1. New, Growing, and Evolving Fields of Genetics, Genomics, and Computational Biology
“Explosion”, “Avalanche”, “Tsunami” of data
Complexity of data
30,000 “genes”
structure, function unknown
pathways (circuits) unknown
so much of biology that we do not know
chromosomal packaging, transcriptional regulation, post-translational modification, evolutionary questions – introns early/late, etc.
Stone Age of medicine for determining drugs and treatment?
Multiple model organism genomes
Requires the basics of genetics and biology
Also represents an increasing need in computational expertise

2. Biomedical information tsunami
overwhelming volume of data
multitude of sources
Taken from Ken Buetow, NCI

3. Incredible developments in biomedical information generation
Taken from Ken Buetow, NCI

4. Treatment of Disease Accelerated by Human Genome Project time
Disease with genetic component
ID genes
Diagnostics
Understanding basic
biological defect
Preventative
medicine
Gene therapy
Drug therapy
Pharmacogenomics
Adapted from Francis Collins

5. Stone Age of Medicine Try compound on model organism No No Does it
work?
Does it
harm/kill?
Try compound on
different model
organism or for
other result
Yes
Yes
Seek approval,
human testing,
clinical trials,
FDA, 16 years,
millions of dollars
Done
Knowledge from the genome
is moving us away from this.

6. Motivation of this course
Intro to Bioinformatics
provides a first exposure to some available computational techniques and resources
however, the emphasis is on utilization
In this course -- I try to emphasize tools and techniques that you would use to go about developing your own computational resources (software, systems, tools, etc).
Computational Methods in Molecular Biology
advanced topics

7. What is genomics? Genomics Functional genomics Proteomics
Mapping, sequencing and analysis of genomes
Functional genomics
Simultaneous study of function of all genes in pathway or system
Proteomics
Simultaneous study of all proteins in a pathway or system

8. What is Bioinformatics?
“… addresses problems related to the storage, retrieval and analysis of information about biological structure, sequence and function."
Altman, R. (1998) Bioinformatics

9. Questions people hope to answer
Can we find the genes and assign them functions?
Can we link genotype to phenotype?
Can we use genotype/phenotype to predict relevant outcome?
Can we predict protein structures and functions?
Can we reconstruct metabolic, signaling, and other pathways?
Can we reconstruct informational networks?
Can we use cross-species comparisons to learn something?

10. Bioinformatics Goals Scope Applications Limitations Future
Understand a living cell and how it functions at the molecular level
Scope
Develop computational tools and databases
Application of these tools and databases to understand living systems
Applications
Knowledge-based drug design; biotechnology of all kinds
Limitations
Dependent on quality information; errors in sequencing or annotation; algorithm errors due to invalid assumptions
Future
Systems biology: molecular simulation of all cellular processes

11. Database construction and curation
Structure
analysis
Applications
Structure
analysis
Sequence
analysis
Function
analysis
Software development
Database construction and curation

12. Topics Biomolecular databases and genome viewers (1)‏
Sequence similarity and alignments (2)‏
Annotation and protein analyses/predictions (3)‏
Pathways, protein interactions and structure (4)‏
Comparative genomics and genetic information (5)‏

13. Gathering knowledge 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
Rembrandt, 1632
Newton, 1726

14. Bioinformatics Bioinformatics Chemistry Biology Mathematics Molecular
Statistics
Bioinformatics
Computer
Science
Informatics
Medicine
Physics

15. 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

16. 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)‏

17. Bioinformatics in the olden days (Cont.)‏
Evolution was studied and methods created
Phylogenetic reconstruction (clustering – NJ method

18. But then the big bang….

19. The Human Genome -- 26 June 2000
Dr. Craig Venter
Celera Genomics
-- Shotgun method
Dr. Francis Collins / Sir John Sulston
Human Genome Project

20. Human DNA
There are about 3bn (3  109) nucleotides in the nucleus of almost all of the trillions (5-10  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 ~1023 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

21. Human DNA (Cont.)‏
All people are different, but the DNA of different people only varies for 0.2% or less. So, only 1 letter in ~1400 is 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).
The Human Genome has recently been announced as complete (in 2004).

