BCB 444/544 Fall 07 Dobbs 1

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1 BCB 444/544 Required Reading (before lecture) Lecture 23 Mon Oct 5 - Lecture 23 Protein Tertiary Structure Chp 5 - pp Protein Tertiary Structure Wed Oct 7 & Thurs Oct 8 - Lecture 24 & Lab 8 RNA Structure/Function & RNA Structure Chp 6 - pp (Terribilini) #23_Oct5 Fri Oct 8 - Lecture 25 Gene Chp 8 - pp New Reading & Homework Assignment Seminars Last Week ALL: HomeWork #4 ( ed & posted online Sat AM) Due: Mon Oct 22 by 5 PM (not Fri Oct 9) Read: Ginalski et al.(2005) Practical Lessons from Protein Structure, Nucleic Acids Res. 33: (PDF posted on website) Although somewhat dated, this paper provides a nice overview of protein structure prediction methods and evaluation of predicted structures. Your assignment is to write a summary of this paper - for details see HW#4 posted online & sent by on Sat Oct 3 Dr. Klaus Schulten (Univ of Illinois) - Baker Center Seminar The Computational Microscope 2:0 PM in E64 Lagomarcino n_seminar.pdf Check out links on Schulten's website (videos, etc) Great seminar - amazing simulations of dynamics in proteins and large macromolecular assemblies Very computationally intensive - very impressive demonstration of power of computation to produce insights not attainable using only experimental approaches 3 4 Seminars this Week Protein Sequence & Structure: Analysis BCB List of URLs for Seminars related to Bioinformatics: Oct 8 Thur - BBMB Seminar 4:0 in 44 MBB Sachdeve Sidhu (Genentech) Phage peptide and antibody libraries in protein engineering and ligand selection Oct 9 Fri - BCB Faculty Seminar 2:0 in 02 ScI Lyric Bartholomay (Ent, ISU) TBA Diamond STING Millennium - Many useful structure analysis tools, including Protein Dossier SwissProt (UniProt) Protein knowledgebase InterPro Sequence analysis tools BCB 444/544 Fall 07 Dobbs

2 Chp 4 - Secondary Structure Where Find "Actual" Secondary Structure? In the PDB SECTION V STRUCTURAL BIOINFORMATICS Xiong: Chp 4 Protein Secondary Structure Secondary Structure for Globular Proteins Secondary Structure for Transmembrane Proteins Coiled-Coil 7 8 How Does Predicted Secondary Structure Compare with Actual? (An example) Chp 5 - Tertiary Structure Actual - Calculated from PDB coordinates by DSSP or author: SECTION V STRUCTURAL BIOINFORMATICS DSSP Author Xiong: Chp 5 Protein Tertiary Structure Query MAATAAEAVASGSGEPREEAGALGPAWDESQLRSYSFPTRPIPRLSQSDPRAEELIENEE GOR V CCCCHHHHHHHHCCHHHHHHCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCHHHHHHCCCC FDM CCCCCCCCCCCCCCCCCEECCCCCCCCCHHHCCCCCCEECCCCCCCCCCHHHHHHHHCCC CDM CCCCHHHHHHCCCCCCCEECCCCCCCCCHHHCCCCCCEECCCCCCCCCCHHHHHHHHCCC Predicted - Using 3 methods (from CMD server, Jernigan Group, ISU) Methods Homology Modeling Threading and Fold Recognition Ab Initio Protein Structural CASP 9 0 Structural Genomics - Status & Goal Structural Genomics Project ~ 20,000 "traditional" genes in human genome (recall, this is fewer than earlier estimate of 30,000) ~ 2,000 proteins in a typical cell > 4.9 million sequences in UniProt (Oct 2007) > 46,000 protein structures in the PDB (Oct 2007) TargetDB: Database of Structural Genomics Targets Experimental determination of protein structure lags far behind sequence determination! Goal: Determine structures of "all" protein folds in nature, using combination of experimental structure determination methods (X-ray crystallography, NMR, mass spectrometry) & structure prediction 2 BCB 444/544 Fall 07 Dobbs 2

