Presented by
BioPilot: Data-Intensive Computingfor Complex Biological Systems
Nagiza SamatovaComputer Science Research Group
Computer Science and Mathematics Division
Sponsored by
the Department of Energy’s Office of ScienceAdvanced Scientific Computing Research
Scientific Discovery through Advanced Computing
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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
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Relative Frequency of Appearance
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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 constructsInference, 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
Goals for enabling data-intensivecomputing in biology
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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
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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 is 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 XT4 uses backbone and 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
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• Ultra-scale docking. The Bayesian potentials allow exploration of 100s and 1000s of protein complexes instead of one or two.
• Docking from independently crystallized subunits. We demonstrated excellent results on complexes reconstructed from ~500 independently crystallized subunits.
• Whole genome predictions. Developed technology has been used to predict 1000s of protein complexes on the genome-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 structural ensembles
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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.
Statistical characterization of noise essential.
Identification rate increased by 23–40%.
NH3-HC-CO- NH-HC-CO- NH-HC-CO- NH-HC-COOHR R R R
20%@5% False Pos. Rate
Increase in identified peptides
40%@1% False Pos. Rate
W. R. Cannon, Computational Biology and Bioinformatics
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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 are demonstrated: • Quantity of identified peptides is 50% increased. • Among highly reliable identifications, increase is many-fold.
A. Gorin, Computer Science and Mathematics Division
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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
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ALTIX
HTCBLAST
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Number of processors10050
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1work factor
ideal
IMG 1.6IMG 1.6
IMG 2.2IMG 2.2
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Trillions of pairwise comparisons
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After: ~1GB
Before: ~2TB
pGraph: Parallel Graph Library for analysis of biological networksMajor 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
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Discovering cell networks with structure and genome context
2007, Sep 727(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 Rhodobacter sphaeroides 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 FecRRpoE ChrR
3D structure of ChrR anti-sigma regulator
ECF regulation
1 ASD
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ASD
S4
S3
anti-
ASD
ASD
S2
S1
Sofia Heidi, Computational Biology and Bioinformatics
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To model microbial communities, the smallest realistic model would include >109 cells x 100 reactions ~1011 reactions.
Currently can handle ~104–106 reactions on a single-processor machine.
Scientific challenge:
Stochastic simulations of biological networks
J. Phys. Chem. B 105, 11026, 2001
Speedup over exact Gillespie SSA ~25
Multinomial Tau-leaping method:
Speedups ~40–200
Probability-weighted dynamic Monte 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 InvestigatorPacific Northwest National [email protected]
Nagiza Samatova
Principle InvestigatorOak Ridge National [email protected]
Christine Chalk
Program ManagerU.S. Department of EnergyOffice of ScienceAdvanced Scientific Computing Research, [email protected]
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