Sesame: a Data Management System for Structural
Proteomics
John L. Markley
Center for Eukaryotic Structural Genomics
National Magnetic Resonance Facility at Madison
Biological Magnetic Resonance Data Bank
Department of Biochemistry, University of WisconsinMadison, Madison WI 53706, USA.
markley@nmrfam.wisc.edu
What data do we need to capture and make available to ourselves and others?
Individual research lab (“R01 research”)
• Information gleaned from databases, literature, and collaborators
• Laboratory resources (people, freezers, rooms, instruments ...)
• Protocols
• Projects and their progress
• Products (what they are, where located, where distributed)
• Primary data collected (sequences, gel scans, HPLC, UV-vis, MS, NMR...)
• Results to be submitted to databases or put in publications
Large consortia (e.g., structural genomics centers)
• Same as above, but on a larger scale
• Data transmitted to specialized databases (TargetDB, PEPCdb)
Shared instrumentation / technology centers
• User requests for data collection, services
• Scheduling / billing
• Protocols (available for distribution and reuse)
• Individual data sets (back-up copy, copy sent to users)
• Reporting for funding agencies and advisory committees
Publicly accessible databases (Genbank, UniProt, PDB, BMRB, Entrez ...)
• Validation tools and pre-submission screening of data
• Tools for data integration and sharing
Necessary ingredients
Vocabularies, ontologies, and data schema for defined domains (with
databases based on these)
Genbank
Uniprot
Protein Data Bank / Biological Magnetic Resonance Data Bank
PubMed, PubChem, ...
Data sharing
Structural genomics groups are leading the way:
TargetDB, PEPCdb, Center databases
Others are planned: Molecular Libraries
Integration of data, simulations, and validation
General goal in many areas
User interfaces that integrate genomic and other biomedical databases
Genome (nucleic acid and protein sequences)
Transcriptome (gene chips, ...)
Interactome (molecular interaction maps)
Structures (3D coordinates and underlying data)
Biophysical and dynamic data (NMR parameters)
Chemicals (small molecule screening, metabolomics)
Images (macromolecular complexes → cells → organisms)
NMR structure determination as an example of the
integration of data, simulations, and validation
Target
Simulations
Data
PDB, BMRB
Spectra of protein
Prediction of spectra from
sequence / homology modeling
Peak identifications
Prediction of 2º structure from
sequence and assignments
Backbone assignments
Prediction of tertiary contacts
NOE and RDC restraint analysis
Sidechain assignments
Prediction of NMR parameters
(shifts, RDCs) from structure
Structure
calculations
Validation of input data and
calculated results
Final
ensemble
Database
deposition
Publication
“Chemical shift priors” can be used to sharpen probabilities in
automated peak identification and assignment
1H-15N
probability density plot (chemical shift priors) for mouse protein
Mm202773 generated from the sequence of the protein. The colors
correspond to the probability scale on the right.
Conformation-dependent
chemical shift densities: 13C
Ala
13C
-sheet
-helix
The parametric descriptions of these distributions are:
sheet:1.65| 1.0| 0.8| 50.9| 0.00| -0.0| 0.0| 0.00
coil: 1.89| 0.0| 0.4| 54.9| 1.43| -0.6| 0.6| 52.5
Conformation-dependent
chemical shift densities
taken two at a time: 1H-13C
Estimated relative densities
in two-dimensional 13C-1H
chemical shift space for
alanine (top four panels) and
methionine (bottom four
panels). In each set of four
panels, the densities
represent: (top left) extended
strand, E, (top right) alpha
helix, H, (lower left) random
coil, R, (lower right)
combination of E + H
Eghbalnia et al., J. Biomol. NMR, in press
PECAN (Protein Energetic Conformational Analysis from NMR
chemical shifts) analysis of secondary structure from assigned
chemical shifts and the protein sequence: example of output
helix
transition
region
extended
Eghbalnia et al., J. Biomol. NMR, in press
Example of the combined use of database, GRID-type computing, and
validation: recalculation of structures of >500 proteins from restraint
data extracted from the PDB
NMR restraints: NOE, J-couplings from PDB “MR” text files
BMRB restraints database (MR grid)
Database of corrected restraints (DOCR)
Filtered database of non-redundant restraints (FRED)
Distributed
computation on
Condor cluster of
>800 workstations
CYANA
>500 CYANA structures
Database of recalculated
structures (RECOORD)
Following final
CNS water
refinement,
validated and
compared
PDB
CNS
>500 CNS structures
Collaboration among BMRB,
EBI, European Validation Group
(NMRQUAL), ....
