How to use computational tools to maximize the

How to use computational tools to maximize the coverage of protein sequence/structure/function space PSI Bottlenecks 1) Not enough connection between modeling and biology/experiment 2) “Modelability” not used in defining families or a dynamic target selection strategy 3) Incomplete use of functional information in model building Murray Lab: Nebojsa Mirkovic, Tonya Silkov, Hunjoong Lee, Frank Indiviglio, Janey Li Honig Lab: Markus Fischer and Donald Petrey

Phosphoinositide signaling processes denotes a phosphoinositide headgroup

Biophysical properties of cellular protein/membrane interactions Intracellular membranes contain distinct lipid compositions and carry different charge densities Binding behavior of a +8 e peptide to membranes carrying different negative charge densities

Proteins that function in phosphoinositide pathways contain multiple membrane binding motifs Motif 1 Motif 2 C 1/DAG C 2/Ca 2+ Protein kinase C– , , PH/PIP 2 C 2/Ca 2+ Phospholipase C– PH/PIP 2 PX/PI 3 P Phospholipase D FYVE/PI 3 P PH/PI FGD 1(a Rho/Rac GEF) Basic/PS PH/PIP 2 GPCR kinase C 2/Ca 2+ Nonpolar Cytosolic phospholipase A 2 ENTH/PIP 2 Prot/prot Epsin 1, AP 180 Myristate Basic/PS Src, MARCKS, (HIV-1 Gag) 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 m. V -25 m. V

Quantitative physical theory for the interaction of proteins with membrane surfaces

Connection among biophysical properties, membrane binding behavior, and subcellular localization No calcium Calcium Phospholipase C C 2 domains Homology models of all isoforms 5 -lipoxygenase C 2 domain Homology model

Structural genomics and proteomics-level studies of lipid-interacting domains: Northeast Structural Genomics and Arabidopsis 2010 Apply what we have learned to whole families BAR domains C 1 domains C 2 domains ENTH domains FERM domains FYVE domains GRAM domains PDZ domains PHD domains PX domains Sec 14 domains START domains VHS domains High-throughout comparative modeling: Leverage structure information

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.

Sky. Line: High-throughput comparative modeling “Modelability”: Create “reliable” models using known structures as templates Nebojsa Mirkovic Proteins 66: 766 PDB Structure Secondary structure DSSP Clustal. W Sequence Multiple alignments PSI-BLAST Modeling alignments Homologues Target reprioritization Mark. Us: Function annotation Homologous structures Family analysis Modeller or Nest Data on homologues Specialized databases Models (species, IDs, coverage, length, e-value, seq. is. ) Non-redundant & unsolved PROSA, p. G score Model quality p. G > 0. 7 Leverage: unique models Web-accessible models database

NESG Models Database Frank Indiviglio

Hunjoong Lee Models Database: http: //156. 145. 102. 40/nesg 3/nesg. php “Leverage”: Number and quality of 3 D models produced from a set of structures as templates PSI 1 and PSI 2: NESG leverage ~220 sequence unique models

Alternative models based on different PDB templates, reliability measures and sequence coverage

Additional search mechanisms: Expand methodology to the entire PDB, create specialized family and genome databases

C 2 domains from phospholipase C isoforms: Comparative functionality Kd 2. 3 x 10 -9 M Kd 2. 6 x 10 -9 M

C 2 domains from phospholipase C isoforms: Comparative functionality Kd 8. 9 x 10 -8 M → 6. 2 x 10 -9 M 4. 0 x 10 -8 M

Differences between d 1 and d 4: Detection of specificity determinants leads to hypotheses for differential regulation Kd 2. 3 x 10 -9 M Kd 8. 9 x 10 -8 M → 6. 2 x 10 -9 M

Whole family modeling: FYVE domains FYVE domain family: Electrostatic properties of models correlate with in vitro binding measurements and subcellular localization: Comparison of different members

FYVE domain family: Electrostatic properties of models correlate with in vitro binding measurements and subcellular localization: Residue substitution of a single family member

Dynamic target re-prioritization is an important strategy Model/Computation Experiment Structure There is no straightforward prescription: Each family has to be dealt with individually “Modelability”: Create “reliable” models using known structures as templates

START domain leverage Modelability (7378) versus 30% sequence identity (2767) 409 395 83 410 36 35 171 341 86 29 16 54 78 356 134 71 63

Collaborations with Experimental Groups Characterize different START domains based on structural information Discriminate whether START domains bind cholesterol or PC (PI) or other ligands Provide leads for chemical library studies for function-interfering compounds Detailed computational analysis and function annotation Fine-grain structure analysis in the absence and presence of potential ligand Experimental characterization: Protein production, SPR analysis, cellular studies Cho Lab: High-throughput analysis of Human and Arabidopsis START domains Clark Lab: Docking studies of ubiquinone into nematode START domain, electron transport

START domains in the Arabidopsis thaliana genome Sky. Line produces quality models for 58 non-redundant sequences versus 35 Arabidopsis START domains detected by sequence searches (Genome Biology 5: R 41) Key Findings (Tonya Silkov) 1. 45 sequences are of the Birch antigen class 2. Two sequences correspond to AHA 1 domains (Activator of Hsp 90 ATPase) SCOP classifies AHA domains as belonging to the Birch antigen superfamily 3. Two sequences predicted in databases as integral membrane proteins of unknown function 4. Five sequences for related models apparently represent a group of uncharacterized plant START domains

Fig. 1 Cross-genomic studies Structure similarity among lipid-binding domains PIP 2 ENTH domain PIP 2 ANTH domain VHS domain Tonya Silkov

ENTH and ANTH: similar topology, different membrane binding mechanism Helix 0 J Biol Chem. 278: 28993 with Cho Lab ENTH ANTH ENTH

Arabidopsis domain with novel dual ENTH and ANTH functionality Tonya Silkov ENTH ANTH From above ENTH Helix 0 ANTH Cho Lab: First 25 amino acids are required for both PIP 2 binding and membrane penetration. Produce enough protein to obtain crystals.

Fig. 1 A novel functional subclass of VHS domains ENTH domain ANTH domain VHS domain Tonya Silkov

A new VHS-related family, “VR domains”, found in other genomes KIAA 1530 (Homo sapiens) XP_420852 (Gallus gallus) CAB 71110 (Arabidopsis thaliana) XP_747424 (Strongylocentrotus purpuratus) Tonya Silkov

Among this subset of VHS domains, the basic surface patch is conserved Hypothesis: It constitutes a phosphoinositide-specific binding site VR domain family of membrane-binding VHS domains Human and Arabidopsis constructs are being examined in the Cho lab Tonya Silkov

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 By constantly updating resources as new information becomes available, we produce a more relevant (dynamic) target selection strategy