Massively Distributed Computingand

An NRPGM Projecton

Protein Structure and Function

Computation Biology LabPhysics Dept & Life Science Dept

National Central University

From Gene to Protein

About Protein

• Function– Storage, Transport, Messengers, Regulation… E

verything 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

Protein Structure & Function

• Primary sequence Native state with 3D structure– Structure function– Expensive and time consuming

• Misfolding means malfunction– Mad cow disease (“prion” misfolds)

The Folding Problem

• Complexity of mechanism & pathway is huge challenge to science and computation technology

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

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

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

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

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

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)

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

Server

System test

Open Multithread( Working Channel)Connect to client

Recv Request

Send Assignment

Get Result

Put Accept

Packet Request

Packet Assignment

Packet Result

Packet Accept

Structure at Server end

Send(COSM)

Receive

Jobs

clients

Protein database

•Temporary databank•Job analysis•Automatic temperature swaps by parallel tem- pering

DatabankHuman

intervention

Exceptions

Structure at Client end

Receive

MD Run

Return result

Delete files

RestartIf crash

Server

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

Multi-temperature Annealing

client

client

client

client

client

client

client

Server

Old temperatures

Swap temps by

Multiple “peptide”parallel

tempering

New temperatures

Data

ban

k

• 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

Potential of Massive Distributive Computing

Learning curve

• Launched –August 2002 • Small PC-cluster – October 2002

– In-house runs to learn codes• Infrastructure for Distributive Computation

– Installation Gromacs & 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

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

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.

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.

Client

Font-End

Server

Backend

Server 1

Backend

Server N

Client

Client

Multi-Server Architecture

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

Current Simulations

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.

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, resu

lts returned to server for swapping after each “run”, or 105 time steps (100 ps)

P = min{1, exp[-(E2-E1)(1/kT1 – 1/kT2)]}

Parallel-tempering (1SOL)

200

250

300

350

400

450

500

550

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

273285300315333348366384405426447471498

Tem

pera

ture

(K

)

Number of runs (in units of 100 ps)

Const temp. (20ns)

Native conformation

Parallel-temp. (1.6ns)

Initial structure

Preliminary result on

1SOL

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

Swap History (1L2Y)

250

300

350

400

450

500

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69

270

290

310

330

350

370

390

410

430

450

470

Number of runs (in units of 100 ps)

Tem

pera

ture

(K

)

Native stateInitial state

PAC 6ns

Preliminary result on

1L2Y(11

peptides)

• 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…

Modifications needed

• 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

Looking ahead

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 associ

ates, programmers, students, etc.

http://protein.ncu.edu.tw

Thank you for your attention