Ashley Hsu
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ashleyjhsu.bsky.social
Ashley Hsu
@ashleyjhsu.bsky.social
390 followers 500 following 0 posts
BME & CS @Duke | Protein Designer @ColumbiaMed
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Reposted by Ashley Hsu
aktaguy.bsky.social
Calling all structural biologists in the NYC metro area! The nysbdg.bsky.social is back for in-person events!

Register here 👉🏼 buff.ly/dGmJeau

I look forward to seeing you there! 😎

#structuralbiology #cyrstallography #cryoEM #NMR
#chromatography #proteinpurification #AKTAsystems
July 4, 2025 at 1:42 PM
Reposted by Ashley Hsu
delalamo.xyz
Protein conformational samplers like BioEmu and AFSample2 are unable to produce states that can be successfully docked against their binding partners. Moreover, many of the generated states are no closer to the target holo conformation than the default apo prediction
Figure 5. RMSD values computed for the binding interface residues between the Holo structure and Apo, 5 representative, or all 50 AFSample2-sampled conformations for all studied complex subunits. Green bars represent RMSD with the Apo structure. Red bars represent RMSD with the cluster centroids. Blue bars represent RMSD with all sampled conformations. The bars show the median RMSD, while the whiskers show the minimum and maximum values. Binding interfaces were defined as the residues within 10 Å of the other partner in the target Holo complex. Subunits are identified by the PDB ID of the complex, the UniProt ID of a protein, and whether it is in the Receptor or Ligand position
May 16, 2025 at 11:20 AM
Reposted by Ashley Hsu
bioinfoadv.bsky.social
🧬 "Flexible fitting of AlphaFold2-predicted models to cryo-EM density maps using elastic network models" explores improving #AlphaFold2 predictions by refining models against #CryoEM maps. doi.org/10.1093/bioa... #StructuralBiology #Bioinformatics
December 6, 2024 at 8:13 PM
Reposted by Ashley Hsu
franknoe.bsky.social
Super excited to preprint our work on developing a Biomolecular Emulator (BioEmu): Scalable emulation of protein equilibrium ensembles with generative deep learning from @msftresearch.bsky.social ch AI for Science.

www.biorxiv.org/content/10.1...
December 6, 2024 at 8:39 AM
Reposted by Ashley Hsu
judith-bernett.bsky.social
🧬💻Transferring my paper posts here, starting off with "Cracking the black box of deep sequence-based protein-protein interaction prediction" doi.org/10.1093/bib/..., which I published together with @itisalist.bsky.social and @dbblumenthal.bsky.social. So what was it about? 1/13 🧵
Overview figure summarizing the major analyses of the paper. In the setting employed by published methods, performances are strongly inflated regardless of model and dataset. When the positive training dataset is randomly rewired, there is only a slight decrease in performance. When the models are evaluated on unseen proteins that are unsimilar to the training proteins, performances become random.
November 28, 2024 at 9:10 AM
Reposted by Ashley Hsu
janhjensen.bsky.social
The Bigger the Better? Accurate Molecular Potential Energy Surfaces from Minimalist Neural Networks
arxiv.org/abs/2411.18121 #compchem
The Bigger the Better? Accurate Molecular Potential Energy Surfaces from Minimalist Neural Networks
Atomistic simulations are a powerful tool for studying the dynamics of molecules, proteins, and materials on wide time and length scales. Their reliability and predictiveness, however, depend directly...
arxiv.org
November 28, 2024 at 9:45 AM
Reposted by Ashley Hsu
estellaan.bsky.social
We did this crazy project where we tried to see if proteins could interact with their mirror image ligand. Seems impossible when proteins need to form 3D structures to interact. But what about if the interaction remains disordered???

www.nature.com/articles/s41...
Stereochemistry in the disorder–order continuum of protein interactions - Nature
Studies on protein–protein interactions using proteins containing d- or l-amino acids show that stereoselectivity of binding varies with the degree of disorder within the complex.
www.nature.com
November 27, 2024 at 8:31 PM
Reposted by Ashley Hsu
delalamo.xyz
Conformational dynamics smoothen the fitness landscapes of enzymes. Who knew? 🧪🧶
Another important biophysical factor underlying how fitness landscapes are traversed during evolution is conformational sampling, i.e. the ability of proteins to adopt multiple conformational substates.48,49 Different conformational substates could have different activities and thus contribute differently to the net fitness of the protein: conformation “A” could represent 5% of the populated states, but catalyze a new reaction that the other conformations cannot. Evolution can then act to smoothly “tune” the function of the protein through remote mutations that shift its conformational equilibrium by relatively stabilizing conformation “A”. This results in a smoother landscape because there are generally many more pathways by which a pre-existing conformational equilibrium between two conformations can be affected by remote mutations than the number of solutions that are available through complete remodeling of an active site. For example, the laboratory evolution of phosphotriesterase into an into arylesterase was tracked with protein crystallography, which revealed that almost all the mutations were remote from the active site and shifted the conformational equilibrium to favor pre-existing states that were beneficial to the new catalytic activity (50). The same process was observed in the evolution of a computationally designed Kemp eliminase, wherein gradual changes in the conformational sampling between inactive and active states resulting in a remarkably smooth evolutionary transition (51). These examples of evolutionary trajectories generated by laboratory evolution demonstrate how conformational flexibility can produce smooth fitness landscapes, and help explain the high frequency of remote mutations observed in evolutionary trajectories (52).
November 27, 2024 at 4:17 AM
Reposted by Ashley Hsu
sukritsingh92.bsky.social
New preprint! We prospectively evaluate structure-based methods and measure how well they classify clinical kinase mutations as inhibitor–resistant or –sensitizing when compared against our NanoBRET assay. Alchemical methods are well-suited to modeling distal mutants! Link: doi.org/10.1101/2024...
November 19, 2024 at 3:22 PM
Reposted by Ashley Hsu
laurenslosky.bsky.social
Can we design allosteric modulators that change a GPCR's preferred G protein? Yes!💥

BAMs at the GPCR-transducer interface change G protein subtype selectivity in predictable ways, enabling rational drug design.

The lab's 1st preprint! Check it out! 🧪💊🧠🟦https://doi.org/10.1101/2024.11.20.624209
🧵👇
Design of allosteric modulators that change GPCR G protein subtype selectivity
G protein-coupled receptors (GPCRs), the largest family of drug targets, can signal through 16 subtypes of Gα proteins. Biased compounds that selectively activate therapy-relevant pathways promise to ...
doi.org
November 22, 2024 at 4:09 PM
Reposted by Ashley Hsu
paulrobustelli.bsky.social
I'm excited to share a new manuscript with Jiaqi Zhu characterizing the conformational ensembles of covalent adducts formed by a disordered protein and small molecule drugs:
biorxiv.org/content/10.1...

Code + Ensembles:
github.com/paulrobustel...
November 24, 2024 at 5:27 PM
Reposted by Ashley Hsu
moalquraishi.bsky.social
#GoodThingsComeToThoseWait Sorry it's taking us a while. Promise that the wait will be well worth it! #OpenFold3 Love all the great work that's already been done #HelixFold3 #Ligo #Chai1 #Protenix #Boltz1
November 20, 2024 at 10:34 AM
Reposted by Ashley Hsu
adambroerman.bsky.social
Excited to join here and announce our new #proteindesign strategy for allosterically controlling the kinetics of protein-protein interactions! Read on for cool applications in cytokine signaling, biosensing, and protein circuits.
November 20, 2024 at 8:26 AM