22. Genome size Organism Number of base pairs X-174 virus 5,386
Epstein Bar Virus 172,282
Mycoplasma genitalium 580,000
Hemophilus Influenza 1.8  106
Yeast (S. Cerevisiae)  106
Human  109
Wheat  109
Lilium longiflorum 90  109
Salamander  109
Amoeba dubia  109

23. Humans have spliced genes…

24. A gene codes for a protein
mRNA
DNA
transcription
translation
CCTGAGCCAACTATTGATGAA
PEPTIDE
CCUGAGCCAACUAUUGAUGAA

25. Genome revolution has changed bioinformatics
More high-throughput (HTP) applications (cluster computing, GRID, etc.)‏
More automatic pipeline applications
More user-friendly interfaces
Greater emphasis on biostatistics
Greater influence of computer science (machine learning, software engineering, etc.)‏
More integration of disciplines, databases and techniques

26. New areas interfacing bioinformatics
Systems Biology
Cellular networks
Quantitative studies
Time processes
Cellular compartmentation
Multi-scale modelling
Link with experiment
Neurobiology
From genome information to behaviour
Brain modelling

27. Protein Sequence-Structure-Function
Ab initio prediction and folding
Sequence
Structure
Function
Threading
Function prediction from structure
Homology searching (BLAST)‏

28. Luckily for bioinformatics…
There are many annotated databases (i.e. DBs with experimentally verified information)‏
Based on evolution, we can relate biological macromolecules and then “steal” annotation of “neighbouring” proteins or DNA in the DB.
This works for sequence as well as structural information
Problem we discuss in this course: how do we score the evolutionary relationships; i.e. we need to develop a measure to decide which molecules are (probably) neighbours and which are not
Sequence – Structure/function gap: there are far more sequences than solved tertiary structures and functional annotations. This gap is growing so there is a need to predict structure and function.

29. What is a database?
A collection of information, usually stored in an electronic format that can be searched by a computer.

30. Databases (DB)‏ Sets of data Stored on computer Explicit data model
What is in the DB? What is not?
Well-defined data structure

31. Analysis methods Searches Comparison Features Keywords or free text
Similarity
Comparison
Sequence-sequence alignment
Structures
Features
Identification of (sites, domains)‏
Prediction of (secondary structure)‏

32. Different Databases in Bioinformatics
Sequence Databases
SNP/mutation DBs
Structure Databases
Expression Databases
Spectral Databases
Metabolism Databases
Drug Databases
Cell/Strain Databases
Organism Databases
Interaction Databases
Function/Ontology DBs
Bibliographic DBs
Disease Databases
Databases of Databases

33. Why So Many Databases? To collect and preserve valuable data
To make data accessible and easily searched
To standardize data representation or data formats
To organize data into knowledge

34. Different Types of Databases
Public (web-accessible, downloadable)
Local or Private (restricted to registered users or not web-accessible)
Archival (anything goes)
Curated (managed data submission)
Specialty databases (special interest)
General databases (wide interest)

35. Where To Go For More Info?
Nucleic Acids Research Web Server Issue (every Jan.)

36. Sequence Databases* GenBank EMBL/trEMBL/UniProt DDBJ PIR SwissProt
EMBL/trEMBL/UniProt
DDBJ
PIR
SwissProt

37. Sequence Databases
Some specialize in DNA sequence data (GenBank, DDBJ)
Some specialize in protein sequence data (PIR, Swiss-Prot, UniProt)
Some are specific for organisms or classes of organisms (yeast, fruitflies, human, certain bacteria)

38. Sequence Annotation
Most sequence databases usually include more information than just the raw sequence data
This additional information is called “annotation” and it may be done either automatically or manually
Annotations include information about gene/protein name, length, position, references, corrections, etc

39. Different Levels of Annotation
Sparse – typical of most archival DNA sequence databases (GenBank, DDBJ)
Moderate – typical of more curated databases or protein-specific databases (PIR, trEMBL)
Detailed – typical of organism-specific databases or databases with a very high level of curation (Swiss- Prot, EcoCyc, BacMap)

