Structure Databases

DNA/Protein structure-function analysis and prediction

Lecture 6

Bioinformatics Section, Vrije Universiteit, AmsterdamSome pics were token from http://www.umanitoba.ca/afs/plant_science/courses

The dictionary definition

Main Entry: da·ta·base

Pronunciation: 'dA-t&-"bAs, 'da- also 'dä-Origin: circa 1962

: a usually large collection of data organized especially for rapid search and retrieval (as by a computer)

- Webster dictionary

WHAT is a database?

A collection of data that needs to be: Structured (standardized data representation) Searchable Updated (periodically) Cross referenced

Challenge: To change “meaningless” data into useful information

that can be accessed and analysed the best way possible.

Organizing data into knowledge

HOW would YOU organise all biological sequences so that the biological information is optimally accessible?

You need an appropriate database management system (DBMS)

DBMS

Internal organization Controls speed and

flexibility

A unity of programs that Store Extract Modify

DatabaseDatabase

StoreStore ExtractExtract ModifyModify

USER(S)USER(S)

DBMS organisation types

Flat file databases (flat DBMS) Simple, restrictive, table

Hierarchical databases (hierarchical DBMS) Simple, restrictive, tables

Relational databases (RDBMS) Complex,versatile, tables

Object-oriented databases (ODBMS) Complex, versatile, objects

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A flat file database

Cell_Stock : "SK11.pEA215.3"Species  "Escherichia coli"Plasmid  "pEA215.3"Experiment       "SK11"Freezer  "AG334 -80C"Box      "Pisum ESTs II"Gridded  "Rack(BF7) Box(Pisum ESTs II)"

Cell_Stock : "SK11.pI206KS"Species  "Escherichia coli"Plasmid  "pI206KS"Experiment       "SK11"Freezer  "AG334 -80C"Box      "Pisum ESTs II"Gridded  "Rack(BF7) Box(Pisum ESTs II)"

Cell_Stock : "SK11.pEA46.2"Species  "Escherichia coli"Plasmid  "pEA46.2"Experiment       "SK11"Freezer  "AG334 -80C"Box      "Pisum ESTs II"Gridded  "Rack(BF7) Box(Pisum ESTs II)"

Collection of records, each containing several data fields.

Disadvantageous Redundancy Force single view of the

data (‘organizer’ and ‘attributes’)

Relational databases

Data is stored in multiple related tables

Data relationships across tables can be either many-to-one or many-to-many

A few rules allow the database to be viewed in many ways

Lets convert the “course details” to a relational database

Student 1 Chemistry Biology A B B A C …..Student 1 Chemistry Biology A B B A C …..

Student 2 Ecology Maths A D A A A …..Student 2 Ecology Maths A D A A A …..

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Course detailsCourse detailsFLAT DATABASE 2FLAT DATABASE 2

Student 2 Ecology Biology A B A A A …..Student 2 Ecology Biology A B A A A …..

Student 1 Chemistry English A A A A A …..Student 1 Chemistry English A A A A A …..........

Name Depart. Course E1 E2 E3 P1 P2Name Depart. Course E1 E2 E3 P1 P2

Student 1 Chemistry Maths C C B A A …..Student 1 Chemistry Maths C C B A A …..

Our flat file database

Normalization 1: remove repeating records (rows)

sID Name dIDsID Name dID

1 Student1 11 Student1 1

2 Student2 22 Student2 2

cID CoursecID Course

1 Biology1 Biology

2 Maths 2 Maths

3 English 3 English

dID DepartmentdID Department

1 Chemistry1 Chemistry

2 Ecology 2 Ecology

1 1 A B B A C …..1 1 A B B A C …..

2 2 A D A A A …..2 2 A D A A A …..

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..

..

..

2 1 A B A A A …..2 1 A B A A A …..

1 3 A A A A A …..1 3 A A A A A …..........

sID cID E1 E2 E3 P1 P2sID cID E1 E2 E3 P1 P2

1 2 C C B A A …..1 2 C C B A A …..

Primary keysPrimary keysForeign keysForeign keys

sID Name dIDsID Name dID

1 Student1 11 Student1 1

2 Student2 22 Student2 2

cID CoursecID Course

1 Biology1 Biology

2 Maths 2 Maths

3 English 3 English gID Grade gID Grade

1 A1 A

2 B 2 B

3 C 3 C

dID Department dID Department

1 Chemistry1 Chemistry

2 Ecology 2 Ecology

wID ProjectwID Project

1 E11 E1

2 E2 2 E2

3 E3 3 E3

4 P1 4 P1

5 P2 5 P2

sID cID gID wIDsID cID gID wID

1 1 1 1 1 1 1 1 1 1 2 21 1 2 2

1 1 2 31 1 2 3

1 1 1 41 1 1 4

1 1 3 5 1 1 3 5

2 1 1 1 2 1 1 1 2 1 1 22 1 1 2

2 1 2 32 1 2 3

2 1 1 42 1 1 4

2 1 1 5 2 1 1 5

Normalization 2: remove repeating records (columns)

