How to discover drug targets in genomics databases

Transcription

1 How to discover drug targets in genomics databases It is the ability to search data - rather than access to data per se - that will determine which companies succeed in the post-genomics era. Dr Mark Swindells, Inpharmatica Ltd Afundamental change is currently taking place in the way pharmaceutical research is conducted. It has become possible to identify candidate drug targets - and even drugs - not in the laboratory but in silico, by searching through the multitude of gene and protein sequences now accumulating in public databases. Companies that fail to take advantage of the revolutionary opportunities which this new approach affords risk being left behind in the increasingly competitive drug development race. Two key developments have made possible the emergence of this new approach. The first has been the astonishing success of genomics. Throughout the 1990s, genomics delivered an ever-increasing flow of sequence data that has now become a torrent. More than half a million protein sequences - mostly predicted from the sequenced DNA of numerous pharmaceutically important organisms - are already in the public domain. Within two years, over 60 microbial genomes, several metazoan species and - most importantly - the human genome will have been fully sequenced and their full repertoire of protein products predicted. For the pharmaceutical industry, the implications of the genomics revolution are difficult to overestimate. Once the full set of sequence data is in the public domain, every pharmaceutical target will effectively be available to everyone - or at least, everyone capable of determining the threedimensional structure and biological function of a protein for which only the primary genetic code and amino acid sequence are available. Herein lies the challenge of the post-genomics era - for the task of interpreting not just one but hundreds of thousands of sequences within vast and labyrinthine archives is a daunting one. Recently, however, techniques have emerged which enable sequences to be analysed in a powerful and efficient way, and it is these techniques - as much as the flood-tide of genomic data - that are making in silico drug discovery a reality. From families to functions: making sense of novel sequences It is not possible to predict the three-dimensional structure, or - therefore - the function of a protein simply on the basis of its primary amino acid sequence. Indeed, the primary amino acid... it is these techniques - as much as the flood-tide of genomic data - that are making in silico drug discovery a reality Innovations in Pharmaceutical Technology 69

2 Figure 1. The detection of similarities between amino acid sequences is fundamental to pharmaceutical research in silico. Conventional techniques can only detect relationships between sequences when at least 25 per cent (that is, one in four) of their residues match. New techniques - such as profile-based searching and especially threading - provide much greater sensitivity, allowing many more relationships to be identified. sequence itself may be difficult to derive from DNA sequence data in eukaryotes. Unfortunately, such primary sequence data is the only information that genomics provides - a limitation which thus far has held back full commercial exploitation of the data now becoming available. The key breakthrough that has solved this problem stems from the realisation that all proteins - even those with sequences that at first sight appear to be considerably different - can be members of families sharing essentially similar structures and functions. Thus, if the sequence for an unknown protein can be shown to be related to a known one, the protein it encodes is likely to belong to the same family and to have similar structural and functional properties. In this way, our knowledge of known proteins can be used to predict the biological role, pharmaceutical potential and even the three-dimensional structure of novel proteins - provided a match can be found between their sequences. The idea of using information from known proteins to make sense of novel sequences is not in itself new. It is more than ten years since it was realised that even proteins with quite different sequences belonged to a small number of families, and this insight has already driven the development of new classes of drugs. In the development of anti-hiv drugs, for instance, the HIV proteinase was found to be half of an aspartic proteinase - a common enzyme class that includes molecules such as renin and pepsin. Since the 3-D structures of these related molecules were already known and inhibitors were readily available, the development of new protease inhibitors - which are now integral to the treatment of HIV infection - was significantly accelerated (1). However, turning this simple insight into a 70 Innovations in Pharmaceutical Technology

