Bioinformatics in The Drug Discovery Process

Bioinformatics in the Drug Discovery ProcessSarah Pollock and Hershel M. SaferCompugen Ltd.72 Pinchas Rosen Street, Tel Aviv 69512, Israel{ sarah, hersh } @compugen.co.ilIntroduction ─ Bioinformatics is the part of molecular biology that involves working with biological data, typically using computers, with the goal of enabling and accelerating biological research. It is ubiquitous in drug discovery; few if any projects are computer-free. Bioinformatics spans a wide range of activities: data capture, automated recording of experimental results; data storage and access, using a multitude of databases and query tools; data analysis; and visualization of raw data and analytical results. Although the first two categories rely on computer skills, the others also require mathematics, molecular biology, and biochemistry. Until recently, most people working in bioinformatics started in one discipline and picked up the others on the job. New interdisciplinary programs of study, though, integrate these subjects.Bioinformatics is web-centric. Most academic groups and many companies make computing tools and data available on the web. An online supplement to this article (/science/armc-2001.htm) contains links to relevant websites. Key databases are also reviewed in each year’s first issue of Nucleic Acids Research (1).This article briefly reviews recent bioinformatics publications, with a focus on tools and databases used to speed drug target discovery and selection. Subjects not covered include genetic, physical, and radiation hybrid mapping; genetics; genotyping and high-throughput screening; molecular and cellular simulation; and automation.WHOLE-GENOME SEQUENCE ANALYSISThe newly available human genome sequence includes the sequence of almost every gene. Initial versions of the transcriptome, the expression level of transcribed sequences in particular tissues under specific conditions, and of the proteome, the proteins in a cell under given conditions and their interactions, are also available. These data are transforming biology and the pharmaceutical industry. Manual analysis of such vast amounts of data is impractical; computerized tools are needed to reap the full benefit of these resources. Genomics has become a major source of drug targets, and bioinformatics is crucial for finding and validating novel targets so as to minimize investment in laboratory resources.Sequencing the Human Genome ─ The most important recent genomics papers are about the draft sequencing of the human genome (2, 3). Two versions were published: one by the Human Genome Project (HGP), which includes 20 sequencing centers in six countries, and one by the company Celera. The primary sequencing articles are accompanied by articles describing bioinformatics analyses of the data.Current technology can sequence DNA segments of up to about 1,000 base pairs (bp; kb for thousand bp and Mb for million bp). A longer stretch is sequenced by breaking it into small fragments, sequencing one or both ends of each, and joining the pieces, so-called shotgun sequencing. The process of joining short sequences is called fragment assembly; it relies on the assertion that two sequences with virtually identical regions are likely from the same location, in the absence of complications from repeats and sequencing errors. Assembly tools include Phrap, FAK, the TIGR assembler, and CAP (4-7). The HGP divided the genome into pieces of 200-350 kb that were sequenced individually and then joined. Celera, though, fragmented the entire genome. Previous assemblers reconstructed entire microbial genomes, but the Celera Assembler was the first to apply whole-genome shotgun sequencing to a target this complex (8). Published in Annual Reports in Medicinal Chemistry, 2001 © Academic Press2 Section─ Topics in Biology Allen, Ed.VThe HGP sequence and automatically-generated annotation are freely available from the websites of the National Center for Biotechnology Information (NCBI), Ensembl, and UCSC. The Celera sequence is available from the Celera website under conditions described there. Comparisons of the sequences reveal that they are globally similar but have local discrepancies, probably mostly the result of mis-assembly (9, 10) This review can mention only a few of the many interesting conclusions. The most striking finding may be the number of protein-coding genes, which both groups estimated as 30,000-40,000. This is lower than the previously estimated 70,000-120,00 genes (11), though recent analyses presaged this result (12, 13). These numbers are surprising because they are not much larger than the 20,000 genes in the nematode or the 12,000 in the fruit fly. This shows that genome complexity does not depend only on the number of genes. Alternative splicing seems to be key to the greater intricacy of humans (14). Analysis also revealed evidence of horizontal transfer of bacterial genes into vertebrates as well as extremely long duplicated regions.Sequencing Other Genomes ─ More than 600 organisms are being sequenced and several dozen genomes are complete. One criterion in choosing an organism to sequence is its medical importance. Microbial and viral genomes help identify drug targets in pathogens; eukaryotic genomes advance understanding of human genetics and physiology. Plant and animal sequencing have also led to significant advances in sequencing and bioinformatics technologies.A gene’s genomic context, including nearby genes and transcription control regions, is needed to understand the gene’s function. Comparative genomics, i.e., comparing conserved sequence regions in multiple organisms, uses conservation of