Sequence - BIOTEC - Biotechnology Center TU Dresden

Introduction based on

Chapter 1

Lesk, Introduction to Bioinformatics

Michael Schroeder

BioTechnological Center

TU Dresden Biotec

Contents

n Molecular biology primer n The role of computer science n Phylogeny n Sequence Searching n Protein structure n Clinical implications n Read chapter 1

By Michael Schroeder, Biotec, 2

23 June 2000: Draft of Human genome sequenced!

n 1953: Watson and Crick discover the structure of DNA n 2000: Draft of human genome is published n “The most wondrous map ever produced by human kind” n “One of the most significant scientific landmarks of all time, comparable with the invention of the wheel or the splitting of the atom”

By Michael Schroeder, Biotec 3

High-throughput biomedicine

n Microarrays n Measure activity of thousands of genes at the same time n Example: n Cancer n Compare activity with and without drug treatment n Result: Hundreds of candidate drug targets n RNAi (Noble prize 2004, Fire and Mello) n Knock-down genes and observe effect n Example: n Infectious diseases n Which proteins orchestrate entry into cell?

n Result: Hundreds of candidate proteins n Atomic force microscopes (Noble prize Binnig) n Pull protein out of membrane and measure force n Example: n Eye diseases resulting fomr misfolding n Result: Hundreds of candidate residues


Drug Discovery

n Challenge: Longer time to market, fewer drugs, exploding costs n Approach: Use of compound libraries and highthroughput screening


HTS and Bioinformatics

n High-throughput technologies have completely changed the work of biomedical researchers n Challenge: Interpret (often large) results of screens n Approach: Before running secondary assays use bioinformatics and IT to assemble all possible information


>1.000.000

Sequences

By Michael Schroeder, Biotec

Good News

>30.000

3D Structures

Number of PubMed Abstracts

14,000,000

12,000,000

10,000,000

8,000,000

6,000,000

4,000,000

2,000,000

0

1960

>16.000.000

Articles

1970 1980

Year

1990 2000

Molecular Biology Database List at Nucleic Acids Research

800

700

600

500

400

300

200

100

0

>700

DBs/Tools

2000 2001 2002 2003 year

2004 2005

7

2010

Bad News: Data != Knowledge

n How to analyse data, how to integrate data?

n Comptuer science to the rescue…


Examlpe: computer science is key for sequencing

n Human genome is a string of length 3.200.000.000

n Shotgun sequencing: Break multiple copies of string into shorter substrings n Example: n shotgunsequencing shotgunsequencing shotgunsequencing n cing en encing equ gun ing ns otgu seq sequ sh sho shot tg uenc un n Computing problem: Assemble strings


Computer science key for sequencing

n sh n sho n shot n otgu n tg n n gun un n ns n n seq sequ n equ n n uenc encing n n en cing n ing

QUESTION: How can you handle long repetitive sequences?

Heeeeelllllllllllooooooo

QUESTION: Why was a draft announced? When was the final version ready?


Yersinia pestis

Arabidopsis thaliana

Buchnerasp.

APS

Aquifex aeolicus

Archaeoglobus fulgidus

Borrelia burgorferi

Mycobacterium tuberculosis

Caenorhabitis elegans

Campylobacter jejuni

Chlamydia pneumoniae

Vibrio cholerae

Drosophila melanogaster

Escherichia

Thermoplasma coli acidophilum

Helicobacter pylori

Mycobacterium leprae mouse

Neisseria meningitidis

Z2491

Plasmodium falciparum

Pseudomonas aeruginosa

Ureaplasma urealyticum rat

Rickettsia prowazekii

Saccharomyces cerevisiae

Salmonella enterica

Bacillus subtilis

Thermotoga maritima

Xylella fastidiosa


Break through of the year 2000

Next quest:

Sequencing a genome for 1000$


Quantity and quality of data lead to ambitious goals

n Understand integrative aspects of the biology of organisms n Interrelate sequence, three-dimensional structure, interactions, function of proteins, nucleic acids and protein-nucleic acid complexes n Travel in time n backward (deduce events in evolutionary history ) and n forward ( deliberate modification of biological systems) n Applications in medicine, agriculture, and other scientific fields


