From Structure to Function

Given a protein structurecan we predict the function of a

protein when we do not have a known homolog in the database ?

A different approach for predictingfunction from structure which does not rely on homology

• To characterize the known protein structures belonging to a specific family

• Find general structural features which areunique to the family

• Use these features to predict new members of the family

EXAMPLE :Predicting new DNA-binding proteins

p53

Many DNA-binding proteins are involved in cancer

Leucine zippers -ribbon

Helix-Turn-Helix Zinc-Finger

Many different folds but all can bind DNA

While DNA-binding proteins have diverse folds they all share a common property:All have positive charged surfaces

Complementing the negative charge of the DNA

Positive(Blue)

Negative(red)

DNA-binding proteins are characterized by positive charged surfaces

But so do proteins that don’t bind nucleic acids

Positive(Blue)

Negative(red)

Strategy for predicting new DNA-binding proteins

1. Build a database of DNA-binding and non DNA-binding proteins

2. Extract the positive electrostatic patch in all proteins in Data Set.

3. Find features that could be used to discriminate the DNA-binding proteins from other proteins.

4. Use the features as a vector to train a machine learning algorithm to identify novel DNA-binding proteins

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Machine learning algorithmfor predicting protein function from structural

features

• SVM (Support Vector Machine) is trained on a set of known proteins that have a common function such as DNA binding (red dots), and in addition, a separate set of proteins that are known not to bind DNA (blue dots)

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• Using this training set of DNA and non-DNA binding protein, an SVM would learn to differentiate between the members and non-members of the family

• Having learned the features of the class (DNA binding proteins), the SVM could recognize a new protein as members or as non-members of the class based on the combination of its structural features.

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DNA binding Non-‘DNA binding

Testing the algorithm for predicting DNA-binding proteins

TP, TN, FP, FNSensitivitySpecificity

PredictingRNA Structure

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proteinRNADNA

According to the central dogma of molecular biology the main role of RNA is to transfer genetic information from DNA to protein

RNA has many other biological functions

• Protein synthesis (ribosome)

• Control of mRNA stability (UTR)

• Control of splicing (snRNP)

• Control of translation (microRNA)

The function of the RNA molecule depends on its folded structure

Nobel prize 2009

Ribosome

Protein structures RNA structures

~Total 90,000 Total ~900

RNA Structural levels

tRNA

Secondary Structure Tertiary Structure

RNA Secondary Structure

U U

C G U A A UG C

5’ 3’

5’G A U C U U G A U C

3’

• RNA bases are G, C, A, U• The RNA molecule folds on itself. • The base pairing is as follows: G C A U G U hydrogen bond.

Stem

Loop

Predicting RNA secondary Structure

Most common approach:

Search for a RNA structure with a

Minimal Free Energy (MFE)

G A U C U U G A U C

U U

C G U A A UG U

G C U A G U

Low energy High energy

U

Free energy model

Free energy of a structure is the sum of all interactions energies

Each interaction energy can be calculated thermodynamicly

Free Energy(E) = E(CG)+E(CG)+…..

The aim: to find the structure with the minimal free energy (MFE)

Why is MFE secondary structure prediction hard?

• MFE structure can be found by calculating free energy of all possible structures

• BUT the number of potential structures grows exponentially with the number of bases

Solution :Dynamic programming (Zucker and Steigler)

Simplifying Assumptions for RNA Structure Prediction

• RNA folds into one minimum free-energy structure.

• The energy of a particular base can be calculated independently– Neighbors do not influence the energy.

Sequence dependent free-energy Nearest Neighbor Model

U U

C G G C A UG CA UCGAC 3’5’

U U

C G U A A UG CA UCGAC 3’5’

Free Energy of a base pair is influenced by

the previous base pair (not by the base pairs further down).

Sequence dependent free-energy values of the base pairs

(nearest neighbor model) U U

C G G C A UG CA UCGAC 3’5’

U U

C G U A A UG CA UCGAC 3’5’

Example values:GC GC GC GCAU GC CG UA -2.3 -2.9 -3.4 -2.1

These energies are estimated experimentally from small synthetic RNAs.

Improvements to the MFE approach

• Positive energy - added for destabilizing regions such as bulges, loops, etc.

• More than one structure can be predicted

Free energy computation

U UA A G C G C A G C U A A U C G A U A 3’A5’

-0.3

-0.3

-1.1 mismatch of hairpin-2.9 stacking

+3.3 1nt bulge -2.9 stacking

-1.8 stacking

5’ dangling

-0.9 stacking-1.8 stacking

-2.1 stacking

G= -4.6 KCAL/MOL

+5.9 4 nt loop

Improvements to the MFE approach

• Positive energy - added for destabilizing regions such as bulges, loops, etc.

• Looking for an ensemble of structures with

low energy and generating a consensus structure

WHY?

RNA is dynamic and doesn’t always fold to the lowest energy structure

RNA fold prediction based on Multiple Alignment

Information from multiple sequence alignment (MSA) can help to predict the probability of positions i,j to be base-paired.

G C C U U C G G G CG A C U U C G G U CG G C U U C G G C C

Compensatory Substitutions

U U

C G U A A UG CA UCGAC 3’

G C

5’

Mutations that maintain the secondary structure

can help predict the fold

RNA secondary structure can be revealed by

identification of compensatory mutations

G C C U U C G G G CG A C U U C G G U CG G C U U C G G C C

U CU GC GN N’G C

Insight from Multiple Alignment

Information from multiple sequence alignment (MSA) can help to predict theprobability of positions i,j to be base-paired.

•Conservation – no additional information•Consistent mutations (GC GU) – support stem•Inconsistent mutations – does not support stem.•Compensatory mutations – support stem.

From RNA structure to Function

Rfam RNA Family databasehttp://www.sanger.ac.uk/Software/Rfam/

Many families of non coding RNAs which have unique functions are characterized by the combination of a conserved sequenceand structure

MicroRNAsan example of an RNA family

miRNAgene

Target gene

maturemiRNA

MicroRNA in Cancer

The challenge for Bioinformatics:

- Identifying new microRNA genes- Identifying the targets of specific microRNA

How to find microRNA genes?

Searching for sequences that fold to a hairpin ~70 nt -RNAfold-other efficient algorithms for identifying stem loops

Concentrating on intragenic regions and introns- Filtering coding regions

Filtering out non conserved candidates-Mature and pre-miRNA is usually evolutionary conserved

How to find microRNA genes?

A. Structure prediction

B. Evolutionary Conservation

Predicting microRNA targets

MicroRNA targets are located in 3’ UTRs, and complementing mature microRNAs

•Why is it hard to find them ??– Base pairing is required only in the seed sequence

(7-8 nt) – Lots of known miRNAs have similar seed sequences

Very high probability to find by chance

3’ UTR of Target gene

mature miRNA

Predicting microRNA target genes

• General methods

- Find motifs which complements the seed sequence (allow mismatches)– Look for conserved target sites– Consider the MFE of the RNA-RNA pairing ∆G

(miRNA+target)– Consider the delta MFE for RNA-RNA pairing

versus the folding of the target

∆G (miRNA+target )- ∆G (target)