Relational Databases

What have we achieved? No repeating information Less storage space Better reality representation Easy modification/management Easy usage of any combination of records

Remember the DBMS has programs to access and edit this information so ignore the human reading limitation of the primary keys

Accessing database information

A request for data from a database is called a query

Queries can be of three forms:Choose from a list of parametersQuery by example (QBE)

• QBE build wizard allows which data to display

Query language

Query Languages

The standard SQL (Structured Query Language) originally called

SEQUEL (Structured English QUEry Language) Developed by IBM in 1974; introduced commercially

in 1979 by Oracle Corp. Standard interactive and programming language for

getting information from and updating a database.

RDMS (SQL), ODBMS (Java, C++, OQL etc)

Querying our biological relational database

Many view are possible …

Plasmid View

Plasmid     Species           Cell StockpEA25       Escherichia coli  SK10.2.pEA25pEA46.2     Escherichia coli  SK11.pEA46.2pEA207.2    Escherichia coli  SK11.pEA207.2pEA214.6    Escherichia coli  MB123.pEA214.6pEA215.3    Escherichia coli  SK11.pEA215.3pEA238.2    Escherichia coli  MB123.3.PEA238.2pEA238.11   Escherichia coli  MB123.3.pEA238.11pEA277.11   Escherichia coli  SK11.pEA277.11pEA303.4    Escherichia coli  SK11.pEA303.4pEA315.2    Escherichia coli  MB123.3.pEA315.2 peB4        Escherichia coli  VB1.eB4

Experiment View

Experiment  Cell Stock       Box               Freezer SK4         SK4.pPS-IAA4-5   Pisum ESTs I      AG334 -80C SK4         SK4.pPS-IAA6     Pisum ESTs I      AG334 -80C SK4         SK4.pTic110      Pisum ESTs I      AG334 -80C SK4         SK4.pToc34       Pisum ESTs I      AG334 -80C SK4         SK4.pToc86       Pisum ESTs I      AG334 -80C SK5         SK5.pAB96.3      Pisum ESTs I      AG334 -80C SK5         SK5.pABR17.10    Pisum ESTs I      AG334 -80C SK5         SK5.pABR18.2     Pisum ESTs I      AG334 -80C SK5         SK5.pI39         Pisum ESTs I      AG334 -80C SK5         SK5.pI49KS       Pisum ESTs I      AG334 -80C SK5         SK5.pI176KS      Pisum ESTs I      AG334 -80C SK5         SK5.pI225KS      Pisum ESTs I      AG334 -80C

Distributed databases

From local to global attitude Data appears to be in one location but is most

definitely not

A definition: Two or more data files in different locations, periodically synchronized by the DBMS to keep data in all locations consistent (A,B,C)

An intricate network for combining and sharing information

Administrators praise fast network technologies!!! Users praise the internet!!!

Data warehouse Periodically, one imports data from databases and store it

(locally) in the data warehouse.

Now a local database can be created, containing for instance protein family data (sequence, structure, function and pathway/process data integrated with the gene expression and other experimental data).

Disadvantage: expensive, intensive, needs to be updated.

Advantage: easy control of integrated data-mining pipeline.

So why do biologists care?

Three main reasons

Database proliferationDozens to hundreds at the moment

More and more scientific discoveries result from inter-database analysis and mining

Rising complexity of required data-combinationsE.g. translational medicine: “from bench to

bedside” (genomic data vs. clinical data)

Biological databases

Like any other databaseData organization for optimal analysis

Data is of different typesRaw data (DNA, RNA, protein

sequences)Curated data (DNA, RNA and protein

annotated sequences and structures, expression data)

Raw Biological dataNucleic Acids (DNA)

Raw Biological dataAmino acid residues (proteins)

Curated Biological DataDNA, nucleotide sequences

Gene boundaries, topologyGene boundaries, topology Gene structureGene structure

Introns, exons, ORFs, splicingIntrons, exons, ORFs, splicing

Expression dataExpression data Mass spectometryMass spectometryIdentify unknown compoundsIdentify unknown compounds

Mass spectometry Mass spectometry (metabolomics, proteomics)(metabolomics, proteomics)

Post-Translational proteinPost-Translational proteinModification (PTM)Modification (PTM)

Curated Biological DataProteins, residue sequences

MCTUYTCUYFSTYRCCTYFSCDExtended sequence information Extended sequence information

Secondary structureSecondary structure

Hydrophobicity, motif dataHydrophobicity, motif data

Protein-protein interactionProtein-protein interaction

Curated Biological data3D Structures, folds

Biological Databases

The NAR Database Issue: http://www.oxfordjournals.org/nar/database/c/

Distributed information

Pearson’s Law: The usefulness of a column of data varies as the square of the number of columns it is compared to.

