sequence alignment algorithms – application to bioinformatics tool development

Dr.S.Parthasarathy, Bharathidasan Univ., Trichy

116 FEB 2006

Sequence Alignment Algorithms – Application to Bioinformatics Tool

Development

Dr. S. ParthasarathyReader and Head

Department of BioinformaticsBharathidasan UniversityTiruchirappalli – 620 024

(E-mail: [email protected])


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Plan

Introduction to BioinformaticsSequence alignment algorithms

Global alignment : Needleman - Wunsch algorithm Local alignment : Smith – Waterman algorithm

– Predict Fold to a protein sequence Methodology Algorithm, Coding & Tool Development Benchmarking

Conclusions


316 FEB 2006

Introduction

Why do we need Bioinformatics?

What is Bioinformatics?

Where is Bioinformatics used?


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Why?

Biological Data Explosion How did Biological Data Explosion

happen?

Sequence Databases are HUGE than the Structure Databases Why so?


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Introduction Biological Data : Genome Projects

Latest Revolution On 26 June, 2000 - Announcement of completion of

the draft of the ‘Human Genome’ ‘Genetic Code of Human Life is Cracked by

Scientists’ Human Genome contains 3.2 x 109 bps Unit of (Genome) sequence length

bps (base pairs) Mbps (Mega base pairs) = 106 bps Gbps (Giga base pairs) = 109 bps huge (human genome equivalent) = 3.2 Gbps

Unit of Genetic distance centiMorgan (cM) - arbitrary unit ; Named for Thomas Hunt

Morgan (e.g. 1 cM = 0.01 recombinant frequency)


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Introduction Biological Data : Genome Projects

16 February 2001 15 February 2001


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Biological Data : Recombinant DNA Technology

Old Revolution 1940 – Role of DNA as the genetic material was confirmed 1953 – Discovery of DNA structure by James Watson & Francis Crick 1966 – Establishment of the Genetic Code 1967 – DNA ligase was isolated – (join two strands of DNA together) – Molecular Glue 1970 – Isolation of Restriction enzyme – Molecular Scissors 1972 – Recombinant DNA molecules were generated at Stanford University, USA 1973 – Joining DNA fragments to the plasmid pSC101 isolated from

E.Coli. They could replicate when introduced into E.Coli. The discoveries of 1972 & 1973 triggered off the biggest

scientific revolution – Genetic Engineering


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Biological Data explosion

GenBank, NCBI, USA 44 Gbps of DNA & 40 Million Sequences (upto 2004) GenBank, National Center for Biotechnology Information, USA

Protein Data Bank (PDB), RCSB, USA 29,000 structures (2004) PDB, Research Collaboratory for Structural Bioinformatics,

USA QUALITY of Data - HIGH

Experimental error in modern genomic sequencing is extremely low

QUANTITY of Data - HUGE With Recombinant DNA technology & genomic sequencing,

size of sequence data bases is increasing very rapidly SEQUENCE Versus STRUCTURE Databases

Sequence Databases are HUGE than Structure DatabasesLeads to Bioinformatics


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What?

What is Bioinformatics?

Define Bioinformatics


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Bioinformatics - Definition

F(i,j) = max {

F(i-1, j-1)+s(xi,yj),

F(i-1, j) – d,

F(i, j-1) – d.}Bioinformatics is an integration of mathematical, statistical and computer methods to analyze biological data. We use computer programs to make inference from the biological data, to make connections among them and to derive useful and interesting predictions.