Improvement in Ramachandran
plot appearance (recalculated –
original structures)
Example of analysis of results from the RECOORD
(REcalculated COORDinates) database)
Improvement in Z-score packing quality
(recalculated – original structures)
Nederveen et al. Proteins, in press
The Sesame Project
Goals: laboratory information management system, collaborative
tools, process pipelining
Started in 1998 as a tool for managing data at the National Magnetic
Resonance Facility at Madison (NMRFAM)
Was adopted by the Center for Eukaryotic Structural Genomics
(CESG) in 2000 and continually expanded and refined since then
Currently used by:
• Three structural genomics consortia (CESG, SGPP, and Berlin
Structure Factory)
• Proteomics project on enzymes of E. coli
• NMR facilities (NMRFAM, Medical College of Wisconsin, Mayo)
• Molecular Interactions Facility (Madison)
• Biological Magnetic Resonance Data Bank
• “R01” labs
Under construction for:
• Metabolomics consortium
• Molecular screening facility
Zolani et al., J. Struct. Funct.
Genomics (2003) 4:11-23
http://www.sesame.wisc.edu
Sesame: basic design features
Multiple-tier system sitting on a commercial relational
database management system
Computational servers do the heavy work and off-load to
distributed (GRID) servers
Users interact via Java2 with clients on any computer linked
to the Web (desktop, notebook, hand-held)
CORBA
Name Server
OR
B
Alibaba
Dispenser
OR
B
AlibabaDB
JDB
C
User
Client
OR
B
Tier 1
Alibaba
OR
B
Tier 2
Sesame
DB
and
FS
Tier 3
Setting up a new virtual laboratory under
Sesame (from the ‘Help Pages’)
• Users can set up a lab or facility
• The “Lab Master” invites members to join the group and
can carry out some customizations
Target Information
• Sesame organizes uploaded information from the
annotated proteome (gene identifier, ORF
sequence, sequences of flanking regions, relevant
data from web-based databases, etc.)
• ORF information includes direct links to all records
in the Sesame data base associated with that ORF
for rapid and efficient data recall and analysis
• Queries can be used to select ORFs on the basis of
chosen criteria (physical properties or annotations
of various kinds) and to organize them into
‘workgroups’ of defined sizes (usually 96) for entry
into either the E. coli or cell-free pipeline
Every record is
associated with a
protocol, so that
laboratory results
can be entered
Variables associated with
cell growth:
• OD at induction
• Wet weight of cells
• Expression level
• Solubility
• Cleavage
“Actions” provide a controlled vocabulary for searchable outcomes of work
• Each lab can create a unique set of controlled vocabulary terms.
• Actions can represent lab activities (e.g., steps in cloning, expression testing,
protein production from cells or cell-free extracts, structure determinations by
X-ray or NMR) or stamps of approval (e.g., cloning acceptable, purification
complete).
• CESG’s entire data dictionary contains 184 defined actions.
• The list of actions can evolve as warranted by the development of new
laboratory processes.
• Actions are linked to a specific workgroup number and protocol so that the
data can be mined at a later date and reports generated.
• Actions shown here pertain to target selection and cloning.