3 The idea of using information from known proteins to make sense of novel sequences is not in itself new method for efficiently searching databases has proved a difficult challenge. To search a database, each sequence of interest must be compared with every single sequence present, and the degree of match determined. Given the sheer size of databases and the complexity of the sequences they contain, search procedures that are both sufficiently rapid and sufficiently sensitive to deliver quality information within a reasonable timescale have proved hard to develop. Recently though, techniques have at last emerged that allow databases to be searched with sufficient power, depth and speed - unlocking a treasure trove of pharmaceutical information. For instance, when a database is searched it is now readily possible to: Identify protein coding sequences within long stretches of DNA, Predict the function of a novel sequence in order to assess its potential as a drug or drug target, Identify all the sequences within the database that have a feature of pharmaceutical interest, such as a specific DNA-binding domain, Find novel, unexploited proteins related to a known drug target, in the hope of finding fresh applications for an established drug series, and Screen for potential toxicities. If, for example, a sequence for a bacterial protein is also present in humans, this will alert the researcher that an antimicrobial drug raised against that target may prove toxic in humans. Pharmaceutical companies have not been slow to recognise these opportunities. For example, the journal Science recently reported that several new proteins found within databases are already entering clinical trials. These include a vascular growth factor that may improve circulation, a bone-strengthening factor for treating osteoporosis, a chemokine that may help protect bone marrow during chemotherapy and a stimulator of wound healing (2). With the emergence of ever richer databases, and the launch of a new generation of more sophisticated sequence-search resources, a pharmaceutical gold-rush is certain to take place over the next few years as companies compete for the riches of the post-genomic world. Sequence-similarity searches: the state of the art The basic principle by which sequence-similarity searches operate is simple: the sequence under investigation is compared with each database sequence in turn, and an attempt made to align (that is, bring into register) the two sequences. Here, we will consider specifically protein-protein comparisons, but the same ideas can equally be applied to protein-dna comparisons. Frequently, only portions of two protein sequences - corresponding to structurally important regions of the protein that have been conserved during evolutionary time - will match, but this difficulty can readily be overcome using publicly available search algorithms. The major problem in sequence-similarity searching is that two proteins may be structurally related even when little or even no similarity remains at the level of the amino acid sequence. Standard sequence-similarity search techniques can only detect relationships between proteins when there is a match between at least 25 per cent of the residues when the two proteins are brought into register. In order to detect more distant relationships, which may be vital to understanding a protein's structure and function, more powerful techniques are needed. One technique that allows matches to be found at lower levels of sequence similarity (down to about 20 per cent) is the profile-based search. In this approach, a 'profile' or family of sequences is used to search the database instead of a single sequence. The profile irons out the idiosyncrasies of individual sequences, while capturing the key elements that characterise the members of the family. It is, therefore, better able to search for matches than any individual sequence. Threading: using structures to interpret sequences Even profile-based searching is poor at finding good sequence-matching when fewer than 20 per cent of residues match, but it is precisely this level of remote similarity that must be detected if sequence databases are to be mined effectively. There is, however, one technique - known as threading - which enables matches to be found between even the most distantly related proteins (3). The secret to the success of threading is to use data on three-dimensional protein structure, coupled with knowledge of the physicochemical properties of amino acids, to determine if a novel sequence is likely to fold in the same way as a sequence for which the structure is known. If it does, a family relationship is confirmed, and the three-dimensional structure of the novel sequence instantly predicted. Currently, the three-dimensional structures of over 10,000 proteins have been determined, primarily by X-ray crystallography, and their full atomic co-ordinates are publicly available in the Protein Data Bank (PDB) database. 72 Innovations in Pharmaceutical Technology

4 Although this is a comparatively small number of proteins, nearly all of the major protein families - and thus most major fold types - are thought to be represented. The term 'threading' derives from the search method in which the query sequence is 'threaded' through the fold structures of proteins where a match is suspected, to determine whether or not it is physicochemically plausible. Threading is such an expert technique that its benefits have not hitherto been commercially available. Recently, however, a commercial threading tool which may be useful to skilled bioinformaticians has been marketed by Molecular Simulations Inc (San Diego, CA, USA), while Inpharmatica (London, UK) has used its expertise in threading algorithms during the construction of its Biopendium database, making the results available to subscribers. Commercial strategies How can pharmaceutical companies best exploit the riches which the post-genomic world now offers? Most companies have accumulated proprietary sequence data in areas of specific interest to them, but to derive maximum value from this data it is necessary to probe it against large databases. In addition, databases can be mined for novel targets and drugs without the search being based on any existing data. Most pharmaceutical companies will wish to adopt both these strategies, using database searches to interpret their own private sequence data and conduct de novo discovery and development programmes. Given these objectives, which databases should pharmaceutical companies use, and how should they mine them? Currently, biotech companies With the emergence of ever richer databases... a pharmaceutical gold-rush is certain to take place over the next few years Figure 2. Although these proteins have similar 3-D structures, their amino acid sequences only match at a few points (shown as an asterisk). New sequence analysis techniques such as threading enable the relationship between proteins to be detected - even at such low levels of sequence similarity. Innovations in Pharmaceutical Technology 73

