Primary and secondary metabolism, and post-translational protein modifications, as portrayed by proteomic analysis of Streptomyces coelicolor



The newly sequenced genome of Streptomyces coelicolor is estimated to encode 7825 theoretical proteins. We have mapped approximately 10% of the theoretical proteome experimentally using two-dimensional gel electrophoresis and matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry. Products from 770 different genes were identified, and the types of proteins represented are discussed in terms of their anno-tated functional classes. An average of 1.2 proteins per gene was observed, indicating extensive post-translational regulation. Examples of modification by N-acetylation, adenylylation and proteolytic processing were characterized using mass spectrometry. Proteins from both primary and certain secondary metabolic pathways are strongly represented on the map, and a number of these enzymes were identified at more than one two-dimensional gel location. Post-translational modification mechanisms may therefore play a significant role in the regulation of these pathways. Unexpectedly, one of the enzymes for synthesis of the actinorhodin polyketide antibiotic appears to be located outside the cytoplasmic compartment, within the cell wall matrix. Of 20 gene clusters encoding enzymes characteristic of secondary metabolism, eight are represented on the proteome map, including three that specify the production of novel metabolites. This information will be valuable in the characterization of the new metabolites.


The genome of a plasmid-free derivative, strain M145, of the Gram-positive bacterium Streptomyces coelicolor A3(2) has recently been completely sequenced and annotated (Bentley et al., 2002). At 8.67 Mb and 7825 annotated genes, it is nearly twice the size of the Escherichia coli (Blattner et al., 1997) and Bacillus subtilis (Kunst et al., 1997) genomes, presumably reflecting the lifestyle of the organism. Streptomycetes are mycelial, saprophytic soil bacteria that undergo complex morphological differentiation and produce a range of diverse secondary metabolites with important applications in human medicine and agriculture (for a review, see Champness, 2000). Spore germination is followed by the development of branched hyphae that grow on and into appropriate substrates. In response to complex but still poorly defined signals, the substrate mycelium produces aerial hyphae that eventually undergo septation to yield chains of unigenomic spores. As the aerial branches grow, the substrate mycelium typically begins to produce the various antibiotics. Before the genome sequencing project began, S. coelicolor was known to produce four antibiotics (one of them plasmid determined), two of which, actinorhodin (Act) and undecylprodigiosin (Red), are pigmented, and a polyketide spore pigment. It is now apparent that there are about 20 gene clusters that are likely to direct the biosynthesis of what may broadly be considered as secondary metabolites (Bentley et al., 2002). In addition, Bentley et al. (2002) predicted more than 800 secreted proteins, 65 sigma factors, 37 Ser/Thr kinase homologues, 85 two-component sensor histidine kinases and a host of transcriptional regulators.

The complete sequencing of an organism's genome immediately allows study of overall gene expression at the levels of mRNA abundance, typically using DNA microarrays (the transcriptome) (Schena, 2000; Lucchini et al., 2001), and protein profiling, by two-dimensional (2D) PAGE coupled with high-throughput matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry (the proteome) (Blackstock and Weir, 1999; Mann et al., 2001). Analysis of the proteome is more complicated than transcriptome analysis because of the diverse physical and chemical properties of proteins, and because of the need for any given protein to be extracted in sufficient quantity not only to be detected, but also to be identified. Thompson and co-workers have used 2D-PAGE extensively to analyse changes in the pattern of radiolabelled protein profiles in pulse–chase experiments in S. coelicolor during growth and in response to stress, but did not have the benefit of the complete genome sequence to use mass spectrometry to identify proteins of interest (Puglia et al., 1995; Vohradsky et al., 1997; 2000). In this study, we have mapped a substantial part of the S. coelicolor proteome using 2D-PAGE and MALDI-TOF peptide mass fingerprint analysis. Particular attention is paid to the representation of proteins involved in secondary metabolic pathways in comparison with those from primary metabolism, and to the extent of regulation by post-translational modification mechanisms.


