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Bioinformatics For MNW 2 nd Year. Jaap Heringa FEW/FALW Integrative Bioinformatics Institute VU (IBIVU) heringa@cs.vu.nl. Current Bioinformatics Unit. Jens Kleinjung (1/11/02) Victor Simosis – PhD (1/12/02) Radek Szklarczyk - PhD (1/01/03) John Romein (1/12/02, Henri Bal).

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bioinformatics for mnw 2 nd year

Bioinformatics For MNW 2nd Year

Jaap Heringa

FEW/FALW

Integrative Bioinformatics Institute VU (IBIVU)

heringa@cs.vu.nl

slide2

Current Bioinformatics Unit

  • Jens Kleinjung (1/11/02)
  • Victor Simosis – PhD (1/12/02)
  • Radek Szklarczyk - PhD (1/01/03)
  • John Romein (1/12/02, Henri Bal)
bioinformatics course 2nd year mnw spring 2003
Bioinformatics course 2nd year MNW spring 2003
  • Pattern recognition
    • Supervised/unsupervised learning
    • Types of data, data normalisation, lacking data
    • Search image
    • Similarity tables
    • Clustering
    • Principal component analysis
    • Discriminant analysis
bioinformatics course 2nd year mnw spring 20031
Bioinformatics course 2nd year MNW spring 2003
  • Protein
    • Folding
    • Structure and function
    • Protein structure prediction
    • Secondary structure
    • Tertiary structure
    • Function
    • Post-translational modification
    • Prot.-Prot. Interaction -- Docking algorithm
    • Molecular dynamics/Monte Carlo
bioinformatics course 2nd year mnw spring 20032
Bioinformatics course 2nd year MNW spring 2003
  • Sequence analysis
    • Pairwise alignment
    • Dynamic programming (NW, SW, shortcuts)
    • Multiple alignment
    • Combining information
    • Database/homology searching (Fasta, Blast, Statistical issues-E/P values)
bioinformatics course 2nd year mnw spring 20033
Bioinformatics course 2nd year MNW spring 2003
  • Gene structure and gene finding algorithm
  • Omics
    • DNA makes RNA makes protein
    • Expression data, Nucleus to ribosome, translation, etc.
    • Metabolomics
    • Physiomics
    • Databases
      • DNA, EST
      • Protein sequence
      • Protein structure
bioinformatics course 2nd year mnw spring 20034
Bioinformatics course 2nd year MNW spring 2003
  • Microarray data
  • Protein structure (PDB)
  • Proteomics
  • Mass spectrometry/NMR/X-ray?
bioinformatics course 2nd year mnw spring 20035
Bioinformatics course 2nd year MNW spring 2003
  • Bioinformatics method development
  • IPR issues
  • Programming and scripting languages
  • Web solutions
  • Computational issues
    • NP-complete problems
    • CPU, memory, storage problems
    • Parallel computing
  • Bioinformatics method usage/application
  • Molecular viewers (RasMol, MolMol, etc.)
gathering knowledge
Gathering knowledge

Rembrandt, 1632

  • Anatomy, architecture
  • Dynamics, mechanics
  • Informatics

(Cybernetics – Wiener, 1948)

(Cybernetics has been defined as the science of control in machines and animals, and hence it applies to technological, animal and environmental systems)

  • Genomics, bioinformatics

Newton, 1726

slide10

Bioinformatics

Chemistry

Biology

Molecular

biology

Mathematics

Statistics

Bioinformatics

Computer

Science

Informatics

Medicine

Physics

slide11

Bioinformatics

“Studying informational processes in biological systems” (Hogeweg, early 1970s)

  • No computers necessary
  • Back of envelope OK

“Information technology applied to the management and analysis of biological data” (Attwood and Parry-Smith)

Applying algorithms with mathematical formalisms in

biology (genomics) -- USA

bioinformatics in the olden days
Bioinformatics in the olden days
  • Close to Molecular Biology:
    • (Statistical) analysis of protein and nucleotide structure
    • Protein folding problem
    • Protein-protein and protein-nucleotide interaction
  • Many essential methods were created early on (BG era)
    • Protein sequence analysis (pairwise and multiple alignment)
    • Protein structure prediction (secondary, tertiary structure)
bioinformatics in the olden days cont
Bioinformatics in the olden days (Cont.)
  • Evolution was studied and methods created
    • Phylogenetic reconstruction (clustering – NJ method
slide15

The Human Genome -- 26 June 2000

Dr. Craig Venter

Celera Genomics

-- Shotgun method

Sir John Sulston

Human Genome Project

human dna
Human DNA
  • There are about 3bn (3  109) nucleotides in the nucleus of almost all of the trillions (3.5  1012 ) of cells of a human body (an exception is, for example, red blood cells which have no nucleus and therefore no DNA) – a total of ~1022 nucleotides!
  • Many DNA regions code for proteins, and are called genes (1 gene codes for 1 protein in principle)
  • Human DNA contains ~30,000 expressed genes
  • Deoxyribonucleic acid (DNA) comprises 4 different types of nucleotides: adenine (A), thiamine (T), cytosine (C) and guanine (G). These nucleotides are sometimes also called bases
human dna cont
Human DNA (Cont.)
  • All people are different, but the DNA of different people only varies for 0.2% or less. So, only 2 letters in 1000 are expected to be different. Over the whole genome, this means that about 3 million letters would differ between individuals.
  • The structure of DNA is the so-called double helix, discovered by Watson and Crick in 1953, where the two helices are cross-linked by A-T and C-G base-pairs (nucleotide pairs – so-called Watson-Crick base pairing).
slide19

DNA compositional biases

  • Base composition of genomes:
  • E. coli: 25% A, 25% C, 25% G, 25% T
  • P. falciparum (Malaria parasite): 82%A+T
  • Translation initiation:
  • ATG is the near universal motif indicating the start of translation in DNA coding sequence.
slide20

Some facts about human genes

  • Comprise about 3% of the genome
  • Average gene length: ~ 8,000 bp
  • Average of 5-6 exons/gene
  • Average exon length: ~200 bp
  • Average intron length: ~2,000 bp
  • ~8% genes have a single exon
  • Some exons can be as small as 1 or 3 bp.
  • HUMFMR1S is not atypical: 17 exons 40-60 bp long, comprising 3% of a 67,000 bp gene
genetic diseases
Genetic diseases
  • Many diseases run in families and are a result of genes which predispose such family members to these illnesses
  • Examples are Alzheimer’s disease, cystic fibrosis (CF), breast or colon cancer, or heart diseases.
  • Some of these diseases can be caused by a problem within a single gene, such as with CF.
genetic diseases cont
Genetic diseases (Cont.)
  • For other illnesses, like heart disease, at least 20-30 genes are thought to play a part, and it is still unknown which combination of problems within which genes are responsible.
  • With a “problem” within a gene is meant that a single nucleotide or a combination of those within the gene are causing the disease (or make that the body is not sufficiently fighting the disease).
  • Persons with different combinations of these nucleotides could then be unaffected by these diseases.
genetic diseases cont cystic fibrosis
Genetic diseases (Cont.)Cystic Fibrosis
  • Known since very early on (“Celtic gene”)
  • Inherited autosomal recessive condition (Chr. 7)
  • Symptoms:
    • Clogging and infection of lungs (early death)
    • Intestinal obstruction
    • Reduced fertility and (male) anatomical anomalies
  • CF gene CFTR has 3-bp deletion leading to Del508 (Phe) in 1480 aa protein (epithelial Cl- channel) – protein degraded in ER instead of inserted into cell membrane
slide24

Genomic Data Sources

  • DNA/protein sequence
  • Expression (microarray)
  • Proteome (xray, NMR,
  • mass spectrometry)
  • Metabolome
  • Physiome (spatial,
  • temporal)

Integrative

bioinformatics

slide25

Genomic Data Sources

Vertical Genomics

genome

transcriptome

proteome

metabolome

physiome

Dinner discussion: Integrative Bioinformatics & Genomics VU

slide26

DNA

transcription

mRNA

translation

Protein

A gene codes for a protein

CCTGAGCCAACTATTGATGAA

CCUGAGCCAACUAUUGAUGAA

PEPTIDE

slide27

Humans have

spliced genes…

slide29

Remark

  • The problem of identifying (annotating) human genes is considerably harder than the early success story for ß-globin might suggest.
  • The human factor VIII gene (whose mutations cause hemophilia A) is spread over ~186,000 bp. It consists of 26 exons ranging in size from 69 to 3,106 bp, and its 25 introns range in size from 207 to 32,400 bp. The complete gene is thus ~9 kb of exon and ~177 kb of intron.
  • The biggest human gene yet is for dystrophin. It has > 30 exons and is spread over 2.4 million bp.
slide30

DNA makes RNA makes Protein:Expression data

  • More copies of mRNA for a gene leads to more protein
  • mRNA can now be measured for all the genes in a cell at ones through microarray technology
  • Can have 60,000 spots (genes) on a single gene chip
  • Colour change gives intensity of gene expression (over- or under-expression)
slide33

High-throughput Biological Data

  • Enormous amounts of biological data are being generated by high-throughput capabilities; even more are coming
    • genomic sequences
    • gene expression data
    • mass spec. data
    • protein-protein interaction
    • protein structures
    • ......
slide34

Protein structural data explosion

Protein Data Bank (PDB): 14500 Structures (6 March 2001)

10900 x-ray crystallography, 1810 NMR, 278 theoretical models, others...

dickerson s formula equivalent to moore s law
Dickerson’s formula: equivalent to Moore’s law

n = e0.19(y-1960)

with y the year.

