With Zhuoqi Zheng, Bo Zhang, @kdidi.bsky.social @jasonyim.bsky.social @josephwatson.bsky.social Hai-Feng Chen, @briantrippe.bsky.social

And thanks as well to ChatGPT for turning my first banal attempt at a tweet-thread into emoji-packed click-bait for real work! 😂

This has been a big team effort – with contributions across 5 timezones 🌍. Many thanks to my fantastic coauthors on our whitepaper: arxiv.org/abs/2502.12479

This is a V0 pilot—we need your input for V1!
🧩 Know of an important motif? Add it to the benchmark!
🏗️ Help improve the pipeline & metrics (sequence-based? Side-chain-level?)
Let’s shape the future of motif scaffolding together!

Surprise: A modern baseline (RFdiffusion) fails on motifs scaffolded into de novo enzymes 15 years ago 🤯
This suggests modern deep learning methods aren’t always better than past methods!

MotifBench fixes this by providing:
🧪 A standardized evaluation pipeline
🏆 30 challenging motifs as test cases
📊Easy-to-use eval scripts and a leaderboard for method comparison
Now, results can be easily and consistently measured.

Recent progress in motif scaffolding has been exciting! But…
❌ Current evaluation are inconsistent, and results incomparable
❌ Widely used test cases are too easy
❌ Reproducibility is difficult
That’s where MotifBench comes in.

Motif scaffolding is a core challenge in protein design:
✅ Input: a motif (small functional substructure)
🎯 Goal: find a scaffolds (full proteins) that preserves the motif’s geometry.
But what's the state of methods for this problem?

🔥 Benchmark Alert! MotifBench sets a new standard for evaluating protein design methods in motif scaffolding.
Why does this matter? Reproducibility & fair comparison have been lacking—until now.
Paper: arxiv.org/abs/2502.12479 | Repo: github.com/blt2114/Moti...
A thread ⬇️