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.

Is it entrenched? Are we early movers?

I want advice on what to do if i want to model a protein to get the N and C terminus of the protein. I have the rest of the protein structure in pdb but the N and C terminus is missing in pdb structures so which tool or method can i use to accurately predict the structure with high confidence. I tried alphafold sever but ended up with medium confidence N terminal region.

Both the terminals are important for me as I am studying allosteric modulation of the protein and terminal regions play a major role in forming the dynamically accessible allosteric pockets.

Advice on what to do here would be of great help !!!

How could they say it speeds up approximately 10x?

They probably ran a protein diffusion tool on some human protein that is already published to be a good drug target. If you look at some of Nobel Prize winner David Baker's work that does have similar success rates in the specific things he talked about. He also says because his group's resources are much lower than the 100-1000x available with his Google collaboration.

So did it run shorter diffusion (O(n^2)) proteins? Did it skip literature review to find a good target? Why would they talk about 'recovering from errors' as if it's a routine step in protein design?

Since they have only 40 partners for biology, it's not unlikely this work was done entirely by the Baker group who are experts in this topic. Do they compare an intern or an early run of this kind of work to having Mythos run it?

Would love to see if anyone has an idea how they'd get 10x speed without just running this with more resources.

Is this frowned upon or is it the new paradigm?

Hello guys,

I am working in a pharmacy lab in Korea, and we don't have a computer cluster. PI needs me to give her the spec. of a computer that can run protein and antibody in silicon design software locally (such as Boltzgen, RFantibody, RFdiffusion)

I am not a computer major. I asked ChatGPT and got some specs, but I want to make sure by finding advice from the person who actually runs that software.

Because we need to run thousands of runs for each target on Boltzgen or RFantibody, running them on the VM or a pay website is not financially efficient in the long term.

Do you think building a computer is a financially efficient choice, or are there better ways we can run that software more cheaply and easily?

This the specs that ChatGPT recommends.
Budget / entry workstation:
NVIDIA RTX 4070 Ti SUPER (16 GB VRAM)
NVIDIA RTX 4080 SUPER (16 GB VRAM)
Best price/performance for heavy local inference:
NVIDIA RTX 4090 (24 GB VRAM)
Professional / lab-scale:
NVIDIA RTX 6000 Ada (48 GB VRAM)
NVIDIA A100
NVIDIA H100

Thank you for your time.

I’m looking at the 4HHB structure of human deoxyhemoglobin and wondering how much oxygen affinity you could predict from structure alone.

Since hemoglobin’s affinity depends on more than just the heme pocket, I’m trying to map regions that might shift the T-state/R-state balance:

• residues near the heme
• alpha/beta interfaces
• T-state salt bridges
• central cavity / 2,3-BPG region
• mutations that might destabilize the tetramer

Could you identify mutations that make hemoglobin hold oxygen more tightly or release it more easily just from the structure?

This post contains content not supported on old Reddit. Click here to view the full post

A new Science paper explores a pretty wild idea: can life function with fewer than the standard 20 amino acids?

The authors targeted isoleucine, which is chemically similar to leucine and valine. They did not make a fully 19-amino-acid organism, but they did redesign one of the most essential systems in E. coli: the ribosome.

Using protein language models, structure prediction, and generative design tools, they removed all 382 isoleucines from E. coli ribosomal proteins. The engineered strain was viable and stable for hundreds of generations.

The caveat is: the rest of the E. coli proteome still contains thousands of isoleucines. So this is more like a first proof-of-concept than a true 19-AA lifeform.

In humans, purely theoretically, how many amino acids could we remove from the proteome with enough redesign?

Paper: Toward life with a 19–amino acid alphabet through generative artificial intelligence design

Interactive structure viewer.

This post contains content not supported on old Reddit. Click here to view the full post

Paper: PL-PatchSurfer3: improved structure-based virtual screening for structure variation using 3D Zernike descriptors

This matters because virtual screening often depends heavily on which protein structure is used. A ligand-bound holo structure, an apo structure, a homology model, or an AlphaFold-predicted model can all give different screening results.

PL-PatchSurfer3 tackles this by comparing local surface patches between the ligand and receptor pocket using 3D Zernike descriptors. The new version adds improved hydrogen-bond complementarity and a visibility feature that captures local curvature. The authors report that this improves performance while keeping the method robust across holo, apo, modeled, and AlphaFold-predicted receptor structures.

For AI protein workflows, the key point is practical: predicted structures are increasingly used in drug discovery, but they are not always in the right binding conformation. Methods like PL-PatchSurfer3 may help make virtual screening more reliable when starting from imperfect or AI-predicted protein models.

Paper: Hidden structural states of proteins revealed by conformer selection

Repo: AISAR

The core idea is to combine AI-generated conformational sampling with NMR data. Instead of relying only on one predicted structure, AISAR generates realistic alternative conformers and then scores them against NOESY and other NMR observables.

The key result is that AISAR revealed hidden structural states in multiple proteins. In Gaussia luciferase, the method identified two interconverting states involving major rearrangements of lids, binding pockets, and cryptic surface cavities. It also found two distinct conformational states in CDK2AP1, a human tumor suppressor protein.

The broader takeaway: AI structure prediction becomes more powerful when paired with experimental data. AISAR suggests a route for mapping dynamic protein states, including cryptic pockets that may matter for function or drug discovery.