On 27 March 2001 there were 12,123 3D protein

structures in the PDB: Dickerson’s formula predicts

12,066 (within 0.5%)!

sequence versus structural data
Sequence versus structural data
  • Despite structural genomics efforts, growth of PDB slowed down in 2001-2002 (i.e did not keep up with Dickerson’s formula)
  • More than 100 completely sequenced genomes

Increasing gap between structural and sequence data

slide37

Bioinformatics

Bioinformatics

Large - external

(integrative) ScienceHuman

Planetary Science Cultural Anthropology

Population BiologySociology

SociobiologyPsychology

Systems Biology

BiologyMedicine

Molecular Biology

Chemistry

Physics

Small – internal (individual)

bioinformatics
Bioinformatics
  • Offers an ever more essential input to
    • Molecular Biology
    • Pharmacology (drug design)
    • Agriculture
    • Biotechnology
    • Clinical medicine
    • Anthropology
    • Forensic science
    • Chemical industries (detergent industries, etc.)
slide39

High-throughput Biological DataThe data deluge

  • Hidden in these data is information that reflects
    • existence, organization, activity, functionality …… of biological machineries at different levels in living organisms

Most effectively utilising this information will prove to be essential for Integrative Bioinformatics

slide40

Data Issues ……

  • Data collection: getting the data
  • Data representation: data standards, data normalisation …..
  • Data organisation and storage: database issues …..
  • Data analysis and data mining: discovering “knowledge”, patterns/signals, from data, establishing associations among data patterns
  • Data utilisation and application: from data patterns/signals to models for bio-machineries
  • Data visualization: viewing complex data ……
  • Data transmission: data collection, retrieval, …..
  • ……
slide42

Bioinformatics

  • “Nothing in Biology makes sense except in the light of evolution” (Theodosius Dobzhansky (1900-1975))
  • “Nothing in bioinformatics makes sense except in the light of Biology”
slide43

Pair-wise alignment

T D W V T A L K

T D W L - - I K

Combinatorial explosion

- 1 gap in 1 sequence: n+1 possibilities

- 2 gaps in 1 sequence: (n+1)n

- 3 gaps in 1 sequence: (n+1)n(n-1), etc.

2n (2n)! 22n

= ~

n (n!)2 n

2 sequences of 300 a.a.: ~1088 alignments

2 sequences of 1000 a.a.: ~10600 alignments!

slide44

Dynamic programmingScoring alignments

Sa,b= +

gp(k) = pi + kpeaffine gap penalties

pi and pe are the penalties for gap initialisation and extension, respectively

slide45

Dynamic programmingScoring alignments

T D W V T A L K

T D W L - - I K

2020

10

1

Gap penalties (open, extension)

Amino Acid Exchange Matrix

Score: s(T,T)+s(D,D)+s(W,W)+s(V,L)+Po+2Px +

+s(L,I)+s(K,K)

slide46

Pairwise sequence alignment

Global dynamic programming

MDAGSTVILCFVG

Evolution

M

D

A

A

S

T

I

L

C

G

S

Amino Acid Exchange

Matrix

Search matrix

Gap penalties (open,extension)

MDAGSTVILCFVG-

MDAAST-ILC--GS

slide47

Global dynamic programming

j-1

i-1

Max{S0<x<i-1, j-1- Pi - (i-x-1)Px}

Si-1,j-1

Max{Si-1, 0<y<j-1 - Pi - (j-y-1)Px}

Si,j = si,j + Max

slide51

Local dynamic programming(Smith & Waterman, 1981)

LCFVMLAGSTVIVGTR

E

D

A

S

T

I

L

C

G

S

Negative

numbers

Amino Acid

Exchange Matrix

Search matrix

Gap penalties

(open, extension)

AGSTVIVG

A-STILCG

slide52

Local dynamic programming(Smith & Waterman, 1981)

j-1

i-1

Si,j + Max{S0<x<i-1,j-1 - Pi - (i-x-1)Px}

Si,j + Si-1,j-1

Si,j + Max {Si-1,0<y<j-1 - Pi - (j-y-1)Px}

0

Si,j = Max

slide54

Sequence database searching – Homology searching

DP too slow for repeated database searches

  • FASTA
  • BLAST and PSI-BLAST
  • QUEST
  • HMMER
  • SAM-T98

Fast heuristics

Hidden Markov modelling

slide55

FASTA

  • Compares a given query sequence with a library of sequences and calculates for each pair the highest scoring local alignment
  • Speed is obtained by delaying application of the dynamic programming technique to the moment where the most similar segments are already identified by faster and less sensitive techniques
  • FASTA routine operates in four steps:
slide56

FASTA

  • Operates in four steps:
  • Rapid searches for identical words of a user specified length occurring in query and database sequence(s) (Wilbur and Lipman, 1983, 1984). For each target sequence the 10 regions with the highest density of ungapped common words are determined.
  • These 10 regions are rescored using Dayhoff PAM-250 residue exchange matrix (Dayhoff et al., 1983) and the best scoring region of the 10 is reported under init1 in the FASTA output.
  • Regions scoring higher than a threshold value and being sufficiently near each other in the sequence are joined, now allowing gaps. The highest score of these new fragments can be found under initn in the FASTA output.
  • full dynamic programming alignment (Chao et al., 1992) over the final region which is widened by 32 residues at either side, of which the score is written under opt in the FASTA output.
fasta output example
FASTA output example

DE METAL RESISTANCE PROTEIN YCF1 (YEAST CADMIUM FACTOR 1). . . .

SCORES Init1: 161 Initn: 161 Opt: 162 z-score: 229.5 E(): 3.4e-06

Smith-Waterman score: 162; 35.1% identity in 57 aa overlap

10 20 30

test.seq MQRSPLEKASVVSKLFFSWTRPILRKGYRQRLE

:| :|::| |:::||:|||::|: |

YCFI_YEAST CASILLLEALPKKPLMPHQHIHQTLTRRKPNPYDSANIFSRITFSWMSGLMKTGYEKYLV

180 190 200 210 220 230

40 50 60

test.seq LSDIYQIPSVDSADNLSEKLEREWDRE

:|:|::| |:::||:|||::|: |

YCFI_YEAST EADLYKLPRNFSSEELSQKLEKNWENELKQKSNPSLSWAICRTFGSKMLLAAFFKAIHDV

240 250 260 270 280 290

slide58

FASTA

  • (1) Rapid identical word searches:
  • Searching for k-tuples of a certain size within a specified bandwidth along search matrix diagonals.
  • For not-too-distant sequences (> 35% residue identity), little sensitivity is lost while speed is greatly increased.
  • Technique employed is known as hash coding or hashing: a lookup table is constructed for all words in the query sequence, which is then used to compare all encountered words in each database sequence.
slide59

FASTA

  • The k-tuple length is user-defined and is usually 1 or 2 for protein sequences (i.e. either the positions of each of the individual 20 amino acids or the positions of each of the 400 possible dipeptides are located).
  • For nucleic acid sequences, the k-tuple is 5-20, and should be longer because short k-tuples are much more common due to the 4 letter alphabet of nucleic acids. The larger the k-tuple chosen, the more rapid but less thorough, a database search.
slide60

BLAST

  • blastp compares an amino acid query sequence against a protein sequence database
  • blastn compares a nucleotide query sequence against a nucleotide sequence database
  • blastx compares the six-frame conceptual protein translation products of a nucleotide query sequence against a protein sequence database
  • tblastn compares a protein query sequence against a nucleotide sequence database translated in six reading frames
  • tblastx compares the six-frame translations of a nucleotide query sequence against the six-frame translations of a nucleotide sequence database.
slide61

BLAST

  • Generates all tripeptides from a query sequence and for each of those the derivation of a table of similar tripeptides: number is only fraction of total number possible.
  • Quickly scans a database of protein sequences for ungapped regions showing high similarity, which are called high-scoring segment pairs(HSP), using the tables of similar peptides. The initial search is done for a word of length W that scores at least the threshold value T when compared to the query using a substitution matrix.
  • Word hits are then extended in either direction in an attempt to generate an alignment with a score exceeding the threshold of S, and as far as the cumulative alignment score can be increased.
slide62

BLAST

Extension of the word hits in each direction are halted

  • when the cumulative alignment score falls off by the quantity X from its maximum achieved value
  • the cumulative score goes to zero or below due to the accumulation of one or more negative-scoring residue alignments
  • upon reaching the end of either sequence
  • The T parameter is the most important for the speed and sensitivity of the search resulting in the high-scoring segment pairs
  • A Maximal-scoring Segment Pair (MSP) is defined as the highest scoring of all possible segment pairs produced from two sequences.
slide63

PSI-BLAST

  • Query sequences are first scanned for the presence of so-called low-complexity regions (Wooton and Federhen, 1996), i.e. regions with a biased composition likely to lead to spurious hits; are excluded from alignment.
  • The program then initially operates on a single query sequence by performing a gapped BLAST search
  • Then, the program takes significant local alignments found, constructs a multiple alignment and abstracts a position specific scoring matrix (PSSM) from this alignment.
  • Rescan the database in a subsequent round to find more homologous sequences Iteration continues until user decides to stop or search has converged
slide64

PSI-BLAST iteration

Query sequence

Q

xxxxxxxxxxxxxxxxx

Gapped BLAST search

Query sequence

Q

xxxxxxxxxxxxxxxxx

Database hits

A

C

D

.

.

Y

PSSM

Pi

Px

Gapped BLAST search

A

C

D

.

.

Y

PSSM

Pi

Px

Database hits

slide66

Multiple alignment profilesGribskov et al. 1987

i

A

C

D

W

Y

0.3

0.1

0

0.3

0.3

Gap

penalties

1.0

0.5

Position dependent gap penalties

slide67

Normalised sequence similarity

The p-valueis defined as theprobability of seeing at least one unrelated score S greater than or equal to a given score x in a database search over n sequences.

