zsasa: a Zig-based engine for high-throughput solvent accessible surface area at proteome scale

Solvent accessible surface area (SASA) is widely used to describe protein stability, ligand binding, mutation effects, and protein–protein interfaces. As structural biology workloads expand to predicted-structure col-lections, trajectories, and large assemblies, SASA tools must combine reproducible calculation with high throughput, low memory use, and workflow-friendly input handling.

We present zsasa, a Zig-based SASA engine with command-line and Python interfaces. zsasa implements the established Shrake–Rupley and Lee–Richards algorithms, provides exact f64/f32 modes and an optional bitmask approximation, and supports batch and trajectory workflows, compressed structure inputs, and configurable atom classification including Chemical Component Dictionary (CCD)-based radii for non-standard components.

In matched Shrake–Rupley validation on 4,370 Escherichia coli AlphaFold Database structures, exact double-precision zsasa reproduced FreeSASA total SASA values to near numerical identity. In 10-thread batch benchmarks on the E. coli and 23,586-structure human AlphaFold collections, zsasa achieved a 2.94-fold speedup over a FreeSASA batch wrapper in exact f64 mode. In bitmask mode, zsasa reached up to a 9.70-fold speedup, using roughly 12.5% to 25% of the comparator peak memory. Trajectory benchmarks exceeded 1,000 frames/s at tens of megabytes of peak memory, and a 4.5-million-atom PDB stress-test file completed in less than 5 s. These results support zsasa as a practical tool for reproducible, low-memory generation of surface-derived structural features at large scale. zsasa is available under the MIT License at https://github.com/N283T/zsasa .