3 Database of Theoretical Structures? PMDB: Protein Model Database also, via NAR's Molecular Biology Database Collection Protein Structure or Protein Folding Problem "Major unsolved problem in molecular biology" In cells: spontaneous assisted by enzymes assisted by chaperones Theoretical structural models (predicted) are no longer accepted by the PDB (since 0/5/06); but, it is possible to search for models deposited earlier: In vitro: many proteins can fold to their "native" states spontaneously & without assistance but, many do not! 3 4 Deciphering the Protein Folding Code Protein Structure Protein Structure or Protein Folding Problem Given the amino acid sequence of a protein, predict its 3-dimensional structure (fold) Inverse Folding Problem Given a protein fold, identify every amino acid sequence that can adopt its 3-dimensional structure Structure is largely determined by sequence BUT: Similar sequences can assume different structures Dissimilar sequences can assume similar structures Many proteins are multi-functional 2 Major Protein Folding Problems: - Determine folding pathway 2- Predict tertiary structure from sequence Both still largely unsolved problems 5 6 Steps in Protein Folding Protein Dynamics - "Collapse"- driving force is burial of hydrophobic aa s (fast - msecs) 2- Molten globule - helices & sheets form, but "loose" (slow - secs) 3- "Final" native folded state - compaction & rearrangement of 2' structures Protein in native state is NOT static Function of many proteins requires conformational changes, sometimes large, sometimes small Globular proteins are inherently "unstable" (NOT evolved for maximum stability) Energy difference between native and denatured state is very small (5-5 kcal/mol) (this is equivalent to ~ 2 H-bonds!) Native state? - assumed to be lowest free energy - may be an ensemble of structures Folding involves changes in both entropy & enthalpy 7 8 BCB 444/544 Fall 07 Dobbs 3

4 Difficulty of Tertiary Structure Folding or tertiary structure prediction problem can be formulated as a search for minimum energy conformation Search space is defined by psi/phi angles of backbone and side-chain rotamers Search space is enormous even for small proteins! Number of local minima increases exponentially with number of residues Tertiary Structure Methods 2 (or 3) Major Methods:. Comparative Modeling: Homology Modeling (easiest!) Threading and Fold Recognition (harder) 2. Ab Initio Protein Structural (really hard) Computationally it is an exceedingly difficult problem! 9 20 Comparative Modeling? Ab Initio Comparative modeling - term is sometimes used interchangeably with homology modeling, but also sometimes used to mean both: homology modeling threading/fold recognition. Develop energy function bond energy bond angle energy dihedral angle energy van der Waals energy electrostatic energy 2. Calculate structure by minimizing energy function usually Molecular Dynamics (MD) or Monte Carlo (MC) Ab initio prediction - impractical for most real (long) proteins Computationally? very expensive Accuracy? Usually poor for all except short peptides (but much improvement recently!) Provides both folding pathway & folded structure 2 22 Comparative Modeling Homology Modeling Two types: ) Homology modeling 2) Threading (fold recognition) Both rely on availability of experimentally determined structures that are "homologous" or at least structurally very similar to target Provide folded structure only. Identify homologous protein sequences (ψ-blast) 2. Among available structures (in PDB), choose one with closest sequence to target as template (can combine steps & 2 by using PDB-BLAST) 3. Build model by placing target sequence residues in corresponding positions on homologous structure & refine by "tweaking" modeled structure (energy minimization) Homology modeling - works "well" Computationally? "relatively" inexpensive Accuracy? higher sequence identity better model Requires ~30% sequence identity with sequence for which structure is known BCB 444/544 Fall 07 Dobbs 4

