Nathaniel Blalock
@nathanielblalock.bsky.social
130 followers 420 following 20 posts
Graduate Research Assistant in Dr. Philip Romero's Lab at Duke/Wisconsin Reinforcement and Deep Learning for Protein Redesign | He/him
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nathanielblalock.bsky.social
We are excited in the @philromero.bsky.social lab to share our new preprint introducing RLXF for the functional alignment of protein language models (pLMs) with experimentally derived notions of biomolecular function!
nathanielblalock.bsky.social
Let me know if you’d like me to clarify anything. I’m happy to talk!
nathanielblalock.bsky.social
Me too 🤪 It is really exciting to be submitting! We definitely learned a lot along the way
Reposted by Nathaniel Blalock
kevinkaichuang.bsky.social
Reinforcement learning with experimental feedback (RLXF) shifts protein language models so that they generate sequences with improved properties

@nathanielblalock.bsky.social @philromero.bsky.social

www.biorxiv.org/content/10.1...
nathanielblalock.bsky.social
Thank you for sharing our work @kevinkaichuang.bsky.social! It means a lot
nathanielblalock.bsky.social
Thank you for posting about our preprint!
nathanielblalock.bsky.social
We apply RLXF across five diverse protein classes to demonstrate its generalizability and effectiveness at generating optimized sequences by learning functional constraints beyond those captured during pre-training
nathanielblalock.bsky.social
Experimental validation reveals the RLXF-aligned model generates a higher fraction of functional sequences, a greater number of sequences more fluorescent than CreiLOV, and the brightest oxygen-independent fluorescent protein variant reported to date
nathanielblalock.bsky.social
We align ESM-2 to experimental fluorescence data from the CreiLOV flavin-binding fluorescent protein. The aligned model learns to prioritize mutations that enhance fluorescence, many of which are missed by the base model
nathanielblalock.bsky.social
RLXF follows a two-phase strategy inspired by RLHF. Supervised Fine-Tuning initializes the model in the right region of sequence space. Proximal Policy Optimization directly aligns sequence generation with feedback from a reward function like a sequence-function predictor
nathanielblalock.bsky.social
Pre-trained pLMs generate highly diverse sequences mirroring statistical patterns from natural proteins. But here's the challenge: they lack an explicit understanding of function, often failing to generate proteins with enhanced or non-natural activities. RLXF bridges this gap!
nathanielblalock.bsky.social
We are excited in the @philromero.bsky.social lab to share our new preprint introducing RLXF for the functional alignment of protein language models (pLMs) with experimentally derived notions of biomolecular function!
Reposted by Nathaniel Blalock
alexwild.bsky.social
Post the amazing science things you have done with federal funding.
nathanielblalock.bsky.social
It was a pleasure meeting you! Y'all are doing super interesting and relevant work. It will be cool to see how we can continue to interact and maybe collaborate in the future!
nathanielblalock.bsky.social
Favorite foods! Tandoori chicken and chili momo's: everestkitchen.ca. Onigiri! www.onigiriya.ca. Pho: www.viethouserestaurant.com.
nathanielblalock.bsky.social
My 1st NeurIPS was a wonderful experience - incredible to see so much research in protein design and reinforcement learning. Here are my favorite papers (and favorite places I got food in Vancouver 😋):
nathanielblalock.bsky.social
Hey Kevin, could I be added? This is really helpful for joining Bluesky! Thank you for doing it
Reposted by Nathaniel Blalock
kevinkaichuang.bsky.social
Three BioML starter packs now!

Pack 1: go.bsky.app/2VWBcCd
Pack 2: go.bsky.app/Bw84Hmc
Pack 3: go.bsky.app/NAKYUok

DM if you want to be included (or nominate people who should be!)