Week 4 – Smarter Coordinates: Guiding Residue Placement with Interaction Context

Hey everyone! 👋

Welcome to the Week 4 update of my GSoC journey. This week marked the transition from reading and preparing into actively writing code, and I made good progress on interaction-driven layout logic for protein residues.

📝 PR Status

To start with, I raised a PR to enable exporting LigNetwork data as a NetworkX graph object, which is currently under review. This change lays the groundwork for more versatile layout strategies and integration with external graph tools.

The updates discussed below — involving custom coordinate generation for residues — have not been raised as a PR yet. I’m still refining and testing the logic before pushing it upstream.

🧠 From Random to Reasoned: Residue Placement

My initial approach used spring_layout from NetworkX — a physics-inspired layout algorithm. While it reduced overlap somewhat, it lacked awareness of which residues interacted with which ligand atoms, leading to visually entangled diagrams (see image below):

Early entanglement issues using spring layout

This made the visualization hard to interpret and missed the goal of contextual clarity.

🧭 A Custom, Interaction-Aware Layout

To address this, I implemented a custom layout function that:

Analyzes interaction data between ligand atoms and protein residues.
Calculates direction vectors from the ligand center to interaction points.
Places residues just outside the ligand, extending outward from their interaction point.
Handles overlap by rotating nearby residues around the interaction anchor.

This results in a layout where residues surround the ligand more meaningfully, visually aligned with their interactions and spaced to avoid clutter.

Here’s a glimpse at the core logic:

direction = interaction_point[:2] - ligand_center
direction_unit = direction / np.linalg.norm(direction)
residue_pos = interaction_point[:2] + direction_unit * base_offset_distance

Fallback placement uses a radial arrangement to fill in the gaps for residues that don’t have clearly defined interaction points, ensuring no residue is left floating or awkwardly positioned.

Although this is a backup option for now, the priority is still to make the NetworkX layout options work by providing more information.

Although, the placement is not yet perfect

🔧 Implementation Notes

I also implemented:

Ring centroid detection for π interactions: This helps precisely locate the center of aromatic rings, crucial for accurately positioning residues involved in pi-stacking or T-shaped pi interactions.
Overlap resolution logic with rotation around interaction points: This feature prevents residues from overlapping by rotating them (by 45 degrees clockwise) around their defined interaction points, ensuring a clear and uncluttered visualization.
A fallback circular distribution strategy for unpositioned residues: For residues without clear interaction points, this strategy arranges them in a circular pattern, preventing them from being left out and maintaining a coherent diagram.

With this new layout engine, diagrams are now much clearer and biologically meaningful, setting a solid foundation for the interaction visualization system I’ll continue to build.

📆 What’s Next?

Next week, I’ll be focusing on improving NetworkX-based layouts by incorporating interaction-aware edge weights and metadata. I’ll also begin integrating the new coordinate system into the LigNetwork rendering pipeline.

Thanks for reading, and see you next week with more updates! 💻🧬