BCB 444/544

1
BCB 444/544

• Lecture 25
• More RNA Structure
• BCB 544 Projects
• 25_Oct19

2
Required Reading (before lecture)

• Mon Oct 15 - Lecture 23
• Protein Tertiary Structure Prediction
• Chp 15 - pp 214 - 230
• Wed Oct 17 Thurs Oct 18 - Lecture 24 Lab 8
  (Terribilini)
• RNA Structure/Function RNA Structure
  Prediction
• Chp 16 - pp 231 - 242
• Fri Oct 18 - Lecture 25 ( Mon Oct 22)
• Gene Prediction
• Chp 8 - pp 97 - 112

3
Homework Assignment

• ALL HomeWork 4 (emailed posted online Sat
  AM)
• Due Mon Oct 22 by 5 PM (not Fri Oct 19)
• Read
• Ginalski et al.(2005) Practical Lessons from
  Protein Structure Prediction, Nucleic Acids Res.
  331874-91. http//nar.oxfordjournals.org/cgi/cont
  ent/full/33/6/1874
• (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 HW4 posted online sent
  by email on Sat Oct 13

4
BCB 544 Only New Homework Assignment

• 544 Extra2 (posted online Thurs?)
• Due Fri Nov 2 by 5 PM
• HW2 is next step in Team Projects
• Will end lecture a few minutes early today - to
  allow time to meet discuss 544 Teams Projects

5
Seminars this Week

• BCB List of URLs for Seminars related to
  Bioinformatics
• http//www.bcb.iastate.edu/seminars/index.html
• Oct 18 Thur - BBMB Seminar 410 in 1414 MBB
• Sachdeve Sidhu (Genentech) Phage peptide and
  antibody libraries in protein engineering and
  ligand selection
• Was great talk!
• Oct 19 Fri - BCB Faculty Seminar 210 in 102 ScI
• Lyric Bartholomay (Ent, ISU) Computational
  Biology and vector-borne disease from the field
  to the bench

6
Another local example Combining Structure
Prediction, 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
7
Chp 16 - RNA Structure Prediction

• SECTION V STRUCTURAL BIOINFORMATICS
• Xiong Chp 16 RNA Structure Prediction
  (Terribilini)
• RNA Function
• Types of RNA Structures
• RNA Secondary Structure Prediction Methods
• Ab Initio Approach
• Comparative Approach
• Performance Evaluation

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RNA Function
This slide has been changed

• Storage/transfer of genetic information
• Newly discovered regulatory functions
• miRNA si RNA pathways, especially
• Catalytic

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RNA types functions
Types of RNAs Primary Function(s)
mRNA - messenger translation (protein synthesis) regulatory
rRNA - ribosomal translation (protein synthesis) ltcatalyticgt
tRNA - transfer translation (protein synthesis)
hnRNA - heterogeneous nuclear precursors intermediates of mature mRNAs other RNAs
scRNA - small cytoplasmic signal recognition particle (SRP) tRNA processing ltcatalyticgt
snRNA - small nuclear snoRNA - small nucleolar mRNA processing, polyA addition ltcatalyticgt rRNA processing/maturation/methylation
regulatory RNAs (siRNA, miRNA, etc.) regulation of transcription and translation, other??
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RNA Structures

• RNA forms complex 3D structures
• Mainly "single-stranded" - but
• Single RNA strandscan self-hybridize to form
• Base-paired regions

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Levels of RNA Structure
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• Like proteins, RNA has primary, secondary, and
  tertiary structure ( quaternary structure, too)
• Primary structure Ribonucleotide sequence
• Secondary structure Helix vs turn (base-paired
  vs single-stranded) Note in RNA, helices often
  involve long-range interactions
• Tertiary structure 3D structure (also due to
  long-range interactions)
• Quaternary structure complex of 2 or more RNA
  strands

Rob Knight Univ Colorado
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Common structural motifs in RNA

• Helices
• Loops
• Hairpin
• Interior
• Bulge
• Multibranch
• Pseudoknots
• Tetraloops

Fig 6.2 Baxevanis Ouellette 2005
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Covalent non-covalent bonds in RNA
This is a new slide

• Primary
• Covalent bonds
• Secondary/Tertiary
• Non-covalent bonds
• H-bonds
• (base-pairing)
• Base stacking