40. Different Levels of Database Annotation*
GenBank (large # of sequences, minimal annotation)
PIR (large # of sequences, slightly better annotation)
SwissProt (small # of sequences, even better annotation)
Organism-specific DB (very small # of sequences, best annotation)

41. Important web sites EBI NCBI www.ebi.ac.uk/
European Bioinformatics Institute
Databases: EMBL, UniProt, Ensembl,…
NCBI
National Center for Biotechnology Information
Databases: PubMed, Entrez, OMIM,…

42. Historical background, 1
“Atlas of Protein Sequence and Structure”, Margaret Dayhoff et al 1965
Printed book with all published sequences
New editions into the 1970s
Basis for Protein Information Resource (PIR), pir.georgetown.edu/
Since 2003 part of UniProt,

43. Historical background, 2
SwissProt
Amos Bairoch, University of Geneva
Swiss Institute of Bioinformatics,
Data from literature, carefully curated
Started in 1986
Since 2003 part of UniProt

44. DB properties Quality Comprehensiveness Redundancy
Error rates, types of errors
Update policy
Comprehensiveness
Data sources
Redundancy
Multiple entries for same biological item?

45. Consider when choosing a DB
Central data type
Data entry and quality
Primary or derived data
Maintainer status
Availability

46. Central data types Nucleotide sequences Protein sequences
EMBL, GenBank
Protein sequences
UniProt (PIR, Swiss-Prot)‏
Genes, genomes
Ensembl, EntrezGene
3D structure
PDB (RSCB)‏

47. Data entry and quality Method of data entry Quality control
Scientists deposit data
Curators enter data (from literature)‏
Quality control
Consistency, redundancy, conflicts
Are checks applied?
Update policy
Regularity
Are errors removed?

48. Primary and Secondary Databases
Primary databases
REAL EXPERIMENTAL DATA
Biomolecular sequences or structures and associated annotation information (organism, function, mutation linked to disease, functional/structural patterns, bibliographic etc.)
Secondary databases
DERIVED INFORMATION
Fruits of analyses of sequences in the primary sources (patterns, blocks, profiles etc. which represent the most conserved features of multiple alignments)
Primary database contains:
Annotation, administration, etc., but also REAL EXPERIMENTAL DATA
Secondary database contains:
Data from primary database(s), extra annotation and administration, and added value (calculation, annotation, etc).
Most conserved features of the multiple alignment such that they are able to provide potent discriminators of family members for newly determined sequences.

49. Primary Databases Sequence Information Genome Information
DNA: EMBL, Genbank, DDBJ
Protein: SwissProt, TREMBL, PIR, OWL
Genome Information
GDB, MGD, ACeDB, ENSEMBL
Structure Information
PDB, NDB, CCDB/CSD
DDBJ - The DNA Data Bank of Japan.
The OWL database is a non-redundant protein sequence database produced from the following source databases: SWISSPROT PIR(1-3) GenBank translations NRL-3D
PIR(1-3) - The Protein Identification Resource
EMBL - The European Molecular Biology DNA Sequence Database
PDB - The Protein Databank (3D structures)
AceDB was originally developed for the C. elegans genome project , from which its name was derived (A C. elegans DataBase). However, the tools in it have been generalized to be much more flexible and the same software is now used for many different genomic databases from bacteria to fungi to plants to man
Cambridge Crystallogrphic Database (CCDB)
Primary database contains:
Annotation, administration, etc., but also REAL EXPERIMENTAL DATA