sequence and higher-order genomic structure to gain insight into gene function. PipMaker compares long DNA sequences to identify conserved regions without prior knowledge of gene structures (15). The Artemis Comparison Tool (ACT), a DNA sequence viewer, displays genomic structure as well as similarities and differences between two genomes (16).NCBI provides tools for comparative genomics in addition to comprehensive cross-links between various databases (17). HomoloGene includes curated and calculated orthologues and homologues in six eukaryotes; LocusLink is an interface for queries about their genetic loci. The Human/Mouse Homology Maps display syntenic regions, i.e., regions that contain similar genes, usually in the same order.Clusters of Orthologous Groups (COGs) are phylogenetic classifications of proteins from 34 complete genomes in 26 major phylogenetic lineages (18). BLAST (see below) is used to find the best match for each protein in each genome. Proteins that are best hits for each other are clustered. Clustered proteins are likely to have similar functions;a COG is a cluster with proteins from three or more lineages. COGs yield functional predictions for poorly characterized genomes. Results do not depend on BLAST cutoff scores and so are unaffected by different evolutionary times between pairs of genomes. Sequence Alignment Tools ─ Database searches to find similar sequences that are putative homologues, i.e., that appear to have a common evolutionary ancestor, are at the core of sequence analysis. Proteins with similar sequences or domains typically have similar structure and often function, though the converse is not always true. Proteins that are at least 25% similar are generally homologous (19).Almost synonymous with database searching, Basic Local Alignment Search Tool (BLAST) may be the most widely used bioinformatics tool (20). BLAST is optimized for speed but sacrifices only minimal sensitivity in searching databases. More sensitive algorithms like that of Smith and Waterman find optimal alignments but are slower (21). Newer tools address features of nucleotide sequences (22, 23), modeling phenomena such as frame shifts, codon insertions and deletions, and introns. Improved sensitivity comes at the price of computation time, though hardware accelerators make some such algorithms reasonable for high-throughput environments.Chap. ? Bioinformatics Pollock, Safer 3 Distinguishing signal from background noise is difficult when looking for homologues that are extremely distant on the evolutionary time scale. Sensitivity is improved by focusing on a protein’s functional domains, which may have distinct origins and functions. Multiple alignment of sequences from a domain in several organisms reveals patterns of conservation, including highly conserved residues, and the patterns can be used to recognize distant homologues. Databases that use patterns are based on multiple alignments that have been either manually optimized or created automatically (24-27). InterPro centralizes protein information from many databases (28).Some new tools use existing sequence databases. BLAST now accommodates sequence gaps. Another version combines alignments into a position-specific scoring matrix and searches with that matrix. This iterative process yields a Position-Specific Iterated version (PSI-BLAST) (29) that provides improved annotation (30). Sequence queries can also be compared to collections of position-specific scoring matrices (31).GenBank contains more than 11 billion bp in more than 10 million sequences and continues to grow exponentially. A BLAST search against all of GenBank takes three times as long as it did a year ago! MEGABLAST compares large sets of long nucleotide sequences up to 10 times as fast as do previous programs, but is best for comparing sequences that differ because of sequencing errors (32). The race between database growth and search speed continues; algorithmic and hardware improvements will be needed to continue the pace of biological discovery.Recognizing and Predicting Genes and Other Sequence Features ─ One post-sequencing challenge is comprehensive genome annotation, including at least the putative start, stop, and structure of each gene, and preferably information about transcription regulatory regions. Similarity searches can identify some novel genes, but many human genes are not very similar to known sequences from other organisms.Ab initio gene prediction finds genes in a (typically eukaryotic) genomic sequence using sequence only, without knowing expressed or translated regions, though possibly using known genes as examples. This is challenging, as eukaryotic genes are complex and less than 2% of the human genome codes for proteins. RefSeq, a non-redundant, curated database of mRNA and protein sequences, contains examples that illustrate this complexity (17): genes have many exons (median 7 / mean 8.8), long introns (1,023 bp / 3,365 bp, with large variance, and many longer than 50 kb), short internal exons (122 bp / 145 bp), and short coding sequences (1,100 bp / 1,340 bp). They also stretch over long genomic extents (14 kb / 27 kb) and are widely separated (2).Methods such as Grail2, GenScan, Genie, and HMMGene (33-36) use probabilistic pattern recognition methods to identify protein coding regions based on gene-related patterns of DNA composition. The latter two programs also integrate sequence similarity to known coding regions and expressed sequence tags (see below).EST analysis ─ An expressed sequence tag (EST) is a partial expressed sequence from a (generally anonymous) cDNA. Large collections of ESTs promised a shortcut to finding most genes before the genome