RNA Structure Predictin 11/02/05 - Iowa State Universityweb.cs.iastate.edu/~cs544/Lectures/1102_RNAStructurePrediction.pdf · RNA Structure & Function Prediction Mon Review - promoter

RNA Structure Predictin 11/02/05

D Dobbs ISU - BCB 444/544X 1

11/02/05 D Dobbs ISU - BCB 444/544X: RNA Structure Prediction 1

11/2/05

RNA Structure Prediction


Announcements

Seminar

12:10 PM Fri BCB Faculty Seminar in E164 Lago How to do sequence alignments on parallel computers Srinivas Aluru, ECprE & Chair, BCB Program http://www.bcb.iastate.edu/courses/BCB691F2005.html


Announcements

BCB 544 Projects - Important Dates:

Nov 2 Wed noon - Project proposals due to David/Drena

Nov 4 Fri 10A - Approvals/responses to students

Dec 2 Fri noon - Written project reports due

Dec 5,7,8,9 class/lab - Oral Presentations (20')

(Dec 15 Thurs = Final Exam)


RNA Structure & FunctionPrediction

Mon Review - promoter predictionRNA structure & function

Wed RNA structure prediction2' & 3' structure predictionmiRNA & target prediction - perhaps..

RNA function prediction?Won't have time to cover this…


Reading Assignment (for Mon/Wed)

Mount Bioinformatics• Chp 8 Prediction of RNA Secondary Structure• pp. 327-355• Ck Errata: http://www.bioinformaticsonline.org/help/errata2.html

Cates (Online) RNA Secondary Structure Prediction Module• http://cnx.rice.edu/content/m11065/latest/


Review last lecture:

RNA Structure & Function




RNA Structure & Function

• RNA structure• Levels of organization• Energetics (more about this on Wed)

• RNA types & functions• Genomic information storage/transfer• Structural• Catalytic• Regulatory


Fig 6.2Baxevanis &Ouellette 2005

Covalent & non-covalent bonds in RNA

Primary: Covalent bonds

Secondary/Tertiary Non-covalent bonds

• H-bonds (base-pairing)• Base stacking


1) G-C, A-U, G-U ("wobble") & variants U can form base-pairs with both A & G

2) Nucleotides in RNA are frequently modified this is not very common in DNA

These features & flexible "single-stranded" RNAbackbone allow for many potential base-pairs

Base-pairing in RNA

See: IMB Image Library of Biological Molecules

Modified bases are especially important) in tRNA: e.g., pseudo-Uridine, rD, 5-CH3-C6-isopentenyl-A

7-CH3-G, many others…



Common structural motifs in RNA

Helices

Loops• Hairpin• Internal• Bulge• Multibranch

Pseudoknots


RNA functions

Storage/transfer of genetic information

• Genomes• many viruses have RNA genomes

single-stranded (ssRNA)e.g., retroviruses (HIV)

double-stranded (dsRNA)

• Transfer of genetic information• mRNA = "coding RNA" - encodes proteins


RNA functions

Structural• e.g., rRNA, which is major structural component of

ribosomes BUT - its role is not just structural, also:

CatalyticRNA in ribosome has peptidyltransferase activity

• Enzymatic activity responsible for peptidebond formation between amino acids ingrowing peptide chain• Also, many small RNAs are enzymes

"ribozymes"

(Gloria Culver, ISU)

(W Allen Miller, ISU)




RNA functionsRegulatory

Recently discovered important new roles for RNAs In normal cells:

• in "defense" - esp. in plants• in normal development

e.g., siRNAs, miRNAAs tools:

• for gene therapy or to modify gene expression

• RNAi (used by many at ISU: Diane Bassham,Thomas Baum, Jeff Essner, Kristen Johansen,Jo Anne Powell-Coffman, Roger Wise, etc.)