The marriage of biology and computer science has created a new field called ‘Bioinformatics’. - Arthur M. Lesk

Bioinformat ics

PEPTIDESE QSEDITPEP

atcggcatgcatcagtcatgcaactg


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Biology Basic Definitions

Cell - It is the building block of living organisms Eukaryotic Cells or organisms have the nucleus

separated from the cytoplasm by a nuclear membrane and the genetic material borne on a number of chromosomes consisting of DNA and Protein

Chromosome The physical basis of heredity. Deeply staining rod-like structures present with the nuclei of

eukaryotesContains DNA and protein arranged in compact mannerReplicate identically during cell divisionSame number of chromosomes present in cells of a particular

species (e.g. Human : 22, X and Y)


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GenomeBasic Definitions

Genome A complete set of chromosomes inherited from one parent

Gene One of the units of inherited material carried on by

chromosomes. They are arranged in a linear fashion on DNAs. Each represents one character, which is recognized by its effect on the individual bearing the gene in its cells. There are many thousand genes in each nucleus.

DNA (Deoxyribo Nucleic Acid) DNA is made up of FOUR bases a t g c – adenine, thymine, guanine, cytosine

Protein Protein is made up of TWENTY different amino acids A T G C ... – Alanine, Threonine, Glycine, Cysteine, …


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Central Dogma

Protein

mRNA

DNA

transcription

translation

CCTGAGCCAACTATTGATGAA

PEPTIDE

CCUGAGCCAACUAUUGAUGAA


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Genome DataHuman & Model Organisms

Most mapping and sequencing technologies were developed from studies of simpler non-human organisms

Non-Human/Model organisms Bacterium Escherichia Coli - 4.6 Mbp Yeast Saccharomyces Cerevisiae - 12.1 Mbp Fruit Fly Drosophila melanogaster - 180.0 Mbp Roundworm C. elegans - 95.5 Mbp Laboratory Mouse Mus musculus - 3.0 Gbp

Human – more complex genome Human Homo sapiens - 3.2 Gbp


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Genome DataHuman (Homo Sapiens)

Genome 1

Chromosomes 23

Genes / DNAs ~ 30,000

Nucleotides 3.2 x 109 bps


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Bioinformatics in Genome Research

Data Collection and Interpretation Collecting and Storing Data

Sequence generated by genome research will be used as primary information source for human biology and medicine

The vast amount of data produced will first need to be collected, stored and distributed

Interpretation of Data Recognizing where genes begin and end Searching a database for a particular DNA sequence

may uncover these homologous sequences in a known gene from a model organism, revealing insights into the function of the corresponding human gene


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Understanding Gene Function

Correct protein function depends on the 3-D or folded structure the protein assumes in biological environments

Understanding protein structure will be essential in determining gene function

Gene Protein

Function Structure


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Where?

Where is Bioinformatics used?

What are the uses of Bioinformatics?

Applications of Bioinformatics


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Bioinformatics Tasks

Sequence Analysis (Protein sequences) Similarity & Homology

pairwise local/global alignment• GCG – Seqlab & Seqweb• Scoring Matrices - PAM, BLOSUM

Database Search BLAST, FASTA

Multiple alignmentClustalW, PRINTS, BLOCKS

Secondary Structure Prediction (from Sequence)Proteins – -Helix, β-Sheet, Turn or coilProtein Folding


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Bioinformatics Tasks Structure analysis – Experimental Determination

X-ray crystallography – 3 dimensional coordinates – Structure Nuclear Magnetic Resonance (NMR)

PDB – Protein Data Bank RasMol – Molecular Viewing Software

High-throughput crystallographic structure determination High flux synchrotron radiation sources (data collection) Multiple anomalous diffraction method (data interpretation)

Bioinformatics - Structure Prediction Homology Modelling – InsightII, SwissPDBViewer, Biosuite ‘ab initio’ method - Monte Carlo Simulation

Protein Structure Classification SCOP - Structural Classification Of Proteins CATH - Class, Architecture, Topology, Homologous superfamily FSSP - Fold Classification based on Structure- Structure alignment of Proteins – obtained by DALI (Distance-matrix ALIgnment)


