From Structure to Function. Given a protein structure can we predict the function of a protein when we do not have a known homolog in the database ?

Slides:



Advertisements
Similar presentations
Gene expression From Gene to Protein
Advertisements

RNA structure prediction. RNA functions RNA functions as –mRNA –rRNA –tRNA –Nuclear export –Spliceosome –Regulatory molecules (RNAi) –Enzymes –Virus –Retrotransposons.
Improving miRNA Target Genes Prediction Rikky Wenang Purbojati.
MiRNA in computational biology 1 The Nobel Prize in Physiology or Medicine for 2006 Andrew Z. Fire and Craig C. Mello for their discovery of "RNA interference.
Basics of Molecular Biology
RNA Structure Prediction
Predicting RNA Structure and Function. Non coding DNA (98.5% human genome) Intergenic Repetitive elements Promoters Introns mRNA untranslated region (UTR)
Predicting RNA Structure and Function
RNA structure prediction. RNA functions RNA functions as –mRNA –rRNA –tRNA –Nuclear export –Spliceosome –Regulatory molecules (RNAi) –Enzymes –Virus –Retrotransposons.
Introduction to Bioinformatics - Tutorial no. 9 RNA Secondary Structure Prediction.
Simultaneous transcription and translation in prokaryotes Green arrow = E. coli DNA Red arrow = mRNA combined with ribosomes.
Non-coding RNA William Liu CS374: Algorithms in Biology November 23, 2004.
Chapter 4 Transcription and Translation. The Central Dogma.
RNA Structure Prediction Rfam – RNA structures database RNAfold – RNA secondary structure prediction tRNAscan – tRNA prediction.
Computational biology seminar
RNA Secondary Structure Prediction
Predicting RNA Structure and Function. Nobel prize 1989Nobel prize 2009 Ribozyme Ribosome RNA has many biological functions The function of the RNA molecule.
Presenting: Asher Malka Supervisor: Prof. Hermona Soreq.
LECTURE 5: DNA, RNA & PROTEINS
MicroRNA genes Ka-Lok Ng Department of Bioinformatics Asia University.
Predicting RNA Structure and Function. Following the human genome sequencing there is a high interest in RNA “Just when scientists thought they had deciphered.
[Bejerano Fall10/11] 1.
. Class 5: RNA Structure Prediction. RNA types u Messenger RNA (mRNA) l Encodes protein sequences u Transfer RNA (tRNA) l Adaptor between mRNA molecules.
Predicting RNA Structure and Function
CISC667, F05, Lec27, Liao1 CISC 667 Intro to Bioinformatics (Fall 2005) Review Session.
Predicting RNA Structure and Function. Nobel prize 1989 Nobel prize 2009 Ribozyme Ribosome.
microRNA computational prediction and analysis
Dynamic Programming (cont’d) CS 466 Saurabh Sinha.
Introduction to RNA Bioinformatics Craig L. Zirbel October 5, 2010 Based on a talk originally given by Anton Petrov.
RNA informatics Unit 12 BIOL221T: Advanced Bioinformatics for Biotechnology Irene Gabashvili, PhD.
Non-coding RNA gene finding problems. Outline Introduction RNA secondary structure prediction RNA sequence-structure alignment.
Protein Tertiary Structure Prediction
Transcription Transcription is the synthesis of mRNA from a section of DNA. Transcription of a gene starts from a region of DNA known as the promoter.
MicroRNA Targets Prediction and Analysis. Small RNAs play important roles The Nobel Prize in Physiology or Medicine for 2006 Andrew Z. Fire and Craig.
Genomics and Personalized Care in Health Systems Lecture 9 RNA and Protein Structure Leming Zhou, PhD School of Health and Rehabilitation Sciences Department.
Intelligent Systems for Bioinformatics Michael J. Watts
RNA Secondary Structure Prediction Spring Objectives  Can we predict the structure of an RNA?  Can we predict the structure of a protein?
MicroRNA identification based on sequence and structure alignment Presented by - Neeta Jain Xiaowo Wang†, Jing Zhang†, Fei Li, Jin Gu, Tao He, Xuegong.
RNA Folding. RNA Folding Algorithms Intuitively: given a sequence, find the structure with the maximal number of base pairs For nested structures, four.
1 Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine Chenghai Xue, Fei Li, Tao He,
RNA Secondary Structure Prediction. 16s rRNA RNA Secondary Structure Hairpin loop Junction (Multiloop)Bulge Single- Stranded Interior Loop Stem Image–
1 TRANSCRIPTION AND TRANSLATION. 2 Central Dogma of Gene Expression.
© Wiley Publishing All Rights Reserved. RNA Analysis.
Lecture 9 CS5661 RNA – The “REAL nucleic acid” Motivation Concepts Structural prediction –Dot-matrix –Dynamic programming Simple cost model Energy cost.
RNA secondary structure RNA is (usually) single-stranded The nucleotides ‘want’ to pair with their Watson-Crick complements (AU, GC) They may ‘settle’
RNA Structure Prediction
Gene expression. The information encoded in a gene is converted into a protein  The genetic information is made available to the cell Phases of gene.
Questions?. Novel ncRNAs are abundant: Ex: miRNAs miRNAs were the second major story in 2001 (after the genome). Subsequently, many other non-coding genes.
Doug Raiford Lesson 7.  RNA World Hypothesis  RNA world evolved into the DNA and protein world  DNA advantage: greater chemical stability  Protein.
Improving Intergenic miRNA Target Genes Prediction Rikky Wenang Purbojati.
RNA Structure Prediction RNA Structure Basics The RNA ‘Rules’ Programs and Predictions BIO520 BioinformaticsJim Lund Assigned reading: Ch. 6 from Bioinformatics:
Introduction to Bioinformatics Algorithms Algorithms for Molecular Biology CSCI Elizabeth White
This seems highly unlikely.
Motif Search and RNA Structure Prediction Lesson 9.
Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine 朱林娇 14S
RNA DBP: modeling and dynamics of RNA Russ Altman Vijay Pande.
MicroRNA Prediction with SCFG and MFE Structure Annotation Tim Shaw, Ying Zheng, and Bram Sebastian.
杜嘉晨 PlantMiRNAPred: efficient classification of real and pseudo plant pre-miRNAs.
RNA Structure Prediction
Rapid ab initio RNA Folding Including Pseudoknots via Graph Tree Decomposition Jizhen Zhao, Liming Cai Russell Malmberg Computer Science Plant Biology.
Protein Synthesis RNA, Transcription, and Translation.
RNAs. RNA Basics transfer RNA (tRNA) transfer RNA (tRNA) messenger RNA (mRNA) messenger RNA (mRNA) ribosomal RNA (rRNA) ribosomal RNA (rRNA) small interfering.
AAA AAAU AAUUC AUUC UUCCG UCCG CCGG G G Karen M. Pickard CISC889 Spring 2002 RNA Secondary Structure Prediction.
DNA, RNA, & Protein Synthesis (12.3) State Standards 2A. Distinguish between DNA and RNA. 2B. Explain the role of DNA in storing and transmitting cellular.
bacteria and eukaryotes
Biochemistry Free For All
Predicting RNA Structure and Function
RNA Secondary Structure Prediction
RNA 2D and 3D Structure Craig L. Zirbel October 7, 2010.
credit: modification of work by NIH
Presentation transcript:

