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Posts by Levitate Bio

Advancing Drug Discovery with Rosetta Pocket Protocols and Boltz-2: Finding and Modeling Druggable Binding Sites In the evolving field of computational drug design, two of the most important challenges are identifying druggable surface pockets on proteins and predicting how strongly small molecules will bind to ...

we've seen customers reaching back to physics based methods in order to solve their molecular modeling problems. In our latest post we explore how you can use the Rosetta "pocket opener" method with Boltz-2 to generate better protein-ligand models than Boltz-2 alone levitate.bio/boltz/ai/ml/...

9 months ago 1 2 0 0
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How to train your model An advanced protein design Bootcamp / Hackathon software developer event Summer RosettaCon 2025 – Bootcamp / Hackathon – Application is open, apply here This immersive, hands-on event i…

How to train your model.

This immersive, hands-on, advanced-level event is designed for researchers who are ready to move beyond “out-of-the-box” machine learning models for protein design. Join us for this Summer RosettaCon 2025 workshop/hackathon, more here: rosettacommons.org/2025/05/30/h...

10 months ago 2 3 0 0
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Levitate Bio Elevate your Protein Design

if you're interested in trying out our implementation of ipSAE along with the rest of our platform, get in touch and mention this post for a free 1 month trial levitate.bio/contact

10 months ago 2 1 0 0
Fixing the Flaws in AlphaFold’s Interface Scoring: Meet Dunbrack’s ipSAE Predicting protein-protein interactions Since AlphaFold2 was published, one of the major applications of the model was accurately predicting the structure of protein-protein interactions. By taking as...

As @rolanddunbrack.bsky.social recently discovered, one of the issues with the ipTM score used AF2 to represent the ipTM score is that it is biased by disordered regions of the protein outside the binding interface. To address this, he implemented a new metric, ipSAE.

levitate.bio/alphafold/mu...

10 months ago 11 4 3 1

there are still some real downsides to not actually modeling the physics

10 months ago 1 0 1 0
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Levitate Bio Elevate your Protein Design

De novo #ai protein binder design with levitate.bio! Watch this video to see how it is done, with no code, no scripts, all in an easy to use GUI in the browser. youtu.be/87U_g8awaEE?...

1 year ago 2 2 0 0
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BindCraft: one-shot design of functional protein binders Protein–protein interactions (PPIs) are at the core of all key biological processes. However, the complexity of the structural features that determine PPIs makes their design challenging. We present B...

BindCraft is a new AI de novo binder design tool from the Correia lab with 10-100% success rates. It's available today as an easy to use API using our API framework so you can deploy it in your own pipeline today.

Contact us at levitate.bio/contact to get started
www.biorxiv.org/content/10.1...

1 year ago 4 1 0 2
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Using Levitate Bench for RFDiffusion based de novo Binder Design
Using Levitate Bench for RFDiffusion based de novo Binder Design YouTube video by Levitate Bio

RFDiffusion is great for de novo design but it can be a bit frustrating to use. Check our our new GUI for making de novo binder design fast and easy! www.youtube.com/watch?v=rwpL...

1 year ago 2 1 0 0
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Levitate Bio Elevate your Protein Design

Come work with us! We're hiring a Scientific Sales Specialist. This is the perfect job if you've got a BS in biology or biochemistry and are looking to start a career in software sales. No sales experience is necessary. For more details on the job and how to apply go to levitate.bio/careers

1 year ago 0 0 0 1
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I sometimes forget how flexible proteins are. It becomes really apparent when you use NMR states for an animation. That poor cofactor is getting pushed around a lot 😅

#sciart #blender3d #biocatalysis

1 year ago 481 82 13 9

Quiz: Side chains R K E Q I M have 9 chi1+chi2 rotamers. They are:

{g+g+} {g+t} {g+g-} {tg+} {tt} {tg-} {g-g+} {g-t} {g-g-}.
4 of these have high energy & v. low populations compared to rest. Which 4? Why?

Leu has only 2 good rotamers. Which ones? why?

Good struct bio final exam Question.

1 year ago 8 1 2 0
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Learning high-accuracy error decoding for quantum processors - Nature A recurrent, transformer-based neural network, called AlphaQubit, learns high-accuracy error decoding to suppress the errors that occur in quantum systems, opening the prospect of using neural-network...

Beyond excited to introduce AlphaQubit, now published in
Nature!

AlphaQubit is a neural network for quantum error correction and achieves state-of-the-art accuracy on simulated and real-world data.