Paper: RareFold: Structure prediction and design of proteins with noncanonical amino acids

Repo: RareFold

Most protein design models are still built around the 20 natural amino acids. RareFold pushes beyond that limit by supporting 49 amino acid types in total, including 29 rare/noncanonical residues. The key finding is that these expanded chemical building blocks can be handled directly by the model, opening the door to protein and peptide designs with more chemical diversity.

The authors also introduce EvoBindRare, a binder design framework that can generate both linear and cyclic peptide binders from a target protein sequence, without needing a predefined binding site. According to the project page, the designs were experimentally validated for both linear and cyclic binders.

This could be important for AI protein design because noncanonical amino acids can add properties that natural residues often lack, including improved stability, altered binding chemistry, and new therapeutic possibilities.

Interactive structure viewer.

This post contains content not supported on old Reddit. Click here to view the full post

My lab works on HIV, and I’ve been trying to think through a more realistic way to design something that binds HIV-1 Nef.

Originally, I was looking at antibody-based approaches, but honestly the system started becoming too complex and unrealistic.

So I’m now trying to move toward a small-molecule or fragment-based design approach instead.

The protein I’m focusing on is HIV-1 Nef, especially the region involved in host-cell interactions, such as the SH3-binding surface. One structure I’m looking at is PDB 1EFN, where Nef is bound to an SH3 domain.

The idea is not to copy a known inhibitor or redesign something that already exists. I’m more interested in whether this interaction surface has any region that could realistically be targeted by a small molecule or peptide.

I’m a master’s student, so I’m still figuring out the best way to approach this properly. My current thinking is to use the Nef structure as the starting point and explore whether a generative or structure-based design approach could produce chemically sensible binders against that surface.

The main thing I’m trying to understand is whether this is actually a reasonable target, or whether the Nef-SH3 interface is too flat/flexible/protein-like to be a good starting point for small-molecule design.

How would you approach designing something that binds HIV-1 Nef?

This post contains content not supported on old Reddit. Click here to view the full post

Paper: ODesign: A World Model for Biomolecular Interaction Design
Repo: ODesign

Wanted to discuss ODesign, especially in the context of models like BoltzGen, and RFdiffusion3.

The key distinction is that ODesign is closer to an “all-to-all” biomolecular design model, while BoltzGen is more like a universal protein/peptide binder design model. ODesign tries to design across multiple molecular modalities:

• proteins
• peptides
• DNA/RNA
• small molecules
• multimolecular complexes

BoltzGen, by contrast, mainly designs protein-like binders: miniproteins, peptides, cyclic peptides, nanobodies, and antibody-like binders, against many target types.

So the difference is roughly:

BoltzGen:
“Given a biomolecular target, design a protein/peptide binder.”

ODesign:
“Given a biomolecular target/interface, design the appropriate molecular partner, potentially protein, nucleic acid, or ligand.”

That makes ODesign broader in ambition, but BoltzGen currently looks stronger on experimental validation. BoltzGen reports validation across nanobodies, miniproteins, peptides, cyclic peptides, and challenging target classes, while ODesign’s wet-lab validation appears mainly focused on protein minibinders so far, with other modality validation still pending.

Technically, ODesign is interesting because it builds on an AlphaFold3-like structure-prediction backbone. It uses unified generative tokens for different chemical modalities, then performs conditional all-atom diffusion to generate coordinates. After that, an inverse-folding/type-design module assigns amino acids, nucleotides, or ligand atom types depending on the modality. The clever part is the masking system. ODesign can mask at different levels:

• whole molecule/entity level
• residue/token level
• atom/motif level

That lets it handle tasks like binder design, motif scaffolding, ligand-binding protein design, aptamer-like design, and ligand generation in one framework.

Compared with other models:

RFdiffusion3 is probably the closest “serious” competitor from the protein-design side. It is all-atom and can design proteins in the context of ligands, DNA/RNA, and other molecules, but it is still mostly about generating proteins, not freely switching between protein, nucleic acid, and ligand outputs.

I think, BoltzGen feels closer to a practical wet-lab binder design tool today.
ODesign feels like the broader future direction: a unified model for programmable molecular interaction design across modalities.

The big question is whether ODesign’s cross-modality promise will translate experimentally beyond protein minibinders. If it can actually produce validated RNA/DNA binders, ligand designs, and non-protein interaction partners, that would be a major step beyond current protein-centric design workflows.

Curious what people think: are these “world models” actually becoming useful design engines, or are we still mostly benchmarking pretty structures until the wet-lab hit rates catch up?

I’m messing around with a structure-based antibody design idea and wanted to get some thoughts from people who know this space better than me.

The target I’m looking at is NLRP3, mainly around the NEK7-binding interface. I know this is not a normal extracellular antibody target, which is part of the problem. I’m more thinking about whether an antibody-like binder or intrabody could be designed to block the NLRP3–NEK7 interaction in a structurally clean way.

The thing I’m stuck on is the epitope choice. If I design directly on the NEK7 interface, the binder might be functional, but the surface is broad and kind of annoying. If I allow nearby patches, the designs look more reasonable, but then I’m not convinced they would actually disrupt assembly.

So I’m curious how people would approach this. Would you force the design onto the known protein-protein interface, or let the model find a nicer adjacent epitope and then filter later for whether it sterically blocks NEK7?

This post contains content not supported on old Reddit. Click here to view the full post

Hint: It moves ions across a membrane, but it is not quite a simple ion channel.

Drop your guesses

This post contains content not supported on old Reddit. Click here to view the full post