From Structure to Function

Given a protein structure can we predict the function of a protein when we do not have a known homolog in the database ?

A different approach for predicting function from structure which does not rely on homology To characterize the known protein structures belonging to a specific family Find general structural features which are unique 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-HelixZinc-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

9 Machine learning algorithm for 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)

10 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. ?

DNA binding Non- ‘DNA binding Testing the algorithm for predicting DNA-binding proteins TP, TN, FP, FN Sensitivity Specificity

Predicting RNA Structure

13 protein RNA DNA 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 structuresRNA structures ~Total 90,000 Total ~900

RNA Structural levels tRNA Secondary Structure Tertiary Structure

RNA Secondary Structure U U C G U A A U G 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 U G 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 U G C A UCGAC 3’ 5’ U U C G U A A U G C A 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 U G C A UCGAC 3’ 5’ U U C G U A A U G C A UCGAC 3’ 5’ Example values: GC GC AU GC CG UA 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 U A G C A G C U A A U C G A U A 3’ A 5’ mismatch of hairpin -2.9 stacking nt bulge -2.9 stacking -1.8 stacking 5’ dangling -0.9 stacking -1.8 stacking -2.1 stacking G= -4.6 KCAL/MOL 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 C G A C U U C G G U C G G C U U C G G C C

Compensatory Substitutions U U C G U A A U G C A 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 C G A C U U C G G U C G G C U U C G G C C U C U G C G N N’ G C

Insight from Multiple Alignment Information from multiple sequence alignment (MSA) can help to predict the probability 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 database Many families of non coding RNAs which have unique functions are characterized by the combination of a conserved sequence and structure

MicroRNAs an example of an RNA family miRNA gene Target gene mature miRNA

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)