live: www.crowdsupply.com/second-bedro...

This will be an assembled product, not just a kit!
Game Bub
An open-source FPGA retro-emulation handheld
www.crowdsupply.com
Reposted by lindsey guan
labonnelab.bsky.social
Published in the Chicago Sun Times today
Reposted by lindsey guan
drannecarpenter.bsky.social
So one approach is to omit these words from your research. The other is for all of us to double down on including these words in EVERYTHING.
rebeccacalisi.bsky.social
This will be the biggest game of Taboo the U.S. has ever seen.
Reposted by lindsey guan
simonmathis.bsky.social
A weekend project from a while back -- this little package (with no dependencies) allows you to interact with pymol remotely.

I use it a lot for my protein design workflows together with @biotite.bsky.social.

Just `pip install pymol-remote`
lindseyguan.bsky.social
To learn more, read out the publication in PRX Life and use the code (github.com/MadryLab/rla). Thanks to Foster Birnbaum, Saachi Jain, Aleksander Madry, and Amy E. Keating for this work! (my bsky account is simple a vessel 🦋) 3/3
GitHub - MadryLab/rla: Residue Level Alignment
Residue Level Alignment . Contribute to MadryLab/rla development by creating an account on GitHub.
github.com
lindseyguan.bsky.social
RLA has been tested on several benchmark sets, including several design libraries of miniprotein designs for a variety of protein targets. For all but two targets, filtering with RLA results in a higher success rate after subsequent AF2-based filtering. 2/3
lindseyguan.bsky.social
✨new work from our lab!✨

RLA is a contrastive-learning approach that assesses sequence-structure compatibility by aligning sequence and structure machine learning representations! RLA can successfully filter protein binder designs. 1/3 🧶

journals.aps.org/prxlife/abst...
Jointly Embedding Protein Structures and Sequences through Residue Level Alignment
Residue Level Alignment integrates protein sequence and structure information in a self-supervised model, improving speed and precision in predicting protein binding and structural stability.
journals.aps.org