5 There is... one technique - known as threading - which enables matches to be found between even the most distantly related proteins Mining value from large sequence databases represents a considerable challenge, even to experienced bioinformaticians such as Incyte (Palo Alto, CA, USA) offer access to subscription databases that contain significant numbers of sequences not yet in the public domain. With each passing month, however, as more sequences become public, it is the ability to search data, rather than access to data per se, that is becoming the decisive determinant of which companies will succeed. Mining value from large sequence databases represents a considerable challenge, even to experienced bioinformaticians, not least because there is such a variety of major databases. Many exist - containing either primary DNA sequence data, translations of the nucleic acid sequences into putative amino acid sequences or further analyses of this data; there is also a number of structural databases, such as the PDB. Considerable expertise is required to select databases appropriately, to sift and clean what is in some cases unreliable data, to collate data for analysis from different sources and to use data-mining tools effectively. Consequently, whereas most pharmaceutical companies are rightly attempting to develop in-house bioinformatics expertise, there has also been a growing demand for specialist products and resources, leading to the emergence of a thriving bioinformatics sector within the wider biotech industry. Off-the-shelf sequence analysis tools are provided by several companies such as Tripos (St Louis, USA) and Molecular Simulations, while a number of companies are now offering both tools and processed data to meet specific needs. For example, Lion Bioscience (Heidelberg, Germany) concentrates on providing companies with data on the analysis of DNA and protein sequences at the primary sequence level, while Structural Bioinformatics (Palo Alto, CA, USA) concentrates its expertise on sequences that are amenable to 3-D modelling. Inpharmatica provides both collaborative services and a proprietary subscription database - the Biopendium - in which both sequence and structural data have been pooled, integrated and analysed in advance to support target identification, prioritisation and selection. The pharmaceutical industry thus has a range of options through which to exploit the opportunities now available. Conclusion Five years ago, few would have predicted that the discovery and evaluation of drug targets in silico would become a major element of pharmaceutical R&D, but this is where the industry is now heading. While the virtual laboratory will never replace the real-world experiment, computational approaches can now be integrated for all aspects of pharmaceutical research - from target identification to small molecule refinement and clinical trials. In fact, it is now possible to think of the whole pharmaceutical process as a computational approach, with confirmatory experiments at each decision-point. Dr Mark Swindells specialises in the development of advanced approaches to protein structure and function prediction, and is the founding Scientific Director at Inpharmatica. Prior to his current position, he spent seven years in Japan working at the Protein Engineering Research Institute ( ) and at Yamanouchi Pharmaceutical Company ( ) where he applied knowledge-based approaches to protein structure analysis and drug design. Dr Swindells has a PhD in "The Analysis and Prediction of Protein Structure" from Birkbeck College & University College London, supervised by Professor Janet Thornton, FRS. As Scientific Director at Inpharmatica, he has steered the creation of the Biopendium in addition to developing an integrated solution for the incorporation of clients proprietary data. References 1. Toh H, Miyata T (1985). Is the AIDS virus recombinant? Nature, 316, Wickelgren I (1999). Mining the genome for drugs. Science, 285, Jones DT, Taylor WR and Thornton JM (1992). A new approach to fold recognition. Nature, 358, Note: Inpharmatica is an emerging company with the mission of becoming the leading supplier of protein structure-function information to enable the discovery and development of new drugs. The Company was formed around the work of Professor Janet Thornton, FRS, a pioneer in the field of bioinformatics, and a team of world-class consultants. A unique combination of techniques and expertise has been integrated into the Biopendium - the world s first comprehensive database of pre-calculated protein stucture-function relationships. 74 Innovations in Pharmaceutical Technology

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