A few biological databases

Nucleotide DatabasesAlternative Splicing, EMBL-Bank, Ensembl, Genomes Server, Genome, MOT, EMBL-Align, Simple Queries, dbSTS Queries, Parasites, Mutations, IMGT

Genome DatabasesHuman, Mouse, Yeast, C.elegans, FLYBASE, Parasites

Protein Databases Swiss-Prot, TrEMBL, InterPro, CluSTr, IPI, GOA, GO, Proteome Analysis, HPI, IntEnz, TrEMBLnew, SP_ML, NEWT, PANDIT

Structure Databases PDB, MSD, FSSP, DALI

Microarray Database ArrayExpress

Literature Databases MEDLINE, Software Biocatalog, Flybase Archives

Alignment DatabasesBAliBASE, Homstrad, FSSP

Structural Databases

Protein Data Bank (PDB) http://www.rcsb.org/pdb/

Structural Classification of Proteins (SCOP)

http://scop.berkeley.edu

http://scop.mrc-lmb.cam.ac.uk/scop/

3D Macromolecular structural data

Data originates from NMR or X-ray crystallography techniques

Total no of structures 48.891 (date: this morning)

If the 3D structure of a protein is solved ... they have it

PDB

PDB content

PDB information

The PDB files have a standard format

Key features

Informative descriptors

PDB-mirror on the WWW

e.g.1AE5

Example output: 1AE5

Protein Structure Initiative (PSI)

Aims at determination of the 3D structure of all Proteins

Organize known protein sequences into families. Select family representatives as targets. Solve the 3D structure of targets by X-ray crystallography

or NMR spectroscopy. Build models for other proteins by homology to solved 3D

structures.

+ many structures solved; - many redundant structures (40%)

SCOP

Structural Classification Of Proteins 3D Macromolecular structural data grouped

based on structural classification

Data originates from the PDB Current version (v1.73) 34494 PDB Entries (Feb 2008). 97178 Domains

SCOP levels bottom-up1.Family: Clear evolutionarily relationshipProteins clustered together into families are clearly evolutionarily related. Generally, this means that pairwise residue identities between the proteins are 30% and greater. However, in some cases similar functions and structures provide definitive evidence of common descent in the absence of high sequence identity; for example, many globins form a family though some members have sequence identities of only 15%.

2.Superfamily: Probable common evolutionary originProteins that have low sequence identities, but whose structural and functional features suggest that a common evolutionary origin is probable are placed together in superfamilies. For example, actin, the ATPase domain of the heat shock protein, and hexakinase together form a superfamily.

3.Fold: Major structural similarityProteins are defined as having a common fold if they have the same major secondary structures in the same arrangement and with the same topological connections. Different proteins with the same fold often have peripheral elements of secondary structure and turn regions that differ in size and conformation. In some cases, these differing peripheral regions may comprise half the structure. Proteins placed together in the same fold category may not have a common evolutionary origin: the structural similarities could arise just from the physics and chemistry of proteins favouring certain packing arrangements and chain topologies.

SCOP-mirror on the WWW …

Enter SCOP at the top of the hierarchy

Keyword search of SCOP entries

CATH

Class, derived from secondary structure content, is assigned for more than 90% of protein structures automatically.

Architecture, which describes the gross orientation of secondary structures, independent of connectivities, is currently assigned manually.

Topology level clusters structures according to their toplogical connections and numbers of secondary structures.

The Homologous superfamilies cluster proteins with highly similar structures and functions. The assignments of structures to topology families and homologous superfamilies are made by sequence and structure comparisons.

CATH-mirror on the WWW …

DSSP

Dictionary of secondary structure of proteins

The DSSP database comprises the secondary structures of all PDB entries

DSSP is actually software that translates the PDB structural co-ordinates into secondary (standardized) structure elements

A similar example is STRIDE

WHY bother???

Researchers create and use the data Use of known information for

analyzing new data New data needs to be screened Structural/Functional information Extends the knowledge and

information on a higher level than DNA or protein sequences

In the end ….

Computers can figure out all kinds of problems, except the things in the

world that just don't add up. James Magary

We should add:For that we employ the human brain,

experts and experience.

Bio-databases: A short word on problems Even today we face some key limitations

There is no standard format• Every database or program has its own format

There is no standard nomenclature• Every database has its own names

Data is not fully optimized• Some datasets have missing information without

indications of it Data errors

• Data is sometimes of poor quality, erroneous, misspelled

• Error propagation resulting from computer annotation

What to take home

Databases are a collection of data Need to access and maintain easily and flexibly

Biological information is vast and sometimes very redundant

Distributed databases bring it all together with quality controls, cross-referencing and standardization

Computers can only create data, they do not give answers

Review-suggestion: “Integrating biological databases”, Stein, Nature 2003