Scenario

n New virus (e.g. SARS) and goal to develop treatment n Scientists isolate genetic material of virus n Screen genome for relationships with previously studied viruses [10] n From virus’ DNA they compute the proteins it produces [1] n Compute proteins’ three-dimensional structure and thereby obtain clues about their functions n Screen for similar proteins sequences with known structure [15] n If any are found n Then interpret difference (homology modelling) [25] n Else predict structure from sequence [55] n Identify or design small molecule blocking relevant active sites of the protein [50] n Design antibodies to neutralize the virus [50] n Index of problem difficulty: n <30: solution exists already, n >30: we cannot solve this (yet)


Life in Time and Space

n Life n A biological organism is a naturally-occurring, self-reproducing device that effects controlled manipulations of matter, energy and information n Time n Species evolve through n natural mutation, n recombination of genes in sexual reproduction, or n direct gene transfer n Read the past in contemporary genomes n Space n Species occupy local ecosystems n Species are composed of organisms n Organisms are composed of cells n Cells are composed of molecules


DNA – the molecule of life

By Michael Schroeder, Biotec,

Proteins

n 20 naturally occurring amino acids in proteins n Non-polar n G glycine, A alanine, P proline, V valine n I isoleucine, L leucine, F phenylalanine, M methionine n Polar n S serine, C cysteine, T threonine, N asparagine n Q glutamine, H histidine, Y tyrosine, W tryptophan n Charged n D aspartic acid, E glutamic acid, K lysine, R arginine n Other classification n H,F,Y,W are aromatic and play role in membrane proteins n Distinguish n atg = adenine-thymine-guanine and n ATG = Alanine-Threonine-Glycine


The genetic code

First

Position

Second

Position

(5Õ end) T C A G

T

TTT Phe TCT Ser

TTC Phe TCC Ser

TAT Tyr TGT Cys

TAC Tyr TGC Cys

C

TTA

TTG

Leu

Leu

TCA

TCG

Ser

Ser

TAA Stop TGA Stop

TAG Stop TGG Trp

CTT Leu CCT Pro CAT His

CTC Leu CCC Pro CAC His

CGT

CGC

Arg

Arg

CTA Leu CCA Pro CAA Gln CGA Arg

CTG Leu CCG Pro CAG Gln CGG Arg

ATT Ile ACT Thr AAT Asn AGT Ser

A

G

ATC Ile

ATA Ile

ACC Thr AAC Asn AGC Ser

ACA Thr AAA Lys AGA Arg

ATG Met* ACG Thr AAG Lys AGG Arg

GTT Val GCC Ala GAT Asp GGT Gly

GTC Val GCC Ala GAC Asp GGC Gly

GTA Val GCA Ala GAA Glu GGA Gly

GTG Val GCG Ala GAG Glu GGG Gly

Third

Position

(3Õ end)

T

C

A

G

T

C

A

G

T

C

A

G

T

C

A

G


Protein Structure

n DNA: n Nucleotides are very similar and hence the structure of

DNA is very uniform n Proteins: n Great variety in threedimensional conformation to support diverse structure and functions n If heated, protein “unfolds” to biologically-inactive structure; in normal conditions protein folds


Paradox

n Translation from DNA sequence to amino acid sequence n is very simple to describe , n but requires immensely complicated machinery

(ribosome, tRNA) n The folding of the protein sequence into its threedimensional structure n is very difficult to describe n But occurs spontaneously


Central Dogma

n

DNA sequence determines protein sequence

n

Protein sequence determines protein structure

n

Protein structure determines protein function


Observables and Data Archives

n Databases in molecular biology cover n Nucleic acid and protein sequences, n Macromolecular structures and functions n Archival databanks of biological information n DNA and protein sequences including annotations n Nucleic acid and protein structures including annotations n Protein expression patterns n Derived Databases n Sequence motifs (“signatures” of protein families) n Mutations and variants in DNA and protein sequences n Classification or relationships (e.g. hierarchy of structures) n Bibliographic databases (PubMed with 17M abstracts) n Collections n of links to web sites n of databases


What is Bioinformatics

n Bioinformatics is the marriage of biology and information technology n Bioinformatics is an integrated multidisciplinary field n Covers computational tools and methods for managing, analysing and manipulating sets of biological data n Disciplines include: n biochemistry, genetics, structural biology, artificial intelligence, machine learning, software engineering, statistics, database theory, information visualisation, algorithm design


Bioinformatics

n Has three components n Creation of databases n Development of algorithms to analyse data n Use of these tools for analysing biological data


Databases: Types of Queries 1/2

n 1. Given a sequence (fragment), find sequences in the database that are similar to it n 2. Given a protein structure (or fragment), find protein structures in the database that are similar to it n 3. Given sequence of a protein of unknown structure, find structures in the database that adopt similar threedimensional structures n 4. Given a protein structure , find sequences in the database that correspond to similar structures.