This probability follows the Poisson distribution (Waterman and Vingron, 1994):

P(x, n) = 1 – e-nP(S x),

where n is the number of sequences in the database

Depending on x and n (fixed)

slide68

Normalised sequence similarityStatistical significance

The E-value is defined as the expected number of non-homologous sequences with score greater than or equal to a score x in a database of n sequences:

E(x, n) = nP(S x)

if E-value = 0.01, then the expected number of random hits with score Sx is 0.01, which means that this E-value is expected by chance only once in 100 independent searches over the database.

if the E-value of a hit is 5, then five fortuitous hits with S x are expected within a single database search, which renders the hit not significant.

slide69

Normalised sequence similarityStatistical significance

  • Database searching is commonly performed using an E-value in between 0.1 and 0.001.
  • Low E-values decrease the number of false positives in a database search, but increase the number of false negatives, thereby lowering the sensitivity of the search.
slide70

HMM-based homology searching

  • Most widely used HMM-based profile searching tools currently are SAM-T98 (Karplus et al., 1998) and HMMER2 (Eddy, 1998)
  • formal probabilistic basis and consistent theory behind gap and insertion scores
  • HMMs good for profile searches, bad for alignment
  • HMMs are slow
slide71

Forward:

  • (i) = P(observed sequence, ending in state i at base t)
  • Backward:
  • ß (i) = P(obs. after t | ending in state i at base t)
  • Viterbi:
  •  (i) = maxP(obs. , ending in state i at base t)

t

t

t

The HMM algorithms

  • Questions:
  • What is the most likely die (predicted) sequence? Viterbi
  • What is the probability of the observed sequence? Forward
  • What is the probability that the 3rd state is B, given the observed sequence? Backward
slide72

HMM-based homology searching

Transition probabilities and Emission probabilities

Gapped HMMs also have insertion and deletion states

slide73

d1

d2

d3

d4

I0

I1

I2

I3

I4

m0

m1

m2

m3

m4

m5

End

Start

Profile HMM: m=match state, I-insert state, d=delete state; go from left to right. I and m states output amino acids; d states are ‘silent”.

slide76

Bio-Data Analysis and Data Mining

  • Existing/emerging bio-data analysis and mining tools for
    • DNA sequence assembly
    • Genetic map construction
    • Sequence comparison and database searching
    • Gene finding
    • ….
    • Gene expression data analysis
    • Phylogenetic tree analysis to infer horizontally-transferred genes
    • Mass spec. data analysis for protein complex characterization
    • ……
  • Current mode of work:

Often enough: developing ad hoc tools for each individual application

slide77

Bio-Data Analysis and Data Mining

  • As the amount and types of data and their cross connections increase rapidly
  • the number of analysis tools needed will go up “exponentially”
    • blast, blastp, blastx, blastn, … from BLAST family of tools
    • gene finding tools for human, mouse, fly, rice, cyanobacteria, …..
    • tools for finding various signals in genomic sequences, protein-binding sites, splice junction sites, translation start sites, …..
slide78

Bio-Data Analysis and Data Mining

Many of these data analysis problems are fundamentally the same problem(s) and can be solved using the same set of tools: e.g. clustering or optimal segmentation by Dynamic Programming

Developing ad hoc tools for each application (by each group of individual researchers) may soon become inadequate as bio-data production capabilities further ramp up

slide79

Bio-data Analysis, Data Mining and Integrative Bioinformatics

To have analysis capabilities covering wide range of problems, we need to discover the common fundamental structures of these problems;

HOWEVER in biology one size does NOT fit all…

Goal is development of a data analysis infrastructure in support of Genomics and beyond

algorithms in bioinformatics
Algorithms in bioinformatics

• string algorithms

• dynamic programming

• machine learning (NN, k-NN, SVM, GA, ..)

• Markov chain models

• hidden Markov models

• Markov Chain Monte Carlo (MCMC) algorithms

• stochastic context free grammars

• EM algorithms

• Gibbs sampling

• clustering

• tree algorithms

• text analysis

• hybrid/combinatorial techniques and more…

slide84

Functional genomics

• Monte Carlo

slide86

Example of algorithm reuse: Data clustering

  • Many biological data analysis problems can be formulated as clustering problems
    • microarray gene expression data analysis
    • identification of regulatory binding sites (similarly, splice junction sites, translation start sites, ......)
    • (yeast) two-hybrid data analysis (for inference of protein complexes)
    • phylogenetic tree clustering (for inference of horizontally transferred genes)
    • protein domain identification
    • identification of structural motifs
    • prediction reliability assessment of protein structures
    • NMR peak assignments
    • ......
slide87

Data Clustering Problems

  • Clustering: partition a data set into clusters so thatdata points of the same cluster are “similar” and points of different clusters are “dissimilar”
  • cluster identification-- identifying clusters with significantly different features than the background
slide88

Application Examples

  • Regulatory binding site identification: CRP (CAP) binding site
  • Two hybrid data analysis
  • Gene expression data analysis

Are all solvable by the same algorithm!

slide89

Other Application Examples

  • Phylogenetic tree clustering analysis
  • Protein sidechain packing prediction
  • Assessment of prediction reliability of protein structures
  • Protein secondary structures
  • Protein domain prediction
  • NMR peak assignments
  • ……
slide90

Integrative bioinformatics @ VU

Studying informational processes at biological system level

  • From gene sequence to intercellular processes
  • Computers necessary
  • We have biology, statistics, computational intelligence (AI), HTC, ..
  • VUMC: microarray facility
  • Enabling technology: new glue to integrate
  • New integrative algorithms
  • Goals: understanding cells in terms of genomes, fighting disease (VUMC)
bioinformatics @ vu
Bioinformatics @ VU

Progression:

  • DNA: gene prediction, predicting regulatory elements
  • mRNA expression
  • Proteins: docking, domain prediction
  • Metabolic pathways: metabolic control
  • Cell-cell communication
slide93

Protein structure and function can be complex…

Pyruvate kinase

Phosphotransferase

b barrel regulatory domain

a/b barrel catalytic substrate binding domain

a/b nucleotide binding domain

1 continuous + 2 discontinuous domains

bioinformatics @ vu1
Bioinformatics @ VU

Qualitative challenges:

  • High quality alignments (alternative splicing)
  • In-silico structural genomics
  • In-silico functional genomics: reliable annotation
  • Protein-protein interactions.
  • Metabolic pathways: assign the edges in the networks
  • Cell-cell communication: find membrane associated components
  • New algorithms
bioinformatics @ vu2
Bioinformatics @ VU

Quantitative challenges:

  • Understanding mRNA expression levels
  • Understanding resulting protein activity
  • Time dependencies
  • Spatial constraints, compartmentalisation
  • Are classical differential equation models adequate or do we need more individual modeling (e.g macromolecular crowding and activity at oligomolecular level)?
  • Metabolic pathways: calculate fluxes through time
  • Cell-cell communication: tissues, hormones, innervations

Need ‘complete’ experimental data for good biological model system to learn to integrate

bioinformatics @ vu3
Bioinformatics @ VU

VUMC

  • Neuropeptide – addiction
  • Oncogenes – disease patterns
  • Reumatic disease

CNCR

  • From synapses to higher order behaviour
  • Addiction

FPP

  • Genetic psychology – twin data bank
slide97

Integrative bioinformatics

  • Integrate data sources
  • Integrate methods
  • Integrate data through method integration (biological model)
bioinformatics tool
Bioinformatics tool

Algorithm

Data

tool

Biological

Interpretation

(model)

slide99

Bioinformatics

  • “Nothing in Biology makes sense except in the light of evolution” (Theodosius Dobzhansky (1900-1975))
  • “Nothing in Bioinformatics makes sense except in the light of Biology”
slide100

Pair-wise sequence alignment

(more than just string matching)

Global dynamic programming

MDAGSTVILCFVG

Evolution

M

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Amino Acid Exchange

Matrix

Search matrix

Gap penalties (open,extension)

MDAGSTVILCFVG-

MDAAST-ILC--GS

slide101

Pair-wise alignment search explosions

T D W V T A L K

T D W L - - I K

Combinatorial explosion

- 1 gap in 1 sequence: n+1 possibilities

- 2 gaps in 1 sequence: (n+1)n

- 3 gaps in 1 sequence: (n+1)n(n-1), etc.

2n (2n)! 22n

= ~

n (n!)2 n

2 sequences of 300 a.a.: ~1088 alignments

2 sequences of 1000 a.a.: ~10600 alignments!

this talk own kitchen
This talk – own kitchen

Three integrative methods to predict protein structural aspects:

  • Iterative multiple alignment + protein secondary structure (Praline)

Intermezzo: 2½-D structure prediction of flavodoxin foldby hand

  • Protein domain delineation based on consistency of multiple ab initio model tertiary structures (SnapDRAGON)
  • Protein domain delineation based on combining homology searching with domain prediction (Domaination)
slide104

Comparing sequences - Similarity Score -

  • Many properties can be used:
  • Nucleotide or amino acid composition
  • Isoelectric point
  • Molecular weight
  • Morphological characters
slide105

Multivariate statistics – Cluster analysis

C1 C2 C3 C4 C5 C6 ..

