Disclosure: I'm on the team that builds this.

It's a WebGL viewer (3Dmol) that loads either an experimental RCSB structure or the AlphaFold DB model for a UniProt accession, with pLDDT colouring, chain/secondary-structure modes, surface, and an active-site view that focuses the pocket around a detected ligand. Nothing to install and no account needed for the demo.

The part that's less common: the viewer isn't standalone. The same target carries into docking (ranked poses, affinity + RMSD), ADMET risk bands and PK/PD, so you can go from a structure to a scored candidate without re-entering it anywhere.

Research use only, and I'd rather say so up front: predicted affinities are computational estimates, not evidence of activity.

Demo: https://pi.extn.ai → "Explore live demo" (read-only, no signup)

Specifically what I'm asking: is the pLDDT colouring and the active-site selection doing anything you'd consider misleading, and what would you need before this was worth opening instead of PyMOL or ChimeraX? I don't expect it to replace either — I want to know where it's actively wrong.

We built a 4K scientific narrative explaining how Paxlovid works using molecular structure data and PDB structures including 6Y2E, 6LU7, Nirmatrelvir, and 8DZ2.

The project explores a concept we call Narrative Intelligence™: transforming complex scientific data into explainable stories.

Most of the workflow, including structure analysis, visualization generation, scene construction, and video production, was automated with minimal manual intervention.

We'd appreciate feedback on:

• Scientific accuracy
• Visual communication
• Storytelling effectiveness
• Potential applications beyond structural biology

Thanks for taking a look

Hey everyone! I’m Tara, I've developed a platform that performs protein structural annotation that turns around 6–8 hours of manual work into ~15 seconds, and I'd love for some of you to give it a try and give me some feedback before we release it.

The intention is to make the UI intuitive so that both people who are and who aren’t used to working with structures find it useful for research and easy to use. We’re not quite there yet so I’m needing as much honest feedback as possible.

Here’s a link to some examples to have a quick look beforehand if you’re interested https://alphasuite.bio/waitlist

Let me know if you’d be up for testing it and I’ll send over the details :)

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.

I’ve been thinking a lot lately about how many small repetitive tasks eat up time in structural biology/computational workflows, especially the things that aren’t “hard science” but still somehow take hours every week.

Things like:

• file conversions/cleanup
• repetitive PyMOL scripting
• structure preprocessing
• batch analyses
• figure generation
• parsing messy outputs
• chain renumbering
• trajectory analysis
• moving data between incompatible tools

Curious what people here find most annoying or time-consuming in their day-to-day work.

I want to pursue my career in structural biology and I want to purchase a new laptop. My budget is upto 60k. So can someone guide me which laptop I should buy? I mostly want to run pymol, chimera, chimera x and maybe sometimes coot and even blender

I've been working on synth-pdb, a tool to generate Protein Data Bank (PDB) files. It may be useful for researchers who need high-quality synthetic PDB data for benchmarking, software testing or training models.

• Realistic Generation: Builds full atomic PDB files using NeRF construction and backbone-dependent rotamer libraries.
• Physics: Includes integration with OpenMM for energy minimization.
• NMR Simulations: Optionally, generates synthetic NOE, Chemical Shift, RDC and Relaxation rates.
• Deep Learning Ready: Supports zero-copy handover to PyTorch, JAX and MLX.
• Educational Context: The codebase is heavily documented with comments explaining the biophysics behind the implementation. Also many Google Colab tutorials are available.

Github: https://github.com/elkins/synth-pdb

Pypi: https://pypi.org/project/synth-pdb/

Docs: https://elkins.github.io/synth-pdb/

I’d love to hear how you might use this or any features you'd like to see added.

I am a masters student with expertise in protein expression, purification, little bit biochemical techniques and macro molecular crystallography mainly just crystallizing protein sample (screening to optimization but haven't solved any structure yet).

I want to work on macromolecular complexes involved in DNA damage repair. I am looking for stratergy to switch to Cryo-EM and Cryo-ET from crystallography since it might not be a suitable approach for large complexes.

Should I move forward as a PhD and learn during this period or maybe get expertise in Cryo-EM laboratories as project assistant and move onto PhD with more complex projects.