Fig 6.2 Baxevanis Ouellette 2005
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RNA Structure Prediction
This slide has been changed

• RNA tertiary structure is very difficult to
  predict
• Focus on predicting RNA secondary structure
• Given an RNA sequence, predict its secondary
  structure
• Almost all methods ignore higher order secondary
  structures such as pseudoknots tetraloops
• Specialized software is available for predicting
  these

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RNA Pseudoknots Tetraloops
This is a new slide

• Often have important regulatory or catalyltic
  functions

Pseudoknot
Tetraloop
http//academic.brooklyn.cuny.edu/chem/zhuang/QD/m
ckay_hr.gif
http//www.lbl.gov/Science-Articles/Research-Revie
w/Annual-Reports/1995/images/rna.gif
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Base Pairing in RNA
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• G-C, A-U, G-U ("wobble") many variants

See IMB Image Library of Biological Molecules
http//www.fli-leibniz.de/ImgLibDoc/nana/IMAGE_NAN
A.htmlbasepairs
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Experimental RNA structure determination?

• X-ray crystallography
• NMR spectroscopy
• Enzymatic/chemical mapping

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RNA Secondary Structure Prediction Methods
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• Two (three, recently) main types of methods
• Ab initio - based on calculating most
  energetically favorable secondary structure(s)
• Energy minimization (thermodynamics)
• Comparative approach - based on comparisons of
  multiple evolutionarily-related RNA sequences
• Sequence comparison (co-variation)
• Combined computational experimental
• Use experimental constraints when available

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RNA Secondary structure prediction - 1
This is a new slide

1. Energy minimization (thermodynamics)

• Algorithms
• Dynamic programming to find
• high probability pairs
• (also, some Genetic algorithms)
• Software
• Mfold - Zuker
• RNAfold (Vienna Package) -Hofacker
• RNAstructure - Mathews
• Sfold - Ding Lawrence

R Knight 2005
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RNA Secondary structure prediction - 2
This is a new slide
2) Comparative sequence analysis (co-variation)

• Algorithms
• Mutual information
• Context-free grammars
• Software
• RNAlifold
• Foldalign
• Dynalign

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RNA Secondary structure prediction - 3
This is a new slide
3) Combined experimental computational

• Experiments
• Map single-stranded vs double-stranded regions
  in folded RNA
• How?
• Enzymes S1 nuclease, T1 RNase
• Chemicals kethoxal, DMS, OH?
• Software
• Mfold
• Sfold
• RNAStructure
• RNAFold
• RNAlifold

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1 - Ab Initio Prediction
This slide has been changed

• Requires only a single RNA sequence
• Calculates minimum free energy structure
• Base-paired regions have lower free energy, so
  methods "attempt to find secondary structure with
  maximal base pairing" (Careful!)
• IMPORTANT Largest contribution to energy is to
  nearest neighbor (base-stacking) interactions,
  not base-pairing!

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Ab Initio Prediction Clarifications
This slide has been changed

• Free energy is calculated based on parameters
  determined in the wet lab
• Correction Use known energy associated with
  each type of nearest-neighbor pair
  (base-stacking) (not base-pair)
• Base-pair formation is not independent multiple
  base-pairs adjacent to each other are more
  favorable than individual base-pairs -
  cooperative - because of base-stacking
  interactions
• Bulges and loops adjacent to base-pairs have a
  free energy penalty

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Ab Initio Prediction What are the assumptions?
This is a new slide

• 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 37?C
• equilibrium stability of structure
• lower values (negative) are more favorable
• Is this assumption valid?
• in vivo? - this may not hold, but we don't
  really know

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Energy minimization What are the rules?
This is a new slide
What gives here?
Why 1.2 vs 1.6?
C Staben 2005
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Energy minimization calculations Base-stacking
is critical
This is a new slide
- Tinocco et al.
C Staben 2005
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Ab initio RNA Structure Prediction Uses
Nearest-neighbor parameters
This is a new slide

• Most methods for ab initio prediction (free
  energy minimization) use nearest-neighbor energy
  parameters (derived from experiment) for
  predicting stability of an RNA secondary
  structure (in terms of ?G at 37?C)
• most available software packages use same set
  of parameters
• - Mathews, Sabina, Zuker