1

2

3

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Raw table

Similarity criterion

Similarity

matrix

Scores

5×5

Cluster criterion

Phylogenetic tree

slide107

Comparing sequences - Similarity Score -

  • Many properties can be used:
  • Nucleotide or amino acid composition
  • Isoelectric point
  • Molecular weight
  • Morphological characters
  • But: molecular evolution through sequence alignment
slide108

Multivariate statistics – Cluster analysis

1

2

3

4

5

Multiple

alignment

Similarity criterion

Similarity

matrix

Scores

5×5

Phylogenetic tree

slide109

Human -KITVVGVGAVGMACAISILMKDLADELALVDVIEDKLKGEMMDLQHGSLFLRTPKIVSGKDYNVTANSKLVIITAGARQ

Chicken -KISVVGVGAVGMACAISILMKDLADELTLVDVVEDKLKGEMMDLQHGSLFLKTPKITSGKDYSVTAHSKLVIVTAGARQ

Dogfish –KITVVGVGAVGMACAISILMKDLADEVALVDVMEDKLKGEMMDLQHGSLFLHTAKIVSGKDYSVSAGSKLVVITAGARQ

Lamprey SKVTIVGVGQVGMAAAISVLLRDLADELALVDVVEDRLKGEMMDLLHGSLFLKTAKIVADKDYSVTAGSRLVVVTAGARQ

Barley TKISVIGAGNVGMAIAQTILTQNLADEIALVDALPDKLRGEALDLQHAAAFLPRVRI-SGTDAAVTKNSDLVIVTAGARQ

Maizey casei -KVILVGDGAVGSSYAYAMVLQGIAQEIGIVDIFKDKTKGDAIDLSNALPFTSPKKIYSA-EYSDAKDADLVVITAGAPQ

Bacillus TKVSVIGAGNVGMAIAQTILTRDLADEIALVDAVPDKLRGEMLDLQHAAAFLPRTRLVSGTDMSVTRGSDLVIVTAGARQ

Lacto__ste -RVVVIGAGFVGASYVFALMNQGIADEIVLIDANESKAIGDAMDFNHGKVFAPKPVDIWHGDYDDCRDADLVVICAGANQ

Lacto_plant QKVVLVGDGAVGSSYAFAMAQQGIAEEFVIVDVVKDRTKGDALDLEDAQAFTAPKKIYSG-EYSDCKDADLVVITAGAPQ

Therma_mari MKIGIVGLGRVGSSTAFALLMKGFAREMVLIDVDKKRAEGDALDLIHGTPFTRRANIYAG-DYADLKGSDVVIVAAGVPQ

Bifido -KLAVIGAGAVGSTLAFAAAQRGIAREIVLEDIAKERVEAEVLDMQHGSSFYPTVSIDGSDDPEICRDADMVVITAGPRQ

Thermus_aqua MKVGIVGSGFVGSATAYALVLQGVAREVVLVDLDRKLAQAHAEDILHATPFAHPVWVRSGW-YEDLEGARVVIVAAGVAQ

Mycoplasma -KIALIGAGNVGNSFLYAAMNQGLASEYGIIDINPDFADGNAFDFEDASASLPFPISVSRYEYKDLKDADFIVITAGRPQ

Lactate dehydrogenase multiple alignment

Distance Matrix

1 2 3 4 5 6 7 8 9 10 11 12 13

1 Human 0.000 0.112 0.128 0.202 0.378 0.346 0.530 0.551 0.512 0.524 0.528 0.635 0.637

2 Chicken 0.112 0.000 0.155 0.214 0.382 0.348 0.538 0.569 0.516 0.524 0.524 0.631 0.651

3 Dogfish 0.128 0.155 0.000 0.196 0.389 0.337 0.522 0.567 0.516 0.512 0.524 0.600 0.655

4 Lamprey 0.202 0.214 0.196 0.000 0.426 0.356 0.553 0.589 0.544 0.503 0.544 0.616 0.669

5 Barley 0.378 0.382 0.389 0.426 0.000 0.171 0.536 0.565 0.526 0.547 0.516 0.629 0.575

6 Maizey 0.346 0.348 0.337 0.356 0.171 0.000 0.557 0.563 0.538 0.555 0.518 0.643 0.587

7 Lacto_casei 0.530 0.538 0.522 0.553 0.536 0.557 0.000 0.518 0.208 0.445 0.561 0.526 0.501

8 Bacillus_stea 0.551 0.569 0.567 0.589 0.565 0.563 0.518 0.000 0.477 0.536 0.536 0.598 0.495

9 Lacto_plant 0.512 0.516 0.516 0.544 0.526 0.538 0.208 0.477 0.000 0.433 0.489 0.563 0.485

10 Therma_mari 0.524 0.524 0.512 0.503 0.547 0.555 0.445 0.536 0.433 0.000 0.532 0.405 0.598

11 Bifido 0.528 0.524 0.524 0.544 0.516 0.518 0.561 0.536 0.489 0.532 0.000 0.604 0.614

12 Thermus_aqua 0.635 0.631 0.600 0.616 0.629 0.643 0.526 0.598 0.563 0.405 0.604 0.000 0.641

13 Mycoplasma 0.637 0.651 0.655 0.669 0.575 0.587 0.501 0.495 0.485 0.598 0.614 0.641 0.000

slide111

Multiple sequence alignmentWhy?

  • It is the most important means to assess relatedness of a set of sequences
  • Gain information about the structure/function of a query sequence (conservation patterns)
  • Construct a phylogenetic tree
  • Putting together a set of sequenced fragments (Fragment assembly)
  • Comparing a segment sequenced by two different labs
  • Many bioinformatics methods depend on it (e.g. secondary/tertiary structure prediction)
slide113

Flavodoxin-cheY multiple alignment

Praline with pre-processing

  • 1fx1 -PKALIVYGSTTGNT-EYTAETIARQLANAG-YEVDSRDAASVEAGGLFEGFDLVLLGCSTWGDDSI------ELQDDFIPLF-DSLEETGAQGRKVACF
  • FLAV_DESDE MSKVLIVFGSSTGNT-ESIaQKLEELIAAGG-HEVTLLNAADASAENLADGYDAVLFgCSAWGMEDL------EMQDDFLSLF-EEFNRFGLAGRKVAAf
  • FLAV_DESVH MPKALIVYGSTTGNT-EYTaETIARELADAG-YEVDSRDAASVEAGGLFEGFDLVLLgCSTWGDDSI------ELQDDFIPLF-DSLEETGAQGRKVACf
  • FLAV_DESSA MSKSLIVYGSTTGNT-ETAaEYVAEAFENKE-IDVELKNVTDVSVADLGNGYDIVLFgCSTWGEEEI------ELQDDFIPLY-DSLENADLKGKKVSVf
  • FLAV_DESGI MPKALIVYGSTTGNT-EGVaEAIAKTLNSEG-METTVVNVADVTAPGLAEGYDVVLLgCSTWGDDEI------ELQEDFVPLY-EDLDRAGLKDKKVGVf
  • 2fcr --KIGIFFSTSTGNT-TEVADFIGKTLGA---KADAPIDVDDVTDPQALKDYDLLFLGAPTWNTG----ADTERSGTSWDEFLYDKLPEVDMKDLPVAIF
  • FLAV_AZOVI -AKIGLFFGSNTGKT-RKVaKSIKKRFDDET-MSDA-LNVNRVS-AEDFAQYQFLILgTPTLGEGELPGLSSDCENESWEEFL-PKIEGLDFSGKTVALf
  • FLAV_ENTAG MATIGIFFGSDTGQT-RKVaKLIHQKLDG---IADAPLDVRRAT-REQFLSYPVLLLgTPTLGDGELPGVEAGSQYDSWQEFT-NTLSEADLTGKTVALf
  • FLAV_ANASP SKKIGLFYGTQTGKT-ESVaEIIRDEFGN---DVVTLHDVSQAE-VTDLNDYQYLIIgCPTWNIGEL--------QSDWEGLY-SELDDVDFNGKLVAYf
  • FLAV_ECOLI -AITGIFFGSDTGNT-ENIaKMIQKQLGK---DVADVHDIAKSS-KEDLEAYDILLLgIPTWYYGE--------AQCDWDDFF-PTLEEIDFNGKLVALf
  • 4fxn -MK--IVYWSGTGNT-EKMAELIAKGIIESG-KDVNTINVSDVNIDELL-NEDILILGCSAMGDEVL-------EESEFEPFI-EEIS-TKISGKKVALF
  • FLAV_MEGEL MVE--IVYWSGTGNT-EAMaNEIEAAVKAAG-ADVESVRFEDTNVDDVA-SKDVILLgCPAMGSEEL-------EDSVVEPFF-TDLA-PKLKGKKVGLf
  • FLAV_CLOAB -MKISILYSSKTGKT-ERVaKLIEEGVKRSGNIEVKTMNLDAVD-KKFLQESEGIIFgTPTYYAN---------ISWEMKKWI-DESSEFNLEGKLGAAf
  • 3chy ADKELKFLVVDDFSTMRRIVRNLLKELGFN--NVEEAEDGVDALNKLQAGGYGFVI---SDWNMPNM----------DGLELL-KTIRADGAMSALPVLM
  • T
  • 1fx1 GCGDS-SY-EYFCGA-VDAIEEKLKNLGAEIVQD---------------------GLRIDGD--PRAARDDIVGWAHDVRGAI--------
  • FLAV_DESDE ASGDQ-EY-EHFCGA-VPAIEERAKELgATIIAE---------------------GLKMEGD--ASNDPEAVASfAEDVLKQL--------
  • FLAV_DESVH GCGDS-SY-EYFCGA-VDAIEEKLKNLgAEIVQD---------------------GLRIDGD--PRAARDDIVGwAHDVRGAI--------
  • FLAV_DESSA GCGDS-DY-TYFCGA-VDAIEEKLEKMgAVVIGD---------------------SLKIDGD--PE--RDEIVSwGSGIADKI--------
  • FLAV_DESGI GCGDS-SY-TYFCGA-VDVIEKKAEELgATLVAS---------------------SLKIDGE--PD--SAEVLDwAREVLARV--------
  • 2fcr GLGDAEGYPDNFCDA-IEEIHDCFAKQGAKPVGFSNPDDYDYEESKS-VRDGKFLGLPLDMVNDQIPMEKRVAGWVEAVVSETGV------
  • FLAV_AZOVI GLGDQVGYPENYLDA-LGELYSFFKDRgAKIVGSWSTDGYEFESSEA-VVDGKFVGLALDLDNQSGKTDERVAAwLAQIAPEFGLS--L--
  • FLAV_ENTAG GLGDQLNYSKNFVSA-MRILYDLVIARgACVVGNWPREGYKFSFSAALLENNEFVGLPLDQENQYDLTEERIDSwLEKLKPAV-L------
  • FLAV_ANASP GTGDQIGYADNFQDA-IGILEEKISQRgGKTVGYWSTDGYDFNDSKA-LRNGKFVGLALDEDNQSDLTDDRIKSwVAQLKSEFGL------
  • FLAV_ECOLI GCGDQEDYAEYFCDA-LGTIRDIIEPRgATIVGHWPTAGYHFEASKGLADDDHFVGLAIDEDRQPELTAERVEKwVKQISEELHLDEILNA
  • 4fxn G-----SY-GWGDGKWMRDFEERMNGYGCVVVET---------------------PLIVQNE--PDEAEQDCIEFGKKIANI---------
  • FLAV_MEGEL G-----SY-GWGSGEWMDAWKQRTEDTgATVIGT----------------------AIVNEM--PDNA-PECKElGEAAAKA---------
  • FLAV_CLOAB STANSIAGGSDIA---LLTILNHLMVKgMLVYSG----GVAFGKPKTHLGYVHINEIQENEDENARIfGERiANkVKQIF-----------
  • 3chy VTAEAKK--ENIIAA---------AQAGAS-------------------------GYVV-----KPFTAATLEEKLNKIFEKLGM------
  • G
  • Iteration 0 SP= 136944.00 AvSP= 10.675 SId= 4009 AvSId= 0.313
slide115