50. Secondary Databases Sequence-related Information
ProSite, REBase
Genome-related Information
OMIM, TransFac
Structure-related Information
DSSP, HSSP, FSSP, PDBFinder
Pathway Information
KEGG, Pathways
Function-related
Enzyme, GO
PROSITE - A Dictionary of Protein Sites and Patterns
(1492 patterns (oct 2001))
EC-Enzyme - The EC Enzyme Classification Database
OMIM - Online Mendelain Inheritance in Man
SWISS-2DPAGE - Two-dimensional Polyacrylamide Gel Electrophoresis Database
REBASE - The Restriction Enzyme Database
Refbase - A Protein Sequence Citation Database
KEGG: Kyoto Encyclopedia of Genes and Genomes
DSSP database of sec struct assignments (and much more) for all of the entries in the PDB. 15xxx entries.
HSSP homology-derived struct of proteins; derived db; merging struct (2&3D) and seq info (1D). Implied sec & tert struct. 15xxx entries.
FSSP families of structurally similar proteins. Structural alignment of proteins in PDB entries.
Secondary database contains:
Data from primary database(s), extra annotation and administration, and added value (calculation, annotation, etc).

51. Databases
Data must be in certain format for the programs to recognize them.
Every database can have its own format, but some data elements are essential for every database: Unique identifier, or accession code 2. Name of depositor 3. Literature references 4. Deposition date 5. The real data

52. 3 examples
1. SwissProt
2. EMBL
3. PDB

53. Quality of databases SwissProt EMBL, PDB
Data is only entered by annotation experts
EMBL, PDB
Everybody can submit data
Data are accepted the way they are submitted

54. SwissProt database Database of protein sequences
Produced by Amos Bairoch (University of Geneva) and the EMBL Data Library
Data derived from:
translations of DNA sequences (from EMBL Database)
adapted from the PIR collection
extracted from the literature
and directly submitted by researchers
SwissProt & SwissNew
July 2001:
~86,600 entries, ~15,000 new entries / year
Swissnew: 53,000 entries
Ca. 200 Annotation experts worldwide
Keyword-organised flatfile
31 miljoen/86593 = 357 aa
20 annotators doen 99% vanhet werk. 200 remote experts doen de 1% probleemgevallen.

55. SwissProt records (1) ID identification line
ID ENTRY_NAME DATA_CLASS; MOLECULE_TYPE; SEQUENCE_LENGTH. ID CRAM_CRAAB STANDARD; PRT; AA.
Format for the ENTRY_NAME:
NAME_SPECIES ( 10 characters) here: Crambin (Crambe abyssinica)
For number of organisms (16) SPECIES has a recognizable name: HUMAN, MOUSE, CHICK, BOVIN, YEAST, ECOLI….
N.B. The ID can change, e.g. serotonine receptors have got a new nomenclature
SWISSPROT:CRAA_HUMAN; alpha crystallin a chain.

56. SwissProt records (2)
AC accession number AC P01542; AC is unique: Name, sequence, everything can change but AC stays the same
DT deposition date DT 21-JUL-1986 (Rel. 01, Created) DT 30-MAY-2000 (Rel. 39, Last sequence update) DT 30-MAY-2000 (Rel. 39, Last annotation update) 1) You can not see what the last annotation update was 2) No depositor record (Implicit: author of first reference)
DT relevance ivm protein vs DNA sequencing

57. SwissProt records (3)
DE description DE CRAMBIN. DE 6-phosphofructo-2-kinase 1 (EC ) (Phosphofructokinase 2 I) 1) General descriptive information 2) Free-format
GN gene name GN THI2.
OS & OC & OG
OS Crambe abyssinica (Abyssinian crambe). OC Eukaryota; Viridiplantae; Embryophyta;Tracheophyta;Spermatophyta; OC Magnoliophyta; eudicotyledons; Rosidae; eurosids II; Brassicales; OC Brassicaceae; Crambe.
Organism Species; Organism Classification; OrGanelle
Organel m.n. mitochondrion & chloroplast