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Bioinformatics Tasks

Protein Engineering Mutations

Alter particular amino acid/base for desired effect Site directed mutagenesis

Identify the potential sites where we can do alterations Applications

Agricultural – Genetically Modified Plants, Vegetables, GM Food

Pharmaceutical – Molecular Modelling base Drug Design Medical – Gene Therapy

DNA Bending Application to Genomes

(Ref: M.G.Munteanu, K.Vlahovicek, S.Parthasarathy, I.Simon and S.Pongor, Rod Models of DNA: Sequence-dependent anisotropic elastic modelling of local phenomena, Trends in Biochemical Sciences, 23 (1998) 341-347)


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Bioinformatics TasksGenomics & Proteomics

Genomics is the study of the structure, content, evolution and functions of genes in genomes

Aims of Genomics To establish an integrated web based

database and research interface To assemble Physical,Genetic and Cytological

maps of the Genome To identify and annotate the complete set of

genes encoded within a genome To provide the resources for comparison with

other genomes


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Proteomics – Proteome

Proteome is the complete collection of proteins in a cell/tissue/organism at a particular time. Unlike genomes, which are stable over the life time of the organism, proteomes change rapidly as each cell response to its changing environment and produces new proteins and at different amounts.

Genome is a more stable entity. An organism has only one genome but many proteomes.

For an organism, there may be one body wide proteome, about 200 tissue proteomes about a trillion (~1012) individual cell proteomes.


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The study of proteomes that includes determining the 3D shapes of proteins, their roles inside cells, the molecules with which they interact, and defining which proteins are present and how much of each is present at a given time.

Proteomics – Definition


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To correlate proteins on the basis of their expression profiles.

To observe patterns in protein synthesis and this observed pattern changes can be used as an indicator of the state of cell and its gene expression.

To characterize bacterial pathogens and to develop novel antimicrobials.

To identify regions of the bacterial genome that encode pathogenic determinants.

To develop drugs and in toxicology – Structural Proteomics

Proteomics as a tool for plant genetics and breeding

Proteomics – Applications


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Systems Biology Systems Biology is a new perspective and emerging

field for research in the post-genomic era. It aims at system level understanding of biological

systems. It studies whole cells/tissues/organisms not by a

traditional reductionist’s approach but by holistic means in a reiterative attempt to model the complete cell/tissue/organism.

It is an integrated and interacting network of genes, proteins and biochemical reactions which give rise to life.


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Systems Biology


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Sequence Alignment Algorithms

Similarity and Homology

Sequence Comparison - Issues

Types of alignments

Algorithms Used


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Sequence similarity and homology

Nature is a tinkerer and not an inventor. New sequences are adapted from pre-existing sequences rather than invented de novo . There exists significant similarity between a new sequence and already known sequences. – Fortunate for computational sequence analysis

Similarity – Measurement of resemblance and differences, independent of the source of resemblance.

Homology – The sequences and the organisms in which they occur are descended from a common ancestor.

If two related sequences are homologous, then we can transfer information about structure and/or function, by homology.


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3-D Structure and Homology

3-D structure patterns (motifs) of proteins are much more evolutionarily conserved than amino acid sequences - This type of Homology search could prove more fruitful

Particular motifs may serve similar functions in several different proteins, information that would be valuable in genome analysis

Only a few protein motifs can be recognised at the sequence level

Development of more analytic capabilities to facilitate grouping protein sequences into motif families will make homology searches more useful


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Sequence ComparisonIssues

Types of alignment Global – end to end matching (Needleman-Wunsch) Local – portions or subsequences matching (Smith-

Waterman) Scoring system used to rank alignments

PAM & BLOSUM matrices Algorithms used to find optimal (or good) scoring

alignments Heuristic Dynamic Programming Hidden Markov Model (HMM)

Statistical methods used to evaluate the significance of an alignment score Z- score, P- value and E- value


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Substitution Matrices

PAM (Point Accepted Mutation) BLOSUM (BLOcks SUbstitution

Matrix)90

62

30

BLOSUM

Close

Default

Distant

40

250

500

PAM


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Types of Algorithms Heuristic

A heuristic is an algorithm that will yield reasonable results, even if it is not provably optimal or lacks even a performance guarantee.

