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Possu Huang Lab @possuhuanglab.bsky.social · 7d

Read our preprint here: www.biorxiv.org/content/10.1... (8/8)

SLAE: Strictly Local All-atom Environment for Protein Representation

Building physically grounded protein representations is central to computational biology, yet most existing approaches rely on sequence-pretrained language models or backbone-only graphs that overlook...

www.biorxiv.org

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Possu Huang Lab @possuhuanglab.bsky.social · 7d

Work done by Yilin Chen, @tianyu.bsky.social , Cizhang Zhao and @hkws.bsky.social . Thank you all! (7/8)

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Possu Huang Lab @possuhuanglab.bsky.social · 7d

SLAE projects all-atom structures onto a smooth manifold! Unguided linear interpolation between conformations in SLAE latent space decodes to coherent intermediates structures. (6/8)

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Possu Huang Lab @possuhuanglab.bsky.social · 7d

SLAE extends our generative coverage assessment SHAPES to all-atom, per-residue-type granularity. Now we can compare de novo all-atom protein design models and spot residue-level environment biases. (5/8)

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Possu Huang Lab @possuhuanglab.bsky.social · 7d

Rich in atomic-environment signal, SLAE features outperform PLMs and task-specific models across diverse, challenging downstream tasks, including binding affinity, thermostability and chemical shift prediction. All-atom structure pretraining is all you need! (4/8)

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Possu Huang Lab @possuhuanglab.bsky.social · 7d

The SLAE latent landscape is organized in meaningful ways beyond amino acid identity. It separates residue embeddings along features including solvent accessibility, secondary structure and structural nativeness. (3/8)

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Possu Huang Lab @possuhuanglab.bsky.social · 7d

We design a deliberately hard two-part task to learn compact, expressive features: a local graph encoder projects each residue’s atomic interactions into a feature vector, while a global decoder learns to compose these local environment tokens into coherent macromolecules. (2/8)

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Possu Huang Lab @possuhuanglab.bsky.social · 7d

Introducing SLAE, our new framework to represent all-atom protein structures with residue local chemical environment tokens!
SLAE reasons over atomic interactions to recover structures and residue pairwise energetics, yielding a generalizable, physics-informed latent space. (1/8)

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Possu Huang Lab @possuhuanglab.bsky.social · Aug 25

💻 Sampling and training code for Protpardelle-1c is now available: github.com/ProteinDesig...

Feedback and requests are welcome!

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Possu Huang Lab @possuhuanglab.bsky.social · Aug 19

Code will be released soon on our GitHub: github.com/ProteinDesig...
Preprint: www.biorxiv.org/content/10.1...
Have fun sampling and training!

Conditional Protein Structure Generation with Protpardelle-1c

We present Protpardelle-1c, a collection of protein structure generative models with robust motif scaffolding and support for multi-chain complex generation under hotspot-conditioning. Enabling sidech...

www.biorxiv.org

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Possu Huang Lab @possuhuanglab.bsky.social · Aug 19

Our new set of all-atom models can sample plausible sidechains without stage-2 sampling. Sequence-dependent partial diffusion behavior occurs when we mask the dummy atoms.

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Possu Huang Lab @possuhuanglab.bsky.social · Aug 19

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Possu Huang Lab @possuhuanglab.bsky.social · Aug 19

We achieve competitive results on MotifBench and the RFdiffusion/La-Proteina motif scaffolding benchmarks with both backbone-only and all-atom models, proposing scaffolds to previously unsolved problems.

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Possu Huang Lab @possuhuanglab.bsky.social · Aug 19

We have a new collection of protein structure generative models which we call Protpardelle-1c. It builds on the original Protpardelle and is tailored for conditional generation: motif scaffolding and binder generation.

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Possu Huang Lab @possuhuanglab.bsky.social · Jul 29

Paper: authors.elsevier.com/a/1lWEe8YyDf...

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Possu Huang Lab @possuhuanglab.bsky.social · Jul 29

We include some additional analysis in the supplement, including secondary structure distributions.

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Possu Huang Lab @possuhuanglab.bsky.social · Jul 29

SHAPES now published in Cell Systems!

Possu Huang Lab @possuhuanglab.bsky.social · Jan 15

New preprint from our group! We propose SHAPES, a set of metrics to quantify the distributional coverage of generative models of protein structures with embeddings at different structural hierarchies and quantify undersampling / extrapolation behaviors.

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Reposted by Possu Huang Lab

Kevin K. Yang 楊凱筌 @kevinkaichuang.bsky.social · Feb 21

All-atom fixed backbone protein sequence design with FAMPNN

@richardshuai.bsky.social Talal Widatalla @possuhuanglab.bsky.social @brianhie.bsky.social

www.biorxiv.org/content/10.1...

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Reposted by Possu Huang Lab

Mohammed AlQuraishi @moalquraishi.bsky.social · Jan 15

I'm organizing a Keystone symposium, along with Liz Kellogg and @possuhuanglab.bsky.social, on machine learning and macromolecules. Mar 23-26 in Keystone, Colorado. We have a great lineup and deadlines are coming up soon!

Machine Learning Applied to Macromolecular Structure and Function | Keystone Symposia

Join us at the Keystone Symposia on Machine Learning Applied to Macromolecular Structure and Function, March 2025, in Keystone, with field leaders!

www.keystonesymposia.org

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Reposted by Possu Huang Lab

Kevin K. Yang 楊凱筌 @kevinkaichuang.bsky.social · Jan 15

A framework for evaluating how well generative models of protein structure match the distribution of natural structures.

@possuhuanglab.bsky.social

www.biorxiv.org/content/10.1...

Generative models capture a biased set of protein structure space

Generative models do not capture the full expressivity of PDB structures

Protein structure embeddings reveal undersampled and de novo structure space

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Possu Huang Lab @possuhuanglab.bsky.social · Jan 15

Preprint: www.biorxiv.org/content/10.1...
Code: github.com/ProteinDesig...
Dataset: zenodo.org/records/1458...

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Possu Huang Lab @possuhuanglab.bsky.social · Jan 15

Our supplement has many additional figures of the rasterized protein structure space, stratified by designable and not designable and spatially organized by ESM3 and ProtDomainSegmentor embeddings.

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Possu Huang Lab @possuhuanglab.bsky.social · Jan 15

One consequence of unbiased sampling of protein structure space is a higher likelihood of finding TERtiary Motifs (TERMs) which involve complex loops, with implications for functional protein design (see Figure 5 legend for group labels).

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Possu Huang Lab @possuhuanglab.bsky.social · Jan 15

Inspired by the FPD metric in EvoDiff for protein sequence distributions, we compute Fréchet distance using protein structure embeddings, also subsetted to designable and non-designable samples (FPD-D and FPD-ND).

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Possu Huang Lab @possuhuanglab.bsky.social · Jan 15

New preprint from our group! We propose SHAPES, a set of metrics to quantify the distributional coverage of generative models of protein structures with embeddings at different structural hierarchies and quantify undersampling / extrapolation behaviors.

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