1. Introduction
based on
Chapter 1
Lesk, Introduction to Bioinformatics

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

3. 23 June 2000: Draft of Human genome sequenced!
1953: Watson and Crick discover the structure of DNA
2000: Draft of human genome is published
“The most wondrous map ever produced by human kind”
“One of the most significant scientific landmarks of all time, comparable with the invention of the wheel or the splitting of the atom”

4. High-throughput biomedicine
Microarrays
Measure activity of thousands of genes at the same time
Example:
Cancer
Compare activity with and without drug treatment
Result: Hundreds of candidate drug targets
RNAi (Noble prize 2004, Fire and Mello)
Knock-down genes and observe effect
Infectious diseases
Which proteins orchestrate entry into cell?
Result: Hundreds of candidate proteins
Atomic force microscopes (Noble prize Binnig)
Pull protein out of membrane and measure force
Eye diseases resulting fomr misfolding
Result: Hundreds of candidate residues

5. Drug Discovery
Challenge: Longer time to market, fewer drugs, exploding costs
Approach: Use of compound libraries and high-throughput screening

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

7. Good News >1.000.000 >16.000.000 Sequences Articles >700
DBs/Tools
>30.000
3D Structures

8. Bad News: Data != Knowledge
How to analyse data, how to integrate data?
Comptuer science to the rescue…

9. Examlpe: computer science is key for sequencing
Human genome is a string of length
Shotgun sequencing: Break multiple copies of string into shorter substrings
Example:
shotgunsequencing shotgunsequencing shotgunsequencing
cing en encing equ gun ing ns otgu seq sequ sh sho shot tg uenc un
Computing problem: Assemble strings

10. Computer science key for sequencingsh
sho
shot
otgu tg
gun un ns
seq
sequ
equ
uenc
encing en
cing
ing
QUESTION: How can you handle
long repetitive sequences?
Heeeeelllllllllllooooooo
QUESTION: Why was a draft
announced? When was the final
version ready?

11. Arabidopsis
thaliana
mouse
rat
Caenorhabitis
elegans
Drosophila
melanogaster
Mycobacterium
leprae
Vibrio
cholerae
Plasmodium
falciparum
tuberculosis
Neisseria
meningitidis
Z2491
Helicobacter
pylori
Xylella
fastidiosa
Borrelia
burgorferi
Rickettsia
prowazekii
Bacillus
subtilis
Archaeoglobus
fulgidus
Campylobacter
jejuni
Aquifex
aeolicus
Thermotoga
maritima
Chlamydia
pneumoniae
Pseudomonas
aeruginosa
Ureaplasma
urealyticum
Buchnerasp.
APS
Escherichia
coli
Saccharomyces
cerevisiae
Yersinia
pestis
Salmonella
enterica
Thermoplasma
acidophilum

12. Break through of the year 2000
Next quest: Sequencing a genome for 1000$

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

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

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

16. DNA – the molecule of life

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

18. The genetic code

19. Protein Structure DNA: Proteins:
Nucleotides are very similar and hence the structure of DNA is very uniform
Proteins:
Great variety in three-dimensional conformation to support diverse structure and functions
If heated, protein “unfolds” to biologically-inactive structure; in normal conditions protein folds

20. Paradox Translation from DNA sequence to amino acid sequence
is very simple to describe,
but requires immensely complicated machinery (ribosome, tRNA)
The folding of the protein sequence into its three-dimensional structure
is very difficult to describe
But occurs spontaneously

21. Central Dogma DNA sequence determines protein sequence
Protein sequence determines protein structure
Protein structure determines protein function

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

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

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

25. Databases: Types of Queries 1/2
1. Given a sequence (fragment), find sequences in the database that are similar to it
2. Given a protein structure (or fragment), find protein structures in the database that are similar to it
3. Given sequence of a protein of unknown structure, find structures in the database that adopt similar three-dimensional structures
4. Given a protein structure, find sequences in the database that correspond to similar structures.

26. Databases: Given sequence, find structure
3. Given sequence of a protein of unknown structure, find structures in the database that adopt similar three-dimensional structures. But How?
Easy: Find similar sequences with known structure!
But: There might be similar structures, whose sequence is not similar!
4. Given a protein structure, find sequences in the database that correspond to similar structures. But How?
Easy: Find similar structures and hence sequences
But: There are so many more sequences with unknown structure that the above method will have only very limited success
1 and 2 are solved, 3 and 4 are active fields of research

27. Databases: Types of Queries 2/2
E.g. for which proteins of known structure involved in disease of disrupted purine biosynthesis in humans, are there related proteins in yeast?
Solution: Virtual databases that provide transparent access to a number of underlying data sources and query and analysis tools