Integrating secondary structure prediction in multiple alignmentVictor Simossis

Praline multiple alignment method

(Heringa, Comp. Chem. 23, 341-364;1999, Comp. Chem., 26, 459-477;2002;

Kleinjung, Douglas & Heringa, Bioinformatics, in press;2002)

  • Combining sequence data and secondary structure prediction (Heringa, Curr. Prot. Pept. Sci., 1 (3), 273-301;2000)
  • Secondary structure methods: PhD, Predator, PSIPred, Jpred, SSPRED,...
using secondary structure in multiple alignment
Using secondary structure in multiple alignment

“Structure more conserved than sequence”

slide117

SECONDARY STRUCTURE (helices, strands)

PRIMARY STRUCTURE (amino acid sequence)

VHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVANALAHKYH

TERTIARY STRUCTURE (fold)

QUATERNARY STRUCTURE (oligomers)

Protein structure hierarchical levels

slide118

SECONDARY STRUCTURE (helices, strands)

PRIMARY STRUCTURE (amino acid sequence)

VHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVANALAHKYH

TERTIARY STRUCTURE (fold)

QUATERNARY STRUCTURE (oligomers)

Protein structure hierarchical levels

slide120

Using secondary structure in multiple alignment

Dynamic programming

search matrix

Amino acid exchange

weights matrices

MDAGSTVILCFV

HHHCCCEEEEEE

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Default

slide121

Flavodoxin-cheYpredicted secondary structure

(PREDATOR)

1fx1 -PK-ALIVYGSTTGNTEYTAETIARQLANAG-YEVDSRDAASVEAGGLFEGFDLVLLGCSTWGDDSI------ELQDDFIPLFDS-LEETGAQGRKVACF

e eeee b ssshhhhhhhhhhhhhhttt eeeee stt tttttt seeee b ee sss ee ttthhhhtt ttss tt eeeee

FLAV_DESVH MPK-ALIVYGSTTGNTEYTaETIARELADAG-YEVDSRDAASVEAGGLFEGFDLVLLgCSTWGDDSI------ELQDDFIPLFDS-LEETGAQGRKVACf

e eeeeeehhhhhhhhhhhhhhh eeeeeeeeeeeehhhhhh eeeee

FLAV_DESGI MPK-ALIVYGSTTGNTEGVaEAIAKTLNSEG-METTVVNVADVTAPGLAEGYDVVLLgCSTWGDDEI------ELQEDFVPLYED-LDRAGLKDKKVGVf

e eeeeeehhhhhhhhhhhhhheeeeeehhhhhh eeeeeeehhhhhh eeeeee

FLAV_DESSA MSK-SLIVYGSTTGNTETAaEYVAEAFENKE-IDVELKNVTDVSVADLGNGYDIVLFgCSTWGEEEI------ELQDDFIPLYDS-LENADLKGKKVSVf

eeeeeehhhhhhhhhhhhhheeeeeeeeeehhhhhhh heeeee

FLAV_DESDE MSK-VLIVFGSSTGNTESIaQKLEELIAAGG-HEVTLLNAADASAENLADGYDAVLFgCSAWGMEDL------EMQDDFLSLFEE-FNRFGLAGRKVAAf

eeeehhhhhhhhhhhhhheeeeehhhhhhhhhhheeeeehhhhhhh hheeeee

2fcr --K-IGIFFSTSTGNTTEVADFIGKTLGAK---ADAPIDVDDVTDPQALKDYDLLFLGAPTWNTGAD----TERSGTSWDEFLYDKLPEVDMKDLPVAIF

eeeee ssshhhhhhhhhhhhhggg b eeggg s gggggg seeeeeee stt s s s sthhhhhhhtggg tt eeeee