In most cases, heuristic methods can be very fast, but they make additional assumptions and will miss the best match for some sequence pairs.

Dynamic Programming The algorithm for finding optimal alignments given an

additive alignment score dynamically (We are going to discuss about it soon.) These type of algorithms are guaranteed to find the

optimal scoring alignment or set of alignments. HMM - Based on Probability Theory – very versatile.


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Global AlignmentNeedleman-Wunsch Algorithm

Formula { F(i-1,j-1) + s(xi,yj)

D F(i, j) = max { F(i-1 , j) - d

H { F(i , j-1) - d

V F(i-1,j-1) D

F(i,j-1) V

F(i-1,j) H F(i,j)


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Global AlignmentNeedleman-Wunsch Algorithm

Gap penalties Linear score f(g) = - gd Affine score f(g) = - d – (g-1) e

d = gap open penalty e = gap extend penaltyg = gap length

Trace back Take the value in the bottom right corner and

trace back till the end. (i.e. align end – end always).

Algorithm complexity It takes O(nm) time and O(nm) memory, where

n and m are the lengths of the sequences.


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Local AlignmentSmith-Waterman Algorithm

Same as Global alignment algorithm with TWO differences. F(i,j) to take 0 (zero), if all other options

have value less than 0. Alignment can end anywhere in the

matrix.Take the highest value of F(i,j) over the wholematrix and start trace back from there.


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Formula { F(i-1,j-1) + S(xi,yj) D

F(i, j) = max F(i-1 , j) - d H F(i , j-1) - d V 0 (if all other value is <

0) }

F(i-1,j-1) D V F(i,j-1)

F(i-1,j) H F(i,j)


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Web based server development

Design the web page to get the dataUse cgi-bin or Perl script to parse the

submitted dataInvoke the corresponding program to

get the appropriate resultsSend the results either by e-mail or

to the web page directly


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Application to Bioinformatics Tool Development

To predict a fold to protein sequence


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To predict a fold to protein sequence

To predict possible folds for a given protein sequence, whose structure is not known

To develop a fold recognition technique / tool that is sensitive in detecting folds of given protein sequences in the twilight zone (sequences sharing less than 25% identity)

Application of the fold recognition strategy to genomic annotation


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‘Twilight Zone’ sequencesExample

Cytochrome Sequences

256b>256B:A CYTOCHROME $B562 (OXIDIZED) - CHAIN A

ADLEDNMETLNDNLKVIEKADNAAQVKDALTKMRAAALDAQKATPPKLEDKSPDSPEMKD FRHGFDILVGQIDDALKLANEGKVKEAQAAAEQLKTTRNAYHQKYR

>256B:B CYTOCHROME $B562 (OXIDIZED) - CHAIN B ADLEDNMETLNDNLKVIEKADNAAQVKDALTKMRAAALDAQKATPPKLEDKSPDSPEMKD FRHGFDILVGQIDDALKLANEGKVKEAQAAAEQLKTTRNAYHQKYR

2ccy >2CCY:A CYTOCHROME $C(PRIME) - CHAIN A

QQSKPEDLLKLRQGLMQTLKSQWVPIAGFAAGKADLPADAAQRAENMAMVAKLAPIGWAK GTEALPNGETKPEAFGSKSAEFLEGWKALATESTKLAAAAKAGPDALKAQAAATGKVCKA CHEEFKQD

>2CCY:B CYTOCHROME $C(PRIME) - CHAIN B QQSKPEDLLKLRQGLMQTLKSQWVPIAGFAAGKADLPADAAQRAENMAMVAKLAPIGWAK GTEALPNGETKPEAFGSKSAEFLEGWKALATESTKLAAAAKAGPDALKAQAAATGKVCKA CHEEFKQD


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ExampleSequences similarity

lalign output for

256b & 2ccy follows …


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ExampleCytochrome Structures

CYTOCHROME STRUCTURES (seq. similarity 24%)CYTOCHROME STRUCTURES (seq. similarity 24%)

256b256b

2ccy2ccy


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Goals

Exploration of suitable fold recognition techniques that are sensitive in detecting similar folds despite low sequence similarity

Identification of functional motifs in proteins at sequence (1D) and structure (3D) level

Development of a protocol that aid in the rapid classification and annotation of genomic data based on functional motifs


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Methodology

Reduction of 3D-structure to 1D-environment string. Environment at each residue position is a function of local secondary structure and extent of exposure to the solvent (based on 3D-1D profile method developed by Eisenberg et al., 1991).