Databases: Given sequence, find structure

n 3. Given sequence of a protein of unknown structure, find structures in the database that adopt similar three-dimensional structures.

But How?

n Easy: Find similar sequences with known structure!

n But: There might be similar structures, whose sequence is not similar!

n 4. Given a protein structure , find sequences in the database that correspond to similar structures.

But How?

n Easy: Find similar structures and hence sequences n But: There are so many more sequences with unknown structure that the above method will have only very limited success n 1 and 2 are solved, 3 and 4 are active fields of research


Databases: Types of Queries 2/2

n E.g. for which proteins of known structure involved in disease of disrupted purine biosynthesis in humans, are there related proteins in yeast?

n Solution: Virtual databases that provide transparent access to a number of underlying data sources and query and analysis tools


Databases: Curation and Quality

n Problems: n Given that there are primary and secondary databases, n how to control updates , n how to propagate change , n how to maintain consistency ?

n Contents (experimental results, annotations, supplementary information) all have there own source of error n Older data were limited by older techniques


Databases: Annotation

n Experimental data (e.g. raw DNA sequence) needs to be enriched with annotations n Source of data n Investigators responsible n Relevant publication n Feature tables (e.g. coding regions) n Problems: n (often) lack of controlled and coherent vocabulary n Computer parseable n Automated annotation needed n SwissProt = ca. 540.000 annotated sequences n TrEMBL = ca. 40 Mio unannotated sequences n Maintanence of annotations (what if error detected?)


Computers and Computer Science

n Relevant areas: n Artificial Intelligence n Machine Learning n Neural networks, rulebased learning n Datamining n Association rules n Software Engineering n Design, implementation, testing of software n Programming n Object-oriented C++,

Java n Imperative: C, Modula,

Pascal, Cobol, Fortran n Logic: Prolog n Funtional: ML n Scripting: Perl, Python n Statistics n Database theory n Design and maintenance of databases n How to index sequences, time series, 3D strucutres n Information Visualisation n Graph drawing, diagrams, cartoons, 3D graphics n Algorithm design n Complexity of algorithms n Efficient data structures


Programming

n We will use Python n Scripting language n Supports string processing well n Widely used in bioinformatics


Biological Classification and

Nomenclature

n Back in 18 th century, Linnaeus, a Swedish naturalist, classified living things according to a hierarchy:

Kingdom, Phylum, Class, Order, Family, Genus,

Species n Generally only genus and species are used for identification n Homo sapiens n Drosophila melanogastor n Bos taurus n Linnaeus’ classification based on observed similarity n Widely reflects biological ancestry


Classification of Humans and Fruit Flies

n Kingdom: n Phylum: n Class: n Order: n Family: n Genus: n Species:

Animalia

Chordata

Mammalia

Primata

Hominidae

Homo sapiens

Animalia

Chordata

Insecta

Diptera

Drosophilidae

Drosophila melanogastor


Homology = derived from common ancestor

n Characteristics derived from a common ancestor are called homologous n E.g. eagle’s wing and human’s arm n Other apparently similar characteristics may have arisen independently by convergent evolution n E.g. eagle’s wing and bee’s wing. The most common ancestor of eagles and bees did not have wings n Homologous characters may diverge functionally n E.g. bones in human middle and jaws of primitive fish


Sequence analysis and Homology

n Sequence analysis gives unambiguous evidence for relationship of species n For higher organisms sequence analysis and the classical tools of comparative anatomy, palaeontology, and embryology are often consistent n For microorganisms there are problems n Classical methods: how to describe features n Sequence analysis: lateral gene transfer


Domains of Life

n Ribosomal RNA is present in all organisms n Based on 15S ribosomal RNAs life is divided n Bacteria n No nucleus (procaryote) n E.g. tuberculosis and E. coli n Archaea n No nucleus (procaryote) n few organisms living in hostile environments (termophiles, halophiles, sulphur reducers, methanogens) n Eukarya n Has a nucleus contained in membrane n Nucleus contains chromosomes n Internal compartments called organelles for specialised biological processes n Area outside nucleus and organelles called cytoplasm n E.g. yeast and human beings


Eukaryotic cell


Domains of Life


Example: Use of sequences to determine phylogenetic relationships

Use ExPASy (www.expasy.ch) to search for pancreatic ribonuclease for horse (Equus caballus), minke whale (Balaenoptera acutorostrata), red kangaroo ( Macropus rufus )

>sp|P00674|RNP_HORSE Ribonuclease pancreatic

(EC 3.1.27.5) (RNase 1) (RNase A) - Equus caballus (Horse).