• RNA aptamers (Marit Nilsen-Hamilton, ISU)


RNA types & functions

regulation of transcription and translation,other??

regulatory RNAs (siRNA,miRNA, etc.)

mRNA processing, poly A addition <catalytic>rRNA processing/maturation/methylation

snRNA - small nuclearsnoRNA - small nucleolar

signal recognition particle (SRP)tRNA processing <catalytic>

scRNA - small cytoplasmic

precursors & intermediates of maturemRNAs & other RNAs

hnRNA - heterogeneous nuclear

translation (protein synthesis)t-RNA - transfer

translation (protein synthesis) <catalytic>rRNA - ribosomal

translation (protein synthesis)regulatory

mRNA - messenger

Primary Function(s)Types of RNAs

L Samaraweera 2005


Thanks to Chris Burge, MITfor following slides

Slightly modified from:Gene Regulation and MicroRNAs

Session introduction presented atISMB 2005, Detroit, MI

Chris Burge [email protected]

C Burge 2005 11/02/05 D Dobbs ISU - BCB 444/544X: RNA Structure Prediction 16

Expression of a Typical Eukaryotic Gene

DNA

…

Transcription

Protein

TranslationmRNA

Splicing

exon intron

AAAAAAAAA

Polyadenylation

Protein Coding Gene

Folding, Modification,Transport, Complex Assembly

Protein Complex

Degradation

Degradation

primary transcript / pre-mRNA

Export

For each of theseprocesses, there isa ‘code’

(set of defaultrecognition rules)

C Burge 2005


Gene Expression Challenges forComputational Biology

• Understand the ‘code’ for each step in gene expression(set of default recognition rules), e.g., the ‘splicing code’

• Understand the rules for sequence-specific recognition ofnucleic acids by protein and ribonucleoprotein (RNP) factors

• Understand the regulatory events that occur at each step andthe biological consequences of regulation

Lots of data

Genomes, structures, transcripts, microarrays, ChIP-Chip, etc.


Sequence-specific Transcription Factors

• have modular organization

» Understand DNA-binding specificity

Yan (ISU) A computational method to identify amino acidresidues involved in protein-DNA interactions

ATF-2/c-Jun/IRF-3 DNA complex

Panne et al. EMBO J. 2004

C Burge 2005




Early Steps in Pre-mRNA Splicing

Matlin, Clark & Smith Nature Mol Cell Biol 2005

• Formation of exon-spanning complex

• Subsequent rearrangement to formintron-spanning spliceosomes whichcatalyze intron excision and exon ligation

hnRNP proteins


Alternative Splicing


Wang (ISU) Genome-wide Comparative Analysis of AlternativeSplicing in Plants

> 50% of human genesundergo alternative splicing

C Burge 2005


Splicing Regulation


ESE/ESS = Exonic Splicing Enhancers/Silencers

ISE/ISS = Intronic Splicing Enhancers/Silencers


C. elegans lin-4 Small Regulatory RNA

We now know that there are hundreds of microRNA genes

(Ambros, Bartel, Carrington, Ruvkun, Tuschl, others)

lin-4 precursor

lin-4 RNA

“Translationalrepression”

V. Ambros lablin-4 RNA

target mRNA

C Burge 2005


MicroRNA Biogenesis

N. Kim Nature Rev Mol Cell Biol 2005


miRNA and RNAi pathways

RISC

Dicerprecursor

miRNA siRNAs

Dicer

“translational repression”and/or mRNA degradation

mRNA cleavage, degradation

RNAi pathwaymicroRNA pathwayMicroRNA primary transcript Exogenous dsRNA, transposon, etc.

target mRNA

Drosha

RISCRISC

C Burge 2005




miRNA Challenges for Computational Biology

• Find the genes encoding microRNAs

• Predict their regulatory targets

• Integrate miRNAs into gene regulatory pathways &networks

Computational Prediction of MicroRNA Genes & Targets

C Burge 2005

Need to modify traditional paradigm of"transcriptional control" primarily by protein-DNAinteractions to include miRNA regulatory mechanisms!


New Today:

RNA Structure Prediction


RNA structure prediction strategies

1) Energy minimization(thermodynamics)

2) Comparative sequence analysis(co-variation)

3) Combined experimental & computational

Secondary structure prediction


Secondary structure prediction strategies

1) Energy minimization (thermodynamics)

• Algorithm:Dynamic programming to findhigh probability pairs(also, some Genetic algorithms)

• Software:Mfold - ZukerVienna RNA Package - HofackerRNAstructure - MathewsSfold - Ding & Lawrence

R Knight 2005



2) Comparative sequence analysis (co-variation)• Algorithm:

Mutual informationContext-free grammars

• Software:ConStructAlifoldPfoldFOLDALIGNDynalign

R Knight 2005 11/02/05 D Dobbs ISU - BCB 444/544X: RNA Structure Prediction 30


3) Combined experimental & computational

• Experiment:Map single-stranded vs double-stranded regions in folded RNA

• How?Enzymes: S1 nuclease, T1 RNaseChemicals: kethoxal, DMS

R Knight 2005




Experimental RNA structure determination?

