Possu Huang Lab

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

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

SHAPES now published in Cell Systems!

FAMPNN architecture

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...

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!

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

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...

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

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.

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).

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).

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.

This is a clever way to use synthetic biology: taking a toxin that overactivate the immune system (superantigen), rationally modify its core components and transform it into a platform of immunotherapy agents.
Congratulations to @haotiandu.bsky.social and @possuhuanglab.bsky.social on the 2 papers!

Checkout out these two bombshell papers from @possuhuanglab.bsky.social @stanfordmedicine.bsky.social, computational design of antigen-specific binders to MHC-I or -II, with applications to next gen targeted therapeutics 🤯

Science in 60 Seconds: Haotian Du, PhD student with Possu Huang’s lab, explains her research on creating novel proteins that expands the possibilities for detecting more cancer types.
@possuhuanglab.bsky.social @haotiandu.bsky.social

Incredible work from BioE Professor’s Possu Huang Lab @possuhuanglab.bsky.social @haotiandu.bsky.social - pioneering novel proteins with new structures and functions, unlocking new possibilities for detecting more cancer types. Congratulations!

@haotiandu.bsky.social is now on BlueSky 😄

Excited to share that TRACeR stories are out🙌

Targeting peptide antigens using a multiallelic MHC I-binding system - Nature Biotechnology A protein platform rapidly develops peptide-focused major histocompatibility complex class I binders with high specificity.

A new system, TRACeR-I, targets peptides on MHC I molecules with high precision, enabling specific tumor and virus cell killing.

It powers CAR-T and BiTE therapies, showing potential to transform cancer and infectious disease treatment.

www.nature.com/articles/s41...

The TRACeR project was led by Haotian Du and Jingjia Liu and a collaborative effort between the Huang Lab at @bioe-stanford.bsky.social, Stanford BioX and the Sgourakis Lab at UPenn/CHOP

@naturebiotech.bsky.social @stanfordmedicine.bsky.social @uofpenn.bsky.social @stanfordbiosci.bsky.social

7/ Our platform offers a convenient strategy for creating MHC binders: from a master library, peptide-specific MHC binders can be isolated simply by a few rounds of yeast surface display enrichment.

6/ We show proof-of-concept targeted killing of tumor cells using TRACeR-I.

5/ The mechanism of TRACeR-1 can achieve discrimination of single point mutants to peptides.

4/ But MHC-I peptides have bulged conformations. For TRACeR-I, we use a set of spatially-defined amino acid residues to “box-in” on the peptide and achieve recognition.

3/ Peptides in MHC-II are in an extended conformation. TRACeR-II uses a surface loop to reach into MHC-II’s peptide binding groove to recognize the peptide. We use a single loop to convey specificity to the peptide in the binding groove.

2/ MHC antigens are central to the immune system and can be leveraged for disease-specific targeting. These peptides are derived from intra-cellular proteins and ingested foreign antigens for presentation by class-I and class-II MHCs, respectively.

1/ In two back-to-back papers, we present our de novo TRACeR platform for targeting MHC-I and MHC-II antigens

TRACeR for MHC-I: go.nature.com/4gcLzn5
TRACeR for MHC-II: go.nature.com/4gj5OQk