sequence was available and were to obviate difficulties of computational gene prediction (37). Although their early promise was not completely fulfilled, ESTs are quite useful. Examination of EST assemblies provided the first hints of the prevalence of alternative splicing in the human genome (38, 39) and led to the detection of many putative SNPs (see below) (40, 41). ESTs are useful molecular landmarks and those from multiple tissues can be used to answer questions about tissue-specific genes and to identify novel proteins (42, 43).GenBank contains more than three million human and almost two million mouse EST sequences, and proprietary collections include millions more. ESTs are typically short, 3’-skewed, and of relatively low quality; many represent only the 3’ UTR; and the databases are highly redundant and poorly annotated. As a result, searching EST databases directly is not terribly efficient. More useful information is found by clustering4 Section─ Topics in Biology Allen, Ed.VESTs and mRNAs based on sequence overlaps, yielding sequences that are longer, of higher accuracy, and that better represent the underlying genes. Databases of EST clusters include UniGene (44), the TIGR Human Gene Index (45), and STACK (46); they are compared in (47). GeneNest adds to UniGene by providing consensus sequences and visualization (48). A current challenge is integrating EST clusters and genomic data to improve gene and transcript predictions by eliminating EST artifacts. Polymorphism analysis ─ Most common sequence variation, or polymorphism, consists of single nucleotide polymorphisms (SNPs), mutations that replace one base pair with another. SNPs serve as genetic markers to track familial inheritance; some are functionally important, affecting disease susceptibility and response to medication (49). The dbSNP database (50) holds more than 1.5 million unique SNPs, more than half from The SNP Consortium (51). About 1.42 million have been mapped to the human genome using MEGABLAST (52). To find SNPs, several copies of DNA from a region, preferably from ethnically diverse individuals, are compared for sequence differences. SNP detection technology and the use of SNPs in genotyping are reviewed in (53).Phred (54), a basecalling program for sequencer traces, estimates an error probability for each base, and this quality score can help differentiate polymorphism from sequencing errors. Other errors can be found by aligning sequences to a high-quality reference sequence. The neighborhood quality standard (NQS) requires high Phred scores and good alignment to the reference sequence around a putative polymorphic site (51). POLYBAYES uses the NQS information and the multiple alignment of ESTs in a cluster to model the probability that a site is polymorphic (55); this allows it to detect paralogous genes as well as sequencing errors.METABOLIC AND REGULATORY PATHWAYS AND NETWORKS The previous section focuses on individual genes and proteins. Understanding how proteins interact, though, is crucial to drug development, as modifying one protein’s behavior affects others as well. Gene expression is often used as a surrogate for protein expression, as the former is easier to measure with current technologies, but they do not correlate perfectly (56). Both are used to model cellular dynamics (57, 58). Gene Expression Profiling ─ Gene expression is measured to find specific genes that are differentially expressed and groups of genes that are co-regulated (59). At present, the most popular technology for measuring gene expression is DNA “chips” or “microarrays,” which perform massively parallel Northern blots (60). Where scientists previously studied a few genes at a time, chips allow efficient genome-wide analysis of expression variation. Expression can also be measured with non-microarray methods, including Serial Analysis of Gene Expression (SAGE) and variants (61). Chip and SAGE results agree well on both absolute and relative expression levels (62), though proper analysis is needed to account for SAGE experimental biases (63-65).A differentially expressed gene may be implicated in the phenotypic differences between samples (66). A typical experiment compares expression at different developmental stages (67, 68), between normal and disease states (69, 70), or under differing environmental conditions (71). The EST cluster databases described above can also be used for expression profiling across multiple libraries (72).Co-regulation is recognized from similar expression levels in multiple states. Many methods have been used to measure profile similarity and to cluster genes with similar expression patterns (73-76). Some works cluster tissues as well, as co-regulation of genes associated with multiple regulatory mechanisms may only be recognized by examining a subset of the tissues (77).A common difficulty in prescribing medical treatment is differentiating between diseases with similar phenotypes. Microarray analyses can distinguish between normal and tumor tissue or between cancers (78-81). The latter paper includes an algorithmChap. ? Bioinformatics Pollock, Safer 5 that identifies multiple cancers in tissues without foreknowledge of different diseases. Microarrays are also used to compare cell lines. Cell line clusters based on expression levels differ from those based on drug responses (82); changes in expression can have striking effects on drug sensitivity and resistance. An elegant use of microarrays is to verify and supplement computational predictions of gene structure (83).Promoters, portions of genes that contain information to turn genes on and off, can be identified using clusters from microarray analysis (84). A