• X-ray crystallography

• NMR spectroscopy

• Enzymatic/chemical mapping


1) Energy minimization method

What are the assumptions?

Native tertiary structure or "fold" of anRNA molecule is (one of) its "lowest" freeenergy configuration(s)

Gibbs free energy = ΔG in kcal/mol at 37°C= equilibrium stability of structure

lower values (negative) are more favorableIs this assumption valid?

in vivo? - this may not hold, but we don't really know


Free energy minimization

What are the rules?

A UA U

A=UA=U

Basepair

ΔG = -1.2 kcal/mole

A UU A

A=UU=A

ΔG = -1.6 kcal/mole

Basepair

What gives here?

C Staben 2005 11/02/05 D Dobbs ISU - BCB 444/544X: RNA Structure Prediction 34

Energy minimization calculations:Base-stacking is criticalAA

UU-1.2

CG

GC-3.0

AU or UA

UA AU-1.6

GC

CG-4.3

AG, AC, CA, GA

UC, UG, GU, CU-2.1

GU

UG-0.3

CC

GG-4.8

XG, GX

YU, UY0

- Tinocco et al.

C Staben 2005


Nearest-neighbor parameters

Most methods for free energy minimizationuse nearest-neighbor parameters (derivedfrom experiment) for predicting stability of anRNA secondary structure (in terms of ΔG at 37°C)

& most available software packages usethe same set of parameters:

Mathews, Sabina, Zuker & Turner, 1999


Energy minimization - calculations:

Total free energy of a specificconformation for a specificRNA molecule = sum ofincremental energy terms for:

• helical stacking (sequence dependent)• loop initiation• unpaired stacking

(favorable "increments" are < 0)





But how many possible conformations for asingle RNA molecule?

Huge number:Zuker estimates (1.8)N possible secondary structures for a sequence of N nucleotides

for 100 nts (small RNA…) =3 X 1025 structures!

Solution? Not exhaustive enumeration… Dynamic programming

O(N3) in timeO(N2) in space/storage

iff pseudoknots excluded, otherwise:O(N6 ), timeO(N4 ), space


2) Comparative sequence analysis(co-variation)

Two basic approaches:

• Algorithms constrained by initial alignmentMuch faster, but not as robust as unconstrained

Base-pairing probabilities determined by a partition function

• Algorithms not constrained by initial alignmentGenetic algorithms often used for finding analignment & set of structures


RNA Secondary structure prediction:Performance?

How evaluate?• Not many experimentally determined structures

currently, ~ 50% are rRNA structures so "Gold Standard" (in absence of tertiary structure):

compare with predicted RNA secondary structure with that determined by comparativesequence analysis (!!??) using Benchmark Datasets

NOTE: Base-pairs predicted by comparative sequenceanalysis for large & small subunit rRNAs are 97% accuratewhen compared with high resolution crystal structures!

- Gutell, Pace


RNA Secondary structure prediction:Performance?

1) Energy minimization (via dynamic programming) 73% avg. prediction accuracy - single sequence

2) Comparative sequence analysis97% avg. prediction accuracy - multiple sequences

(e.g., highly conserved rRNAs)much lower if sequence conservation is lower &/or

fewer sequences are available for alignment3) Combined - recent developments:

combine thermodynamics & co-variation& experimental constraints? IMPROVED RESULTS


RNA structure prediction strategies

Requires "craft" & significant user input & insight1) Extensive comparative sequence analysis to predict

tertiary contacts (co-variation)e.g., MANIP - Westhof

2) Use experimental data to constrain model buildinge.g., MC-CYM - Major

3) Homology modeling using sequence alignment & reference tertiary structure (not many of these!)

4) Low resolution molecular mechanicse.g., yammp - Harvey

Tertiary structure prediction

Documents

RNA Structure Predictin 11/02/05 - Iowa State Universityweb.cs.iastate.edu/~cs544/Lectures/1102_RNAStructurePrediction.pdf · RNA Structure & Function Prediction Mon Review - promoter