5 Threading - Fold Recognition Identify best fit between target sequence & template structure. Develop energy function 2. Develop template library 3. Align target sequence with each template in library & score 4. Identify top scoring template (D to 3D alignment) 5. Refine structure as in homology modeling Threading - works "sometimes" Computationally? Can be expensive or cheap, depends on energy function & whether "all atom" or "backbone only" threading is used Accuracy? in theory, should not depend on sequence identity (should depend on quality of template library & "luck") Usually, higher sequence identity to protein of known structure better model Threading: the Motivation Basic premise: The number of unique structural folds in nature is fairly small (probably ) Statistics from Protein Data Bank (>46,000 structures) Prior to Structural Genomics Project, 90% of "new" structures submitted to PDB were similar to existing folds in PDB - suggesting that almost all folds in nature have been identified Thus, chances for a protein to have a native-like structural fold in PDB are quite good Note: Proteins with similar structural folds could be either homologs or analogs Steps in Threading Threading Goal - & Issues Target Sequence ALKKGF HFDTSE Find correct sequence-structure alignment of a target sequence with its native-like fold in template library (usually derived from PDB) Structure Templates Structure database - must be "complete" Can't build a good model if there is no good template in library! Sequence-structure alignment algorithm: Bad alignment Bad score! Energy function or Scoring Scheme:. Align target sequence with template structures in fold library (usually from the PDB) 2. Calculate energy score to evaluate "goodness of fit" between target sequence & template structure 3. Rank models based on energy scores Must distinguish correct sequence-fold alignment from incorrect sequence-fold alignments Must distinguish correct fold from close decoys reliability assessment - How determine whether predicted structure is correct? (or even close?) Threading: Template database Threading: Energy function Build a database of structural templates e.g., ASTRAL domain library derived from the PDB Two main methods (& combinations of these) Structural profile (environmental) physicochemical properties of amino acids Sometimes, supplement with additional decoys e.g., generated using ab initio approach such as Rosetta (Baker) Contact potential (statistical) based on contact statistics from PDB famous one: Miyazawa & Jernigan (ISU) BCB 444/544 Fall 07 Dobbs 5

6 Protein Threading: Typical energy function A Local Example: Rapid Threading Approach for Protein Structure What is "probability" that two specific residues are in contact? Total energy: E p + E s + E g How well does a specific residue fit structural environment? Alignment gap penalty? Goal: Find a sequence-structure alignment that minimizes energy function Kai-Ming Ho, Physics Haibo Cao Yungok Ihm Zhong Gao James Morris Cai-zhuang Wang Drena Dobbs, GDCB Jae-Hyung Lee Michael Terribilini Jeff Sander Cao H, Ihm Y, Wang, CZ, Morris, JR, Su, M, Dobbs, D, Ho, KM (2004) Three-dimensional threading approach to protein structure recognition Polymer 45: Motivations for & Assumptions of Ho Threading Algorithm Simplify: Template structure representation Goal: Develop a threading algorithm that: Is simple & rapid enough to be used in high throughput applications Is relatively "insensitive" to sequence similarity between target protein sequence & sequence of template structure (to enhance detection of remote homologs & structures that are similar due to convergent evolution) Can be used to answer questions such as: What are predicted structures of all "unassigned" ORFs in Arabidopsis? Does Arabidopsis have a protein with structure similar to mammalian Tumor Necrosis Factor (TNF)? Assumptions: Native state of a protein is lowest free energy state Hydrophobic interactions drive protein folding 33 Template structure ( N! N contact matrix) Cij =, if rij! 6. 5 Cij = 0, Yungok Ihm Å C (contact) Otherwise A neighbor in sequence (non-contact) i j N 34 Simplify: Target Sequence Representation Simplify: Energy Function Miyazawa-Jernigan (MJ) model: inter-residue contact energy M(i,j) is a quasi-chemical approximation based on pairwise contact statistics extracted from known protein structures in the PDB: 20 X 20 matrix = 20 values ("letters") Li-Tang-Wingreen (LTW): factorize the MJ interaction matrix to reduce the number of parameters associated with amino acids from 20 to 20 q values Hydrophobic-Polar (HP): represent amino acids as either H (hydrophobic) or polar (P); Dill et al demonstrated the utility of this simple binary alphabet representation: 2 values Compare results with 20 vs 20 vs 2 letter representations How low can we go? Interaction counts only if two hydrophobic amino acid residues are in contact At residue level, pair-wise hydrophobic interaction is dominant: E = Σi,j Cij Uij Cij : contact matrix Uij = U(residue I, residue J) MJ: U = Uij LTW: U = Qi*Qj HP: U = {,0} 35 Yungok IhmBCB 444/544 F07 ISU Dobbs #23 - Protein Tertiary Structure 36 BCB 444/544 Fall 07 Dobbs 6

