engleski

An expert system for in silico drug design and discovery

INTRODUCTION Bacterial natural products are important sources of chemical diversity for commercial exploitation by the pharmaceutical industry. In the past few years there has been a lot of interest in generating new compounds as potential drug candidates by manipulating the programming of biosynthetic gene-clusters in vitro. In order to assist this process a novel expert system for in silico drug design and discovery was developed. METHODOLOGY The expert system has a server-client architecture, with analysis being carried out on the server and a Java user interface for the client which can be PC, Mac or Linux. RESULTS AND DISCUSSION This expert system consists of two suites of programs for semi-automatic DNA sequence analysis (ClustScan) and for the generation of novel gene-clusters by virtual homeologous recombination (CompGen). ClustScan and CompGen are used to generate two specialised databases. The CSDB is a ClustScan database of well-characterised polyketide and nonribosomal peptide natural products. The database contains 170 well annotated natural product gene-clusters. Conversely, r-CSDB is a virtual compound database for molecular modelling studies developed by the use of the CompGen program package and contains more than 20.000 novel compounds. In silico studies are only useful if it is possible to generate strains producing them. Continuing progress in synthetic biology will improve methods to achieve that. CONCLUSION A major issue for the pharmaceutical industry is maintaining a continuous supply of promising new leads for drug development. We propose that recombinatorial biosynthesis offers a new and exciting strategy whereby large and chemically diverse libraries of polyketides can first be screened in silico and then generated in the laboratory for further new lead development. Given that many polyketides are used clinically as antimicrobials, this new expert system comes at an important time when ever increasing numbers of pathogens are becoming resistant to our current antibiotic armoury.

Natural products; gene-clusters; annotation; recombinatorial biosynthesis; databases

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