FLAV_ANASP SKK-IGLFYGTQTGKTESVaEIIRDEFGND--VVTL-HDVSQAE-VTDLNDYQYLIIgCPTWNIGEL--------QSDWEGLYSE-LDDVDFNGKLVAYf

eeeee hhhhhhhhhhhheeehhhhhhhhhheeeeeehhhhhhhhheeeeee

FLAV_ECOLI -AI-TGIFFGSDTGNTENIaKMIQKQLGKD--VADV-HDIAKSS-KEDLEAYDILLLgIPTWYYGEA--------QCDWDDFFPT-LEEIDFNGKLVALf

eee hhhhhhhhhhhheee hhh hhhhhhheeeee hhhhheeeeee

FLAV_AZOVI -AK-IGLFFGSNTGKTRKVaKSIKKRFDDET-MSDA-LNVNRVS-AEDFAQYQFLILgTPTLGEGELPGLSSDCENESWEEFLPK-IEGLDFSGKTVALf

eeehhhhhhhhhhhhhhhhhhhhhhheeeeehhhhhhhhheeeeee

FLAV_ENTAG MAT-IGIFFGSDTGQTRKVaKLIHQKLDG---IADAPLDVRRAT-REQFLSYPVLLLgTPTLGDGELPGVEAGSQYDSWQEFTNT-LSEADLTGKTVALf

eeeehhhhhhhhhhhhhhhhhhhhhheeeee hhhhheeeee

4fxn ----MKIVYWSGTGNTEKMAELIAKGIIESG-KDVNTINVSDVNIDELLNE-DILILGCSAMGDEVL------E-ESEFEPFIEE-IST-KISGKKVALF

eeeee ssshhhhhhhhhhhhhhhtt eeeettt sttttt seeeeee btttb ttthhhhhhhhst t tt eeeee

FLAV_MEGEL M---VEIVYWSGTGNTEAMaNEIEAAVKAAG-ADVESVRFEDTNVDDVASK-DVILLgCPAMGSEEL------E-DSVVEPFFTD-LAP-KLKGKKVGLf

hhhhhhhhhhhhhheeeee hhhhhhhh eeeeeeeeee

FLAV_CLOAB M-K-ISILYSSKTGKTERVaKLIEEGVKRSGNIEVKTMNL-DAVDKKFLQESEGIIFgTPTY-YANI--------SWEMKKWIDE-SSEFNLEGKLGAAf

eeehhhhhhhhhhhhhh eeeeee hhhhhhhhhheeee hhhhhhhhheeeee

3chy ADKELKFLVVDDFSTMRRIVRNLLKELGFNN-VEEAEDGV-DALNKLQAGGYGFVISD---WNMPNM----------DGLELLKTIRADGAMSALPVLMV

tt eeee s hhhhhhhhhhhhhht eeeesshhhhhhhhhheeeee s sss hhhhhhhhhh ttttt eeee

1fx1 GCGDS-SY-EYFCGAVDAIEEKLKNLGAEIVQD---------------------GLRIDGD--PRAARDDIVGWAHDVRGAI--------

eee s ss sstthhhhhhhhhhhttt ee s eeees gggghhhhhhhhhhhhhh

FLAV_DESVH GCGDS-SY-EYFCGAVDAIEEKLKNLgAEIVQD---------------------GLRIDGD--PRAARDDIVGwAHDVRGAI--------

eee hhhhhhhhhhhheeeeeeeeeehhhhhhhhhhhhhh

FLAV_DESGI GCGDS-SY-TYFCGAVDVIEKKAEELgATLVAS---------------------SLKIDGE--P--DSAEVLDwAREVLARV--------

eee hhhhhhhhhhhheeeeehhhhhhhhhhh

FLAV_DESSA GCGDS-DY-TYFCGAVDAIEEKLEKMgAVVIGD---------------------SLKIDGD--P--ERDEIVSwGSGIADKI--------

hhhhhhhhhhhh eeeeee eee

FLAV_DESDE ASGDQ-EY-EHFCGAVPAIEERAKELgATIIAE---------------------GLKMEGD--ASNDPEAVASfAEDVLKQL--------

e hhhhhhhhhhhhhheeeeeeehhhhhhhhhhh

2fcr GLGDAEGYPDNFCDAIEEIHDCFAKQGAKPVGFSNPDDYDYEESKSVRD-GKFLGLPLDMVNDQIPMEKRVAGWVEAVVSETGV------

eee ttt ttsttthhhhhhhhhhhtt eee b gggs s tteet teesseeeettt ss hhhhhhhhhhhhhhhht

FLAV_ANASP GTGDQIGYADNFQDAIGILEEKISQRgGKTVGYWSTDGYDFNDSKALR-NGKFVGLALDEDNQSDLTDDRIKSwVAQLKSEFGL------

hhhhhhhhhhhhhheeeehhhhhhhhhhhhhhhh

FLAV_ECOLI GCGDQEDYAEYFCDALGTIRDIIEPRgATIVGHWPTAGYHFEASKGLADDDHFVGLAIDEDRQPELTAERVEKwVKQISEELHLDEILNA

hhhhhhhhhhhhhheeeehhhhhhhhhhhhhhhhhh

FLAV_AZOVI GLGDQVGYPENYLDALGELYSFFKDRgAKIVGSWSTDGYEFESSEAVVD-GKFVGLALDLDNQSGKTDERVAAwLAQIAPEFGLS--L--

ehhhhhhhhhhhhhheeeee hhhhhhhhhhh

FLAV_ENTAG GLGDQLNYSKNFVSAMRILYDLVIARgACVVGNWPREGYKFSFSAALLENNEFVGLPLDQENQYDLTEERIDSwLEKLKPAV-L------

hhhhhhhhhhhhhhheeeehhhhhhh hhhhhhhhhhhh

4fxn G-----SYGWGDGKWMRDFEERMNGYGCVVVET---------------------PLIVQNE--PDEAEQDCIEFGKKIANI---------

e eesss shhhhhhhhhhhhtt ee s eeees ggghhhhhhhhhhhht

FLAV_MEGEL G-----SYGWGSGEWMDAWKQRTEDTgATVIGT----------------------AIVNEM--PDNAPE-CKElGEAAAKA---------

hhhhhhhhhhheeeee eeeeh hhhhhhhh

FLAV_CLOAB STANSIA-GGSDIALLTILNHLMVK-gMLVYSG----GVAFGKPKTHLG-----YVHINEI--QENEDENARIfGERiANkV--KQIF--

hhhhhhhhhhhhhheeeeehhhh hhhhhhhhhhhhhhh h

3chy -----------TAEAKKENIIAAAQAGASGY-------------------------VVK----P-FTAATLEEKLNKIFEKLGM------

ess hhhhhhhhhtt seeees s hhhhhhhhhhhhhhht

G

Enough to predict 5() topology

slide123

Flavodoxin-cheY multiple alignment/ secondary structure iteration

cheY SSEs

3chy-AA SEQUENCE|| AA |ADKELKFLVVDDFSTMRRIVRNLLKELGFNNVEEAEDGVDALNKLQAGGYGFVISDWNMP|

3chy-ITERATION-0|| PHD | EEEEEEEHHHHHHHHHHHHHHHHH E HHHHHHHHHHHHHEEE |

3chy-ITERATION-1|| PHD | EEEEEEEEHHHHHHHHHHHHHHHHHHHHHHH EEEEEE |

3chy-ITERATION-2|| PHD | EEEEEEEEHHHHHHHHHHHHHHHHHHHHHHH EEEEEE |

3chy-ITERATION-3|| PHD | EEEEEEEEHHHHHHHHHHHHHH EEE HHHHHH EEEEE |

3chy-ITERATION-4|| PHD | EEEEEEEEHHHHHHHHHHHHHH HHHHHHH EEEEE |

3chy-ITERATION-5|| PHD | EEEEEEEEHHHHHHHHHHHHHH EEE HHHHHH EEEEE |

3chy-ITERATION-6|| PHD | EEEEEEEE HHHHHHHHHHHHHH HHHHHHHH EEEEEE |

3chy-ITERATION-7|| PHD | EEEEEEEE HHHHHHHHHHHHHH EEE HHHHHH EEEEE |

3chy-ITERATION-8|| PHD | EEEEEEEE HHHHHHHHHHHHHH HHHHHHH EEEEEE |

3chy-ITERATION-9|| PHD | EEEEEEEE HHHHHHHHHHHHHHHHHHHHHHHH EEEEE |

3chy-AA SEQUENCE|| AA |NMDGLELLKTIRADGAMSALPVLMVTAEAKKENIIAAAQAGASGYVVKPFTAATLEEKLNKIFEKLGM|

3chy-ITERATION-0|| PHD | HHHHHHEEEEEE HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH |

3chy-ITERATION-1|| PHD | HHHHHHEEEEEE HHHHHHHHHHHHHHHHHHHHH EEE HHHHHHHHHHHHHH |

3chy-ITERATION-2|| PHD | HHHHHHEEEEEE HHHHHHHHHHHHHHHHHH EEE HHHHHHHHHHHHHH |

3chy-ITERATION-3|| PHD | HHHHHHHHHHHHHHHHHHHHHHHHHHHHHH EEE HHHHHHHHHHHHHH |

3chy-ITERATION-4|| PHD | HHHHH EEEEE HHHHHHHHHHHHHHHHH EEE HHHHHHHHHHHHHH |

3chy-ITERATION-5|| PHD | HHHHHHHH EEEEE HHHHHHHHHHHHHHHH EEE HHHHHHHHHHHHHH |

3chy-ITERATION-6|| PHD | HHHHHHHH EEEEE HHHHHHHHHHHHHHHH EEEE HHHHHHHHHHHHHH |

3chy-ITERATION-7|| PHD | HHHHHHHH EEEEEE HHHHHHHHHHHHHHHH EEE HHHHHHHHHHHHHH |

3chy-ITERATION-8|| PHD | HHHHHHHH EEEEE HHHHHHHHHHHHHHHH EEE HHHHHHHHHHHHHH |

3chy-ITERATION-9|| PHD | HHHHHHHH EEEEE HHHHHHHHHHHHHHH EEEE HHHHHHHHHHHHHH |

slide124

4fxn-AA SEQUENCE|| AA |MKIVYWSGTGNTEKMAELIAKGIIESGKDVNTINVSDVNIDELLNEDILILGCSAMGDEV|

4fxn-ITERATION-0|| PHD | EEEEEHHHHHHHHHHHHHHHEEE EEEEE |

4fxn-ITERATION-1|| PHD | EEEEEHHHHHHHHHHHHHHHEEEE EEEEE |

4fxn-ITERATION-2|| PHD | EEEEE HHHHHHHHHHHHHHHEEEE EEEEE |

4fxn-ITERATION-3|| PHD | EEEEE HHHHHHHHHHHHHHH E EEEEE |

4fxn-ITERATION-4|| PHD | EEEEEE HHHHHHHHHHHHHHHEEEE EEEEE |

4fxn-ITERATION-5|| PHD | EEEEEEHHHHHHHHHHHHHHHEE EEEEE |

4fxn-ITERATION-6|| PHD | EEEEEEHHHHHHHHHHHHHHHEEEE EEEEE |

4fxn-ITERATION-7|| PHD | EEEEEEHHHHHHHHHHHHHHH EE EEEEE |

4fxn-ITERATION-8|| PHD | EEEEEEHHHHHHHHHHHHHHH EEE EEEEE |

4fxn-ITERATION-9|| PHD | EEEEEHHHHHHHHHHHHHHH EEE EEEEE |

4fxn-AA SEQUENCE|| AA |LEESEFEPFIEEISTKISGKKVALFGSYGWGDGKWMRDFEERMNGYGCVVVETPLIVQNE|

4fxn-ITERATION-0|| PHD | EEEEE HHHHHHHHHHHHHHHHHEEE EEE |

4fxn-ITERATION-1|| PHD | HHHH EEEEEHHHHHHHHHHHHHHHEEE EE |

4fxn-ITERATION-2|| PHD | HHHHHHHHHHHHEEEEEEHHHHHHHHHHHHHHH EEE EE |

4fxn-ITERATION-3|| PHD | HHHHHHHHHHHH EEEEE HHHHHHHHHHHHHHH EEE EE |

4fxn-ITERATION-4|| PHD | HHHHHHHHHHHH EEEEE HHHHHHHHHHHHHHHHHEEE E |

4fxn-ITERATION-5|| PHD | HHHHHHHHHHHH EEEEE HHHHHHHHHHHHHHHHHEEE E |

4fxn-ITERATION-6|| PHD | HHHHHHHHHHHH EEEEEEHHHHHHHHHHHHHHHHEEE E |

4fxn-ITERATION-7|| PHD | HHHHHHHHHHHH EEEEEHHHHHHHHHHHHHHHHH EEE E |

4fxn-ITERATION-8|| PHD | HHHHHHHHHHHHEEEEE HHHHHHHHHHHHHHHHH EEE E |

4fxn-ITERATION-9|| PHD | HHHHHHHHHHHHEEEEEEHHHHHHHHHHHHHHHHEEE E |

4fxn-AA SEQUENCE|| AA |PDEAEQDCIEFGKKIANI|

4fxn-ITERATION-0|| PHD | HHHHHHHHHHHHH |

4fxn-ITERATION-1|| PHD | HHHHHHHHHHHHH |

4fxn-ITERATION-2|| PHD | HHHHHHHHHHHHH |

4fxn-ITERATION-3|| PHD | HHHHHHHHHHHHH |

4fxn-ITERATION-4|| PHD | HHHHHHHHHHHH |

4fxn-ITERATION-5|| PHD | HHHHHHHHHHHHH |

4fxn-ITERATION-6|| PHD | HHHHHHHHHHHH |

4fxn-ITERATION-7|| PHD | HHHHHHHHHHHHH |

4fxn-ITERATION-8|| PHD | HHHHHHHHHHHHH |

4fxn-ITERATION-9|| PHD | HHHHHHHHHHHH |

slide125

Optimal segmentation of predicted secondary structures by Dynamic Programming

H score

The recorded values are used in a weighted function according to their secondary structure type, that gives each position a window-specific score. The more probable the secondary structure element, the higher the score.