7 Energy calculation: Contact energy Summary of Ho Threading Procedure Miyazawa-Jernigan (MJ) matrix: Statistical potential 20 parameters M = C M F I L V W C M F I L Template Structure i j N Contact Matrix Cij =, Cij = 0, if rij < 6 5 Å otherwise (a neighbor in sequence) Li-Tang-Wingreen (LTW): 20 parameters ~ M ij = j Contact Energy: Ec = ( QiCijQj +! Cij) ij= Yungok Ihm N " C2{( qi + ")( q + ") +!} qi ~ solubility Qi ~ hydrophobicity C contact matrix Qi =! qi!# with # =! , " =! Sequence AVFMRIHNDIVYNDIANTTQ Scoring Function Yungok Ihm Sequence Vector S = ( QA, QV, QF,..., QE) = (0.7997, ,.97, ) Contact Energy N Ec = " QiCijQj +! ij= Can complexity be further reduced? Consider simplifying structure representation, too Examine eigenvectors of contact matrix ALKKGF HFDTSE Hydrophobic Contacts ~ # TCT = T ~ ( = N $ i= ~! ivit 2 N $ i= ~! ivivi) T ~ "! V T 2 ri = N ~ 2 " i( ViT ) ~ " i( ViT )! i= 2 Sequence Structure Sequence Contact Matrix Sequence D Profile Haibo Cao (D 3D problem) (D 2D problem) (D D problem) C :contact matrix!i :i-th eigenvalue of C V i :i-th eigenvector V :eigenvector with largest eigenvalue T :protein sequence of the template structure i :fraction of hydrophobic contacts from i-th eigenvector r Haibo Cao Represent contact matrix by its dominant eigenvector (D profile) Threading Alignment Step - now fast! Align target sequence vector (D) with eigenvector profile of template structure (D) D Profile P = V First eigenvector (with highest eigenvalue) dominates the overlap between sequence and structure Higher ranking (rank > 4) eigenvectors are sequence blind Maximize the overlap between the Sequence (S) and the profile (P) S P allowing gaps Calculate contact energy using the alignment: E c New profile P = CP Cao et al Haibo Cao Polymer 45 (2004) BCB /544 F07 ISU Dobbs #23 - Protein Tertiary Structure BCB 444/544 Fall 07 Dobbs 7

