11/02/05 d dobbs isu - bcb 444/544x: rna structure prediction1 11/2/05 rna structure prediction

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 & Function Prediction

Mon Review - promoter prediction

RNA 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:

Catalytic RNA in ribosome has peptidyltransferase activity

• Enzymatic activity responsible for peptide bond formation between amino acids in growing 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 Types of RNAs Primary Function(s)

mRNA - messenger translation (protein synthesis) regulatory

rRNA - ribosomal translation (protein synthesis) <catalytic>

t-RNA - transfer translation (protein synthesis)

hnRNA - heterogeneous nuclear

precursors & intermediates of mature mRNAs & other RNAs

scRNA - small cytoplasmic signal recognition particle (SRP)tRNA processing <catalytic>

snRNA - small nuclear snoRNA - small nucleolar

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

regulatory RNAs (siRNA, miRNA, etc.)

regulation of transcription and translation, other??

L Samaraweera 2005


Thanks to Chris Burge, MITfor following slides

Slightly modified from: Gene Regulation and MicroRNAs

Session introduction presented at ISMB 2005, Detroit, MI

Chris Burge [email protected]

C Burge 2005


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 these processes, there is a ‘code’

(set of default recognition rules)

C Burge 2005


Gene Expression Challenges for Computational 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 of nucleic acids by protein and ribonucleoprotein (RNP) factors

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

Lots of data

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

C Burge 2005


Sequence-specific Transcription Factors

• have modular organization

» Understand DNA-binding specificity

Yan (ISU) A computational method to identify amino acid residues 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 form intron-spanning spliceosomes which catalyze intron excision and exon ligation

hnRNP proteins

C Burge 2005


Alternative Splicing


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

> 50% of human genes

undergo alternative splicing

C Burge 2005


Splicing Regulation


ESE/ESS = Exonic Splicing Enhancers/Silencers

ISE/ISS = Intronic Splicing Enhancers/Silencers

C Burge 2005


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

“Translational repression”

V. Ambros lablin-4 RNA

target mRNA

C Burge 2005


MicroRNA Biogenesis

N. Kim Nature Rev Mol Cell Biol 2005

C Burge 2005


miRNA and RNAi pathways

RISC

Dicerprecursor

miRNA siRNAs

Dicer

“translational repression”and/or mRNA degradation

mRNA cleavage, degradation

RNAi pathway

microRNA pathway

MicroRNA 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-DNA interactions 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 find high probability pairs(also, some Genetic algorithms)

• Software: Mfold - ZukerVienna RNA Package -

Hofacker RNAstructure - MathewsSfold - Ding & Lawrence

R Knight 2005



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

Mutual informationContext-free grammars

• Software: ConStructAlifoldPfoldFOLDALIGNDynalign

R Knight 2005



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 an RNA molecule is (one of) its "lowest" free energy configuration(s)

Gibbs free energy = G in kcal/mol at 37C

= 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

G = -1.2 kcal/mole

A UU A

A=UU=A

G = -1.6 kcal/mole

Basepair

What gives here?

C Staben 2005


Energy minimization calculations:Base-stacking is critical

AAUU

-1.2 CGGC

-3.0

AU or UAUA AU

-1.6 GCCG

-4.3

AG, AC, CA, GAUC, UG, GU, CU

-2.1 GUUG

-0.3

CCGG

-4.8 XG, GXYU, UY

0

- Tinocco et al.

C Staben 2005


Nearest-neighbor parameters

Most methods for free energy minimizationuse nearest-neighbor parameters (derived from experiment) for predicting stability of an RNA secondary structure (in terms of G at 37C)

& most available software packages use the same set of parameters:

Mathews, Sabina, Zuker & Turner, 1999


Energy minimization - calculations:

Total free energy of a specific conformation for a specific RNA molecule = sum of incremental energy terms for:

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

(favorable "increments" are < 0)



But how many possible conformations for a single 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 time O(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 alignment

Much faster, but not as robust as unconstrained

Base-pairing probabilities determined by a partition function

• Algorithms not constrained by initial alignment

Genetic algorithms often used for finding an alignment & 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

comparative sequence analysis (!!??) using Benchmark Datasets

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

- Gutell, Pace


RNA Secondary structure prediction: Performance?

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

sequence2) Comparative sequence analysis

97% avg. prediction accuracy - multiple sequences (e.g., highly conserved rRNAs)much lower if sequence conservation is lower &/or fewer sequences are available for alignment

3) Combined - recent developments: combine thermodynamics & co-variation

& experimental constraints? IMPROVED RESULTS


RNA structure prediction strategies

Requires "craft" & significant user input & insight

1) Extensive comparative sequence analysis to predict tertiary contacts (co-variation)

e.g., MANIP - Westhof2) Use experimental data to constrain model

building e.g., MC-CYM - Major

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

4) Low resolution molecular mechanics e.g., yammp - Harvey

Tertiary structure prediction

11/02/05 d dobbs isu - bcb 444/544x: rna structure prediction1 11/2/05 rna structure prediction

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