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Ab Initio Energy Calculation
This slide has been changed

• Search for all possible base-pairing patterns
• Calculate total energy of each structure based on
  all stabilizing and destabilizing forces

• Total free energy for a specific RNA conformation
  Sum of incremental energy terms for
• helical stacking
• (sequence dependent)
• loop initiation
• unpaired stacking

(favorable "increments" are lt 0)
Fig 6.3 Baxevanis Ouellette 2005
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Dot Matrices

• Can be used to find all possible base pair
  patterns
• Compare input sequence to itself and put a dot
  where there is a complimentary base

R Knight 2005
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Dynamic Programming
This slide has been changed

• Finding optimal secondary structure is difficult
  - lots of possibilities
• Compare RNA sequence with itself
• Apply scoring scheme based on energy parameters
  for base stacking, cooperativity, and penalties
  for destabilizing forces
• Find path that represents most energetically
  favorable secondary structure

31
Problem with DP Approach

• DP returns SINGLE lowest energy structure
• There may be many structures with similar
  energies
• Also, predicted secondary structure is only as
  good as energy parameters used
• Solution return multiple structures with near
  optimal energies

32
Popular Ab Initio Prediction Programs

• Mfold
• Combines DP with thermodynamic calculations
• Fairly accurate for short sequences, less
  accurate as sequence length increases
• RNAfold
• Returns multiple structures near predicted
  optimal structure
• Computes larger number of potential secondary
  structures than Mfold, so uses a simplified
  energy function

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2 - Comparative Prediction Approaches

• Use multiple sequence alignment
• Assume related sequences fold into same secondary
  structure

34
Co-variation patterns in MSAs are critical

• RNA functional motifs are conserved
• To maintain RNA structure during evolution, a
  mutation in a base-paired residue must be
  compensated for by a mutation in residue with
  which it pairs
• Comparative methods search for co-variation
  patterns in MSAs

35
Consensus Structures

• Predict secondary structure of each individual
  sequence in a MSA
• Compare all structures and try to identify a
  consensus structure

36
Popular Comparative Prediction Programs

• Two main types
• Require user to provide MSA
• RNAalifold
• No MSA required
• Foldalign
• Dynalign

37
RNAalifold

• Requires user to provide MSA
• Creates a scoring matrix combining minimum free
  energy and co-variation information
• DP used to identify minimum free energy structure

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Foldalign

• User provides pair of unaligned RNA sequences
• Constructs alignment computes conserved
  structure
• Suitable only for relatively short sequences

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Dynalign

• User provides two unaligned input sequences
• Calculates possible secondary structures using
  algorithm similar to Mfold
• Compares multiple structures from both sequences
  to find a common structure

40
3 - Popular Programs that use Combined
Computational Experimental Approaches

• Mfold
• Sfold
• RNAStructure
• RNAFold
• RNAlifold

41
Comparison of Predictions for Single RNA using
Different Methods
JH Lee 2007
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Comparison of Mfold Predictions -/ Constraints
Mfold plus constraints -54.84 kcal/mol
Mfold -126.05 kcal/mol
JH Lee 2007
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Performance Evaluation
This slide has been changed

• Ab initio methods? correlation coefficient
  20-60
• Comparative approaches? correlation coefficient
  20-80
• Programs that require user to supply MSA are more
  accurate
• Comparative programs are consistently more
  accurate than ab initio
• Base-pairs predicted by comparative sequence
  analysis for large small subunit rRNAs are 97
  accurate when compared with high resolution
  crystal structures! - Gutell, Pace
• BEST APPROACH? Methods that combine
  computational prediction (ab initio
  comparative) with experimental constraints (from
  chemical/enzymatic modification studies)

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BCB 544 "Team" Projects

• 544 Extra HW2 is next step in Team Projects
• Write 1 page outline
• Schedule meeting with Michael Drena to discuss
  topic
• Read a few papers
• Write a more detailed plan
• You may work alone if you prefer
• Last week of classes will be devoted to Projects
• Written reports due Mon Dec 3 (no class that
  day)
• Oral presentations (15-20') will be Wed-Fri Dec
  5,6,7
• 1 or 2 teams will present during each class
  period
• See Guidelines for Projects posted online