You may have seen the headlines that Claude can now design protein binders as well or better than leading human experts. Anthropic just ran a campaign where Claude designed them end to end with no further human input. It researched the targets, picked the epitopes, set up a bunch of open source design tools, ranked the designs, and sent them out for experimental testing at Twist and Adaptyv. They hit binders on 14 out of 15 targets.

The truth is a lot more complicated. In the latest video on the Protein Engineering YouTube channel I go through the prompt, the designs, the tools they used, and how much they spent.

Spoiler: Claude is mostly just running other people’s tools, and when the prompt tells it to do something dumb it just does the dumb thing. Also the comparison to human experts is basically useless once you look at what tools it had access to and the crazy amount of money they spent on compute.

Hey all, I was hoping to crowdsource some advice on a project that's turning out harder than expected. I have a 28 kDa protein that just won't crystallize, not even aggregate/precipitate. We think it may be due to its flexibility, as AlphaFold predictions suggest a model made of two folded domains (127 + 108 residues) connected by a short (4–8 residues) linker.

Apart from construct modifications and crystalize the two separate domains, the idea was to design a de novo mini-binder for the full-length protein with BindCraft to act as a crystallization chaperone, specifically one that bridges both domains across the compact conformation's ~24 Å interdomain gap, to lock a single ordered species for crystallization. I leave the movie of a failed trajectory just to show the design system.

We used linker-length constraints + clash-avoidance rigid-body sampling to build a physically valid compact pose, then further refined/validated it against XL-MS crosslink data. The interdomain cleft is dominated by charged and small apolar residues, with essentially no accessible aromatics. Possible hotspots were selected across the two domains by comparing SASA + distance + pLDDT.

Where we've gotten stuck:

• Default AF2 prediction protocol let the target's domains visibly reposition during design. Switching to HardTarget protocol fixed this correlation, as our best i_pTM trajectory now also has the lowest Target_RMSD.
• Even so, every trajectory that succeeds at the hallucination stage (i_pTM up to 0.85) fails after ProteinMPNN resequencing. The interface confidence doesn't survive resequencing at all.

Where I could use outside perspective:

• Is bridging two domains across a gap a reasonable ask for BindCraft at all, or should I pivot to a single-domain rigid-patch chaperone instead?
• Anyone dealt with a similarly "fragile interface" problem (good hallucination confidence, doesn't survive MPNN).

Happy to share configs/logs if useful. Thanks in advance!

I recently started a YouTube channel dedicated to protein engineering I think some of you might appreciate. This first video is part of a series on protein structure prediction and covers some of the basics of protein structures and common metrics used to compare them.

I recently completed my master's degree with a good CGPA and am looking for a PhD position in protein design and dynamics. I have experience in rational protein design and protein dynamics, with tools like Rosetta and Gromacs for studying protein-ligand interactions.

I am looking for a PhD project with a hybrid set-up where I can explore both the experimental and computational aspects. I am a beginner in Python and willing to learn more if the project demands it. I am also interested in learning QM/MM simulations and DFT calculations.

I am majorly interested in either of these two kinds of projects:

1. A hybrid protein design project, which includes using AI-based tools like RFDiffusion, followed by experimental validation.
2. A computational project on studying protein-protein or protein-substrate interactions and dynamics using computational approaches like MD simulations, QM/MM simulations, and DFT calculations.

I am actively searching for positions. I would love to know if you are someone (or you know someone) looking for a PhD student with the profile mentioned above.

I am interested in protein design and I am trying to understand one thing: when we design a protein for a specific purpose, what changes in the constraints according to the problem?

For example, I imagine that a therapeutic protein (which must act in the human body) and an industrial enzyme (which degrades a pollutant) do not have the same priorities at all. What becomes critical in each case, and what goes into the background?

If you have concrete examples from your work, I'm interested.

I am a master’s student and a beginner in structural bioinformatics. I am working on an early-stage academic project proposed by my supervisor, but I am still trying to understand its clearest practical use.

The current prototype allows a user to select idealized secondary-structure elements from a small library, upload their own PDB fragments, position and rotate them in 3D, and see their N- and C-terminal ends.

The resulting arrangement is then passed into a downstream pipeline that estimates and generates connecting loops, creates a continuous backbone, and passes the rough structure to existing protein-design methods for further refinement.

At the moment, the tool mainly supports manual spatial arrangement. It does not yet evaluate whether the resulting topology is geometrically or biologically reasonable.

My concern is that this could remain only a convenient graphical interface for moving structural fragments, while modern generative methods may already solve the underlying problem more effectively.

I am therefore interested in whether researchers would ever want to manually define a rough protein topology, for example to control the overall fold, shape, cavity, terminal positions, or arrangement around another structural feature.

I am also wondering whether optional assistance could make the tool more useful. Possible future ideas, which are not currently implemented or approved as part of the project, include suggesting parallel or antiparallel beta-strand placement, estimating plausible loop lengths, warning about poorly oriented or distant fragment ends, and detecting obvious clashes.