Extract residue environment profiles of the available protein structures.

A scoring matrix is generated from a library of profiles. Each matrix element is the information value of a residue in the given environment.

A library of environment strings is created for the available protein fold structures.

The probe sequence is queried against this library to look for best matches.


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Scoring Table

PredictFold

Fold library

3D-Profiles

New Sequence

FOLD PREDICTION

1D-Environment Sequence

Annotate New sequence

Workflow


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Residue Environments

_Exposed Partially buried

Buried_

_Helix

_CoilStrand_


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The residue environments are described by 1. the area (A) of the

residue buried in the protein

2. the fraction (f) of side-chain area that is covered by polar atoms (O and N)

3. the local secondary structure


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CLASS Area (A) Å2 FRACTION (f)

BURIED 1 (B1) A > 114 f < 0.45BURIED 2 (B2) 0.45 < f < 0.58BURIED 3 (B3) f > 0.58

PARTIAL 1 (P1) 40 < A < 114 f < 0.67PARTIAL 2 (P2) f > 0.67

EXPOSED (E0) A < 40 f > 0.67


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Residue Environment classes

We have 6 classes based on the extend of exposure to solvent

We have 3 classes based on secondary structure – Alpha Helix(A), Beta Sheet (B) & Coil(C)

Total : 6 x 3 = 18 environmentsB1A,B1B,B1C, B2A,B2B,B2C, B3A,B3B,B3C

P1A,P1B,P1C, P2A,P2B,P2C, E0A,E0B,E0C.For example

B1A - Buried 1 Alpha Helix P2B - Partially Buried 2 Beta Sheet E0C - Exposed 0 Coil


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Scoring Table The scoring table used in this case is a 20 x 18

matrix, constructed from a statistical analysis of the profile library (consisting of 1200 protein structures) provided by PROFILES_3D module of Insight II (Accelrys Inc.)

The scores Sij are calculated using the formula

Sij = ln [ P(i : j) / Pi ] x 100 where P(i : j) is the probability of finding

residue i in the environment j and Pi is the overall probability of finding residue i in any environment.


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Scoring Table

The scoring table contains measure of the compatibility of the 20 amino acids with the 18 environmental classes.

The individual matrix elements are propensities (information values) for the amino acid residues.