8 Parameters for alignment? How incorporate secondary structure? Gap penalty: Insertion/deletion in helices or strands is strongly penalized; smaller penalties for in/dels in loops Gap penalties apply to alignment score only, not to energy calculation Size penalty: If a target residue and aligned template residue differ in radius by > 0.5Å and if residue is involved in > 2 contacts, alignment is penalized Size penalties apply to alignment score only, not to energy calculation ALKKGFG HFDTSE Loop Helix Predict secondary structure of target sequence (PSIPRED, PROF, JPRED, SAM, GOR V) N + = total number of matches between predicted & actual secondary structure of template N - = total number of mismatches N s = total number of residues selected in alignment Global fitness : f = + (N + - N - ) / N s Emod = f * Ethreading Yungok IhmBCB Yungok Ihm BCB /544 F07 ISU Dobbs #23 - Protein Tertiary Structure /544 F07 ISU Dobbs #23 - Protein Tertiary Structure How much better is this fit than random? Eshuffle : Shuffled Sequence vs Structure Erelative = Emod Eshuffled Performance Evaluation? "Blind Test" CASP5 Competition (CASP7 is most recent) (Critical Assessment of Protein Structure ) Given: Amino acid sequence Goal: Predict 3-D structure (before experimental results published) E score modifed to reflect fit with predicted 2' structure Avg E score for same sequence shuffled (randomized) many times Yungok Ihm 46 Typical Results: (well, actually, our BEST Results): HO = #-Ranked CASP5 for this Target Overall Performance in CASP5 Contest ~8th out of 80 (M. Levitt, Stanford) Target 74 PDB ID = MG7 T74_ T74_2 Predicted Structure Actual Structure FR Fold Recognition (targets manually assessed by Nick Grishin) Rank Z-Score Ngood Npred NgNW NpNW Group-name Ginalski Skolnick Kolinski Baker BIOINFO.PL Shortle BAKER-ROBETTA Brooks Ho-Kai-Ming Jones-NewFold FR NgNW - number of good predictions without weighting for multiple models FR NpNW - number of total predictions without weighting for multiple models Cao, Ihm, Wang, BCB 444/544 Dobbs, F07 ISU Ho Dobbs #23 - Protein Tertiary Structure BCB 444/544 Fall 07 Dobbs 8

9 CASP - Check it out! Critical Assessment of Protein Structure CASP7 contest : Provides assessment of automated servers for protein structure prediction (LiveBench, CAFASP, EVA) & URLs for them Another Convenient List of Links for Protein Servers diction_software Related contests & resources: Protein Function (part of CASP) CAPRI = Critical Assessment of Predicted Interactions New: CASPM = CASP for M = Mutant proteins Predict effects of small (point) mutations, e.g., SNPs SECTION V Chp 3 - Protein Structure Visualization, Comparison & Classification Xiong: Chp 3 STRUCTURAL BIOINFORMATICS Protein Structure Visualization, Comparison & Classification Protein Structural Visualization Protein Structure Comparison Protein Structure Classification Protein Structure Comparison Methods 3 Basic Approaches for Aligning Structures (see Xiong textbook for details). Intermolecular 2. Intramolecular 3. Combined But, very active research area - many recent new methods 3 Popular Methods: DALI = Distance Matrix Alignment of Structures (Holm) FSSP Database SSAP = Sequential Structure Alignment Program (Orengo) CATH Database CE = Combinatorial Extension (Bourne) VAST at NCBI URLS: Another local example: Combining Structure, Machine Learning & "Real" (wet-lab) Experiments to Investigate the Lentiviral Rev Protein: A Step Toward New HIV Therapies Susan Carpenter (Washington State Univ) Wendy Sparks Yvonne Wannemuehler Drena Dobbs, GDCB Jae-Hyung Lee Michael Terribilini Kai-Ming Ho, Physics Yungok Ihm Haibo Cao Cai-zhuang Wang Gloria Culver, BBMB Laura Dutca Macromolecular interactions mediated by Rev protein in lentiviruses (HIV & EIAV) Cytoplasm Tat Rev Nucleus pre-mrna Spliceosome AAAA Provirus NUCLEAR IMPORT Early: Regulatory Proteins Rev AAAA (protein-protein) RNA BINDING (protein-rna) Rev Rev AAAA MULTIMERIZATION Rev Rev AAAA (protein-protein) NUCLEAR EXPORT (protein-protein) Late: Structural Proteins Progeny RNA 53 Susan Carpenter 54 BCB 444/544 Fall 07 Dobbs 9

