1. Greg Challis Department of Chemistry, University of Warwick, UK
Lecture 1: Introduction to computer workshops
Microbial Metabolites: Signals to Drugs
Dubrovnik, Croatia, 21 August - 29 August 2010
Greg Challis
Department of Chemistry, University of Warwick, UK
Govind Chandra & Mervyn Bibb
Department of Molecular Microbiology, John Innes Centre, UK

2. Overview Cloning and sequencing of secondary metabolite gene clusters
Analysis of raw sequence data
Cryptic (orphan) gene clusters in microbial genomes
Nonribosomal peptide biosynthesis

3. Cloning of secondary metabolic biosynthesis gene clusters
Identify putative biosynthetic gene in genome of producer
e.g. by PCR using degenerate primers
Construct large insert genomic library
e.g. fosmid or cosmid library
Screen library for clones containing putative biosynthetic gene
e.g. using PCR or colony hybridisation
Sequence and assemble insert of isolated clones
e.g. by sending to a company for shotgun sequencing

4. TCTAGATCGATCCCGCGAAATTAATACGACTCACTATAGGGGAATTGTGAGCGGATAACAATTCCCTCGA GAAGGAGATATACATATGGCAGATCTGAGCAAACTCTCCGATTCTCGCACCGCCCAGCCGGGCCGCATCG TCCGCCCATGGCCGCTGTCTGGCTGCAATGAATCCGCATTGCGTGCTCGCGCCCGGCAGCTTCGGGCACA CCTGGACCGTTTTCCGGACGCGGGCGTGGAGGGCGTGGGTGCGGCATTGGCCCACGACGAGCAGGCGGAC GCAGGTCCGCATCGTGCGGTGGTTGTTGCTTCATCGACCTCAGAATTACTGGATGGTCTGGCCGCGGTGG CCGATGGTCGCCCGCATGCGAGCGTCGTACGCGGGGTTGCGCGTCCTTCTGCCCCGGTAGTGTTTGTGTT TCCTGGGCAGGGGGCACAGTGGGCAGGTATGGCGGGCGAGCTGCTTGGCGAGTCGCGCGTGTTCGCTGCC GCCATGGACGCCTGTGCTCGCGCGTTCGAACCTGTGACAGACTGGACGCTTGCACAGGTCCTGGATAGCC CTGAACAAAGCCGCCGCGTTGAAGTGGTCCAGCCAGCGTTATTCGCCGTGCAAACTTCGCTAGCGGCGCT CTGGCGTTCCTTTGGCGTGACCCCAGATGCTGTGGTTGGCCATTCAATTGGTGAATTAGCAGCGGCGCAT GTTTGCGGTGCCGCAGGTGCGGCGGATGCAGCGCGCGCAGCGGCACTGTGGAGTCGCGAGATGATTCCGT TGGTGGGCAACGGCGACATGGCCGCTGTCGCTCTGTCGGCAGATGAAATTGAACCACGTATCGCGCGCTG GGACGATGACGTAGTGCTGGCGGGCGTCAACGGTCCGCGGTCCGTCCTGTTGACAGGGTCACCTGAACCC GTAGCTCGTCGTGTGCAGGAACTGAGCGCCGAGGGCGTACGCGCCCAGGTAATCAATGTTAGCATGGCTG CGCATAGCGCTCAGGTTGATGACATCGCTGAGGGTATGCGTAGTGCCCTGGCGTGGTTTGCCCCAGGCGG CTCCGAAGTTCCGTTCTACGCCTCACTGACCGGCGGTGCGGTTGATACCCGTGAGTTAGTAGCCGATTAC TGGCGTCGTTCTTTTCGGCTACCGGTACGGTTTGATGAAGCGATCCGCAGTGCCTTGGAAGTAGGCCCGG GTACGTTTGTCGAAGCGAGCCCGCATCCTGTGTTGGCGGCGGCGCTGCAACAGACCCTGGATGCCGAAGG TTCAAGCGCGGCTGTTGTACCTACACTGCAGCGTGGTCAAGGGGGCATGCGTCGCTTCCTGTTGGCCGCG GCCCAGGCTTTCACTGGCGGCGTCGCGGTTGACTGGACGGCCGCTTACGATGATGTTGGTGCCGAACCAG GTTCGCTGCCTGAGTTCGCTCCGGCCGAAGAAGAGGACGAGCCGGCAGAGTCCGGGGTTGATTGGAACGC ACCGCCACACGTGCTCCGCGAACGTCTGCTGGCTGTGGTGAACGGGGAGACCGCAGCTCTTGCAGGCCGC GAAGCTGACGCAGAGGCGACCTTTCGCGAATTAGGTCTCGATTCTGTGTTAGCAGCCCAGCTGCGCGCGA AAGTCAGCGCGGCCATTGGCCGTGAAGTGAATATTGCGCTGTTATATGACCATCCAACCCCGCGTGCACT TGCGGAGGCACTGTCTAGTGGGACGGAAGTAGCGCAACGCGAGACTCGCGCCCGTACAAACGAAGCTGCA CCTGGCGAACCAATTGCGGTAGTAGCGATGGCATGTCGTTTACCGGGCGGTGTATCGACCCCTGAAGAGT