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Scoring Table

Class A C D E F G H I K L M N P Q R S T V W Y

B1A -77 -43 -248 -215 128 -222 -34 111 -137 130 126 -176 -156 -138 -180 -243 -172 74 111 27B1B -105 -45 -180 -159 96 -235 -226 150 -304 107 51 -218 -77 -203 -152 -256 -127 118 92 17B1C -54 -59 -263 -201 140 -278 -61 93 -278 106 91 -261 59 -84 -235 -299 -141 100 96 52B2A -65 15 -80 -58 87 -204 82 55 -94 71 102 -48 -97 16 -11 -133 -67 41 101 86B2B -152 -72 -208 -20 132 -222 -5 107 -83 36 49 -26 -86 -79 -41 -82 -114 71 83 130B2C -81 -62 -197 -113 104 -171 54 81 -212 77 100 -56 -7 -87 -44 -123 -103 66 162 114B3A -44 -138 -43 9 -22 -109 61 -2 56 16 87 -7 -111 16 110 -101 -69 -29 86 50B3B -108 -79 -76 -42 37 -229 80 26 35 14 -68 -33 -1 52 84 -71 -10 16 7 109B3C -87 -98 -83 -46 71 -61 104 -54 8 29 23 9 -11 10 71 -61 -48 -40 112 125P1A 76 95 -28 -43 -85 -46 -91 -6 -50 -30 -42 -58 -41 -32 -51 47 39 30 -129 -88P1B 59 128 -61 -68 -61 -22 -53 9 -201 -81 -40 -92 -65 -238 -89 49 95 44 34 -9P1C 49 129 34 -59 -129 -39 -121 -28 -72 -33 -90 -26 64 -57 -88 59 55 -9 -125 -140P2A -2 -70 29 62 -135 -58 17 -59 66 -46 -27 -2 -25 62 56 -38 -13 -62 -109 -55P2B -52 -87 -3 41 -56 -87 -49 -35 55 -133 -76 0 -101 10 19 49 79 8 -71 -30P2C -25 -81 51 28 -84 -42 20 -94 47 -68 -83 51 44 25 24 17 8 -74 -42 -43E0A 44 -17 44 60 -181 63 -6 -236 7 -137 -90 32 5 29 -20 16 -20 -125 -126 -170E0B 14 -4 -30 -37 -83 175 -76 -139 -154 -160 -62 1 -88 -12 -112 65 -17 -166 81 -3E0C 14 -35 23 4 -163 110 -41 -163 -10 -114 -130 41 25 -3 -41 34 8 -80 -206 -104


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1565 Functional forms

Scan PDB to identify all the structures having these folds

Identify a representative structure with resolution 2.5Å or better

Quality of the structure

(Occupancy, R-Factor, Stereochemistry)

968 Chains

Fold Library


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DALI / FSSP Fold Library

DALI : http://www.ebi.ac.uk/dali Touring protein fold space with

DALI/FSSP. Lisa Holm and Chris Sander, Nucleic Acid Research, (1998), 26, 316-319

Mapping the Protein Universe, Lisa Holm and Chris Sander, Science, (1996), 273, 595-602


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Sequence ComparisonDetails

Type of Alignment Local - portions or subsequences

matching Smith-Waterman Algorithm

Scoring Table : 3D-1D matrixAlgorithm used : Dynamic

ProgrammingAlignment Score : Z- Score


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Formula { F(i-1,j-1) + S(xi,yj) D

F(i, j) = max F(i-1 , j) - d H F(i , j-1) - d V 0 (if all other value is <

0) }

F(i-1,j-1) D V F(i,j-1)

F(i-1,j) H F(i,j)


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Gap Penalties

Gap penalties Linear score f(g) = - gd Affine score f(g) = - d – (g-1) e

d = gap open penalty e = gap extend penalty

g = gap length

Gap penalty values used are d = 500 e = 50


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Local Alignment

Trace back Alignment can end anywhere in the

matrix Take the highest value of F(i,j) over the

whole matrix and start trace back from there.

Algorithm complexity It takes O(nm) time and O(nm) memory,

where n and m are the lengths of the sequences.


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Significance of an Alignment Score

Statistical methods used to evaluate the significance of an alignment score Z-score, P-value and E-value

Significance of Score Z- score = (score – mean)/std. dev

Measures how unusual our original match is. Z 5 are significant.

P- value measures probability that the alignment is no better than random. (Z and P depends on the distribution of the scores)

P 10-100 exact match. E- value is the expected number of sequences that give

the same Z- score or better. (E = P x size of the database)

E 0.02 sequences probably homologous


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Benchmarking

All 968 proteins in the fold library were profiled on each of the other members

A histogram indicating the rank and the number of sequences which got the self score as the highest, is shown in Figure.


