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Macromolecular Structure Database Structural Database Infrastructure Services for Europe. www.ebi.ac.uk/msd. The MSD databases. The MSD actually consists of two separate databases: - PowerPoint PPT Presentation
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Macromolecular Structure DatabaseStructural Database Infrastructure Services for Europe
www.ebi.ac.uk/msd
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The MSD databases
The MSD actually consists of two separate databases: the archive database is highly normalized, with
thousands of relationships linking some 400 tables; the deposition database is the definitive archive for all structural data at MSD
the search database is a much simpler, denormalized database, with data items duplicated and aggregated into 40 much wider tables, making it more amenable to searching and retrieval of data : the MSDSD
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What is the MSDSD database A relational database primarily developed in Oracle
that stores the data derived from the PDB together with reference and other derived information
Simple to understand for the novice biologist and fast in performance for the database non-expert
Originates from the internal MSD archive database that ensures accuracy and data integrity
In MSDSD naming and other summary information is repeated from every level of the hierarchy to the next one in order to be closer to the familiar PDB data
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Main MSDSD features
The symmetry has been expanded and the information of the quaternary biological assemblies is directly available External Information like binding sites and secondary
structure has been derived on the assembly level The original PDB asymmetric unit is also available Includes and provides clear database relations
with the ligands “data mart” and other reference information
Includes information and cross-references to external databases (NCBI taxonomy, UniProt, SCOP etc)
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Data mining is a term that is applied very loosely within bioinformatics to describe any type of data analysis. Almost without exception the analysis of molecular biology is hypothesis based where the search for information has a target that is defined by the knowledge of the biological context of the data.
DATA Analysis
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“Analysis of data in a database using tools which look for trends or anomalies without knowledge of the meaning of the data.”“True data mining software does not just change the presentation, but discovers previously unknown relationships among the data.
(Webopedia and other technical dictionaries)”
Data Mining
was first “invented” by IBM
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Traditional analysis is via “verification-driven analysis”
Requires hypothesis of the desired information (target)Requires correct interpretation of proposed query
Discovery-driven data miningFinds data with common characteristicsResults are ideal solutions to discovery Finds results without previous hypothesis
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“catalytic triad” : text string matchingAtomic coordinates : coordinate superpositionMathematical graph : graph matchingHIS,ASP,SER : data hierarchy knowledge
So what is Hypothesis driven data analysis ?
Define a target = hypothesis Search for target There are/are-not “hits”
Verify/negate hypothesis Distribution is centred on target
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For example, it is possible to find the presence of catalytic triads within the PDB by selecting an example structure and then using a matching technique such as coordinate superposition or graph analysis to screen this against all the coordinate data within the PDB. This will identify the presence of similar residue configurations to the search target and result in a distribution of hits centered on the original search model.
HOWEVER we can only find similar objects distributed about this target.
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Discovery-driven data mining
Finds data with common characteristicsResults are ideal solutions to discovery Finds results without previous hypothesisTarget is mathematical – so has no scientific
dependency
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Mining techniques
Creation of predictive models : future data expectation
Link analysis : connections between data objects
Database segmentation : classification Deviation detection : finding outliers.
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Finds new things !But not what it means !
So what is this data mining ?
Given multiple sets of primary data)Characters, numbers, Function(numbers),….
Find anomaliesTo many : numerical occurrenceData variation : DerivativesSingularities
Correlations and clustersWithin primary datawith other data (dependent variables)
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Discovery driven data mining of the PDB
Analysis of 3-dimensional coordinatesDefined common patterns of atomic interactions
locallyDB segmentation - active sites & common packing featuresLink analysis - Similarity between different functional
groupDefined globally
DB segmentation - common patterns of super-secondary str’
Link analysis - common folds in diverse protein familiesOutlier detection - unique folds
Nucleic Acid sequencesDefine information content using information
technology
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Issues
Systematic “error” propagates as solution 300 lysozyme structures return as a strong solution
Results cannot be found below the noise level Need to characterise the noise level Need to improve signal/noise ratio (S/N) to see information
Target is not biologically defined It does not give you the biological answer Results should reproduce known biology Can give you new results not previously observed
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Data selection
Cannot leave in 300 lysozyme structures ! Select by sequence similarity at 70% exact
alignmentDifferent “phase space” to select data
Remove structures with resolution < 2.5A Remove NMR (different statistics) Remove pre-1982 etc. Geometrical analysis criteria to check for
outliers
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Off the shelf products
Main problem – they “all” do column correlation – but this requires row analysis Ie you can find whether x coordinates are more
correlated to y coordinates than z coordinates Slow
I tried the above on 1e3 of data and it took hours; not much chance on 1.6e9 data then.
Money often
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Local atomic interactions
Data 3D coordinatesAtom typesResidue types
Convert coordinates to distances - easier to compare, no need to superpose coordinates.
Create 3D Hash table of triplets of distances between “points”
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Local atomic interactions
Merge triplets Any pair of N-fold
interactions are a (N+1) interaction if they have (N-1) equivalence.
Just keep going until no more (N+1) interaction are found.
Time = 8 seconds (Digital alpha ES40)
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Local atom interactions
Define key atoms/groups-of-atoms as run time parameters. Solves problem of residue
symmetry Approximation for speed This is a hypothesis
External definition of residue equivalence (PHE TYR) for released data. Improves Signal/Noise ratio. This is a hypothesis
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Is this data mining ?
Basic 3D correlation of distances is Program can be run without any prior definitions.
Addition of key atoms and residue equivalence introduces biology and chemistry introduces hypothesis regarding what is important. Without adding this information you get very little out.
Improvement to the method should spot this without being told !
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This idea has been re-implementedCore analysis on distance onlyStatistical analysis of residue equivalence is
carried out – will find residue equivalenceBit slower now – 2 minutes
To use MSD assembly dataMust be able to normalise by chain similarity
to remove common features due to structure.Can use MSD similarity tables for this.
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Refining the answers
This analysis produces approximate geometrical results
For each “solution”, a second full All vs. All LSQ overlay is performed handles symmetry in D,E,R,P,Thandles different residue overlays
Clusters results using average linkage Writes average + superposed coordinates
+ ligands.
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Catalytic quartet
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Electrostatic interaction
Ligands are found close by rather than associated with the residues
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N-linked glycosolation binding site +?
Spot the non-sugar
This glycosolation site is the same as active site found in “1a53” – indol-3-glycerolphosphate synthase
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Summary
Creates 1000’s of results Returns many metal and catalytic sites 50% have at least 2 of 3 residues as
sequence neighbours 30% have associated ligands
http://www.ebi.ac.uk/msd-srv/MSDtemplate/
See T.J.Oldfield (2003) PROTEINS: Structure, Function, and Genetics 49, 510-528. T.J.Oldfield (2002) Acta Cryst. D57, 1421-1427
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Data mining – not idiot proof
Date of birth and age will give 100 % correlation
Authors for structure submission will be correlated to authors on primary citation.
“Lysozyme” is the most common fold pattern
36 spelling’s of E.Coli will mask results. Requires representative sets
Statistically valid ones too ! Signal/Noise ratio is a problem : hit the
noise and the calculation grows rapidly
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Representative sets and clusteringAnother talk
Data mining fold
Information technological analysis of genomes