KESPAMKFERQHMDSGSTSSSNPTYCNQMMKRRNMTQGWCKPVNTF

VHEPLADVQAICLQKNITCKNGQSNCYQSSSSMHITDCRLTSGSKY

PNCAYQTSQKERHIIVACEGNPYVPVHFDASVEVST

Use sequence alignment to determine evolutionary relationship


Sequence alignment

1.

Global match : align all of one with all of the other sequence (mismatches, insertions, deletions)

And.

--so ,.from.hour.to.hour.we.r

ipe .and.r

ipe

|||| |||||||||||||||||||||||| ||||||

And.

then ,.from.hour.to.hour.we.r

ot.and.r

ot-

2.

Local match : find region in one sequence that matches the other (mismatches, insertions, deletions ; ends can be ignored)

My .care.is.

loss .of.care,.by.

old .care.

d on e,

||||||||| ||||||||||||| |||||| ||

Your .care.is.

gain .of.care,.by.

new .care.

w on


Sequence alignment

3. Motif search : find matches of short sequence in long sequence

Option: perfect,

1 mismatch, mismatches+gaps+insertions+deletions m atch

|||| for the w atch to babble and to talk is most tolerable


Sequence alignment

4. Multiple sequence alignment

No.sooner.---met.--------.but.they.look’d

No.sooner.look’d.--------.but.they.lo-v’d

No.sooner.lo-v’d.--------.but.they.sigh’d

No.sooner.sigh’d.--------.but.they.--asked.one.another.the.reason

No.sooner.knew.the.reason.but.they.-------------sought.the.remedy

No.sooner. .but.they.


Example: Multiple alignment

Use sequence alignment to determine evolutionary relationship…

Example: horse, whale and kangaroo

Expected: horse and whale are placental mammals, kangaroo is marsupial

Multiple alignment with CLUSTAL-W

(http://www.genome.jp/tools/clustalw) multiple sequence alignment computer program main parameters: gap opening/extension penalty


FASTA format

>sp|P00674|RNP_HORSE Ribonuclease pancreatic (EC 3.1.27.5)

(RNase 1) (RNase A) - Equus caballus (Horse).

KESPAMKFERQHMDSGSTSSSNPTYCNQMMKRRNMTQGWCKPVNTFVHEPLADVQAICLQ

KNITCKNGQSNCYQSSSSMHITDCRLTSGSKYPNCAYQTSQKERHIIVACEGNPYVPVHF

DASVEVST

>sp|P00673|RNP_BALAC Ribonuclease pancreatic (EC 3.1.27.5)

(RNase 1) (RNase A) - Balaenoptera acutorostrata (Minke whale) (Lesser rorqual).

RESPAMKFQRQHMDSGNSPGNNPNYCNQMMMRRKMTQGRCKPVNTFVHESLEDVKAVCSQ

KNVLCKNGRTNCYESNSTMHITDCRQTGSSKYPNCAYKTSQKEKHIIVACEGNPYVPVHF

DNSV

>sp|P00686|RNP_MACRU Ribonuclease pancreatic (EC 3.1.27.5)

(RNase 1) (RNase A) - Macropus rufus (Red kangaroo)

(Megaleia rufa).

ETPAEKFQRQHMDTEHSTASSSNYCNLMMKARDMTSGRCKPLNTFIHEPKSVVDAVCHQE

NVTCKNGRTNCYKSNSRLSITNCRQTGASKYPNCQYETSNLNKQIIVACEGQYVPVHFDA

YV


Multiple Alignment with ClustalW

(http://www.genome.jp/tools/clustalw)

CLUSTAL W (1.82) multiple sequence alignmen sp|P00674|RNP_HORSE sp|P00673|RNP_BALAC sp|P00686|RNP_MACRU

