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Protein Folding: Massively Distributed Computing Challenges & Solutions

Explore the complexity of protein structure and function computation in biology using massively distributed computing technology. Discover the challenges and potential of molecular dynamics simulations for protein studies. Learn how distributed computing utilizes idle PC power to accelerate research in genomic medicine. Join the project to contribute to scientific advancements in protein research.

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Protein Folding: Massively Distributed Computing Challenges & Solutions

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  1. Massively Distributed ComputingandAn NRPGM ProjectonProtein Structure and Function Computation Biology LabPhysics Dept & Life Science DeptNational Central University

  2. From Gene to Protein

  3. About Protein • Function • Storage, Transport, Messengers, Regulation… Everything that sustains life • Structure: shell, silk, spider-silk, etc. • Structure • String of amino acid with 3D structure • Homology and Topology • Importance • Science, Health & Medicine • Industry – enzyme, detergent, etc. • An example – 3hvt.pdb

  4. Protein Structure & Function • Primary sequence  Native state with 3D structure • Structure function • Expensive and time consuming • Misfolding means malfunction • Mad cow disease (“prion” misfolds)

  5. The Folding Problem • Complexity of mechanism & pathway is huge challenge to science and computation technology

  6. Molecular Dynamics (MD) • Molecular’s behavior determined by • Ensemble statistics • Newtonian mechanics • Experiment in silico • All-atom w. water • Huge number of particles • Super-heavyduty computation • Software for macromolecular MD available • CHARMm, AMBER, GROMACS

  7. Simple Statistics on MD Simulation • Atoms in a typical protein and water simulation 32000 • Approximate number of interactions in force calculation 109 • Machine instructions per force calculation 1000 • Total number of machine instructions 1023 • Typical time-step size 10–15 s • Number of MD time steps 1011 steps • Physical time for simulation 10–4 s • Total calculation time (CPU: P4-3.0G ) days 10,000

  8. Protein Studies byMassively Distributed Computing A Project in National Research Program on Genomic Medicine • Scientific • Protein folding, structure, function, protein-molecule interaction • Algorithm, force-field • Computing • Massive distributive computing • Education • Everyone and Anyone with a personal PC can take part • Industry – collaborative development

  9. Distributed Computing • Concept • Computation through internet • Utilize idle PC power (through screen-saver) • Advantage • Inexpensive way to acquire huge computation power • Perfectly suited to task • Huge number of runs needed to beat statistics • Parallel computation not ALWAYS needed • Massive data - good management necessary • Public education – anyone w/ PC can take part

  10. Hardware Strategies • Parallel computation (we are not this) • PC cluster • IBM (The blue gene), 106 CPU • Massive distributive computing • Grid computing (formal and in the future) • Server to individual client (now in inexpensive) • Examples: SETI, folding@home, genome@home • Our project: protein@CBL

  11. Software Components • Dynamics of macromolecules • Molecular dynamics, all atomistic or mean-field solvent • Computer codes • GROMACS (for distributive comp; freeware) • AMBER and others(for in-house comp; licensed) • Distributed Computing • COSM - a stable, reliable, and secure system for large scale distributed processing (freeware)

  12. COSM’s Structure Client System tests (test all Cosm functions) Self-tests Connect to server Send Request Recv Assignment Running Simulation Put Result Get Accept Packet Request Packet Assignment Packet Result Packet Accept

  13. Protein database • Temporary • databank • Job analysis • Automatic • temperature • swaps by • parallel tem- • pering Databank Human intervention Jobs Exceptions Send(COSM) clients Receive Structure at Server end

  14. Server Receive If crash MD Run Restart Return result Delete files Structure at Client end

  15. Multi-temperature Annealing • Project suited for multi-temperature runs – Parallel Tempering • Two configurations with energy and temperature (E1, T1) and (E2, T2) Temperature swapped with probability P = min{1, exp[-(E2-E1)(1/kT1 – 1/kT2)]} • Mode of operation • Send same peptide at different temperature to many clients; let run; collect; swap T’s by multiple parallel tempering; randomly redistribute peptides with new T’s to clients

  16. Server client Old temperatures client Swap temps by Multiple “peptide” parallel tempering client Databank client client client New temperatures client Multi-temperature Annealing

