Atomistic Protein Folding Simulations on the Submillisecond Timescale Using Worldwide Distributed Computing Qing Lu CMSC 838 Presentation

CMSC 838T – Presentation Overview u Overview of talk  Motivation  Challenge  Methods l Ensemble Dynamics l  Evaluation  Observations

CMSC 838T – Presentation Motivation u Atomistic simulation of protein folding  understand dynamics of folding  real-time folding in full atomic detail  large-scale parallelization methods u Benefits  protein folding & disease l protein self-assemble to function l proteins misfold  diseases  nanotechnology l nanomachines l self-assemble on the nanoscale

CMSC 838T – Presentation Challenge u Difficulties  limited by current computational techniques l fastest folding in microseconds l one CPU: 1ns/day, 30 years l 10,000 fold computational gap u 1,000 CPUs, 1 microsecond / day  traditional parallelization scheme l hard to scale to a large amount of processors l extremely fast communication l complexity of coordination l expensive supercomputers u cost u time-sharing

CMSC 838T – Presentation Method u ensemble dynamics  a new simulation algorithm  parallel simulation u  heterogeneous network, Internet  large-scale distributed platform

CMSC 838T – Presentation Simulation of Dynamics u free energy barrier  progress from one state to another: transition  thermal fluctuations to push system over free energy barrier u previous approaches: sampling  maybe stuck in meta-stable free energy minima  expensive computational cost of sampling

CMSC 838T – Presentation Ensemble Dynamics u application scenario  waiting time of transitions dominates total time  protein folding l transition: free energy barrier crossing  coupled simulations: transition coupling u Algorithm  M independent simulations from a initial condition  first simulation to cross free energy barrier l M times less to cross barrier than average time  restart M simulations with the new location after transition u Near linear speed up in #processors  exponential kinetics: f(t) = 1 – exp(-k t)  If k * t is small, f(t) = k * t  M simulations  M * f(t) = M * k * t folding events

CMSC 838T – Presentation Limitations u barrier crossing probability  exponential assumptions u correct transition detection  transition: free energy barrier crossing  a large variance in energy: threshold  correct detection is not guaranteed u multiple possible transition  not addressed  selection of the first transition

CMSC 838T – Presentation Distributed Computing u Distributed simulations  M processors for each run  simulate folding in atomic detail on each processor  restart once a crossing barrier event occurs u Implementation:  worldwide distributed computing: Internet  started in October 2000 l more than 200,000 participants l 10,000 CPU-years in the first 12 months

CMSC 838T – Presentation

CMSC 838T – Presentation u client-server architecture  server assign jobs(work unit) to client  client sends back results after computation  ~100K data transfer between client and server u why is ensemble dynamics good for  CPU intensive job: a few hours, often days  connection speed: modem, good enough  suitable for

CMSC 838T – Presentation Work u  search for intelligent life outside Earth  data analysis of signals u  find drug therapy for HIV  how drugs interact with various HIV virus mutations u distributed projects  Divide-and-Conquer  CPU intensive jobs  small pieces of data(kilobytes) transfer  communication not a major concern

CMSC 838T – Presentation Evaluation u  based on Tinker molecular dynamics code  voluntary participants worldwide, over 400,000 CPUs u simulate folding and unfolding  folding rates  simulations on small proteins

CMSC 838T – Presentation Folding Rates

CMSC 838T – Presentation Folding & Unfolding

CMSC 838T – Presentation Observations u Sampling  too expensive to run for a long timescales  waste too much time lingering in local energy minima u Ensemble dynamics  speed up simulations of dynamics  biological meaning of simulations results?  results on large protein folding?  limitations: correct transition detection, transition probability u  cheap way to achieve super computation power  huge distributed computing platform: over 400,000 CPUs  an efficient approach for CPU intensive job u Complexity of problems and size of data increase rapidly  find better algorithm is preferable to buying supercomputers