Transcript 22_Binkowski - PSI Structural Biology Knowledgebase

Protein Surface Analysis for
Functional Analysis and Prediction
T. Andrew Binkowski and Andrzej Joachimiak
2009 NIGMS Workshop: Enabling Technologies for Structural Biology
March 4-6, 2009
Outline
How Can Surface Analysis Aid Your Structural
Genomics Effort?
 Protein Surfaces
 Comparing Surfaces of Proteins
 Surface Analysis in the Structural Genomics Pipeline
 The Global Protein Surface Survey
Functional Inference in Proteins
 Transfer function based on similarity to
a protein with known biological activity
 Sequence
 30-70%
 Functional sites result from spatial
interactions of key residues in
diverse regions of primary sequence
 Structure
 Reveal more distant relationships
 1 fold ~ many functions; vice versa
 Example: generalized secondary
structural element
 Different SSE can bring residues in
spatial proximity
(Jaroszewski & Godzick, ISMB 00)
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Functional Inference in Proteins
 Functional surfaces may be the most conserved structural features of
proteins
 Surfaces performing identical biochemical activity can be found within different
protein scaffolds or in the absence clear evolutionary relationships
Novel heme-monooxygenase
•12% sequence identity
• a/b vs. all a
•Experimentally verified activity
 Exploit ability of proteins to preserve local spatial residue patterns
 Presents another opportunity to infer insightful ideas about their biological function
and mechanisms
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Surfaces of Proteins
 Surface:
 Local grouping of solvent accessible
atoms
 Pockets:
 Empty concavity on a protein surfaces
into which solvent can gain access
 Identifying surfaces:
 Methods:

Solvent accessibility, Geometry, Grids,
Spheres
 Applications:

CASTp, Surfnet, Pocket, Ligsite, Pass
 Our approach:
 Computational geometry (alpha

shape)
 CASTp, PDB, Swiss-Prot,
Catalytic Site Atlas
Ligand binding surfaces:
 Exclusion contact surface
(solvent accessibility difference)
Muck & Edelsbrunner, ACM Tran Graph, 1994; Edelsbrunner, Facello, Liang, Disc Appl Math, 1996; Liang, Edelsbrunner, Woodward, Protein Sci,
1998
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Global Protein Surface Survey
http://gpss.mcsg.anl.gov
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Comparing Surfaces of Proteins
 SurfaceScreen
 Methodology for identifying similarly
shaped proteins and aligning them
Surface
 Optimizes two components
 Global Shape
 Perceived similarity
 Size and scale, independent of
Global Surface
Shape Filtering
chemistry
 Local physicochemical texture
 Preserved atom/residue orientation
 Conservation of chemical
complimentarity
Surface Shape
Alignment
Constrained Spatial
Surface Refinement
Apply Scoring
Functions
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Comparing Surfaces of Proteins:
Global Shape Similarity
 Surface Shape Signatures (SSS)
 Represent signature of a surface

as distribution sampled from a
shape function (Osada et. Al., 2002)
Comparison of probability
distributions
 Kolmogorov-Smirnov
 Earth Mover’s Distance
 ATP Binding sites
 protein kinase CK2 from Z. mays (b)
 phosphopantetheine


adenylyltransferase from E. coli (c)
maltose/maltodextrin transport
protein from E. coli (d,cyan chain A,
light blue chain B)
50 non-homologous sites (< 30%
sequence identity)
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Spatial Surface Alignment Refinement
 Combinatorial comparison of
residue sets in “neighborhood”
 Maintain “like” correspondence

of types
Maximum common residues
 Enumerate and evaluate
alignment orientations
 Find optimal superposition
using SVD of correlation matrix
(Umeyama 1991)
Heme binding pockets of myoglobin from different organisms.
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Evaluating Surface Alignments

RMSD Distance:
 Estimate the probability of obtaining a specific
RMSD for nres
 Compute random surface alignments (108) and build
lookup tables
 RMSD variants:


cRMSD (coordinate)
oRMSD (orientation)
 Surface Volume Overlap:
VAB  VA  VB  VA B
SVOTAB 
VAB
VAA  VBB  VAB
 Interpretation of SVOT is not
straightforward
 Need global and local
lSVOTab 
Vab
Vaa  Vbb  Vab
gSVOTAB 
VAB
VAA  VBB  VAB
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Benchmarking Surface Alignments
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Heme Binding Site Retrieval
 Heme (iron-protoporphyrin IX)
 Multi-functional (i.e.oxygen binding/transport,
electron transfer and redox)
 Binding on 20 different folds
 Between proteins <2% seq. id.
seq. & fold
 Query myoglobin (gray) against PDB
structure to identify hemoproteins
surface analysis
 Retrieval rate (area under ROC curve)
 Sequence: 68.7%
 Structure (SSM): 64.4%
 Surface: 95.8%
 Detection of convergent heme binding
site on IsdG from S. aureus
 Missing characteristic sequence motif
 12% seq id; different scaffold
 Experimentally verified
monooxygenase activity
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ATP: Retrieval of a Flexible Ligand
 Adenosine 5’-triphosphate multifunctional nucleotide (i.e.cell signaling, enegry transfer)
 58 unique EC classifications #.#.#.#
 Conformational flexibility
 Retrieval rates for 4 conformations (79.1%-85.4%); method is tolerant to flexible ligands
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Prediction and Validation of
GDP Binding Surface
 Structure of F420-0:gamma-glutamyl
ligase from A. fulgidus
 Large binding surface was searched
to support functional predictions and
GDP binding surface is identified
 Posed GDP based on superposition of
surfaces (red)
 Co-crystallization experiments
validates prediction
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Surface Analysis in the Structural
Genomics Pipeline
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Exploiting Protein Surfaces in
Structural Genomics
 Developing surface-based
tools to address specific
needs of structural genomics
pipeline
 Ligands for co-crystallization
 Aid in the assignment of