motif that appears upstream of co-regulated genes may be a promoter (85-88). Promoters and expression clusters can be uncovered simultaneously (89, 90). Upstream regions of similar genes from multiple organisms can also be compared, with the required level of motif conservation depending on the phylogenetic distance between the organisms (91). Proteomics ─ Proteomics studies networks of proteins by measuring, among other things, protein expression. Protein activity is regulated by post-translational modification and degradation; these cannot yet be predicted from DNA sequence. Proteomics measures protein expression directly, not via gene expression, thus achieving better accuracy. Current work uses 2-dimensional polyacrylamide gel electrophoresis (2D-PAGE) and mass spectrometry. New separation and characterization technologies, such as protein microarrays and high-throughput chromatography, are being developed.In 2D-PAGE, samples to be compared are run through separate gels; corresponding spots that look different may indicate differential expression. Comparing spots has been time-consuming and error-prone, but new tools vastly improve both speed and accuracy (92). Spot intensity is measured to estimate expression level and mass spectrometry is used to identify proteins from spots of interest (93). Tools for analyzing gel images and mass spectra are described in (94). Proteins from organisms that are not well represented in protein databases can sometimes be identified based on proteins from other species using mutation-tolerant methods (95). Differential expression can be measured by labeling samples with different tags (96).Control sequences (standards) are typically used to calibrate mass spectrometry measurements. Internal standards added to samples can confound identification; external standards in different spots may not yield sufficient precision. Both suffer because calibration varies even over the time needed to process a sample plate. A recent approach corrects for systematic deviations by first using a low-precision database search. The proteins found are used to estimate calibration parameters and a second search uses greater precision, taking account of the recalibration (97). Metabolic and Regulatory Networks ─ Cellular processes result in large part from proteins interacting with each other and with other cellular components. Some research tries to identify interactions; other efforts use interactions to reverse-engineer metabolic and regulatory networks. Appropriate intervention points for therapeutic agents can be gleaned from an understanding of these networks. Comprehensive protein-protein interaction maps have been published for Saccharomyces cerevisiae (98) and Helicobacter pylori (99). Groups are working on maps for other organisms and for specific pathways in humans.Cross-species comparisons can be used to infer interactions. A phylogenetic profile is a list of species in which a protein is expressed. Proteins with the same profile are likely to be in a pathway that occurs only in certain organisms (100). Conservation of relative gene position identifies sets of genes that occur together, in the same order, in multiple species (101); this approach has been more informative in prokaryotes than in eukaryotes (102). Domain fusion looks for domains from two proteins in one organism that are contained in a single protein in a second organism (103, 104). Fusions may have an evolutionary advantage from more efficient interactions if the domains are in a common pathway. The protein with fused domains is called a “Rosetta stone” because it helps explain the relationship between the separated proteins in the other organism.6 Section─ Topics in Biology Allen, Ed.VStudying all tissues under all conditions to elucidate all interactions is a tall order. Recent work draws on existing research reports. Text mining tools extract interactions reported in document collections such as Medline. One approach is to compile a list of proteins and look for entries separated by appropriate verbs, such as two protein names with “phosphorylate” in between (105-107). A second approach parses text completely and looks for interactions (108, 109). The latter is more comprehensive but existing tools cannot fully evaluate all text. Other tools skip parsing altogether, using co-occurrence of protein names to deduce functional relationships (110, 111).Network complexity is illustrated by the Boehringer Mannheim “Biochemical Pathways” wall charts. Most reconstruction focuses on pathways, non-branching portions of networks. Databases of reconstructed pathways include KEGG (112), EcoCyc and MetaCyc (113), and DIP (114). Reconstructing pathways from interaction data is quite difficult. Some tools simultaneously recognize interactions and reconstruct pathways (115-117), often using statistical techniques (118-121).Mathematical models incorporate gene and protein expression to deduce pathway structure. Stoichiometric, thermodynamic, biochemical, and other constraints are used to model expression over time (122-127). Comparing pathways can yield functional insights; comparisons based on sequence (128) and on Enzyme Commission numbers of pathway components (129) have been developed. Reconstructed networks can predict output compounds based on input nutrients and required precursors (130).PROTEIN SECONDARY AND TERTIARY STRUCTURE Predicting protein three-dimensional structure is an important open problem not only in bioinformatics, but in general high performance computing. Structure being the key to function, determining a protein’s structure is a key step toward elucidating its role. The subfield of protein-ligand docking