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Benchmarking

797

633 3 2 17 29 54

0

200

400

600

800

1000

1 2 3 4 5 6 7 8

Rank

No

. o

f S

equ

ence

s


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Benchmarking

Report 797 retain the self as the highest score 63 report the self to have the second highest score There were about 100 proteins that have ranks

between 5 and 100. Limitations

Prediction is restricted to the 968 folds in the library The algorithm is insensitive to partially folded

sequences Specific to globular proteins and not for membrane

proteins Sequences that fold in the presence of cofactors and

ligands are not accounted for


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Web based server development

Design the web page to get the dataUse cgi-bin or Perl script to parse the

submitted dataInvoke the corresponding program to get

the appropriate resultsSend the results either by e-mail or to the

web page directlyPrepare a ‘user manual’ to describe the

salient features of the server


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Conclusions

PredictFold – A program to predict possible folds for a new protein sequence based on the 3D-1D profile method

Benchmarking results show the reliability of the method

There are lot of scopes for further improvements


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Future Directions

To update the fold library by including more known folds

To use the predicted secondary structure information of the given sequence also

To optimise the source code for efficient handling of genome sequences, automatically

To combine results from other algorithms ORF, HMM, etc. to detect remote homologs

To develop & maintain a web-based sever for fold recognition


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BT versus IT

Bioinformatics including Biotechnology (BT) requires lot of Information Technology (IT) skills for Genomic annotation projects

Bioinformatics is one of the potential areas for IT professionals also

Genome Projects will be the next huge task for IT industries (like the Y2K problem in the past)

BT will take on IT soon … in the near future …


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Conclusions

Developing Web based Bioinformatics tools Develop/modify useful algorithms Generate computer source codes Create/Maintain Web based server

Using existing Web based tools efficientlyEthical issues

Bioethics & Biosafety : Ensure always that any bioinformatics tool harmful to environment & society has neither been developed nor been used by you

Cloning of human, Terminator technology, GM Food, etc.


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References (latest) Arthur M. Lesk, Introduction to Bioinformatics, Oxford University

Press, New Delhi (2003). D. Higgins and W. Taylor (Eds), Bioinformatics- Sequence

structure and databanks, Oxford University Press, New Delhi (2000).

R.Durbin, S.R.Eddy, A.Krogh and G.Mitchison, Biological Sequence Analysis, Cambridge Univ. Press, Cambridge, UK (1998).

A. Baxevanis and B.F. Ouellette, Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins, (Third Edition) Wiley-Interscience, Hoboken, NJ (2005).

G.Gibson and S.V.Muse, A Primer of Genome Science, Sinauer Associates, USA (2002).

N. C. Jones and P. A. Pevzner, An Introduction to Bioinformatics Algorithms, Ane Books, New Delhi (2005).

Michael S. Waterman, Introduction to computational Biology, Chapman & Hall, (1995).

J. A. Clasel and M. P. Deutscher (Eds), Introduction to Biophysical Methods for Protein and Nucleic Acid Research, Academic press, New York (1995).

D.S. T.Nicholl, An Introduction to Genetic Engineering, (Second Edition) Cambrdige Univ. Press, UK (2002).


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References

3D-1D Profile method J.U.Bowie, E.Luthy & D.Eisenberg, Science, 253,

164-170 (1991).Ostensible Recognition of Folds (ORF)

method Rajeev Aurora and George D.Rose, Proc. Natl.

Acad. Sci. (USA), 95(6), 2818-2823 (1998).Superfamily Hidden Markov Model (SHMM)

method A.Krogh, M.Brown, IS.Mian, K.Sjolander and

D.Haussler, J. Mol. Biol. 235(5), 1501-31 (1994).


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ImportantBioinformatics Resources

Databases & Tools NCBI, NIH - www.ncbi.nlm.nih.gov EMBL, EBI - www.ebi.ac.uk ExPasy, Swiss - www.expasy.org DDBJ - www.ddbj.nig.ac.jp PDB - www.rcsb.org/pdbSoftware Accelrys - www.accelrys.com/products

GCG, Insight II, Cerius II, Discovery Studio TCS - www.atc.tcs.co.in/biosuite/

BIOSUITE Jalaja Technologies - www.jalaja.com

GENOCLUSTER


7216 FEB 2006

Thank You

sequence alignment algorithms – application to bioinformatics tool development

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