KESPAMKFERQHMDSGSTSSSNPTYCNQMMKRRNMTQGWCKPVNTFVHEPLADVQAICLQ 60

RESPAMKFQRQHMDSGNSPGNNPNYCNQMMMRRKMTQGRCKPVNTFVHESLEDVKAVCSQ 60

-ETPAEKFQRQHMDTEHSTASSSNYCNLMMKARDMTSGRCKPLNTFIHEPKSVVDAVCHQ 59

*:** **:*****: :......*** ** *.**.* ***:***:**. *.*:* *

KNITCKNGQSNCYQSSSSMHITDCRLTSGSKYPNCAYQTSQKERHIIVACEGNPYVPVHF 120

KNVLCKNGRTNCYESNSTMHITDCRQTGSSKYPNCAYKTSQKEKHIIVACEGNPYVPVHF 120

ENVTCKNGRTNCYKSNSRLSITNCRQTGASKYPNCQYETSNLNKQIIVACEG-QYVPVHF 118

:*: ****::***:*.* : **:** *..****** *:**: :::******* ******

DASVEVST 128

DNSV---- 124

DAYV---- 122

* *


Example: Number of Aligned Residues

Horse and Minke whale: 95

Minke whale and Red kangoroo: 82

Horse and Red kangoroo: 75

Conclusion: Horse and whale share the most identical residues


New Example:

Elephant and Mammoth

Mitochondrial cytochrome b from

Siberian woolly mammoth (Mammuthus primigenius) preserved in arctic permafrost

African elephant (Loxodonta africana)

Indian elephant (Elephans maximus)

Q: To which one is the Mammuth more closely related?


Indian elephant: sp|P24958|CYB_LOXAF

Mammoth: sp|P92658|CYB_MAMPR

African elephant: sp|O47885|CYB_ELEMA

MTHIRKSHPLLKIINKSFIDLPTPSNISTWWNFGSLLGACLITQILTGLFLAMHYTPDTM 60

MTHIRKSHPLLKILNKSFIDLPTPSNISTWWNFGSLLGACLITQILTGLFLAMHYTPDTM 60

MTHTRKFHPLFKIINKSFIDLPTPSNISTWWNFGSLLGACLITQILTGLFLAMHYTPDTM 60

*** ** ***:**:**********************************************

TAFSSMSHICRDVNYGWIIRQLHSNGASIFFLCLYTHIGRNIYYGSYLYSETWNTGIMLL 120



************************************************************

LITMATAFMGYVLPWGQMSFWGATVITNLFSAIPYIGTNLVEWIWGGFSVDKATLNRFFA 180

LITMATAFMGYVLPWGQMSFWGATVITNLFSAIPYIGTDLVEWIWGGFSVDKATLNRFFA 180

LITMATAFMGYVLPWGQMSFWGATVITNLFSAIPYIGTNLVEWIWGGFSVDKATLNRFFA 180

**************************************:*********************

LHFILPFTMIALAGVHLTFLHETGSNNPLGLTSDSDKIPFHPYYTIKDFLGLLILILLLL 240

LHFILPFTMIALAGVHLTFLHETGSNNPLGLTSDSDKIPFHPYYTIKDFLGLLILILFLL 240

FHFILPFTMVALAGVHLTFLHETGSNNPLGLTSDSDKIPFHPYYTIKDFLGLLILILLLL 240

:********:***********************************************:**

LLALLSPDMLGDPDNYMPADPLNTPLHIKPEWYFLFAYAILRSVPNKLGGVLALLLSILI 300

LLALLSPDMLGDPDNYMPADPLNTPLHIKPEWYFLFAYAILRSVPNKLGGVLALLLSILI 300

LLALLSPDMLGDPDNYMPADPLNTPLHIKPEWYFLFAYAILRSVPNKLGGVLALFLSILI 300

******************************************************:*****

LGLMPLLHTSKHRSMMLRPLSQVLFWTLTMDLLTLTWIGSQPVEYPYIIIGQMASILYFS 360

LGIMPLLHTSKHRSMMLRPLSQVLFWTLATDLLMLTWIGSQPVEYPYIIIGQMASILYFS 360

LGLMPLLHTSKHRSMMLRPLSQVLFWTLTMDLLTLTWIGSQPVEHPYIIIGQMASILYFS 360

**:*************************: *** **********:***************

IILAFLPIAGVIENYLIK 378

IILAFLPIAGMIENYLIK 378

IILAFLPIAGMIENYLIK 378

**********:*******


Example: Elephant and Mammoth

Mammoth and African elephant have 10 mismatches,

Mammoth and Indian elephant 14.

