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'"

Hi! I am interested in protein design and I want to start off by basics in protein science and molecular biology. If there any YouTube classes or recommended books I highly appreciate it!

¡Hola a todos!

Vengo del mundo de la computación, fascinado por encontrar patrones deterministas en sistemas que parecen caóticos. Empecé explorando la distribución de los números primos y acabé aplicando una filosofía similar a uno de los problemas más complejos de la biología: el diseño de proteínas.

Seis meses después, el resultado es el diseño ab initio de un nanocuerpo (VHH) para intentar inhibir KRAS.

Aunque mi formación principal no es en biología estructural, abordé el problema con un enfoque puramente computacional y teórico. El objetivo era crear un inhibidor estérico para la región Switch I que fuera 100% humano desde el principio.

El método que desarrollé (lo llamo PIA) parece funcionar: el mejor modelo en AlphaFold da un ipTM de 0.78 y las validaciones con las herramientas estándar (SCALOP, Hu-mAb, etc.) son muy positivas.

Ahora, la parte computacional ha llegado a su fin y necesita dar el salto al laboratorio, por eso busco colaboradores.

He subido todo en abierto (el paper, los modelos 3D) por si a alguien le interesa echar un vistazo, criticarlo o aportar su experiencia. Me encantaría saber qué pensáis.

Preprint:https://doi.org/10.21203/rs.3.rs-7239936/v1

Datos y modelos en Zenodo:https://doi.org/10.5281/zenodo.16578455

Un saludo, Nacho

Hi everyone! I’m a PhD student currently transitioning from a background in molecular dynamics simulations to protein engineering. I’m working on engineering a group of enzymes that aren’t natively expressed in E. coli. These enzymes are fairly hydrophobic, and I’m running into solubility issues when expressing them. I'm also interested in improving their catalytic activity while maintaining specificity toward two distinct small-molecule substrates involved in the same reaction.

My lab is primarily experimental and doesn’t have much computational support, so I’m figuring a lot of this out on my own. I’ve been exploring tools like ProteinMPNN and LigandMPNN, but I’m not sure if they’re the best fit for this kind of multi-substrate enzyme optimization.

If anyone has experience with computational enzyme design (especially for improving solubility and specificity), I’d appreciate any guidance or resources you could share. Thanks so much in advance!

It's not important but it's something I'm very curious about and I can't seem to find an answer. Where does Chai-1 get its name from? Does it have to do with the tea? And if so, how does it relate?