Abstract
The size and complexity of 3D macromolecular structures available in the Protein Data Bank is constantly growing. Current tools and file formats have reached limits of scalability. New compression approaches are required to support the visualization of large molecular complexes and enable new and scalable means for data analysis. We evaluated a series of compression techniques for coordinates of 3D macromolecular structures and identified the best performing approaches. By balancing compression efficiency in terms of the decompression speed and compression ratio, and code complexity, our results provide the foundation for a novel standard to represent macromolecular coordinates in a compact and useful file format.
Highlights
The Protein Data Bank (PDB) [1], the archive for 3D structures of biological macromolecules, has rapidly grown over the last few years
Compression of 3D coordinates of macromolecular structures and lossless compression approaches based on different encoding algorithms
We investigated compression approaches for 3D coordinates of macromolecular structures
Summary
The Protein Data Bank (PDB) [1], the archive for 3D structures of biological macromolecules, has rapidly grown over the last few years. New integrative methods that combine multiple modelling and experimental techniques, most notably Electron Microscopy, determine structures of up to the megadalton (MDa) range at atomic resolution [2,3,4]. Such large complexes bring major challenges to analysis and visualization tools since transfer and processing of the structural data is slow. Limitations in network bandwidth and client side memory further reduce the visualization performance on the web and mobile devices These bottlenecks are caused by inefficiencies of the file formats currently used by the PDB to store macromolecular structures.
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