5. Artemis

6. Sequencing
Shift from long reads - low coverage to short reads - high coverage.
The read lengths of 454 are approaching those of capillary and gel methods.
Illumina can now give read lengths of 100 nucleotides.
Coupled with some clever strategies such as paired end sequencing, we can get long high quality contigs from the short reads coming out of the machines.

7. Assembly Affected by both quality and length of the reads.
High GC (or AT) presents another hurdle to assembly.
High coverage helps but only to a limited extent.
Assembly can suffer due to very high coverage.
Best left to people who do this for a living.
But you do need to understand the process enough to be able to do some independent quality checks.

8. No more “finishing”
Assembly
Primer
Design
Sequencing
Cycle till all gaps were closed and all ambiguities resolved.
Because we just want a sequence we can mine with some degree of confidence, there is no need for the sequence to be finished to a single contig.

9. Beware Multiple contigs Uncertainty about the correctness of contigs.
It is better to have a few more contigs than to have wrongly assembled ones.

10. A B C A C B A C B

11. Mining Contigs can be searched for clusters.
Clusters may be scattered over several contigs due to mis-assembly.
blastp: Fast, but will not find any proteins which have not been called in the contigs.
tblastn: Slower. Search a nucleotide database with a protein query. Also helps by indicating potentially adjacent contigs and wrongly assembled ones.
Use both.
Make a cosmid library and sequence the positive cosmids.

12. Annotation
ORF calling
rRNAS
tRNAs
Rfam
RAST

13. Sequence gazing n contigs RAST n GenBank files
These are plain text files. Do not open in any word processor. Use notepad or download a decent text editor from the web.
Sequence visualisation — Artemis.
Sequence comparison — ACT.
The Artemis ACT workshop manual
takes over from here.

14. ‘Cryptic’ (orphan) biosynthetic gene clusters
Present in many of the 739 sequenced microbial genomes
e.g. Streptomyces avermitilis
Streptomyces coelicolor
Bacillus subtilis
Pseudomonas fluorescens
Pseudomonas syringae
Nostoc punctiforme
Aspergillus nidulans
Polyketide synthases
Nonribosomal peptide
synthetases
Terpene synthases
May prove a valuable new source of bioactive metabolites

15. Genome sequence of the model antibiotic-producer Streptomyces coelicolor M145

16. Gene clusters directing complex metabolite biosynthesis in the S
Gene clusters directing complex metabolite biosynthesis in the S. coelicolor genome

17. NRPSs are metabolic assembly lines – penicillin biosynthesis as an example

18. NRPSs are metabolic assembly lines – penicillin biosynthesis as an example

19. NRPSs are metabolic assembly lines – penicillin biosynthesis as an example

20. NRPSs are metabolic assembly lines – penicillin biosynthesis as an example

21. NRPSs are metabolic assembly lines – penicillin biosynthesis as an example

22. NRPSs are metabolic assembly lines – penicillin biosynthesis as an example

23. NRPSs are metabolic assembly lines – penicillin biosynthesis as an example

24. Prediction of NRPS module substrate specificity
GrsA DASVWEMFMALLTGASLYIILKDTINDFVKFEQYINQKEITVITLPPTYVVHL-----DPERILSIQTLITAGSATSPSLVNKWKEK--VTYINAYGPTETTI
Ncs1-M1 DIAVWELLAAFVGGARLVIAEHRLRGVVPHLPELMTDHRVTVAHFVPSVLEELLGWMADGGRVG-LRLVVCGGEAVPPSQRDRLLALSGARMVHAYGPTETTI
GrsA D A W T I A A I
Ncs1-M1 D I W H V G A I
Challis, Ravel and Townsend, Chem. Biol. (2000) 7,
Stachelhaus, Mootz and Marahiel, Chem Biol. (1999) 6,

25. NRPS-PKS

26. Questions? About things in the talk About the manuals
About computing in relation to sequence analysis in general

27. BLASTP

28. Artemis Comparison Tool (ACT)

29. ORF Finder