1 year ago 48 13 1 0
Figure 3 from the preprint.  its a 9 panel figure, and the figure caption makes for decent alt text so here it is:

(A) AfCycDesign predicted model for design GAB_D8 bound to GABARAP shown as surface, with
hotspot residues highlighted in green. (B) Affinity determination of GAB_D8 using SPR. SPR
sensorgram from 9-point single-cycle kinetics experiments (5-fold dilution, highest concentration 20
µM). Experimental data are shown in orange and global fits are shown with black lines. The dissociation
constant (KD) is also shown on the plot. (C) Superposition of chains E and F from the X-ray crystal
structure of GAB_D8 bound to GABARAPL1 and the AfCycDesign model. (D) AfCycDesign predicted
model for design GAB_D23 bound to GABARAP shown as surface, with hotspot residues highlighted in
green. (E) Affinity determination of GAB_D23 using SPR. SPR sensorgram from 9-point single-cycle
kinetics experiments (5-fold dilution, highest concentration 20 µM). Experimental data are shown in pink
and global fits are shown with black lines. The dissociation constant (KD) is also shown on the plot. (F)
Alignment of chains A and B from the X-ray crystal structure of GAB_D23 bound to GABARAP and the
AfCycDesign model. (G) Alignments of GAB_D8 and GAB_D23 macrocycle models to X-ray crystal
structures show close matches. (H) Comparison of GAB_D8 and GAB_D23 binding modes in the design models. (I) Competitive AlphaScreen response vs. concentration plot, IC50 from the average of
three experiments. Donor and acceptor beads in the assay are bound to GABARAP and
GABARAP-binding peptide K1, respectively.

Figure 3 from the preprint. its a 9 panel figure, and the figure caption makes for decent alt text so here it is: (A) AfCycDesign predicted model for design GAB_D8 bound to GABARAP shown as surface, with hotspot residues highlighted in green. (B) Affinity determination of GAB_D8 using SPR. SPR sensorgram from 9-point single-cycle kinetics experiments (5-fold dilution, highest concentration 20 µM). Experimental data are shown in orange and global fits are shown with black lines. The dissociation constant (KD) is also shown on the plot. (C) Superposition of chains E and F from the X-ray crystal structure of GAB_D8 bound to GABARAPL1 and the AfCycDesign model. (D) AfCycDesign predicted model for design GAB_D23 bound to GABARAP shown as surface, with hotspot residues highlighted in green. (E) Affinity determination of GAB_D23 using SPR. SPR sensorgram from 9-point single-cycle kinetics experiments (5-fold dilution, highest concentration 20 µM). Experimental data are shown in pink and global fits are shown with black lines. The dissociation constant (KD) is also shown on the plot. (F) Alignment of chains A and B from the X-ray crystal structure of GAB_D23 bound to GABARAP and the AfCycDesign model. (G) Alignments of GAB_D8 and GAB_D23 macrocycle models to X-ray crystal structures show close matches. (H) Comparison of GAB_D8 and GAB_D23 binding modes in the design models. (I) Competitive AlphaScreen response vs. concentration plot, IC50 from the average of three experiments. Donor and acceptor beads in the assay are bound to GABARAP and GABARAP-binding peptide K1, respectively.

This allows the models to "understand" the cyclic aspect of a cyclic peptide. And it seems to work pretty well! Decent looking designs within < 1Å of the crystal structure and <1nM IC50s. The code and models arent out yet but we look forward to tryin git out.

1 year ago 1 0 0 0
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Accurate de novo design of high-affinity protein binding macrocycles using deep learning The development of macrocyclic binders to therapeutic proteins typically relies on large-scale screening methods that are resource-intensive and provide little control over binding mode. Despite consi...

Here's an interesting new paper from IPD: www.biorxiv.org/content/10.1...

Cyclic peptides are a compelling format for a drug but a lot of computational design tools don't handle them well. Here they modified AF2 and RFDiffusion with a rotational encoding 1/

1 year ago 3 1 1 0

We're obviously going to use this account to talk about what we do, but we're also going to use it to share developments in other parts of the field that we think are particularly interesting

1 year ago 1 0 0 0
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Levitate Bio Elevate your Protein Design

Hello! We're a software company which spun out of Cyrus Bio. We sell cloud based tools to help make state of the art protein engineering and modeling methods more accessible. We're owned by the Rosetta Commons Foundation which will use our profits to support academic research.

levitate.bio

1 year ago 3 2 1 0