How a small advisor script grew legs
PyPAn was not born as a grand idea. It began its life as a dissertation project: a tiny Django web app named MMAT that simply advised users on which protein structure modeling method to use. Homology, threading, ab initio — it gave sensible recommendations based on the input sequence and quietly went back to sleep. It worked. But it wasn’t enough.
Real protein analysis workflows are messy. Researchers rarely need one neat answer. They need to stitch together multiple tools, check properties, align sequences, analyze structures, and somehow keep their sanity. So I did what most developers do at this point. I overbuilt it.
When a script became a platform
PyPAn grew from an advisor into a full-fledged desktop application. The central idea was simple: put the most common protein analysis steps in one place and save users the pain of opening ten different tools. The implementation was anything but simple.
The toolkit expanded fast. Sequence analysis uses Biopython to calculate a dozen physicochemical properties. Multiple sequence alignment runs through a Clustal-Ω wrapper. Structural analysis generates Ramachandran plots and calculates solvent accessibility and radius of gyration. All these parts lived under one roof, wrapped in a clean GUI that didn’t require users to touch the command line.
Building a GUI that scientists actually use
Writing the underlying logic was the easy part. Making it accessible through a friendly interface was the real test. GUI design in scientific software is often treated as an afterthought, but here it became the backbone. I wanted PyPAn to feel approachable even to those who are more comfortable pipetting than programming.
The application was designed to guide users through logical workflows rather than dumping all features at once. It was less “choose your own adventure” and more “follow the breadcrumbs to get results.”
The long road to publication
PyPAn was practical, useful, and reasonably stable. But publishing a software tool that is an integrator rather than an entirely new algorithm is a different kind of challenge. Reviewers loved the utility but questioned its novelty. It collected rejections like trading cards before finally landing at a journal that valued what it offered: a unified, user-friendly protein analysis platform.
In many ways, PyPAn taught me more about scientific communication than about coding. Building something is one skill. Convincing the world it matters is another entirely.