58. SwissProt records (4)
RN References RN [1] RP SEQUENCE. RX MEDLINE; RA Teeter M.M., Mazer J.A., L'Italien J.J.; RT "Primary structure of the hydrophobic plant protein crambin."; RL Biochemistry 20: (1981).
CC Comments or notes CC -!- FUNCTION: THE FUNCTION OF THIS HYDROPHOBIC PLANT SEED PROTEIN CC IS NOT KNOWN. CC -!- MISCELLANEOUS: TWO ISOFORMS EXISTS, A MAJOR FORM PL (SHOWN HERE) CC AND A MINOR FORM SI. CC -!- SIMILARITY: BELONGS TO THE PLANT THIONIN FAMILY.
RP = keyword, waar de referentie over gaat, bijvoorbeeld: STRUCTURE, SEQUENCE.
Als [RP= Sequence], is de eerste auteur van deze referentie de depositor van de data.
CC CATALYTIC ACTIVITY
CC TISSUE SPECIFICITY

59. SwissProt records (5)
DR Database Cross Reference DR PIR; A01805; KECX. DR PDB; 1CRN; 16-APR-87. DR PDB; 1CBN; 31-JAN-94. DR PDB; 1CCM; 31-OCT-93. DR PDB; 1CCN; 31-JAN-94. DR PDB; 1CNR; 31-AUG-94. DR PDB; 1AB1; 12-AUG-97. DR INTERPRO; IPR001010; -. DR PFAM; PF00321; plant_thionins; 1. DR PRINTS; PR00287; THIONIN. DR PROSITE; PS00271; THIONIN; 1.
KW Keyword Not standardized (under control of depositor) KW Thionin; 3D-structure.
C-C-x(5)-R-x(2)-[FY]-x(2)-C
[The three C's are involved in disulfide bonds]
plant thionins signature
InterPro - Integrated Resource of ProteinDomains and Functional Sites
PRINTS is a compendium of protein fingerprints. A fingerprint is a group of conserved motifs used to characterise a protein family; its diagnostic power is refined by iterative scanning of OWL. Usually the motifs do not overlap, but are separated along a sequence, though they may be contiguous in 3D-space. Fingerprints can encode protein folds and functionalities more flexibly and powerfully than can single motifs: thedatabase thus provides a useful adjunct to PROSITE.

60. SwissProt records (6) FT Feature table data
FT DISULFID FT DISULFID FT DISULFID FT VARIANT P -> S (IN ISOFORM SI). FT VARIANT L -> I (IN ISOFORM SI). FT STRAND FT HELIX FT TURN FT HELIX FT TURN FT STRAND FT TURN

61. Feature table
Other features: post-translational modifications, binding sites, enzyme active sites, local secondary structure or other characteristics reported in the cited references. Sequence conflicts between references are also included.
FT CONFLICT MISSING (IN REF. 2).
FT MUTAGEN G->R,L,M: DNA BINDING LOST.
FT MOD_RES PHOSPHORYLATION (BY PKC).
FT LIPID 1 1 MYRISTATE.
FT CARBOHYD GLUCOSYLGALACTOSE.
FT METAL COPPER (POTENTIAL).
FT BINDING HEME (COVALENT).
FT PROPEP ACTIVATION PEPTIDE.
FT DOMAIN EXTRACELLULAR (POTENTIAL).
FT ACT_SITE ACCEPTS A PROTON DURING CATALYSIS.
FT VARSPLIC GRP -> DVR (IN SHORT FORM).

62. SwissProt records (7)
SQ sequence header SQ SEQUENCE 46 AA; MW; 919E68AF159EF722 CRC64;
Sequence data TTCCPSIVAR SNFNVCRLPG TPEALCATYT GCIIIPGATC PGDYAN
// Termination line
Dit getal - 919E68AF159EF722 CRC64 – is de zogenaamde check-sum. Een getal berekend door de computer om te controleren of de data nog correct is.

63. EMBL database Nucleotide database EMBL & EMNEW July 2001:
EMBL: 3,951,820 entries, EMNEW: 323,703
EMEST*: 8,092,600, EMNEWEST*: 619,777
*) EMEST/EMNEWEST = EST-section of EMBL, EST = expressed sequence tag
EMBL records follows roughly same scheme as SwissProt
Obligatory deposit of sequence in EMBL (or SwissProt) before publication
EMEST = EST-sectie van de EMBL database. EST – expressed sequence tag.

