biopilot: data-intensive computing for complex …...2 samatova_biopilot_sc07 biological research is...

Presented by

BioPilot: Data-Intensive Computingfor Complex Biological Systems

Nagiza Samatova

Computer Science Research Group

Computer Science and Mathematics Division

Sponsored by

the Department of Energy’s Office of Science

Advanced Scientific Computing Research

Scientific Discovery through Advanced Computing

2 Samatova_BioPilot_SC07

Biological research is becoming a high-throughput, data-intensive science, requiringdevelopment and evaluation of new methods and algorithms for large-scale

computational analysis, modeling, and simulation

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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- translationalmodifications 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 ofcellular networks

Integration of bioinformatics and biomolecular modelingand simulation

• Structure, function, and dynamics of complex biomolecular system inappropriate environments

• Comparative analysis of large-scale molecular simulation trajectories

• Event recognition in biomolecular simulations

• Multi-level modeling of protein models and their conformational space

Goals for enabling data-intensivecomputing in biology


Comparative molecular trajectoryanalysis with BioSimGrid

• Routine protein simulations generateTB trajectories.

• Routine analysis tools (ED) requiremultiple passes.

• Correlation analyses have randomaccess patterns.

• Comparative analyses requiremultiple/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 materialtransport

T. P. Straatsma, Computational Biology and Bioinformatics


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 permitmeaningful computational simulation/molecular dynamics.Goal is to reduce the errors in initial models.

• We multi-align the group of neighbors usingsequence and structure information to findthe most stable parts of the domain.Extracted stable core structure is 20 30%closer in terms of RMSD to the target thanto any of the original templates.

• More flexible parts of target are modeledlocally by choosing most sequentiallysimilar loops from the library of localsegments found in all the homologues.

• A genetic algorithm conformation searchstrategy using Cray XT4 uses backboneand sidechain angles as parameters.Each GA step is evaluated by minimization.

A computed template compared

with the target, improved from 3.4A

initially to 2.3A

E. Uberbacher, Biological and Environmental Sciences Division


• Ultra-scale docking. The Bayesian potentials allowexploration of 100s and 1000s of protein complexes insteadof one or two.

• Docking from independently crystallized subunits. Wedemonstrated excellent results on complexes reconstructedfrom ~500 independently crystallized subunits.

• Whole genome predictions. Developed technology hasbeen used to predict 1000s of protein complexes on thegenome-wide level in several organisms.

Best structure. The set with the largest common

kernel always includes nearly the best native

structure present in the ensemble of 10,000

folded structures.

Quality estimator. The size of the largest common

kernel (obtained from the connectivity graph)

provides an excellent estimate of how close the

selected structure is to the native ones.

A. Gorin, Computer Science and Mathematics Division

Analysis of ultra-large structuralensembles


Predictive proteome analysis usingadvanced 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 toolsidentify only 10 20%of spectra fromproteomics analysis.

• Fragmentation patternsare highly sequence-specific.

• Statisticalcharacterization ofnoise essential.

• Identification rateincreased by 23–40%.

NH3-HC -CO- NH-HC -CO- NH-HC -CO- NH-HC -COOH

R R R R

20%@5% False Pos. Rate

Increase in identified peptides

40%@1% False Pos. Rate

W. R. Cannon, Computational Biology and Bioinformatics


In-depth analysis of MS proteomicsdata flows

Mass-spectrometry-based proteomics is one of the richest sources of thebiological information, but the data flows are enormous (100,000s ofsamples each consisting of 100,000s of spectra).

Computationally, bothadvanced graph algorithmsand memory-based indexes(> 1 TB are required forin-depth analysis of thespectra).

Two principally new capabilities are demonstrated: • Quantity of identified peptides is 50% increased. • Among highly reliable identifications, increase is many-fold.

A. Gorin, Computer Science and Mathematics Division


ScalaBLAST

• Standalone BLAST applicationwould take >1 year for IMG(>1.6 million proteins) vs IMGand >3 years for IMG vs nr.

• “PERL script” approach breakseven modest clusters becauseof poor memory management.

Scientific challenge:

Good scaling on both shared anddistributed memory architectures

C. S. Oehmen, Computational Biology and Bioinformatics

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ALTIX

HTCBLAST

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10000

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1work factor

ideal

IMG 1.6

IMG 2.2

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hours

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Trillions of pairwise comparisons


After: ~1GB

Before: ~2TB

pGraph: Parallel Graph Library foranalysis of biological networks

Major features:

• Memory reduction by 1000 times

• Scalability to 100s of processors

• FPT-based search space reduction theory

Find cliques Merge cliques

2,109

vertices

16,169

edges

4123 modulesModule

851

“common-target”

associations

Association

pGraph

Nagiza Samatova, Computer Science and Mathematics Division


Discovering cell networks withstructure and genome context

2007, Sep 7

27(5): 793

The conserved ASD domain links ~30%

of ECF sigma and anti-sigma pairs in the

signal transduction cascade, e.g.,

response to iron and 1O2 with FecIR,

RpoE-ChrR proteins.

Many combinations of different sigma ( )

and sensor (S) domains exist. Two levels

of positional clustering, (1) domain and

(2) gene neighbor, generate vast

permutations between protein pairs.

The structure of the ChrR anti-sigma factor from Rhodobactersphaeroides reveals domains which help explain the

combinatoric complexity of gene regulation in response to

environmental conditions. An advanced toolkit for graph,

genome context, and visual analytics was used to study ECF

sigma factor regulation.

FecI FecR

RpoE ChrR

3D structure of ChrRanti-sigma regulator

ECF regulation

1 ASD

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ASD

S4

S3

anti-

ASD

ASD

S2

S1

Sofia Heidi, Computational Biology and Bioinformatics


• To model microbial communities, the smallest realistic model wouldinclude >109 cells x 100 reactions ~1011 reactions.

• Currently can handle ~104–106 reactions on a single-processor machine.

Scientific challenge:

Stochastic simulations of biologicalnetworks

• J. Phys. Chem. B 105, 11026,2001

• Speedup over exact GillespieSSA ~25

Multinomial Tau-leaping method:

• Speedups ~40–200

Probability-weighted dynamicMonte Carlo method…

H. Resat, Computational Biology and Bioinformatics

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Number of phosphorylation binding sites

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Contacts

Tjerk Straatsma

Principal Investigator

Pacific Northwest National Laboratory

[email protected]

Nagiza Samatova

Principle Investigator

Oak Ridge National Laboratory

[email protected]

Christine Chalk

Program Manager

U.S. Department of Energy

Office of Science

Advanced Scientific Computing Research, SciDAC

[email protected]


biopilot: data-intensive computing for complex …...2 samatova_biopilot_sc07 biological research is...

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