10 Rev is essential for lentiviral replication Rev is a small nucleoplasmic shuttling protein (HIV Rev 5 aa; EIAV Rev 65 aa) Recognizes a specific binding site on viral RNA: Rev Responsive Element (RRE) Interacts with CRM to export incompletely spliced viral RNAs from nucleus to the cytoplasm Specific domains of Rev mediate nuclear localization, RNA binding, and nuclear export Critical role of Rev in lentiviral replication makes it an attractive target for antiviral (AIDs) therapy Problem: no high resolution Rev structure! not even for HIV Rev, despite intense effort ($$) Why?? Rev aggregates at concentrations needed for NMR or X- ray crystallography What about insights from sequence comparisons? "undetectable" sequence similarity among Revs from different lentiviruses (eg, EIAV vs HIV <0%) But: We know that lentiviral Rev proteins are functionally "homologous" - even in highly diverse lentiviruses Approach: Hypothesis: Rev proteins from diverse lentiviruses share structural features critical for function Computationally model structures of lentiviral Rev proteins - using structural threading algorithm (with Ho et al) Predict critical residues for RNA-binding, protein interaction - using machine learning algorithms (with Honavar et al ) Test model and predictions - using genetic/biochemical approaches (with Carpenter & Culver) - using biophysical approaches (with Andreotti & Yu groups) Initially: focus on EIAV Rev & RRE EIAV Rev HIV- Rev Functional domains: EIAV vs HIV Rev exon exon RBM Folding? RRDRW ERLE KRRRK 6 NLS/RBM RQARRNRRRRWR NLS - Nuclear Export Signal NLS - Nuclear Localization Signal RBM - putative RNA Binding Motif Predicted EIAV Rev Structure Comparison of Predicted Rev Structures EIAV FIV HIV SIV Dimer HIV Dimer Yungok IhmBCB Yungok IhmBCB /544 F07 ISU Dobbs #23 - Protein Tertiary Structure /544 F07 ISU Dobbs #23 - Protein Tertiary Structure BCB 444/544 Fall 07 Dobbs 0

11 Predicted vs Experimental Structure of N-terminal region of HIV Rev Location of functional residues EIAV Rev? A B C Leu36,45,49: On surface, consistent with role in nuclear export Leu95 & Leu09: Buried in core, critical hydrophic contacts for fold? Predicted Structure HIV Rev N-terminus NMR Structure Overlay HIV Rev N-terminal Alignment of Predicted Peptide & NMR Structures (Battiste & Williamson) Putative RBM Yungok IhmBCB Yungok IhmBCB /544 F07 ISU Dobbs #23 - Protein Tertiary Structure 6 444/544 F07 ISU Dobbs #23 - Protein Tertiary Structure Mutate hydrophobic residues predicted to be critical for helical packing in core Functional Analysis of Rev Structural Mutants in vivo (CAT assay) L65 vs L95 & L09 Single mutants: Leu to Ala Leu to Asp Double mutants: Leu to Ala Single Ala Mutation L A Single Asp Mutation L D Insert charged aa in hydrophobic core Negligible effect on Rev activity Dramatic change in Rev activity? L65 L95 L09 Activity of Rev Structural Mutants Double Ala Mutation L L A A Reduction in Rev activity? Sham pcdna3 RI A D L65 A D L95 A D L09 L65A L95A L65A L09A L95A L09A Controls Single Mutations Double Mutations Yungok Ihm 63 Wendy Sparks 64 EIAV Rev Functional domains: EIAV vs HIV Rev RBM Folding? Red - RNA interaction Green - Protein interaction - Nuclear Export Signal NLS - Nuclear Localization Signal RBM - putative RNA Binding Motif Putative RNA-binding Motifs & Predicted RNA-binding Residues Mapped onto Predicted EIAV Rev Structure ERLE KRRRK NLS Yungok Ihm RRDRW HIV- Rev RRDRW ERLE KRRRK ARRHLGPGPT QHTPSRRDRW IREQILQAEV LQERLEWRIR NLS/RBM RQARRNRRRRWR HFREDQRGDF SAWGDYQQAQ ERRWGEQSSP RVLRPGDSKRRRKHL Michael Terribilini 66 BCB 444/544 Fall 07 Dobbs