Protocols are organized so that only the fields and actions relevant to a
given protocol are visible to users
Body of the protocol
List of CESG actions
NIH database tag
mapped to each action
Fields for data entry – default
values can also be entered
Other features of the Sesame LIMS
In addition to documenting the progress of targets,
Sesame can perform other functions:
• Predict restriction digest patterns
• Store information about laboratory ‘resources’,
such as primer prefixes, protease types, and
storage locations
• Create orders for oligonucleotide primers
• Store images and files
• Create reports (XML and summary reports)
Multiple windows can be “tiled” on the browser screen for
ease in making comparisons
As another example, here is the Sesame record
for a sample prepared for mass spectrometery
On scrolling down, one sees that images and files have been appended
JM 7203 xray LA 7196
Match to: gi|22328616 Score: 111 At4g14165
F-box family protein-related [Arabidopsis thaliana]
Found in search of I:\CESG4\jm7203.mgf
Nominal mass (Mr): 30619; Calculated pI value: 9.03
NCBI BLAST search of gi|22328616 against nr
Unformatted sequence string for pasting into other applications
Taxonomy: Arabidopsis thaliana
Cleavage by Trypsin: cuts C-term side of KR unless next residue is P
Sequence Coverage: 16%
Matched peptides shown in Bold Red
1
51
101
151
201
251
MENKHNPTSH
QQQFMHGNSW
IPKTSDCFHM
FGPPPPSNPW
QGITVLANDT
SSSVQFYGAQ
TSHTWSELPE
KLAPYGRSMI
VYKDHKLYFL
KVLATKLVVT
GGFIRNTIYF
WFVPSFKH
Expected At4g14165.1 mw 30595
LNPCVPLGTL
MRYAMQVRGQ
NKTGSFKIFD
VTGKVLKVEE
SASHGNNTHD
LPDKSCPKTH
LAPTVLGINR
FCGDIPQQTF
MGGARPRTWS
IYIFNLETQK
PLADLIPPRQ
TWKGDTCWNQ
EWSVKVERSQ
FRVFESMLLD
TEPLHTLDSY
Sesame module for crystallization
screening (‘Well’)
• Well is the Sesame module
that manages information
about crystallization screens
• This includes the
composition of the screen
(the software performs
automatic volume
calculations)
• Screens are linked to
specific sample records
• Sesame controls the
screening robot
Sesame module for crystallization
screening (‘Well’)
• Well is the Sesame module that manages
information about crystallization screens
• This includes the composition of the
screen (the software performs automatic
volume calculations)
• Screens are linked to specific sample
records
• Sesame controls the screening robot
In structural genomics, most protein-protein
interactions detected are homo-oligomers
At5g06450.1: Arabidopsis fold-space target
At5g06450.1:
Arabidopsis fold-space
target
X-ray structure
1VK0
• 28% identity over 104 residues with P0445H04.27 from Oryza sativa.
• Structure is similar to trimeric cyclic viral exonucleases, but At5g06450.1
is a homohexamer with C6 symmetry
• Structure is reminiscent of processivity factors in nucleic acid modifying
enzymes
• Is a nucleic acid pulled through its center? Each subunit has a disordered
positively charged sequence YKYKGS with aromatic rings for stacking?
Sesame is flexible and able to accommodate changes in
protocols and decision points
New CESG pipeline
being tested
Construct design
PCR cloning -> DNA
15-50 g scale
mRNA -> protein -> cell-free production and solubility screen
Flexi®Vector plasmids
1-5 mg scale
Protein from E. coli cells
Fluidigm chip
crystallization
screening
Protein from cell-free
Screening:
Yield
MS
Functional assays
NMR 15N-1H HSQC
or 1H screening
Sesame module for protein-protein interactions: ‘Rukh’
• The Rukh module is designed to manage yeast
two-hybrid (Y2H) screens, and to track the whole
Y2H screening process from the initial screen
setup through the validation steps.
• Rukh is also used to score positives based on
absorbance data, visual selection from gel images
or other criteria, and to generate work lists for a
Tecan robot to reformat the plates.
Sesame interface for Y2H screening
Functions
Plate reader data shown for a bait.