Future Studies
Discovery
electron density
Functional annotation tools
Drive further studies (i.e.
ligand binding, discovery)
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Crystallization/Structure Improvement
Partially Solved
or Low Quality
Structure
Surface Identification
Search GPSS for
Binding Sites
Co-crystallization
Experiments
Introduction of GDP to F420-0:gamma-glutamyl ligase from A.
fulgidus
improves resolution from 2.8 to 1.9 Angstroms and orders loop
regions.
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Assisted Electron Density Assignment
 Unidentified ligand density
 Construct surface surrounding density
and search against ligand surface
library
 Does not require entire structure
to be built
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Assisted Electron Density Assignment
 Applicable to ligands of various
molecular weights and sizes
 Fructose (pdb id=1zx5)
 NADP (pdb id=2ag8)
 Suggest a list in cases of
ambiguity
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Landscape Analysis: ATP
 Classification based on surface similarity
shows functional families have preferred
(not necessarily unique) surfaces and
conformation
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Automated Protein Kinase Classification
 All-against-all surface comparison of all protein

kinases in the PDB
 Color labeled by expert annotation (KinBase)
Surface clustering identifies:
 Dual substrate specificity of CK2 proteins
 Active/inactive states
 Similarity detected between MAP p38 kinase and
Abelson leukemia virus tyrosine kinase (Abl) with
bound cancer drug STI-571
 MAP kinase has unique DFG “out” conformation not
previously seen in ser/thr kinases
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Function Sleuth
 Conserved protein of unknown
function (VCA0319) from V. cholerae
 apc29617
 Unique arrangement of common
structural motifs
 Problematic for secondary structure
and fold analysis
 Surface analysis identifies DNA
binding surface and 5 putative metal
binding sites
 All 5 metal binding sites showed
strong preference for Mg
 Putative metalloregulated repressor
with Mg-regulated mechanism of DNA
binding
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Function Sleuth
1bdb
NAD
1hoh
MGD
2qwr
ANP
1jbw
ACQ
Target APC7761 (3fd3)
Agrobacterium tumefaciens str. C58
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Function Sleuth
1i9c
Target APC61725 (3fz5)
Rhodobacter sphaeroides 2.4.1
 Top 17 most similar surfaces bind B12
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Global Protein Surface Survey
 SurfaceScreen for PSI ‘function sleuth’
targets
 Automated analysis of largest 5 surfaces
(per chain and unit)
 Technical Note:

DOE INCITE on Blue/GeneP at ANL
http://gpss.mcsg.anl.gov
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Conclusion
 Comparing surfaces of proteins can be a useful
tool with many applications
 Functional characterization
 Assisted electron density assignment
 Automated classification
 Global Protein Surface Survey
 http://gpss.mcsg.anl.gov
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Acknowledgements
ANL/MCSG
H. An,
G. Babnigg,
L. Bigelow,
A. Binkowski,
C-s. Chang,
S. Clancy,
G. Cobb,
M. Cuff,
M. Donnelly,
C. Giometti,
W. Eschenfeldt,
Y. Fan,
C. Hatzos,
R. Hendricks
G. Joachimiak,
H. Li,
L. Keigher,
Y-c. Kim,
N. Maltseva,
E. Marland,
S. Moy,
R. Mulligan,
B. Nocek,
J. Osipiuk,
M. Schiffer,
ANL/MCSG
A. Sather,
G. Shackelford,
L. Stols,
K. Tan,
C. Tesar,
R-y. Wu,
L. Volkart,
R-g. Zhang,
M. Zhou,
ANL/SBC
N. Duke,
S. Ginell,
F. Rotella
Univ. of Virginia
W. Minor,
M. Chruszcz,
M. Cyborowski,
M. Grabowski,
P. Lasota,
P. Miles,
M. Zimmerman,
H. Zheng
Univ. College
London @ EBI,
J. Thornton,
C. Orengo,
M. Bashton,
R. Laskowski,
D. Lee,
R. Marsden,
D. McKenzie,
A. Todd,
J. Watson
Northwestern Univ.
W. Anderson,
O. Kiryukhina
D. Miller,
G. Minasov,
L. Shuvalova,
X. Yang,
Y. Tang
G. Montelione,
Ruthgers Univ. NESGC
T. Terwilliger,
Los Alamos, ITCSG
Z. Derewenda, Univ.
of Virginia, ITCSG
Z. Dauter, NCI
J. Liang, Univ.
of Illinois
D. Sherman, U. Michigan
Washington
Univ.
D. Fremont,
T. Brett,
C. Nelson,
Univ. of Texas
SWMC
Z. Otwinowski,
D. Borek,
A. Kudlicki,
A. Q. Mei,
M. Rowicka
Funding: NIH and DOE
Univ. of Toronto
A. Edwards,
C. Arrowsmith,
A. Savchenko,
E. Evdokimova,
J. Guthrie,
A. Khachatryan,
M. Kudrytska,
T. Skarina,
X. (Linda) Xu
Univ. of Chicago
O. Schneewind,
D. Missiakas,
P. Gornicki,
S. Koide, ITCSG
W-j. Tang,
B. Roux,
J. L. Robertson
M.R. Rosner,
T. Kossiakoff, ITCSG
V. Tereshko,
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Thank you