12 Express & purify -ERev deletion mutants -ERev binds specifically to RRE in vitro RBM Folding? -ERev NE S NLS UV crosslinking Competition BSA sense antisense BSA No protein No cold RRE Cold RRE Marker Undigested 32 P-RRE Jae-Hyung BCB Lee444/544 F07 ISU Dobbs #23 - Protein Tertiary Structure 67 Jae-Hyung BCB Lee444/544 F07 ISU Dobbs #23 - Protein Tertiary Structure 68 EIAV Rev: Binding s vs Experiments Structure PREDICTED: Protein binding residues RNA binding residues + + KRRRK RRDRW GPLESDQWCRVLRQSLPEEKISSQTCIARRHLGPGPTQHTPSRRDRWIREQILQAEVLQERLEWRI VALIDATED: QRGDFSAWGDYQQAQERRWGEQSSPRVLRPGDSKRRRKHL Protein binding residues RNA binding residues WT RBM FOLD? NLS/RBM RRDRW ERLE KRRRK Roles of Putative RNA Binding Motifs? RBD RBD NLS RRDRW ERLE KRRRK AADAA AALA KAAAK ERDE Jae-Hyung Lee Lee et al (2006) J Virol 80:3844 Terribilini et al (2006) PSB :45 Jae-Hyung Lee BCB /544 F07 ISU Dobbs #23 - Protein Tertiary Structure Rev RNA Binding Motifs: Predicted vs Experiment Structure PREDICTED: Protein binding residues RNA binding residues + + KRRRK RRDRW GPLESDQWCRVLRQSLPEEKISSQTCIARRHLGPGPTQHTPSRRDRWIREQILQAEVLQERLEWRI VALIDATED: QRGDFSAWGDYQQAQERRWGEQSSPRVLRPGDSKRRRKHL Protein binding residues RNA binding residues Jae-Hyung Lee WT AADAA AALA ERDE KAAAK RBM FOLD? RRDRW ERLE KRRRK AADAA AALA KAAAK ERDE NLS/RBM Summary: s vs Experiments KRRRK RRDRW GPLESDQWCRVLRQSLPEEKISSQTCIARRHLGPGPTQHTPSRRDRWIREQILQAEVLQERLEWRI Lee et al (2006) J Virol 80: ERLE Terribilini et al (2006) PSB : RBM FOLD NLS/RBM RRDRW ERLE KRRRK QRGDFSAWGDYQQAQERRWGEQSSPRVLRPGDSKRRRKHL BCB 444/544 Fall 07 Dobbs 2

13 Conclusions & Future Directions Combination of computational & wet lab approaches revealed that: EIAV Rev has a bipartite RNA binding domain Two Arg-rich RBMs are critical RRDRW in central region (but not ERLE) KRRRK at C-terminus, overlapping the NLS Based on computational modeling, the RBMs are in close proximity within the 3-D structure of protein Lentiviral Rev proteins & their cognate RRE binding sites may be more similar in structure than has been appreciated Future: Computational: Use Rev-RRE model system to discover "predictive rules" for protein-rna recognition Experimental? Experimentally determine the structure of Rev-RRE complex!!! Lee et al (2006) Terribilini et al (2006) J Virol 80:3844 BCB 444/544 PSB :45 F07 ISU Dobbs #23 - Protein Tertiary Structure Building Designer Zinc Finger DNA-binding Proteins J Sander, P Zaback, F Fu, J Townsend, R Winfrey D Wright, K Joung, L Miller, D Dobbs, D Voytas Chp 6 - RNA Structure SECTION V STRUCTURAL BIOINFORMATICS Xiong: Chp 6 RNA Structure (Terribilini) Introduction Types of RNA Structures RNA Secondary Structure Methods Ab Initio Approach Comparative Approach Performance Evaluation Wright et al (2006) Sander et al (2007) Nature Protocols Nucleic Acids Res 76 BCB 444/544 Fall 07 Dobbs 3