Restrictions:

H only if ws>=4

E only if ws>=2

E score

C score

? score

Region

window size

Segmentation score (Total score of each path)

2

6

sequence position

Max score

5

Offset

Label

H

slide126

Example of an optimally segmented secondary structure prediction library for sequence 3chy

3chy ---------------GYVV-----KPFTAATLEEKLNKIFEKLGM------

3chy <- 1fx1 ??????????????? ee ?? hhhhhhhhhhhhhh ????????

3chy <- FLAV_DESDE ??????????????? ee ?? hhhhhhhhhhhhhhh ????????

3chy <- FLAV_DESVH ??????????????? ee ?? hhhhhhhhhhhhhh ????????

3chy <- FLAV_DESGI ??????????????? eee ?? ??hhhhhhhhhhhhh ????????

3chy <- FLAV_DESSA ??????????????? eee ?? ??hhhhhhhhhhhhh ????????

3chy <- 4fxn ??????????????? eee ?? hhhhhhhhhhhhh ?????????

3chy <- FLAV_MEGEL ????????????????eee ?? hh?hhhhhhhhhhh ?????????

3chy <- 2fcr e ? eeeeeee hhhhhhhhhhhhhhh ??????

3chy <- FLAV_ANASP ? eeeeeee hhhhhhhhhhhhhhh ??????

3chy <- FLAV_ECOLI eeeeeee hhhhhhhhhhhhhhh hhhhh

3chy <- FLAV_AZOVI ? eeeeeee hhhhhhhhhhhhhhh ????

3chy <- FLAV_ENTAG e eeeeeeee hhhhhhhhhhhhhhhh? ??????

3chy <- FLAV_CLOAB eeeeeee hhhhhhhhhh ???????????

3chy <- 3chy --------------- ----- hhhhhhhhhhhhhh ------

Consensus ---------------EEEE----- HHHHHHHHHHHHH ------

Consensus-DSSP ...............****.....****xx***************......

PHD --------------- ----- HHHHHHHHHHHHHH ------

PHD-DSSP ...............xxxx.....******************x**......

DSSP ...............EEEE.....SS HHHHHHHHHHHHHHHT ......

LumpDSSP ...............EEEE..... HHHHHHHHHHHHHHH ......

what to do with a multiple alignment
What to do with a multiple alignment?
  • Use it to eyeball and detect structural/functional features
  • Use it to make a profile and search a database for homologs
  • Give it to other bioinformatics methods and predict secondary structure, functional residues, correlated mutations, phylogenetic trees, etc.
rules of thumb when looking at a multiple alignment ma
Rules of thumb when looking at a multiple alignment (MA)
  • Hydrophobic residues are internal
  • Gly (Thr, Ser) in loops
  • MA: hydrophobic block -> internal -strand
  • MA: alternating (1-1) hydrophobic/hydrophilic => edge -strand
  • MA: alternating 2-2 (or 3-1) periodicity => -helix
  • MA: gaps in loops
  • MA: Conserved column => functional? => active site
rules of thumb when looking at a multiple alignment ma1
Rules of thumb when looking at a multiple alignment (MA)
  • Active site residues are together in 3D structure
  • Helices often cover up core of strands
  • Helices less extended than strands => more residues to cross protein
  • -- motif is right-handed in >95% of cases (with parallel strands)
  • MA: ‘inconsistent’ alignment columns and match errors!
  • Secondary structures have local anomalies, e.g. -bulges
rules of thumb when looking at a multiple alignment ma2
Rules of thumb when looking at a multiple alignment (MA)
  • Active site residues are together in 3D structure
  • Helices often cover up core of strands
  • Helices less extended than strands => more residues to cross protein
  • -- motif is right-handed in >95% of cases (with parallel strands)
  • MA: ‘inconsistent’ alignment columns and match errors!
  • Secondary structures have local anomalies, e.g. -bulges
periodicity patterns
Periodicity patterns

Burried -strand

Edge -strand

-helix

burried and edge strands
Burried and Edge strands

Parallel -sheet

Anti-parallel -sheet

slide134

Flavodoxin-cheY example: 5()

  • 1fx1 -PKALIVYGSTTGNT-EYTAETIARQLANAG-YEVDSRDAASVEAGGLFEGFDLVLLGCSTWGDDSI------ELQDDFIPLF-DSLEETGAQGRKVACF
  • FLAV_DESDE MSKVLIVFGSSTGNT-ESIaQKLEELIAAGG-HEVTLLNAADASAENLADGYDAVLFgCSAWGMEDL------EMQDDFLSLF-EEFNRFGLAGRKVAAf
  • FLAV_DESVH MPKALIVYGSTTGNT-EYTaETIARELADAG-YEVDSRDAASVEAGGLFEGFDLVLLgCSTWGDDSI------ELQDDFIPLF-DSLEETGAQGRKVACf
  • FLAV_DESSA MSKSLIVYGSTTGNT-ETAaEYVAEAFENKE-IDVELKNVTDVSVADLGNGYDIVLFgCSTWGEEEI------ELQDDFIPLY-DSLENADLKGKKVSVf
  • FLAV_DESGI MPKALIVYGSTTGNT-EGVaEAIAKTLNSEG-METTVVNVADVTAPGLAEGYDVVLLgCSTWGDDEI------ELQEDFVPLY-EDLDRAGLKDKKVGVf
  • 2fcr --KIGIFFSTSTGNT-TEVADFIGKTLGA---KADAPIDVDDVTDPQALKDYDLLFLGAPTWNTG----ADTERSGTSWDEFLYDKLPEVDMKDLPVAIF
  • FLAV_AZOVI -AKIGLFFGSNTGKT-RKVaKSIKKRFDDET-MSDA-LNVNRVS-AEDFAQYQFLILgTPTLGEGELPGLSSDCENESWEEFL-PKIEGLDFSGKTVALf
  • FLAV_ENTAG MATIGIFFGSDTGQT-RKVaKLIHQKLDG---IADAPLDVRRAT-REQFLSYPVLLLgTPTLGDGELPGVEAGSQYDSWQEFT-NTLSEADLTGKTVALf
  • FLAV_ANASP SKKIGLFYGTQTGKT-ESVaEIIRDEFGN---DVVTLHDVSQAE-VTDLNDYQYLIIgCPTWNIGEL--------QSDWEGLY-SELDDVDFNGKLVAYf
  • FLAV_ECOLI -AITGIFFGSDTGNT-ENIaKMIQKQLGK---DVADVHDIAKSS-KEDLEAYDILLLgIPTWYYGE--------AQCDWDDFF-PTLEEIDFNGKLVALf
  • 4fxn -MK--IVYWSGTGNT-EKMAELIAKGIIESG-KDVNTINVSDVNIDELL-NEDILILGCSAMGDEVL-------EESEFEPFI-EEIS-TKISGKKVALF
  • FLAV_MEGEL MVE--IVYWSGTGNT-EAMaNEIEAAVKAAG-ADVESVRFEDTNVDDVA-SKDVILLgCPAMGSEEL-------EDSVVEPFF-TDLA-PKLKGKKVGLf
  • FLAV_CLOAB -MKISILYSSKTGKT-ERVaKLIEEGVKRSGNIEVKTMNLDAVD-KKFLQESEGIIFgTPTYYAN---------ISWEMKKWI-DESSEFNLEGKLGAAf
  • 3chy ADKELKFLVVDDFSTMRRIVRNLLKELGFN--NVEEAEDGVDALNKLQAGGYGFVI---SDWNMPNM----------DGLELL-KTIRADGAMSALPVLM
  • T
  • 1fx1 GCGDS-SY-EYFCGA-VDAIEEKLKNLGAEIVQD---------------------GLRIDGD--PRAARDDIVGWAHDVRGAI--------
  • FLAV_DESDE ASGDQ-EY-EHFCGA-VPAIEERAKELgATIIAE---------------------GLKMEGD--ASNDPEAVASfAEDVLKQL--------
  • FLAV_DESVH GCGDS-SY-EYFCGA-VDAIEEKLKNLgAEIVQD---------------------GLRIDGD--PRAARDDIVGwAHDVRGAI--------
  • FLAV_DESSA GCGDS-DY-TYFCGA-VDAIEEKLEKMgAVVIGD---------------------SLKIDGD--PE--RDEIVSwGSGIADKI--------
  • FLAV_DESGI GCGDS-SY-TYFCGA-VDVIEKKAEELgATLVAS---------------------SLKIDGE--PD--SAEVLDwAREVLARV--------
  • 2fcr GLGDAEGYPDNFCDA-IEEIHDCFAKQGAKPVGFSNPDDYDYEESKS-VRDGKFLGLPLDMVNDQIPMEKRVAGWVEAVVSETGV------
  • FLAV_AZOVI GLGDQVGYPENYLDA-LGELYSFFKDRgAKIVGSWSTDGYEFESSEA-VVDGKFVGLALDLDNQSGKTDERVAAwLAQIAPEFGLS--L--
  • FLAV_ENTAG GLGDQLNYSKNFVSA-MRILYDLVIARgACVVGNWPREGYKFSFSAALLENNEFVGLPLDQENQYDLTEERIDSwLEKLKPAV-L------
  • FLAV_ANASP GTGDQIGYADNFQDA-IGILEEKISQRgGKTVGYWSTDGYDFNDSKA-LRNGKFVGLALDEDNQSDLTDDRIKSwVAQLKSEFGL------
  • FLAV_ECOLI GCGDQEDYAEYFCDA-LGTIRDIIEPRgATIVGHWPTAGYHFEASKGLADDDHFVGLAIDEDRQPELTAERVEKwVKQISEELHLDEILNA
  • 4fxn G-----SY-GWGDGKWMRDFEERMNGYGCVVVET---------------------PLIVQNE--PDEAEQDCIEFGKKIANI---------
  • FLAV_MEGEL G-----SY-GWGSGEWMDAWKQRTEDTgATVIGT----------------------AIVNEM--PDNA-PECKElGEAAAKA---------
  • FLAV_CLOAB STANSIAGGSDIA---LLTILNHLMVKgMLVYSG----GVAFGKPKTHLGYVHINEIQENEDENARIfGERiANkVKQIF-----------
  • 3chy VTAEAKK--ENIIAA---------AQAGAS-------------------------GYVV-----KPFTAATLEEKLNKIFEKLGM------
  • G
  • Iteration 0 SP= 136944.00 AvSP= 10.675 SId= 4009 AvSId= 0.313
slide141