is useful in rational drug design. Laboratory prediction is time consuming and expensive, so researchers have been working on computerized prediction for several decades. Exact computational prediction is difficult (131, 132), but sophisticated algorithms to find approximate solutions continue to be developed.The Critical Assessment of Methods of Protein Structure Prediction (CASP) tracks progress in this field bi-annually. Computational groups predict structures of proteins whose structures have been found in the laboratory before the latter results are released. The predictions are assessed by experts. Tools are classified as using one of three approaches: comparative modeling looks for amino acid similarity to proteins of known structure; fold recognition predicts folds in regions that do not share amino acid similarity with known structures; and ab initio tools work directly from physical principles. Results of CASP 3 defined the technology as of 1998 (133-135). CASP 4 took place in 2000, and the upcoming publication of its results will define the current state of the art.WORKING WITH COMPLEX DATASETSThe difficulties of genomic analysis are compounded by data complexities. Datasets are large, ill-structured, and noisy. Combining data from multiple sources is challenging, as even basic terms have multiple meanings. A gene, for example, may refer to the stretch of genomic DNA that encodes a protein (sometimes including the promoter), the exons in that region, the coding sequence only, the resulting mRNA transcript, etc. Data Management and Integration ─ Most genomic data are stored in flat files or commercial relational databases, but object-oriented databases are finding increasing use. AceDB, a non-commercial database created as part of the Caenorhabditis elegans sequencing project, has a flexible data model that is easily extended to handle new kinds of data (136). It includes displays and other tools for use with genomic data.Gene expression, as a new research area, has benefited from integrating databases that are geographically dispersed, with different formats and terminology. Standards for storing, reporting, and exchanging data have been proposed (137-139); an alternative isChap. ? Bioinformatics Pollock, Safer 7 to combine data sources in a centralized, curated database (140, 141). Data storagestandards have also been proposed for protein interactions and pathways (142).Groups working in established areas often cannot retrofit standards to their existing databases. One way to enhance interoperability is to add standard, object-oriented interfaces (143-146). Another is to create tools that access distributed databases, but present a user interface that makes the databases appear integrated (147). A standard for exchanging many kinds of biological data, based on XML, is described in (148).Several databases contain different kinds of data integrated by locus. Often these contain similar data from different sources, such as multiple radiation hybrid maps (149). Sometimes they include different kinds of data (51, 150-153) or more complex representations, such as the genes contained in a metabolic pathway (154) or data on protein interaction and function (155). InterPro is perhaps the most comprehensive of such databases, and includes web links to many data sources (28).Gene Ontologies ─ Database integration is crucial for comparative genomics, large-scale inference of characteristics of novel genes in one organism from known properties of genes in other organisms. This requires automatic combination of different data sources. Ontologies are structured, controlled vocabularies. An ontology for biological function in Escherichia coli is described in (156). More recently, the Gene Ontology Consortium has built ontologies describing a gene’s molecular function, the biological processes that use it, and its cellular location (157). Tools for sharing ontologies exist (158), but results of using ontologies for database integration have yet to be published. Visualization ─ Computational methods cannot yet rival the ability of the human eye to pick out patterns. Many visualization tools show annotated genomic sequences. An early example is Genotator (159), which shows color-coded annotations such as predicted genes, promoters, splice sites, and putative homologies in parallel to the sequence. Different levels of detail can be viewed by zooming in and out. Artemis is a newer tool that allows calculations on the sequence or its CDS features, such as G+C content and amino acid properties such as hydrophobicity (16). This area continues to develop rapidly, with new tools constantly emerging. A recent focus is comparing both sequence and rearrangements, as discussed above (160-162).Most recent visualization tools build on the availability of web browsers. Re-usable interface components, typically in Java, for displaying common genomic objects are available (163, 164). Displays of 3D molecular structures may be the most striking visualization tools; experience with these and other display tools helps researchers understand how to take advantage of visual perception in the design of new tools (165). Acknowledgments ─ We thank A. Diber, M. Edelman, Z. Fligelman, R. Gill-More, N. Kaplan, G. Naveh, and P. Safer for their help.References1. Special issue: Biological databases, Nucleic Acids Res., 29, 1 (2001).2. International Human Genome Sequencing Consortium, Nature, 409, 860 (2001).3. J.C. Venter, M.D. Adams, E.W. Myers, P.W. Li, et al., Science, 291, 1304 (2001).4. P. Green, /UWGC/analysistools/phrap.htm, 2001.5. E.W. Myers, J. Comput. Biol., 2, 275 (1995).6. 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