Issues Surrounding the Use of Open-Source PyMOL to Visualize Large Complexes

1. Issues Surrounding the Use of Open-Source PyMOL to Visualize Large Complexes Warren L. DeLano, Ph.D. DeLano Scientific LLC

2. Overview • Project Overview • Some “Biggish” examples • Animations • Intro/Demo (if there’s time)

3. What is DeLano Scientific LLC? • LLC = Limited Liability Company • Mission: to create, share, and support molecular software for all research scientists, students, and educators, with minimal restrictions on usage. • DeLano Scientific can be thought of as: • A private software company with an independent quasi-academic, scientific focus. or • An independent quasi-academic laboratory funded as a private scientific software company.

4. Open-Source vs.Free for Academics • PyMOL is true Open-source in that it does not discriminate against commercial entities – it is free for all for unrestricted reuse. • However, extra benefits are provided to those individuals, laboratories, schools, and companies who voluntarily sponsor the effort with their contributions.

5. How are the Bills Paid? • Sustainable funding now derived from purchases of PyMOL licenses and support agreements. • Therefore, we are directly accountable to hundreds (eventually thousands?) of users in Academia and Industry for our continued existence and growth. • To survive as a business, we must rapidly come up with practical solutions to broad visualization needs for today and the future.

6. Visualization of Large Complexes • Software Issues • What can you do with PyMOL? • Where does PyMOL break down? • What can be done about it? • Data Format Issues • Existing limitations in the data files. • PyMOL is a mostly a follower/consumer in this area.

7. PyMOL Is a General Tool • PyMOL is good for many things, but not great for very many of them. • User base spans the full range: students, small molecule chemists, crystallographers, up through CryoEM. • Best uses of PyMOL at present: • Communication of “molecular scenes”. • Generation of high quality images. • Scripting and generation of animations.

8. Visualization of Large Complexes • Can combine molecular data with volumetric data. • Can compute volumes from atomic coordinates. • Can compute isosurfaces and color-by-potential (no “volume” rendering yet)

9. Can PyMOL Visualize Large Complexes? • Well…maybe. Let’s try some…

12. Limits of PyMOL • There are some practical limits… • Maximum = 1 million atoms on a 1 GB machine. • The comfortable limit on Linux is about a 1/4 of that. • The practical limit on Windows is only about 150,000 atoms per GB of RAM due to VM problems. • PyMOL is optimized for performance and image quality, but not memory usage.

13. Outlook for Improvement? • Unless specifically prioritized by users, memory usage is not likely to improve much due to architectural constraints in the code. • However, it may be possible to improve Windows memory behavior to approach that of Unix. • 8+ GB RAM should become attainable over the next several years as 64-bit computers become more common. • G5, Athlon64, Itanium2, etc.

14. Map File Formats: • PyMOL does has volume data support: • Electronic density • XPLOR, CCP4, O/Brix • EM reconstructions • No specific file formats yet • Electrostatic Potentials • Delphi/Grasp, Biosym Grids, Mead AVS Grids

15. Limits on PyMOL Map Handling • Fast but wasteful • PyMOL currently stores vertices and levels • 4 * sizeof(float) = 16 bytes/voxel • Largest practical map with 1 GB RAM 200 ^ 3 ? • Current user base hasn’t yet bumped up against PyMOL’s limits – but it may happen.

16. Summary on Large Complexes • PyMOL may be useful for working with asymmetric units. • However, more specialized tools will probably be needed for visualization of million-atom biological-units.

17. Animations • Rendered Movies • Real Time OpenGL Movies

19. Real Time Animations

20. Demo