  17. Potential of Massive Distributive Computing • Simulation of folding a small peptide for 100ns • Each run (105 simulation steps; 100 ps) ~100 min PC time • 1000 runs (100 ns) per “fold” ~105 min • Approx. 70 days on single PC running 24h/day • Ideal client contribute 8h/day • 100 clients 70x3/100 = 2 days per fold • 10,000 clients 50 folds/day(small peptide) • Mid-sized protein needs > 1 ms to fold • 7x105 days on single PC • 10,000 clients 210 days • 106 clients (!!) 2~3 days

  18. Learning curve • Launched –August 2002 • Small PC-cluster – October 2002 • In-house runs to learn codes • Infrastructure for Distributive Computation • InstallationGromacs & COSM – January-March 2003 • Test runs and debugging • IntraLaboratory test run – March-October 2003 • NCU test run – July-October 2003 • Launched on WWW – 20 November 2003 • Traffic jam – multiple server (see next slide) • Scientific studies > November 2003

  19. In-House Test Runs • Time – Began March 2003 • Clients • About 25 PCs in CBL and outsiders ( MS-Window ) • Goal – test and debug • Test server-client communication • Lots of debugging • Test data distribution, collection and management • Test parallel tempering

  20. Multi-Server Architecture Client Font-End Server Client Backend Server 1 Client Step 1: Client sends request to Front-End Server Step 2: FES assigns IP of a Back-End Server i to client Step 3: Client requests job from BESi Step 4: BESi sends job to client Step 5: Client sends result to BESi Repeat cycle. Backend Server N

  21. Current status and Plans for immediate future • Last beta version Pac v0.9 • Released on July 15 • To lab CBL members & physics dept • About 25 clients • First alpha version Pac v1.0 released October 1 2003 • Current version Pac v1.2 • Releases for distributed computing on 20 November 2003 • In search of clients • Portal in “Educities”http://www.educities.edu.tw/~2,500 downloads, ~500 real clients • PC’s in university administrative units • City halls and county government offices • Talks and visits to universities and high schools

  22. 1SOL: (20 res.) A Pip2 and F-Actin-Binding Site Of Gelsolin, Residue 150-169. One helix. 1ZDD: (35 res.) Disulfide-Stab-ilized Mini Protein A Domain. Two helices. 1L2Y: (20 res.) NMR Structure Of Trp-Cage Miniprotein Construct Tc5B; synthetic. Current Simulations

  23. A small test case – 1SOL • Target peptide – 1SOL.pdb • 20 amino acids; 3-loop helix and 1 hairpin; 352 atoms; ~4000 bonds interaction • Unit time step= 1 fs • Compare constant temperature and parallel-tempering • Constant T @ 300K • Parallel-tempering with about 20 peptides, results returned to server for swapping after each “run”, or 105 time steps (100 ps)

  24. Parallel-tempering (1SOL) Temperature (K) Number of runs (in units of 100 ps) P = min{1, exp[-(E2-E1)(1/kT1 – 1/kT2)]}

  25. Initial structure Native conformation Const temp. (20ns) Parallel-temp. (1.6ns) Preliminary result on 1SOL

  26. A second test case – 1L2Y • Simulation target – Trp-Cage • 20 amino acids, 2 helical loops • A short, artificial and fold-by-itself peptide • Have been simulated with AMBER • Folding mechanism not well understood

  27. Temperature (K) Number of runs (in units of 100 ps) Swap History (1L2Y)

  28. Preliminary result on 1L2Y (20 peptides) Native state Initial state PAC 6ns

  29. Modifications needed • Reduce size of water box • Save computation time • Rewrite the energy function • Ignore the water-water interaction • Increase cut-off radius • Try different simulation algorithms for changing pressure and temperature • Others…

  30. Looking ahead • Better understanding of annealing procedure • Better understanding of energetics • Expand client community • Develop serious collaboration with biologists • Structure biologists, e.g., NMR people • Protein function people • Drug designers • “…investigation of motions that have particular functional implications and to obtain information that is not accessible to experiment.” Karplus and McCammon, Nature Strct. Biol. 2002

  31. The Team • Funded by NRPGM/NSC • Computational Biology Laboratory Physics Dept & Life Sciences Dept National Central University • PI: Professor HC Lee (Phys & LS/NCU) • Co-PI: Professor Hsuen-Yi Chen (Phys/NCU) • Jia-Lin Lo (PhD student) • Jun-Ping Yiu (MSc Res. Assistant) • Chien-Hao Wei (MSc RA) • Engin Lee ( MSc student ) • PDF (TBA) • We are looking for collaborators, research associates, programmers, students, etc.

  32. http://protein.ncu.edu.tw

  33. Thank you for your attention

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