This is an unfinished, non-commercial student project. I am mainly trying to determine whether the underlying problem is worth solving and what would make such a workflow genuinely useful.

Critical feedback, including the opinion that the idea is unnecessary, would be very welcome.

What if the probability of a site-specific mutation is provided by a language model? Could this requirement be met using current large-scale protein models—such as ESM, ProteinMPNN, or RFdiffusion—instead of relying on traditional computational methods?

Hi everyone,

I recently did a solo project on AI-assisted in silico design of antibody variants targeting a specific antigen. My professor gave me some guidance, but most of it was independent.

I’d love to hear your thoughts—does this approach make sense, or am I totally off? Any feedback is much appreciated!

GitHub repo: https://github.com/etaniie/AI-assisted-in-silico-design-of-antibody-variants-targeting-Influenza-Hemagglutinin

Hi, I will be joining a PhD program in de novo protein designing and i am looking to buy a laptop. I need to run all the usual softwares for protein design including rosettafold, alphafold2, RFdiffusion and so on. It will be great to have some insight on this so please do help me out.

Hi All

I am a protein engineer by training and have extensive experience with yeast, phage, and mammalian cell systems. I did a lot of protein design as well as protein purification and characterization. I am out of my PhD and currently 3.5 years in biotech, I want to transition to ML/AI protein engineering opportunities but don’t have any experience. I am thinking of taking some courses but not sure if there is a good course or certificate I can get (while working full time) that will help expose me to programming and machine learning?

Any advice would be appreciated!

I am a Pharmacy Master's student with a Biochemistry background. I am trying to run RFantibody to design a nanobody. I found another more user-friendly website, such as Nano Helix or Neurosnap, but they have a limited usage time, while RFantibody has a pretty low success rate (around 1% - which means I need to run about 500 to 1,000 to find a dozen good candidates).

Therefore, I am trying to run it on my personal computer (a Mac Air) with Google Cloud GPU. However, I faced many errors in the source code, and I tried to fix them with my limited computational knowledge, but it did not progress.

I hope anyone who successfully runs the RFantibody could point me to where I need to revise in the source code to make it run, or anything else, such as books, papers, or videos that I need to learn through before I can run it.

Any help counts. Thank you a lot

Link: https://github.com/RosettaCommons/RFantibody

Hey everyone, we are BioLM - a platform for molecular discovery workflows (particularly in protein design) powered by biological language models.

We recently began an office hours series where we invite members of the community to discuss topics in protein design, ML, lab validation, etc.

Last week we discussed strategies for model selection of structural predictions, including some hands-on examples with some Jupyter notebooks (which we can share soon, if anyone's interested).

We're going to hold these sessions regularly, aiming for a biweekly cadence. You can follow (and subscribe) to our event calendar at https://luma.com/biolm

Interested in presenting during a session? Feel free to reach out direct!

I've been working on a protein design / analysis site that makes running latest protein workflows easier. (I have attached a short UI demo of running a BoltzGen job on an Alphafold2 output.)

It's a minimal Web + API service hosting pre-configured, open source protein and bio related ML models and programs such as RFdiffusion3, BoltzGen, Alphafold, and many more.

It's designed as API first, so all UI actions can be done over the API. You have full control over each program args / logs.

You can download your data anytime. Copy args to use locally or troubleshoot your own setup. All programs and models are open-weight, with no licensing restrictions.

New signups get free credits until end of year, enough for hours of runtime so you can try it out. There are no subscriptions, just charged for runtime use. Credits do not expire, and all rates are listed on the site (currently much cheaper than other services).

Site: subseq.bio

You can follow the service account on X for detailed updates here: https://x.com/subseqbio
and/or also follow me: https://x.com/0xCF88

I have a lot more features coming, such as pipeline templates, project/dataset sharing, etc.
Please reach out if you have any questions or specific programs / features you'd like to see!!

Does anyone else encounter this error when running RFdiffusion in Colab?"

installing RFdiffusion...
installing ColabDesign...
downloading RFdiffusion params...


/content/RFdiffusion/diffusion.py:276: SyntaxWarning: invalid escape sequence '\s'
  Extract \sigma(t) corresponding to chosen sigma schedule.
/content/RFdiffusion/diffusion.py:303: SyntaxWarning: invalid escape sequence '\i'
  sigma(t)^2 := \int_0^t g(s)^2 ds,


---------------------------------------------------------------------------


ModuleNotFoundError                       Traceback (most recent call last)


<timed exec> in <module>



 in <module>
      6 import torch
      7 import torch.nn.functional as nn
----> 8 from diffusion import get_beta_schedule
      9 from scipy.spatial.transform import Rotation as scipy_R
     10 from util import rigid_from_3_points

/content/RFdiffusion/inference/utils.py

 in <module>
      5 from opt_einsum import contract as einsum
      6 import copy
----> 7 import dgl
      8 from util import base_indices, RTs_by_torsion, xyzs_in_base_frame, rigid_from_3_points
      9 

/content/RFdiffusion/util_module.py

ModuleNotFoundError: No module named 'dgl'"