28. Databases: Curation and Quality
Problems:
Given that there are primary and secondary databases,
how to control updates,
how to propagate change,
how to maintain consistency?
Contents (experimental results, annotations, supplementary information) all have there own source of error
Older data were limited by older techniques

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

30. Computers and Computer Science
Relevant areas:
Artificial Intelligence
Machine Learning
Neural networks, rule-based learning
Datamining
Association rules
Software Engineering
Design, implementation, testing of software
Programming
Object-oriented C++, Java
Imperative: C, Modula, Pascal, Cobol, Fortran
Logic: Prolog
Funtional: ML
Scripting: Perl, Python
Statistics
Database theory
Design and maintenance of databases
How to index sequences, time series, 3D strucutres
Information Visualisation
Graph drawing, diagrams, cartoons, 3D graphics
Algorithm design
Complexity of algorithms
Efficient data structures

31. Programming We will use Python Scripting language
Supports string processing well
Widely used in bioinformatics

32. Biological Classification and Nomenclature
Back in 18th century, Linnaeus, a Swedish naturalist, classified living things according to a hierarchy: Kingdom, Phylum, Class, Order, Family, Genus, Species
Generally only genus and species are used for identification
Homo sapiens
Drosophila melanogastor
Bos taurus
Linnaeus’ classification based on observed similarity
Widely reflects biological ancestry

33. Classification of Humans and Fruit Flies
Kingdom: Animalia Animalia
Phylum: Chordata Chordata
Class: Mammalia Insecta
Order: Primata Diptera
Family: Hominidae Drosophilidae
Genus: Homo Drosophila
Species: sapiens melanogastor

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

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

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

37. Eukaryotic cell

38. Domains of Life

39. Example: Use of sequences to determine phylogenetic relationships
Use ExPASy ( to search for
pancreatic ribonuclease for
horse (Equus caballus),
minke whale (Balaenoptera acutorostrata),
red kangaroo (Macropus rufus)
>sp|P00674|RNP_HORSE Ribonuclease pancreatic (EC ) (RNase 1) (RNase A) - Equus caballus (Horse). KESPAMKFERQHMDSGSTSSSNPTYCNQMMKRRNMTQGWCKPVNTFVHEPLADVQAICLQKNITCKNGQSNCYQSSSSMHITDCRLTSGSKYPNCAYQTSQKERHIIVACEGNPYVPVHFDASVEVST
Use sequence alignment to determine evolutionary relationship

40. Sequence alignment
Global match: align all of one with all of the other sequence (mismatches, insertions, deletions) And.--so,.from.hour.to.hour.we.ripe.and.ripe |||| |||||||||||||||||||||||| |||||| And.then,.from.hour.to.hour.we.rot-.and.rot-
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.done, ||||||||| ||||||||||||| |||||| || Your.care.is.gain.of.care,.by.new.care.won

41. Sequence alignment 3. Motif search:
find matches of short sequence in long sequence
Option:
perfect,
1 mismatch,
mismatches+gaps+insertions+deletions
match |||| for the watch to babble and to talk is most tolerable

42. 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.

43. 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 (
multiple sequence alignment computer program
main parameters: gap opening/extension penalty

44. FASTA format
>sp|P00674|RNP_HORSE Ribonuclease pancreatic (EC ) (RNase 1) (RNase A) - Equus caballus (Horse).
KESPAMKFERQHMDSGSTSSSNPTYCNQMMKRRNMTQGWCKPVNTFVHEPLADVQAICLQ
KNITCKNGQSNCYQSSSSMHITDCRLTSGSKYPNCAYQTSQKERHIIVACEGNPYVPVHF
DASVEVST
>sp|P00673|RNP_BALAC Ribonuclease pancreatic (EC ) (RNase 1) (RNase A) - Balaenoptera acutorostrata (Minke whale) (Lesser rorqual).
RESPAMKFQRQHMDSGNSPGNNPNYCNQMMMRRKMTQGRCKPVNTFVHESLEDVKAVCSQ
KNVLCKNGRTNCYESNSTMHITDCRQTGSSKYPNCAYKTSQKEKHIIVACEGNPYVPVHF
DNSV
>sp|P00686|RNP_MACRU Ribonuclease pancreatic (EC ) (RNase 1) (RNase A) - Macropus rufus (Red kangaroo) (Megaleia rufa).
ETPAEKFQRQHMDTEHSTASSSNYCNLMMKARDMTSGRCKPLNTFIHEPKSVVDAVCHQE
NVTCKNGRTNCYKSNSRLSITNCRQTGASKYPNCQYETSNLNKQIIVACEGQYVPVHFDA YV

45. 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
DAYV
* *

46. 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

47. 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?