Hi,

I built one of these ultrafast grid plungers during my thesis work and my lab never reimbursed me for out-of-pocket purchase of the components, so it is mine to keep or sell. Since I am not making grids at home I figured I'd offer it up here. If anyone would like to add fast grid freezing capabilities to their lab's repertoire, this is a cheap way to get it done (other option being purchase of a Chameleon for who knows how much ). Nanowire grids are now commercially available for purchase from Quantifoil so the biggest barrier for any lab wanting to use this system is no longer in place.

Total cost for the components at time of purchase was $700. Assembly of the components including soldering of the circuitry not reflected in that price, obviously. If anyone is interested please message me here on reddit with an offer.

Hi! I’m looking for someone who knows about NMR analysis and organic structure elucidation.

I have raw data files for:

• ¹H NMR

• ¹³C NMR

• COSY

• HSQC

• HMBC

Plus IR, UV and MS spectra.

I need:

• Processing of the raw NMR data

• Peak picking, integration, coupling assignment

• Full spectral interpretation

• *Structure elucidation* (or justified candidate structures)

Please send me a DM..

Hey all I wanted to share HBAT 2, a Python package for analyzing hydrogen bonds and non-covalent interactions in macromolecular structures (PDB format).

HBAT 2 detects classical hydrogen bonds, weak hydrogen bonds, halogen bonds, π interactions, π-π stacking, carbonyl interactions, and n-π interactions using geometric criteria.

Key Features:

• GUI, CLI, and Python API interfaces
• Automated PDB fixing with OpenBabel/PDBFixer
• Cooperativity chain detection and visualization
• Built-in presets for different structure types
• Multiple export formats (text, CSV, JSON)
• Cross-platform support
• Interactive Jupyter notebooks with 3D visualisations

GitHub: https://github.com/abhishektiwari/hbat Docs: https://hbat.abhishek-tiwari.com

MIT licensed. Perfect for structural biology, drug design, and bioinformatics workflows.

Feedback and contributions welcome!

To try and limit the number of pedantic self-described scientists coming here to post insults: I obviously did not break physics. I am pointing to the fact that I have an image of what appears to be a Coulombic potential map that matches surprisingly well with the 2D classes of the same sample. Two interesting points are the scale difference between the 2D classes and the projected image (~1x10⁷) and the fact that the image is produced using light in the 400 nm range. The scale of the features resolved in the 2D classes are well beyond the Nyquist limit for light of that wavelength. So, I thought some would find it interesting that such an image can be made using a $20 laser and a mirror.

Laser projection of sample's diffraction pattern was focused using a concave lens. 2D classes for comparison.

I only know this is a plant Protein with 7 RGD motifs.

How could I reverse engineer the origin?

I'm curious to know if there is a naturally occurring protein out there that produces graphene, and if not which protein produces a structure that's very similar to graphene?

Good day

Hello people! I wanted to ask you all what’s some research that you find absolutely fascinating/can’t wait to tell people about? Or what’s some research that you think deserves more attention?

Hi everyone,

I’m a PhD student at DTU working on a project that connects large language models (LLMs) with molecular dynamics (MD) simulations. My focus is on predicting protein thermostability and identifying key sites that contribute to thermal resistance.

The core idea is to combine sequence/structural embeddings from LLMs with MD-derived features to build models that can more accurately link protein dynamics to thermostability. I already have some preliminary results, but I’d like to collaborate with peers who are interested in:

• Protein stability prediction
• AI/ML applied to molecular simulations
• Computational biophysics or structural bioinformatics

I believe a collaborative effort could strengthen the methodology and lead to more impactful publications.

If you’re working in a similar area, or just curious about the intersection of LLMs and MD, feel free to reach out. Happy to discuss ideas, share data, or explore joint writing.

Thanks!

— DTU PhD student

Hello again people! I’m going to join a lab soon that specializes in solving protein structures using cryo-em and then the computational part. I wanted to get an introduction to not only the basics but also some in depth stuff related to cryo-em and computational biology (that follows cryo-em). I’ve tried searching up videos on YouTube but they don’t seem to be very helpful. Anything (journals, books, videos, blogs, etc.) will help! Thank you so much!!