64. Protein Data Bank (PDB)
Databank for macromolecular structure data (3- dimensional coordinates)
Obligatory deposit of coordinates in the PDB before publication
~16,000 entries (October 2001)
PDB file is a keyword-organised flat-file (80 column)
human readable
every line starts with a keyword (3-6 letters)
platform independent
Started ca. 25 years ago (on punche cards!)
Naast eiwittenook DNA & RNA!!

65. PDB records (1)
Filename= accession number= PDB Code 1) Filename is 4 positions (often 1 digit & 3 letters, e.g. 1CRN) 2) Be aware: 0HYK means entry HYK does not contain coordinates
HEADER describes molecule & gives deposition date HEADER PLANT SEED PROTEIN APR CRN 1CRND 1
CMPND name of molecule COMPND CRAMBIN CRN 4
SOURCE organism SOURCE ABYSSINIAN CABBAGE (CRAMBE ABYSSINICA) SEED CRN 5

66. PDB records (2) AUTHOR AUTHOR W.A.HENDRICKSON,M.M.TEETER 1CRN 6 JRNL
The depositor
JRNL
JRNL AUTH M.BLABER,X.-J.ZHANG,B.W.MATTHEWS L 10
JRNL TITL STRUCTURAL BASIS OF ALPHA-HELIX PROPENSITY AT TWO L 11
JRNL TITL 2 SITES IN T4 LYSOZYME L 12
JRNL REF SCIENCE V L 13
JRNL REFN ASTM SCIEAS US ISSN L 14
REMARK Not standardized: many different REMARK records & subrecords! REMARK 1 REFERENCE CRNC 10 REMARK 1 AUTH M.M.TEETER,W.A.HENDRICKSON CRN 16 REMARK 1 TITL HIGHLY ORDERED CRYSTALS OF THE PLANT SEED PROTEIN 1CRN 17 REMARK 1 TITL 2 CRAMBIN CRN 18 REMARK 1 REF J.MOL.BIOL V CRN 19 REMARK 1 REFN ASTM JMOBAK UK ISSN CRN 20 REMARK CRN 21 REMARK 2 RESOLUTION. 1.5 ANGSTROMS CRN 22

67. PDB records (3)
SEQRES Sequence of protein; Be aware: Not always all 3D-coordinates are present for all the amino acids in SEQRES!! SEQRES THR THR CYS CYS PRO SER ILE VAL ALA ARG SER ASN PHE 1CRN 51 SEQRES ASN VAL CYS ARG LEU PRO GLY THR PRO GLU ALA ILE CYS 1CRN 52 SEQRES ALA THR TYR THR GLY CYS ILE ILE ILE PRO GLY ALA THR 1CRN 53 SEQRES CYS PRO GLY ASP TYR ALA ASN CRN 54
HET & FORMUL
metals, cofactors, ions, etc. HET NAD A NAD CO-ENZYME MDH 219 HET SUL A SULFATE MDH 220 HET NAD B NAD CO-ENZYME MDH 221 HET SUL B SULFATE MDH 222 FORMUL 3 NAD 2(C21 H28 N7 O14 P2) MDH 223 FORMUL 4 SUL 2(O4 S1) MDH 224 FORMUL 5 HOH *471(H2 O1) MDH 225
HEADER OXIDOREDUCTASE(NAD(A)-CHOH(D)) APR MDH MDH 3
COMPND CYTOPLASMIC MALATE DEHYDROGENASE (E.C ) MDH 4
SOURCE PORCINE (SUS $SCROFA) HEART MDH 5

68. PDB records (4)
HELIX/SHEET/TURN Secondary structure elements as provided by the crystallographer (subjective) HELIX H1 ILE PRO /10 CONFORMATION RES 17, CRN 55 SHEET S1 2 CYS ILE CRN 58 TURN T1 PRO TYR CRN 59
SSBOND disulfide bridges SSBOND 1 CYS CYS CRN 60 SSBOND 2 CYS CYS CRN 61
CRYST1, ORIGX1, ORIGX2, ORIGX3, SCALE1, SCALE2, SCALE3 crystallographic parameters CRYST P CRN 63 ORIGX CRN 64 ORIGX CRN 65 ORIGX CRN 66 SCALE CRN 67 SCALE CRN 68 SCALE CRN 69