Significant?

Q1: can we tell from these sequences alone that they are closely related?

Q2: differences are small – do they come from selection, random noise or drift

Strategies needed difference judging of similiarities


Excursion:

Similarity and Homology

Important difference:

Similarity is the measurement of resemblance of sequences

Homology: common ancestor

Similarity is gradual, homology is either true or false

Similarity = now, homology = past events

Homology is only very rarely directly observed (e.g. lab population, clinical study of viral infection)

Homology is inferred from sequence similarity


Example: Homology/Similarity

The assertion that the cytochrome b sequences are

homologues means that there is a common ancestor

BUT:

1. Maybe cytochrome b functionally requires so many conserved residues and will hence occur in many species (  In fact, This is not the case here)

2. Maybe cytochrome b has to function this way in elephant-like species, but in fact started out from different ancestors (i.e. convergent evolution )Mammoth are homolgues – are also ribonuclease sequences homologues? Difference is much bigger

3. Maybe mammoth and african elephant have only fewer mismatches, because Indian elephant’s DNA mutated faster

4. Maybe all of them acquired cytochrome b through a virus

( horizontal gene transfer )


Examples: Conclusion

Classical methods confirm that for pancreatic ribonuclease (Horse – whale - kangoroo) inferring homology from similarity is justified

But to answer whether Mammoth are closer to African or

Indian elephants is too close to call (non-significant)

Problems with inferring phylogeny from gene and protein sequence comparison

Wide range of variation (possibly below statistical significance)

Different rates of evolution for different branches of the evolutionary tree

Even if relationship - which sequence came first?


Inferring Phylogenies with SINES and LINES

Pylogeneticist’s dream of features:

‘all-or-none’ character

Irreversible appearance

Solution:

SINES and LINES (Short and Long Interspersed

Nuclear Elements)

Repetitive, non-coding sequences in eukaryotic genomes

>30% in human genome, >50% in some plants

SINES = 70-500 base pairs long, up to 10 6 copies

LINES up to 7000 base pairs, up to 10 5 copies

They enter genome by reverse transcription of RNA


A practical example:

Fatherhood

The picture shows a Southern blot of DNA from different family members, probed using a mini-satellite.

You can work out which of F1 and F2 is the father of child C, by observing which bands they have in common.

(Reproduced from "Essential Medical Genetics" by M.Connor and

M.Ferguson-Smith, with permission from Blackwell Science.)


Why SINES are useful in phylogeny

Either present or absent

Inserted at random in non-coding portion of genome i.e. SINE has no important function so that convergent evolution can be excluded

Presence of a SINE in two species and absence in a third implies that first two species are more closely related

SINE insertion appears to be irreversible

Temporal order

Presence of a SINE in two species and absence in a third implies that ancestor of first two species is younger than ancestor of all three


Example revisited

Q: What is the closest land-based relative of the whales?

Classical palaeontology links Cetacea (whales, dolphins, porpoises) with Artiodactyla

(including e.g. cattle)

Belief that Cetaceans diverged before Artiodactyla split into suborder of

Suiformes (e.g. pigs),

Tylopoda (e.g. camels, llamas),

Ruminantia (e.g. deer, cattle, goats, sheep, antelopes, giraffe)


Example revisited

Sequence comparison results

Based on mitochondrial DNA, pancreatic ribonuclease, fibrinogen, and others

Closest relatives of whales are hippopotamuses (share 4

SINES)

These two are closest to Ruminantia


Searching for Similar

Sequences with PSI-Blast

False negatives :

300 out of 1000 are not found

Any search method for sequences should be

Sensitive: pick up distant relationships

Selective: reported relationships are true

Example: database with (among others) 1000 globin sequences

Globin familiy (oxygen transport) of proteins occurs in many species

Proteins have same function and structure

But there are pairs of members of the family sharing less than 10% identical residues

Sequence Database

1000 Globin

Sequences

900 Search results

True positives:

700 out of 900 are really globins

False positives :

200 out of 900 are not globins


Searching for Distant Relationships with PSI-BLAST

How can we find distant relationships without increasing the false negatives?

PSI-BLAST:

Position Sensitive Iterated – Basic Linear Alignment

Sequence Tool

Identifies conserved patterns within the sequences

Improves Sens and Spec

Score via intermediaries may be better than score from direct comparison

A

50%

B

50%

C

Only 10%


PSI-BLAST Example

Human PAX-6 gene (SwissProt ID P26367) has homologues in many different species

(human, Drosophila, etc.)

