In collaboration with

BioPilot: Data-Intensive Computingfor Complex Biological Systems

Pacific Northwest National LaboratoryOak Ridge National Laboratory

Sponsored bythe Department of Energy

Office of Science

Program Managers:Drs. Gary Johnson and Anil Dean

2 Samatova_BioPilot_0611

Goals for enabling data intensivecomputing in biology

Biological research is becoming a high-throughput, data-intensive science, requiring development and evaluation of new methods and algorithms for

large-scale computational analysis, modeling, and simulation

-80 -60 -40 -20 0 20 40 60 800

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Figure 1

Rel

ativ

e F

req

uen

cy o

f A

ppe

ara

nce

Fragment Ion Mass Offset

-80 -60 -40 -20 0 20 40 60 800

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Figure 1

Rel

ativ

e F

req

uen

cy o

f A

ppe

ara

nce

Fragment Ion Mass Offset

Computational algorithms for proteomics Improve the efficiency and reliability of protein identification and

quantification. Peptide identification using MS/MS using pre-computed spectral

databases Combinatorial peptide search with mutations, post- translational

modifications and cross-linked constructs.

Inference, modeling, and simulation of biological networks

Reconstruction of cellular network topologies using high-throughput biological data

Stochastic and differential equation-based simulations of the dynamics of cellular networks.

Integration of bioinformatics and biomolecular modeling and simulation

Structure, function, and dynamics of complex biomolecular system in appropriate environments.

Comparative analysis of large-scale molecular simulation trajectories Event recognition in biomolecular simulations Multi-level modeling of protein models and their conformational space.

3 Samatova_BioPilot_0611

Comparative molecular trajectoryanalysis with BioSimGrid

Routine protein simulations generate TB trajectories

Routine analysis tools (ED) require multiple passes

Correlation analyses have random access patterns

Comparative analyses require multiple/distributed trajectory access

Comparative Trajectory Analysis Tools

ENERGY

GRADIENT

OPTIMIZE

DYNAMICS

THERMODYNAMICS

QMD

QM/MM

ET

QHOP

INPUT

PROPERTY

PREPARE

ANALYZE

ESP

VIB

Classical Force Field

DFT

SCF: RHF UHF ROHF

MP2: RHF UHF

MP3: RHF UHF

MP4: RHF UHF

RI-MP2

CCSD(T): RHF

CASSCF/GVB

MCSCF

MR-CI-PT

CI: Columbus Full Selected

Integral API

Geometry

Basis Sets

PEigS

pFFT

LAPACK

BLAS

MA

Global Arrays

ecce

ChemIO

NWCHM

The analysis of molecular dynamicsTrajectories presents a large,data-intensive analysis problem:

The Scientific Challenge:

Integrated molecular simulationand comparative moleculartrajectory analysis:

Enzymatic reaction mechanisms Molecular machines Molecular basis of signal and

material transport

T.P.Straatsma, Computational Biology and Bioinformatics

4 Samatova_BioPilot_0611

d

Building accurate protein structures Homology models can be built from related protein structures, but even

with proper alignment, usually have about 4A RMS error–too large to permit meaningful computational simulation/molecular dynamics.Goal to reduce the errors in initial models

We multi-align the group of neighbors using sequence and structure information to find the most stable parts of the domain. Extracted stable core structure is 2030% closer in terms of RMSD to the target than to any of the original templates

More flexible parts of target are modeled locally by choosing most sequentially similar loops from the library of local segments found in all the homologues

A genetic algorithm conformation search strategy using Cray XT3 uses backbone and sidechain angles as parameters.Each GA step is evaluated by minimization

A computed template compared to the target, improved from 3.4A initially to 2.3A

E. Uberbacher, Biological and Environmental Sciences Division

5 Samatova_BioPilot_0611

High-resolution protein docking High-throughput protein interaction data generation

provides many interacting proteins. However, the best docking programs (Glide, GOLD) recognize proper docking site and position between known docking partners a small fraction of the time

We take advantage of a reduced search space for contacting protein surfaces but sample all relative conformations on a grid

Surface normals are anti-aligned with those on the other protein surface in the docking procedure. Complexity of the search is r * N1 * N2 with steric clashes filtered

Bayesian scoring evaluates each configuration based on specific residue pair distances at each interface: Score=sum of Log ratios for interface/decoys contact frequencies over all contacts in the patch

Scoring scheme is effective at separating false configurations from true. It recognizes 1/3 of correct configurations as the top choice and a second third

within the top three choices.

