What is Bioinformatics? A Proposed Definition and Overview of the ...

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349What is Bioinformatics?ed from a common ancestral gene, andparalogues, proteins that are related bygene duplication within a genome [35].Normally, orthologues retain the samefunction while paralogues evolve distinct,but related functions [36].An important concept that arises fromthese observations is that of a finite “partslist” for different organisms [37-39]: aninventory of proteins contained within anorganism, arranged according to differentproperties such as gene sequence, proteinfold or function. Taking protein folds as anexample, we mentioned that with a fewexceptions, the tertiary structures of proteinsadopt one of a limited repertoireof folds. As the number of different foldfamilies is considerably smaller than thenumber of genes, categorising the proteinsby fold provides a substantial simplificationof the contents of a genome. Similar simplificationscan be provided by other attributessuch as protein function. As such, weexpect this notion of a finite parts list tobecome increasingly common in futuregenomic analyses.Clearly, an essential aspect of managingthis large volume of data lies in developingmethods for assessing similarities betweendifferent biomolecules and identifyingthose that are related. There are well-documentedclassifications for all of the maintypes of data we described earlier. Althoughdetailed descriptions of these classificationsystems are beyond the scope ofthe current review, they are of great importanceas they ease comparisons betweengenomes and their products. Links to themajor databases are available from oursupplementary website.3.2 Data IntegrationThe most profitable research in bioinformaticsoften results from integrating multiplesources of data [40]. For instance, the3D coordinates of a protein are more usefulif combined with data about the protein’sfunction, occurrence in different genomes,and interactions with other molecules. Inthis way, individual pieces of informationare put in context with respect to otherdata. Unfortunately, it is not alwaysstraightforward to access and crossreferencethese sources of information becauseof differences in nomenclature andfile formats.At a basic level, this problem is frequentlyaddressed by providing externallinks to other databases. For example inPDBsum, web-pages for individual structuresdirect the user towards correspondingentries in the PDB, NDB, CATH, SCOPand SWISS-PROT databases. At a moreadvanced level, there have been efforts tointegrate access across several data sources.One is the Sequence Retrieval System, SRS[41], which allows flat-file databases to beindexed to each other; this allows the userto retrieve, link and access entries fromnucleic acid, protein sequence, proteinmotif, protein structure and bibliographicdatabases. Another is the Entrez facility[42], which provides similar gateways toDNA and protein sequences, genomemapping data, 3D macromolecular structuresand the PubMed bibliographic database[43].A search for a particular gene in eitherdatabase will allow smooth transitions tothe genome it comes from, the proteinsequence it encodes, its structure, bibliographicreference and equivalent entries forall related genes. In our own group, we havedeveloped the SPINE [44] and PartsList[39] web resources; these databases integratemany types of experimental data andorganise them using the concept of thefinite “parts list” we described above.4. “…UNDERSTAND andOrganise the Information…”Having examined the data, we can discussthe types of analyses that are conducted.Asshown in Table 1, the broad subject areas inbioinformatics can be separated accordingto the type of information that is used. Forraw DNA sequences, investigations involveseparating coding and non-coding regions,and identification of introns, exons andpromoter regions for annotating genomicDNA [45, 46]. For protein sequences, analysesinclude developing algorithms forsequence comparisons [47], methods forproducing multiple sequence alignments[48], and searching for functional domainsfrom conserved sequence motifs in suchalignments. Investigations of structuraldata include prediction of secondary andtertiary protein structures, producingmethods for 3D structural alignments [49,50], examining protein geometries usingdistance and angular measurements, calculationsof surface and volume shapes andanalysis of protein interactions with othersubunits, DNA, RNA and smaller molecules.These studies have lead to molecularsimulation topics in which structural dataare used to calculate the energetics involvedin stabilising macromolecular structures,simulating movements within macromolecules,and computing the energiesinvolved in molecular docking. The increasingavailability of annotated genomicsequences has resulted in the introductionof computational genomics and proteomics– large-scale analyses of complete genomesand the proteins that they encode. Researchincludes characterisation of proteincontent and metabolic pathways betweendifferent genomes, identification of interactingproteins, assignment and prediction ofgene products, and large-scale analyses ofgene expression levels. Some of these researchtopics will be demonstrated in ourexample analysis of transcription regulatorysystems.Other subject areas we have included inTable 1 are: development of digital librariesfor automated bibliographical searches,knowledge bases of biological informationfrom the literature, DNA analysis methodsin forensics, prediction of nucleic acid structures,metabolic pathway simulations, andlinkage analysis – linking specific genes todifferent disease traits.In addition to finding relationships betweendifferent proteins, much of bioinformaticsinvolves the analysis of one typeof data to infer and understand the observationsfor another type of data. An exampleis the use of sequence and structuraldata to predict the secondary and tertiarystructures of new protein sequences [51].These methods, especially the former, areoften based on statistical rules derivedfrom structures, such as the propensity forcertain amino acid sequences to produceMethod Inform Med 4/2001
350Luscombe, Greenbaum, GersteinParadigm shifts during the past couple of decades have taken much of biology away from thelaboratory bench and have allowed the integration of other scientific disciplines, specificallycomputing. The result is an expansion of biological research in breadth and depth. The vertical axisdemonstrates how bioinformatics can aid rational drug design with minimal work in the wet lab.Starting with a single gene sequence, we can determine with strong certainty, the proteinsequence. From there, we can determine the structure using structure prediction techniques. Withgeometry calculations, we can further resolve the protein’s surface and through molecularsimulation determine the force fields surrounding the molecule. Finally docking algorithms canprovide predictions of the ligands that will bind on the protein surface, thus paving the way for thedesign of a drug specific to that molecule. The horizontal axis shows how the influx of biologicaldata and advances in computer technology have broadened the scope of biology. Initially with a pairof proteins, we can make comparisons between the between sequences and structures ofevolutionary related proteins. With more data, algorithms for multiple alignments of severalproteins become necessary. Using multiple sequences, we can also create phylogenetic trees totrace the evolutionary development of the proteins in question. Finally, with the deluge of data wecurrently face, we need to construct large databases to store, view and deconstruct theinformation. Alignments now become more complex, requiring sophisticated scoring schemes andthere is enough data to compile a genome census – a genomic equivalent of a population census –providing comprehensive statistical accounting of protein features in genomes.Fig. 2Organizing and understanding biological datadifferent secondary structural elements.Another example is the use of structuraldata to understand a protein’s function;here studies have investigated the relationshipdifferent protein folds and theirfunctions [52, 53] and analysed similaritiesbetween different binding sites in the absenceof homology [54]. Combined withsimilarity measurements, these studies provideus with an understanding of how muchbiological information can be accuratelyMethod Inform Med 4/2001transferred between homologous proteins[55].4.1 The Bioinformatics SpectrumFig. 2 summarises the main points weraised in our discussions of organisingand understanding biological data – thedevelopment of bioinformatics techniqueshas allowed an expansion of biologicalanalysis in two dimension, depth andbreadth. The first is represented by thevertical axis in the figure and outlines apossible approach to the rational drugdesign process. The aim is to take a singlegene and follow through an analysis thatmaximises our understanding of theprotein it encodes. Starting with a genesequence, we can determine the proteinsequence with strong certainty. From there,prediction algorithms can be used to calcu-
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