TF for eye development

Mutations in:

Human: no or deformed iris

Drosophila: no eyes, expressed in wing or leg ectopic eyes

PSI-Blast at NCBI site (www.ncbi.nlm.nih.gov)


Result


Result

• Description of sequence

• Max score – linked to data that show where sequences match

• Total score - includes scores from non-contiguous portions of the subject sequence that match the query

• Query coverage

• Identity - % of a sequence with the highest percentage of identical bases

• E-Value

• Accession number – linked to Gene bank record


Result

BLASTP 2.2.28+

RID: 6D2U321501N

Database: All non-redundant GenBank CDS translations+PDB+SwissProt+PIR+PRF excluding environmental samples from WGS projects

33,121,465 sequences; 11,555,699,950 total letters

Query= gi|6174889|sp|P26367.2|PAX6_HUMAN RecName: Full=Paired box protein

Pax-6; AltName: Full=Aniridia type II protein; AltName:

Full=Oculorhombin

Length=422

Score E

Sequences producing significant alignments: (Bits) Value ref|NP_000271.1| paired box protein Pax-6 isoform a [Homo sap... 870 0.0 ref|XP_004264012.1| PREDICTED: paired box protein Pax-6 isofo... 869 0.0 ref|XP_003910122.1| PREDICTED: paired box protein Pax-6 isofo... 869 0.0 ref|XP_004683008.1| PREDICTED: paired box protein Pax-6 isofo... 869 0.0 ref|XP_005064880.1| PREDICTED: paired box protein Pax-6 isofo... 868 0.0 ref|NP_001035735.1| paired box protein Pax-6 [Bos taurus] >re... 868 0.0 gb|AAA59962.1| oculorhombin [Homo sapiens] 868 0.0 ref|NP_037133.1| paired box protein Pax-6 [Rattus norvegicus]... 868 0.0 gb|EAW68233.1| paired box gene 6 (aniridia, keratitis), isofo... 869 0.0

...


Introduction to Protein Structure

Proteins play a variety of roles:

Structural (viral coat proteins, horny outer layer of human and animal skin, cytoskeleton)

Catalysis of chemical reactions (enzymes)

Transport and Storage (e.g. haemoglobin)

Regulation (e.g. hormones)

Receptor and signal transduction

Genetic transcription

Recognition (cell adhesion molecules)

Antibodies and other proteins of the immune system


Proteins

Are large molecules

Only small part – the active site – is functional

Evolve by structural changes produced by mutations in the amino acid sequence

Ca. 21.000 human proteins structures are now known

Overall 90.000 protein structures in PDB

Can be obtained by X-ray crystallography or nuclear magnetic resonance (NMR)


Structure of Proteins

Backbone and side chain

Residue i-1

, Residue i

, Residue i+1

,

S i-1

S i

S i+1

Side chain (variable)

| | |

…N-C

α

-C-N-C

α

-C-N-C

α

-C… Main chain (constant)

|| || ||

O O O

Polypeptide chain folds into a curve in space

Common structural feature

Alpha-helix

Beta-sheet

Turns and Loops


Hierarchy of Architecture

Primary structure : Amino acid sequence

Secondary structure : Helices, sheets, loops, hydrogen-bonding pattern of main chain

Tertiary structure : Assembly and interactions of helices, sheets, etc.

Quaternary structure : Assembly of monomers

Evolution can merge proteins

E.g.: 5 enzymes in E. coli = 1 protein in fungi Aspergillus nidulans catalyze successive steps in biosynthesis of aromatic amino acids

E.g.: Globins form tetramers in mammalian haemoglobin and dimers in ark clam Scaoharca inaequivalvis


Protein Structure

DHAP to GAP in Glycolyse

Triosephosphate isomerase from Bacillus stearothermophilus

Highly efficient enzyme appearing in most species


Extra layer of Architecture: supersecondary structure

Alpha-helix hairpin

Beta hairpin

Beta-alpha-beta unit

= Patterns of interaction between helices and sheets


Hierarchy of Architecture

Supersecondary structures:

Alpha-helix hairpin

Beta hairpin

Beta-alpha-beta unit

Domains:

Compact unit, single chain, independent stability

Modular proteins:

Multi-domain

Copies of related domains or “mix-and-match”