Surface normals for human Ras-related protein Rap-1A [PDB:1C1Y, shown in CPK (orange) and RAF kinase (cyan)]

Blue curve decoys, yellow curve true

A. Gorin, Computer Science and Mathematics Division

6 Samatova_BioPilot_0611

Predictive proteome analysis using advanced computation and physical modelsScientific Challenge:Data sizes: 30,000 to 200,000 spectra from a single experiment to becompared with as many as millions of theoretical spectra

Computational tools identify only 1020%of spectra from proteomics analysis:

Fragmentation patterns are highly sequence-specific

Generic patterns result in low identification rate

Physical models not used in current analyses becauseof computational expense

W.R.Cannon, Computational Biology and Bioinformatics

NH3-HC-CO- NH-HC-CO- NH-HC-CO- NH-HC-COOHR R R R

7 Samatova_BioPilot_0611

In-depth analysis of MS proteomicsdata flowsMass-spectrometry-based proteomics is one of the richest sources of the biological information, but the data flows are enormous (100,000s of samples each consisting of 100,000s of spectra)

Computationally, both advanced graph algorithms and memory-based indexes (> 1 TB are required forin-depth analysis of the spectra)

Two principally new capabilities demonstrated • Quantity of identified peptides is 100% increased • Among highly reliable identifications, increase is many fold

A. Gorin, Computer Science and Mathematics Division

8 Samatova_BioPilot_0611

ScalaBLAST

Standalone BLAST application would take >1 year for IMG (>1.6 million proteins) vs IMG and >3 years for IMG vs nr

“PERL script” approach breaks even modest clusters becauseof poor memory management

Scientific Challenge:

Good scaling on both shared and distributed memory architectures

C.S.Oehmen, Computational Biology and Bioinformatics

0 150

sca

lin

g f

acto

r

number of processors10050

0

40

80

120

MPP2

ALTIX

HTCBLAST

0 1000

Qu

erie

s p

er (

pro

c*m

inu

te)

number of processors10050

0

3

4

6

10000

5

2

1work factor

ideal

9 Samatova_BioPilot_0611

To model microbial communities, the smallest realistic model would include > 109 cells x 100 reactions ~ 1011 reactions

Currently can handle ~ 104106 reactions on a single-processor machine

Scientific Challenge:

Stochastic simulations of biological networks

Speedups ~40-200

J. Phys. Chem. B 105, 11026, 2001

Speedup over exact Gillespie SSA ~25

Probability-weighted dynamic Monte Carlo method

Multinomial Tau-leaping method

H. Resat, Computational Biology and Bioinformatics

1 4

number of phosphorylation binding site

32 5 876 9 1211100

250

500

750

1000

1250

1500

ave

rag

e ru

nn

ing

ti m

e ( s

eco

nd

s)

10 Samatova_BioPilot_0611

Uncovering mechanismsof transcriptional controlStochastic fluctuations in molecular populations have consequences for gene regulation. Our theoretical and simulation studies predicted that noise frequency content is determined by underlying gene circuits, leading to mapping between gene circuit structure and noise frequency range. Predictions are validated experimentally

Model of shift of frequency range distribution shape due to negative feedback. Red bars (e) represent unregulated circuit distribution;blue bars represent distributionfor circuit with negative auto-regulation

Dashed box and arrow show shiftof the low-frequency trajectoriesto center of distribution whilehigher-frequency trajectoriesare unaffected

2006 Feb 2; 439(7076):608

N. Samatova, Computer Science and Mathematics Division

11 Samatova_BioPilot_0611

Contacts

Dr. Gary JohnsonActing Program Manager(301) 903-5800e-mail: garyj@er.doe.gov

11 Presenter_date

Dr. Anil Dean Program Manager (301) 903-1465

e-mail: deane@mics.doe.gov