Transcript Diana Murray

Lateral organization and electrostatic control of signaling
Protein/membrane interactions
Lipid sequestration by membrane associated proteins
Membrane-mediated protein/protein interactions
Subcellular re-localization upon ligand binding
+25 mV
-25 mV
High-throughput modeling of cellular signaling proteins
Protein families involved in membrane-mediated subcellular targeting
Within and across families, within and across genomes
→ Developed methods to reliably model all instances of peripheral proteins
Tonya Silkov:
Nebojsa Mirkovic:
Hunjoong Lee:
Frank Indiviglio:
Paul Murray:
Anna Mulgrew-Nesbitt:
Janey Li:
Zheng Li:
Meng Wang:
Mariam Konate:
Arabidopsis 2010 project
Phosphoinositide-binding domains in the human genome
Structural genomics high-throughput modeling
Database structures for integrating structures and predicited models
Formation of retroviruses and host/virion interactions
Phosphoinositide-modifying enzymes: PTEN and PLCs
Synaptic vesicle exocytosis
Simulation of the structure and dynamics of phosphoinositides
Integration of structure-focused tools into geWorkbench
Modeling of phosphoinositide signaling
http://ph7wnu.cpmc.columbia.edu/
Biophysical properties of cellular protein/membrane interactions
Intracellular membranes contain
distinct lipid compositions and
carry different charge densities
Binding behavior of a +8e peptide
to membranes carrying
different negative charge densities
Proteins that function in phosphoinositide pathways contain
multiple membrane binding motifs that mediate
specific and non-specific interactions with lipids and membrane surfaces
Motif 1
Motif 2
C1/DAG
C2/Ca2+
Protein kinase C–,,
PH/PIP2
C2/Ca2+
Phospholipase C–
PH/PIP2
PX/PI3P
Phospholipase D
FYVE/PI3P
PH/PI
FGD1(a Rho/Rac GEF)
Basic/PS
PH/PIP2
GPCR kinase
C2/Ca2+
Nonpolar
Cytosolic phospholipase A2
Myristate
Basic/PS
Src, MARCKS, (HIV-1 Gag)
Basic/PS
Farnesyl
K-Ras, G
Multiple inputs: Temporal and spatial control of subcellular targeting
through coincidence counting
Many peripheral proteins, especially those involved in subcellular
targeting , are either highly basic or charge polarized.
+25 mV
-25 mV
Quantitative physical theory for the interaction of proteins with membrane surfaces
Ligand-induced electrostatic switches:
Neutralization of charge around hydrophobic groups
Calcium-binding and phosphoinositide-binding domains
10R-LO C2
Endofin FYVE
Synaptotagmin I C2A domain: Interaction with 2:1 PC/PS
Agreement with experimental measurements:
Residue substitutions, ionic strength and % acidic lipid dependence
F234
Electrostatics plus hydrophobic:
–7.0 kcal/mol prediction
– 6.4 kcal/mol experiment
Janey Li
Connection among biophysical properties,
membrane binding behavior, and subcellular localization
No calcium
Calcium
Phospholipase C C2 domains
5-lipoxygenase C2 domain
Paul Murray
All lipid-binding domains in all model genomes
Use what we have learned computationally and experimentally to develop:
1. More complete lists of peripheral proteins of known structure from the PDB;
2. Detect and model all instances of peripheral proteins in sequence databases;
3. Discover new instances, novel functionalities, new families;
4. Create databases to house this information;
5. Use this information to annotate protein sequences of unknown function.
Structural Genomics and Protein Family Analysis
How to use computational tools to maximize the coverage of
protein sequence/structure/function space
“Leverage”: Number and quality of 3D models produced from a set of structures as templates
PSI1 and PSI2: NESG leverage ~220 sequence unique models
NCBI non-redundant (nr) database: 6.4 M protein sequences
PDB Protein Data Bank:
0.048 M protein structure
→ A judicious selection of structural targets with the modeling in hand,
will provide almost complete coverage of protein sequence/structure space.
There are many different ways to define “protein family”
“Modelability”: Create “quality” models using known structures as templates
Leverage for START domains: Modelability (7378) versus 30% sequence identity (2767)
General ingredients for a homology model
1. Structural template with
similarity to query
2. High quality alignment between query
sequence and template sequence
It’s possible to say something about function if computed
biophysical properties are similar or otherwise meaningful
SkyLine: High-throughput comparative modeling tool
PDB Structure
DSSP
Secondary structure
ClustalW
Sequence
Nebojsa Mirkovic
Meng Wang
Hunjoong Lee
Multiple
alignments
PSI-BLAST
Modeling alignments
Homologues
Non-redundant
& unsolved
Modeller or Nest
Family analysis
Data on homologues
Specialized
databases
PROSA, pG score
Model quality
pG > 0.7
MarkUs: Function
annotation
Homologous structures
(species, IDs, coverage,
length, e-value, seq. is.)
Models
Target
reprioritization
Leverage: unique models
Web-accessible
models database
Models Database Frank Indiviglio
Tonya Silkov, Frank Indiviglio
Fig. 1
Structure similarity among lipid-binding domains
ENTH domain
ANTH domain
VHS domain
Tonya Silkov
ENTH and ANTH have similar structural topology but
different mechanism of PIP and membrane binding
Helix 0
J Biol Chem. 278:28993
with Cho Lab
ENTH
ANTH
ANTH
ENTH
Arabidopsis domain with both ENTH and ANTH functionality
Tonya Silkov
ENTH
ANTH
From above
ENTH
Helix 0
Helix 0
ANTH
Cho Lab: First 25 amino acids
are required for both PIP2
binding and membrane
penetration.
Fig. 1
Structure similarity among lipid-binding domains
ENTH domain
ANTH domain
VHS domain
A new VHS-related family, “VR domains”, found in other genomes
KIAA1530
(Homo sapiens)
XP_420852
(Gallus gallus)
CAB71110
(Arabidopsis thaliana)
XP_747424
(Strongylocentrotus purpuratus)
Tonya Silkov
Among this subset of VHS domains,
the basic surface patch is conserved
VR domain family of membrane-binding VHS domains
Tonya Silkov
Phosphoinositide signaling processes
denotes
a phosphoinositide
headgroup
Nebojsa Mirkovic
Zheng Li
Mariam Konate
The ability to construct a quality model of a sequence is a more strategic
definition of a protein family member
Allows for the discovery of distantly related members
With function annotation, allows for the discovery of new sub-groups
Structures + Sequences -> Models + Function annotation (Markus)
More comprehensive coverage of protein sequence/structure/function space