69. PDB records (5)
ATOM one line for each atom with its unique name and its x,y,z coordinates ATOM N THR CRN 70 ATOM CA THR CRN 71 ATOM C THR CRN 72 ATOM O THR CRN 73 ATOM CB THR CRN 74 ATOM OG1 THR CRN 75 ATOM CG2 THR CRN 76 ATOM N THR CRN 77 ATOM CA THR CRN 78 ATOM C THR CRN 79 ATOM O THR CRN 80
TER record terminates the amino acid chain ATOM OD1 ASN CRN 394 ATOM ND2 ASN CRN 395 ATOM OXT ASN CRN 396 TER ASN CRN 397
One TER-record per molecule. So if protein is a dimer, than two TER-records.

70. PDB records (6)
HETATM atomic coordinate records for atoms within “HET & FORMUL”-lines (metals, cofactors, ions, …) and for water molecules HETATM 5158 AP NAD B MDH5495 HETATM 5159 AO1 NAD B MDH5496 HETATM 5160 AO2 NAD B MDH HETATM O HOH MDH5544 HETATM O HOH MDH5545 HETATM O HOH MDH5546

71. DB vs. Interface Confusion: Interface is not same as DB!
Interface is the method of access
Database (DB) is the data itself
Same DB accessed by different interfaces (UniProt from ExPASy or EBI)‏
One interface may be used to access different databases (SRS)‏

72. Maintainer status Large, academic, public institute
EBI, NCBI
Quasi-academic institute
TIGR, SIB
Research group or scientist
Company

73. Availability Publicly available, no restrictions
EMBL, GenBank
Available, but with copyright
May not be re-used in other DB
UniProt
Commercial
Copyright
May be accessible to academics at no charge

74. DB identifiers Identify a DB item uniquely A primary key for an item
Permanent
“Accession code”
E.g. P01112
Use this for UniProt, EMBL, etc
“Entry name”
E.g. RASH_HUMAN
Warning: may change!
P01112
RASH_HUMAN

75. Accession codes and updates
DB items may be merged or split
Two sequence entries merged, e.g. they were actually the same protein
A sequence entry split, e.g. actually from two different genes
Primary accession code
The new, recommended code
Secondary accession code
The old; kept for trackability
Version numbers in some DBs

76. Nucleotide sequence DBs
Primary
EMBL,
GenBank,
Collaboration and synchronization
Data submitted directly from sequencing projects, scientists
Large, with subdivisions
Redundant, fragments, rather messy…
Publicly available, no restrictions

77. Genome DBs, 1 Nucleotides, but complete genome
Usually high quality, careful annotation
Ensembl
Eukaryotes
Automatic annotation, links to other DBs SG
Vega
Manually curated, built on top of Ensembl

78. Genome DBs, 2 UniGene EntrezGene (LocusLink)‏
“An organized view of the transcriptome”
EntrezGene (LocusLink)‏
d=Retrieve&dopt=full_report&list_uids=3265
Species-specific databases
SGD, Saccharomyces cerevisiae,
Berkeley Drosophila Genome Project,

79. Protein sequence DBs UniProt Swiss-Prot TrEMBL www.uniprot.org
UniProtKB = UniProt Knowledge Base
UniProt = Swiss-Prot + PIR + TrEMBL
Swiss-Prot
Credible sequences
Manual expert annotation
TrEMBL
Translations of EMBL nucleotide sequences
Automatic, basic annotation
Eventually integrated into Swiss-Prot

80. UniProt ExPASY interface (Swiss Inst Bioinfo)‏ EBI interface
EBI interface
srv/uniProtView.do?proteinId=RASH_HUMAN&pag er.offset=0
Same data, different look; your choice

81. Protein sequence domains
Domains, motifs, families
Patterns of similar residues in a section of a protein sequence
Often a functional and structural unit
The presence of a domain: hints on function
Pfam
Protein sequence domains
Hidden Markov Models, software HMMER