Classification of Protein Structure

All Alpha : mostly alpha helices

All Beta : mostly beta sheets

Alpha+Beta : Helices and sheets in different parts of the molecule, no beta-alpha-beta units

Alpha/Beta : Helices and sheets assembled from beta-alpha-beta units

Alpha/Beta linear

Alpha/Beta barrel

Little or no secondary structure


SCOP: Structural Classification of Proteins

top

CLASS

All alpha (284) All Beta (174) Alpha+Beta (376) Alpha/Beta (147)

FOLD

Trypsin-like serine proteases (1)

SUPERFAMILY

= evolutionary related, similar structure, not necessarily similar sequence

Immunoglobulin-like (23)

Transglutaminase (1) Immunoglobulin (6)

FAMILY

= set of domains with similar sequence


C1 set domains

(antibody constant)

V set domains

(antibody variable)

72

Pymol


Engrailed homeodomain (1enh)

Transcription factor important in development

Used to study protein folding

Utrophin calmodulin homology domain (1bhd)

Actin binding

Closely relatd to dystrophin, whose lack causes muscular dystrophies (weak muscles)

Cytochrome c, rice (1ccr)

Electron transport across mitochondrial membrane


DNA-binding domain of HIN recombinase (1hcr)

74


Engrailed homeodomain (1enh)

75

Fibronectin III domain (1fna)

Found on cell surface

Mannose-binding protein (1npl)

Barnase (1brn)

Cleaves RNA and is lethal if intracellular and not inhibited by barstar


TATA-box-binding protein (1cdw)

76

OB-domain from Lys-tRNA synthetase (1bbw)

Scytalone dehydratase (3std)

Alcohol dehydrogenase, NADbinding domain (1ee2)

Break down of alcohol into simpler compounds


Adenylate kinase (3adk)

Energy production

77

Chemotaxis receptor methyltransferase (1af7)


Pancreatic spasmolytic polypeptide (2psp)

Thiamine phosphate synthase (2tps)

78

Protein Structure Prediction and

Engineering

If sequence of amino acids contains enough information to specify three-dimensional structure of proteins, it should be possible to devise algorithm for prediction

Secondary structure prediction : Which segments of the sequence are helices, which strands?

Fold recognition : Given library of known structures with their sequences and a sequence with unknown structure , can we find the structure that is most similar

Homology modelling

Given two homologous sequences , one with one without structure.

If between 30 and 50% of the residues are identical, the structure can serve as a model


Critical Asessment of Structure

Prediction (CASP)

Chicken lysozyme KVFGRCELAAAMKRHGLDNYRGYSLGNWVCAAKFESNFNTQATNRNTDGS

Baboon alpha-lactalbumin KQFTKCELSQNLY--DIDGYGRIALPELICTMFHTSGYDTQAIVEND-ES

Chicken lysozyme TDYGILQINSRWWCNDGRTPGSRNLCNIPCSALLSSDITASVNCAKKIVS

Baboon alpha-lactalbumin TEYGLFQISNALWCKSSQSPQSRNICDITCDKFLDDDITDDIMCAKKILD

Chicken lysozyme DGN-GMNAWVAWRNRCKGTDVQA-WIRGCRL-

Baboon alpha-lactalbumin I--KGIDYWIAHKALC-TEKL-EQWL--CE-K


Clinical Implications of

Sequencing

Fast and reliable diagnosis of disease and risk:

Easy diagnosis (with symptoms)

In advance of appearance (e.g. Huntington)

In utero diagnosis (e.g. cystic fibrosis: thick secretions in lung)

Genetic counselling

Customized treatment (predict response to therapy/side effects)

E.g. childhood leukaemia is treated with toxic drug 6-mercaptopurine.

Small fraction of patients used to die as they lack enzyme thiopurine methyltransferase.

Identify drug targets

Nowadays targets are: ½ receptors, ¼ enzymes, ¼ hormones

7% have unknown targets

Gene therapy

Replace defective genes or supply gene products (insulin for diabetes and Blood Factor VIII for haemophilia)

However : Most diseases do not have a single genetic cause!


Quick check

By now you should

Have read chapter 1

Know the main data sources (sequence and structure)

Know the role that bioinformatics plays

Understand the difference between homology and similarity

Understand what sequence comparison and alignment are

Understand how they can be useful for phylogenetic studies

Understand primary, secondary, tertiary structure

Be able to assess the assumptions made and the quality of data