48. 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
**************************************:*********************
LHFILPFTMIALAGVHLTFLHETGSNNPLGLTSDSDKIPFHPYYTIKDFLGLLILILLLL 240
LHFILPFTMIALAGVHLTFLHETGSNNPLGLTSDSDKIPFHPYYTIKDFLGLLILILFLL 240
FHFILPFTMVALAGVHLTFLHETGSNNPLGLTSDSDKIPFHPYYTIKDFLGLLILILLLL 240
:********:***********************************************:**
LLALLSPDMLGDPDNYMPADPLNTPLHIKPEWYFLFAYAILRSVPNKLGGVLALLLSILI 300
LLALLSPDMLGDPDNYMPADPLNTPLHIKPEWYFLFAYAILRSVPNKLGGVLALFLSILI 300
******************************************************:*****
LGLMPLLHTSKHRSMMLRPLSQVLFWTLTMDLLTLTWIGSQPVEYPYIIIGQMASILYFS 360
LGIMPLLHTSKHRSMMLRPLSQVLFWTLATDLLMLTWIGSQPVEYPYIIIGQMASILYFS 360
LGLMPLLHTSKHRSMMLRPLSQVLFWTLTMDLLTLTWIGSQPVEHPYIIIGQMASILYFS 360
**:*************************: *** **********:***************
IILAFLPIAGVIENYLIK 378
IILAFLPIAGMIENYLIK 378
**********:*******

49. 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

50. 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

51. 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)

52. 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?

53. 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 = base pairs long, up to 106 copies
LINES up to 7000 base pairs, up to 105 copies
They enter genome by reverse transcription of RNA

54. 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.)

55. 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

56. 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)

57. 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

58. Searching for Similar Sequences with PSI-Blast
False negatives:
300 out of 1000
are not found
Searching for Similar Sequences with PSI-Blast
Sequence Database
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
1000 Globin
Sequences
900 Search
results
True positives:
700 out of 900
are really globins
False positives: 200 out of 900
are not globins

59. 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 B C
50%
Only 10%

60. 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 (

61. Result

62. 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

63. 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| |sp|P |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_ | paired box protein Pax-6 isoform a [Homo sap
ref|XP_ | PREDICTED: paired box protein Pax-6 isofo
ref|XP_ | PREDICTED: paired box protein Pax-6 isofo
ref|XP_ | PREDICTED: paired box protein Pax-6 isofo
ref|XP_ | PREDICTED: paired box protein Pax-6 isofo
ref|NP_ | paired box protein Pax-6 [Bos taurus] >re
gb|AAA | oculorhombin [Homo sapiens]
ref|NP_ | paired box protein Pax-6 [Rattus norvegicus]
gb|EAW | paired box gene 6 (aniridia, keratitis), isofo
...

64. 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

65. 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 human proteins structures are now known
Overall protein structures in PDB
Can be obtained by X-ray crystallography or nuclear magnetic resonance (NMR)

66. Structure of Proteins Backbone and side chain | | |
Residue i-1, Residue i, Residue i+1, Si Si Si Side chain (variable)
| | |
…N-Cα-C-N-Cα-C-N-Cα-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

67. 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

68. Protein Structure
DHAP to GAP in Glycolyse
Triosephosphate isomerase from Bacillus stearothermophilus
Highly efficient enzyme appearing in most species

69. Extra layer of Architecture: supersecondary structure
Alpha-helix hairpin
Beta hairpin
Beta-alpha-beta unit
= Patterns of interaction between helices and sheets

70. 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”

71. 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

72. SCOP: Structural Classification of Proteins
top
All alpha (284)
All Beta (174)
Alpha/Beta (147)
Alpha+Beta (376)
CLASS
Immunoglobulin-like (23)
Trypsin-like serine proteases (1)
FOLD
Immunoglobulin (6)
Transglutaminase (1)
SUPERFAMILY
= evolutionary related, similar structure, not necessarily similar sequence
FAMILY
= set of domains with similar sequence
C1 set domains (antibody constant)
V set domains (antibody variable)

73. Pymol

74. 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)

75. Engrailed homeodomain (1enh)

76. 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)

77. Scytalone dehydratase (3std) OB-domain from Lys-tRNA synthetase (1bbw)
Alcohol dehydrogenase, NAD-binding domain (1ee2)
Break down of alcohol into simpler compounds
Adenylate kinase (3adk)
Energy production

78. Chemotaxis receptor methyltransferase (1af7)
Thiamine phosphate synthase (2tps)
Pancreatic spasmolytic polypeptide (2psp)

79. 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

80. 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

81. 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!

82. 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