82. Macromolecular 3D structure
Protein Data Bank, PDB
Oldest computer-based bio-DB (1971)‏
3D structures of proteins, oligonucleotides
X-ray crystallography and NMR
SCOP, Structural Classification
Hierarchical scheme of classification
Similar 3D structures in families

83. Others, 1 OMIM Online Mendelian Inheritance in Man
Human genes and genetic disorders
d=190020

84. Others, 2 GeneCards GeneLynx Aggregate database; human
Links from gene to other DBs
GeneLynx
Aggregate database; human, rat, mouse

85. Databases
Flat-file
Relational
Object-oriented

86. Flat-file databases Each entry is its own text file
Easy to set up and understand
Computationally intensive to search
No connections between records
Doesn’t work for large databases

87. Relational databases Data is organized in sets of tables
Columns represent fields and rows represent records
Columns are indexed with attribute that can be cross-referenced by other tables
Databases are queried using a structured query language (SQL)‏
Require much more planning to set up, but much faster and easier to access than flat files

88. Object oriented databases
Store data as objects
Allows hierarchical associations to be made
Makes some programming tasks easier
Better suited to complex data types (i.e. protein-protein interactions where there are multiple interactions between proteins

89. Exercise Text-based searching of sequence databases
Refining searches using query logic (Boolean)‏
Retrieving data and formatting it for other programs

90. Exercise 1: Sequence database searching by keyword
Purpose:
To practice with Boolean search terms to learn how to refine searches based on keywords.
To familiarize yourself with navigating between databases within the Entrez system.
To become familiar with text searches of integrated biological databases through Entrez.
Activities:
Find references to the genes in the literature
Locate the database references for a particular human gene.
Practice query searching with limits and wildcards.

91. 1 AND 2 1 2 lipocalin AND disease (60 resulten)‏ 1 OR 2 1 2
lipocalin OR disease
( resultaten)‏
1 NOT 2 1 2
lipocalin NOT disease
(530 resultaten)‏ 91

92. Text based search of NCBI Entrez
Query 1: human MAP kinase inhibitor
How many nucleotide, protein and gene records does this query return?
Query 2: put quotes around the original query
How many records are returned from the 3 databases if you search with quotes around the entire query?

93. Query 3: Drop the quotes and limit the records to human only.
How many records were returned from the nucleotide, protein and Gene database?
From this final query, look at the records returned from the Gene database and go through the annotations of the first records.
What family of kinase inhibitors is represented?
With what kinases do they interact?
What is the function of this family of kinase inhibitors?
Despite searching for kinase inhibitors, why were so many kinase records returned?
What strategy did you employ to restrict the searches to records from human only?

94. Query 4: Turn off any limits that you may have set in the previous searches and do the following query of Entrez: human catalase.
How many records were returned from the nucleotide, protein and Gene databases?
What is the official gene symbol for human catalase?
What are 2 proteins that catalase interacts with?
On what KEGG pathways is it located (include pathway name and ID)?
What reaction does it catalyze?
What is the enzyme classification number for human catalase?
What co-factors are required for catalase to catalyze this reaction?

95. Query 5: Toll-like receptor genes.
Answer the following questions about human Toll-like receptor proteins.
How many human toll-like receptors are there?
What are their Refseq accession numbers, official genes symbols and gene names?
Which ones have a mouse homolog?
What is the mouse homolog Refseq accession number?
What are the adapter molecules for each toll-like receptor?
What is the major role of toll-like receptors in human immunology?
Which toll-like receptors dimerize with each other?

96. Query 6: Do the following query of Entrez: HIV CCR5 receptor
How many records were returned in the nucleotide, protein and Gene databases?
What is the official gene symbol for the HIV CCR5 receptor?
What are two aliases for the official gene symbol?
What is the offical gene name for HIV CCR5 receptor?
What does the Gene record tell you about its structure?
What is its importance in the biology of an HIV infection?
How many exons doe the gene have?
How long is the protein?