Those above the cutoff go forward
to the next screen in the process.
Screen evaluation
and refinement
Visualization and scoring of
gels is used in creating the
next screen to be used in the
process.
Example of data retrieval:
searching for information
on a recent structure
determined at CESG
(domain from ORF
At3g03410.1)
Search by ID reveals that two versions of this gene have been studied: the full-length and a “chunk”
scroll
scroll more
By clicking on the ORF number, one can view available annotation captured from other databases
The list of actions shows that the structure of the domain has been determined and deposited in the PDB
Structure of the 67 aa domain (nCML) of At3g3410.1
nCML
N-terminal
domain from
human
calmodulin
Song, Zhao, Thao, Frederick & Markley (2004) J. Biomol. NMR 30, 451-456
PDB 1TIZ; BMRB 6209
EF-hands can be “open” or “closed”
Different responses of the EF-hands of CaM and calbindin
to Ca2+ binding are thought to be responsible for their
different physiological roles in calcium signaling
First EF-hand
Calmodulin (CaM)
Calbindin
Ca2+-loaded CaM
Ca2+-loaded calbindin
Ca2+-loaded nCML (this work)
closed
closed
open
closed
open
Second EF Hand
closed
closed
open
closed
closed
The Arabidopsis nCML studied here has a different signature,
which may indicate the existence of a new calcium signaling
pathway in plants.
Arabidopsis has 6 CaM proteins and 50 CaM-related proteins
such as nCML, the domain studied here.
Mapping of the ligand binding site of
At2g24940.1 by NMR
Red: [1H,15N]-HSQC of
At2g24940.1
Green: [1H,15N]-HSQC of
At2g24940.1 in the presence of
progesterone (ratio 1:1)
Blue: Site with the greatest
chemical shift perturbations (HN
>0.15ppm)
HN ={((H)2+(N/5)2)/2}1/2.
Summary
Domains currently covered by Sesame
• Bioinformatics (data from and to data banks)
• Molecular biology
• Protein chemistry
• Proteomics
• Molecular interactions
• NMR spectroscopy
• Crystallomics and X-ray crystallography
Additional domains under construction
• Metabolomics
• Small molecule screening
Challenges
• Education of users of the LIMS
• Motivation of users to capture all data correctly
Feedback from data summaries used to track
progress and guide next steps
Automate data entry from instrumentation
• Evolution of the LIMS to handle scientific domains at
increasing depth and breadth
Nomenclature issues
Interfacing with instrumentation and software
Acknowledgments
Sesame staff members
Zsolt Zolnai
John Cao
Peter Lee
Jing Li
Michael Runnels
Jianhua Zhang
Sesame collaborators
Wim Hol (UW, Seattle)
Michael Hoffman
Eileen Maher
Hartmut Oschkinat
(Berlin)
Michael Sussman
NMRFAM staff
members
Arash Bahrami Hamid
Eghbalnia
Liya Wang
Milo Westler
BMRB staff members
Eldon Ulrich
Jurgen Doreleijers
Jundong Lin
Steve Mading
Dimitri Maziuk
David Tolmie
Chris Schulte
Kent Wenger*
Hongyang Yao
Collaborators
Hideo Akutsu (Osaka)
Helen Berman (PDB)
Yannis Ioannidis
(Athens, Greece)
Miron Livny*
John Westbrook (PDB)
*UW-Madison Computer
Sciences
CESG staff members
David Aceti
Craig Bingman
Brian Fox
Zach Miller*
Craig Newman
George Phillips
Jikui Song
Zhaohui Sun
Brian Volkman (MCW)
Others on the
recalculation project
Alexandre Bonvin
Peter Güntert
Robert Kaptein
Sander Nabuurs
Aart Nederveen
Michael Nilges
Chris Spronk
Wim Vranken
Grant support
National Institutes of Health
National Institute for
General Medical Sciences
National Center for Research Resources
Biomedical Technology Program
National Library of Medicine
National Science Foundation