Building flavodoxin

try again

2

1

3

4

5

RH

slide147

Flavodoxin family - TOPS diagrams

(Flores et al., 1994)

4

3

2

5

4

3

1

2

5

1

protein structure evolution
Protein structure evolution

Insertion/deletion of secondary structural elements can ‘easily’ be done at loop sites

slide149

Protein structure evolution

Insertion/deletion of structural domains can ‘easily’ be done at loop sites

N

C

slide150

Integrating protein multiple alignment, secondary and tertiary structure prediction to predict

structural domains in sequence data

SnapDRAGON

  • Richard A. George
  • George R.A. and Heringa, J. (2002) J. Mol. Biol., 316, 839-851.
slide151

A domain is a:

  • Compact, semi-independent unit (Richardson, 1981).
  • Stable unit of a protein structure that can fold autonomously (Wetlaufer, 1973).
  • Recurring functional and evolutionary module (Bork, 1992).
  • “Nature is a ‘tinkerer’ and not an inventor” (Jacob, 1977).
slide152

The DEATH Domain

  • Present in a variety of Eukaryotic proteins involved with cell death.
  • Six helices enclose a tightly packed hydrophobic core.
  • Some DEATH domains form homotypic and heterotypic dimers.

http://www.mshri.on.ca/pawson

slide153

Delineating domains is essential for:

  • Obtaining high resolution structures (x-ray, NMR)
  • Sequence analysis
  • Multiple sequence alignment methods
  • Prediction algorithms (SS, Class, secondary/tertiary structure)
  • Fold recognition and threading
  • Elucidating the evolution, structure and function of a protein family (e.g. ‘Rosetta Stone’ method)
  • Structural/functional genomics
  • Cross genome comparative analysis
slide154

Structural domain organisation can be nasty…

Pyruvate kinase

Phosphotransferase

b barrel regulatory domain

a/b barrel catalytic substrate binding domain

a/b nucleotide binding domain

1 continuous + 2 discontinuous domains

slide155

SECONDARY STRUCTURE (helices, strands)

PRIMARY STRUCTURE (amino acid sequence)

VHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVANALAHKYH

TERTIARY STRUCTURE (fold)

QUATERNARY STRUCTURE

Protein structure hierarchical levels

slide156

SECONDARY STRUCTURE (helices, strands)

PRIMARY STRUCTURE (amino acid sequence)

VHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVANALAHKYH

TERTIARY STRUCTURE (fold)

QUATERNARY STRUCTURE

Protein structure hierarchical levels

slide157

SECONDARY STRUCTURE (helices, strands)

PRIMARY STRUCTURE (amino acid sequence)

VHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVANALAHKYH

TERTIARY STRUCTURE (fold)

QUATERNARY STRUCTURE

Protein structure hierarchical levels

slide158

SECONDARY STRUCTURE (helices, strands)

PRIMARY STRUCTURE (amino acid sequence)

VHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVANALAHKYH

TERTIARY STRUCTURE (fold)

QUATERNARY STRUCTURE

Protein structure hierarchical levels

slide159

Domain prediction using DRAGON

Distance Regularisation Algorithm for Geometry OptimisatioN

(Aszodi & Taylor, 1994)

  • Folds proteins based on the requirement that (conserved) hydrophobic residues cluster together.
  • First constructs a random high dimensional Ca distance matrix.
  • Distance geometry is used to find the 3D conformation corresponding to a prescribed target matrix of desired distances between residues.
slide160

The DRAGON target matrix is inferred from:

  • A multiple sequence alignment of a protein (old)
    • Conserved hydrophobicity
  • Secondary structure information (SnapDRAGON)
    • predicted by PREDATOR (Frishman & Argos, 1996).
    • strands are entered as distance constraints from the N-terminal Ca to the C-terminal Ca.
slide161

Multiple alignment

C distance

matrix

Target

matrix

Predicted secondary structure

N

N

3

N

N

100 randomised

initial matrices

100 predictions

CCHHHCCEEE

Input data

N

  • The C distance matrix is divided into smaller clusters.
  • Seperately, each cluster is embedded into a local centroid.
  • The final predicted structure is generated from full embedding of the multiple centroids and their corresponding local structures.
slide162

SnapDragon

Generated folds by Dragon

Multiple alignment

Boundary recognition

Predicted secondary structure

Summed and Smoothed Boundaries

CCHHHCCEEE

slide163

SnapDRAGON

Domains in structures assigned using method by Taylor (1997)

1

2

3

Domain boundary positions of each model against sequence

Summed and Smoothed Boundaries (Biased window protocol)

snapdragon
SnapDRAGON
  • Is very slow (can be hours for proteins>400 aa) – cluster computing implementation
  • Uses consistency in the absence of standard of truth
  • Goes from primary+secondary to tertiary structure to ‘just’ chop protein sequences
  • SnapDRAGON webserver is underway
slide165

Integrating protein sequence database

searching and on-the-fly domain recognition

DOMAINATION

Richard A. George

Protein domain identification and improved sequence searching using PSI-BLAST

(George & Heringa, Prot. Struct. Func. Genet., in press; 2002)

slide166

Domaination

  • Current iterative homology search methods do not take into account that:
    • Domains may have different ‘rates of evolution’.
    • Common conserved domains, such as the tyrosine kinase domain, can obscure weak but relevant matches to other domain types
    • Premature convergence (false negatives)
    • Matrix migration / Profile wander (false positives).
slide167

PSI-BLAST

  • Query sequence is first scanned for the presence of so-called low-complexity regions (Wooton and Federhen, 1996), i.e. regions with a biased composition (e.g. TM regions or coiled coils) likely to lead to spurious hits, which are excluded from alignment.
  • Initially operates on a single query sequence by performing a gapped BLAST search
  • Then takes significant local alignments found, constructs a ‘multiple alignment’ and abstracts a position specific scoring matrix (PSSM) from this alignment.
  • Rescans the database in a subsequent round to find more homologous sequences -- Iteration continues until user decides to stop or search converges
slide168

PSI-BLAST iteration

Query sequence

Q

xxxxxxxxxxxxxxxxx

Gapped BLAST search

Query sequence

Q

xxxxxxxxxxxxxxxxx

Database hits

A

C

D

.

.

Y

PSSM

Pi

Px

Gapped BLAST search

A

C

D

.

.

Y

PSSM

Pi

Px

Database hits

slide169

DOMAINATION

Chop and Join

Domains

slide170

Identifying domain boundaries

Sum N- and C-termini of

gapped local alignments

True N- and C- termini are

counted twice (within 10 residues)

Boundaries are smoothed using two

windows (15 residues long)

Combine scores using biased

protocol:

if Ni x Ci = 0

then Si = Ni+Ci

else Si = Ni+Ci +(NixCi)/(Ni+Ci)

slide171

Identifying domain deletions

  • Deletions in the query (or insertion in the DB sequences) are identified by
    • two adjacent segments in the query align to the same DB sequences (>70% overlap), which have a region of >35 residues not aligned to the query. (remove N- and C- termini)

DB

Query

slide172

Identifying domain permutations

  • A domain shuffling event is declared
    • when two local alignments (>35 residues) within a single DB sequence match two separate segments in the query (>70% overlap), but have a different sequential order.

b a

DB

Query

a b

slide173

Identifying continuous and discontinuous domains

  • Each segment is assigned an independence score (In).
  • If In>10% the segment is assigned as a continuous domain.
  • An association score is calculated between non-adjacent
  • fragments by assessing the shared sequence hits to the
  • segments. If score > 50% then segments are considered as
  • discontinuous domains and joined.
slide174

Create domain profiles

  • A representative set of the database sequence fragments that overlap a putative domain are selected for alignment using OBSTRUCT (Heringa et al. 1992). > 20% and < 60% sequence identity (including the query seq).
  • A multiple sequence alignment is generated using PRALINE (Heringa 1999, 2002; Kleinjung et al., 2002).
  • Each domain multiple alignment is used as a profile in further database searches using PSI-BLAST (Altschul et al 1997).
  • The whole process is iterated until no new domains are identified.
slide175

Significant sequences found in database searches

At an E-value cut-off of 0.1 the performance of DOMAINATION

searches with the full-length proteins is 15% better than PSI-BLAST

slide176

Summary

  • Algorithmic integration issues:
  • Integrating data categories
  • Integrating alternative methods (consensus)
  • Making an web-integrated genomics pipeline that combines it all
big task ahead @ vu
Big task ahead @ VU

Needs:

  • People
  • Teams with an interest in Integrative Bioinformatics
  • HTC/Dedicated cluster computing
slide178

Acknowledgements

VU CvB

FEW

FALW

Victor Simossis – NIMR to VU (1 November 2002)

Jens Kleinjung – NIMR to VU (1 December 2002)

Hans Westerhoff – FALW, VU

Henri Bal – CS, FEW, VU

Hans van Beek – VUMC/FALW, VU