Analysis of the proteome using 2D-PAGE

Streptomyces coelicolor M145 produces the pigmented antibiotics actinorhodin (Act) and the prodigiosin complex (Red) in a growth phase-dependent manner ( Takano et al., 1992 ; Gramajo et al., 1993 ). Thus, in liquid minimal medium supplemented with casamino acids (SMM), pigment production begins at the transition between exponential growth and stationary phase. In order to observe proteins from both primary and secondary metabolic pathways, pigmented mycelium from transition phase cultures was harvested and disrupted directly into the strongly denaturing isoelectric focusing buffer UTCHAPS (see Experimental procedures ). Protein extracts were separated by 2D-PAGE using several different immobilized pH gradient (IPG) strips for the first-dimension separation (pH 4–7, pH 6–11, pH 4.5–5.5 and pH 5.5–6.7), 12.5% SDS–PAGE for the second and silver nitrate staining for protein detection ( Fig. 1 ). The proteins visible on each gel were counted after spot detection of scanned gel images using phoretix 2D image analysis software, and compared with the total number expected assuming that every potential gene was expressed ( Table 1 ). To calculate the number of spots expected, proteins in the theoretical proteome with predicted isoelectric point values within the pH range of the IPG strip used, and with molecular weights between 8 kDa and 140 kDa (the detectable range using 12.5% PAGE) were counted and multiplied by a factor of 1.2, representing the observed average number of protein spots per gene (see Table 1 ). Separation using the isoelectric point (pI) range pH 4–7 produced 1051 detectable spots, representing 19% of the total number of proteins predicted for this region. Excluding secreted and membrane proteins, which would not generally be expected in the protein extract, the proportion was 25%. In the pI range pH 6–11, these numbers were 8% for the theoretical total proteome and 12% for the theoretical extractable proteome. Using the narrow-range IPG strips pH 4.5–5.5 and pH 5.5–6.7, 1555 and 913 spots were detected, respectively, corresponding to 82% and 41% of the total number of predicted extractable proteins. Thus, the higher resolution and higher loading possible on these ‘zoom gels’ approximately doubled the number of proteins detectable.

Figure 1.

A proteome map of S. coelicolor showing 2D-PAGE separations using IPG strips pH 4–7, pH 6–11, pH 4.5–5.5 and pH 5.5.-6.7. The number of spots identified is given at the foot of each reference gel. Protein spots from metabolic pathways that were identified at positions inconsistent with their predicted amino acid sequences, and suggestive of post-translational modification, are marked in yellow (primary metabolism) or red (secondary metabolism). Blue spots correspond to other examples of post-translational modifications that are mentioned in the text and in Table 3 . Separations have been performed on numerous occasions and generally show high spatial reproducibility.

Table 1. .   Estimate of the proportion of the S. coelicolor proteome that can be detected by silver staining of 2D-PAGE separations of transition phase protein extracts and MALDI-TOF identification of protein spots illustrating the extent of post-translational modifications.
 Isoelectric point range (pH)
  • a

    . On silver-stained gels.

  • b

    . The total number of gene products with theoretical isoelectric point values and molecular weights within the range of the gel separation (8–140 kDa) was multiplied by 1.2, the observed average number of protein spots per gene as a result of post-translational modifications.

  • c

    . Same as a, but excluding proteins annotated as being membrane or secreted.

  • d

    . From colloidal Coomassie-stained gels.

Protein spots detecteda10511555913379
Percentage of expected19%63%37%8%
protein spots detectedbof 5418of 2462of 2470of 4792
Percentage of expected25%82%41%12%
extractable protein spots detectedcof 4221of 1886of 2218of 3112
Protein spots identifiedd443459193210
Different gene products represented374360170179
Ratio of protein spots per gene product1.

Identification of separated proteins: towards a proteome map and database

Protein spot identification was performed exclusively on gels stained with colloidal Coomassie G250 and mapped back onto the analytical silver-stained gels in Fig. 1. Although silver staining is 5–10 times more sensitive than colloidal Coomassie staining (and about 100 times more sensitive than conventional Coomassie staining), further mass spectrometry analysis of protein spots is much more efficient after staining with colloidal Coomassie than with silver. A total of 1305 protein spots were unambiguously assigned to sequenced genes using MALDI-TOF peptide mass fingerprint analysis, at a success rate of ≈ 90% (Table 1). There is some redundancy in these figures, with several spots being represented in more than one pI range. On each gel, some proteins were identified in more than one position, indicative of post-translational modifications. The average number of protein spots per gene product was 1.2. The products from 770 different genes were assigned to the proteome map, representing ≈ 10% of the genome and corresponding to many different classes of proteins (Table 2). The master gel reference maps are the basis for the production of a publicly accessible proteome database (, see last Results section).

Table 2. .  Types of proteins identified on the proteome map.
Protein classaNumber ingenomeaNumber identifiedb
Hypothetical1049 68 (6%)
Conserved hypothetical1322114 (8%)
Secreted  530 30 (5%)
ABC transport ATP binding  109 16 (14%)
Lipoproteins  124 10 (8%)
Sigma factors   65  0 (0%)
Antisigma factors    7  2 (28%)
Antisigma factor antagonists   15  1 (6%)
Two-component system regulators   80 11 (13%)
Other regulators  592 36 (6%)
Two-component system kinases   85  0 (0%)
Ser/Thr kinases   37  1 (2%)
Other3923482 (12%)
All7816770 (10%)

Analysis of co- and post-translational modifications

The results of MALDI-TOF identification of protein spots summarized in Table 1 indicated a significant amount of regulation at the post-transcriptional level in S. coelicolor, as in most other bacteria. Table 3 lists those gene products that were identified at pI and/or molecular weight co-ordinates significantly different from predicted values. In general, these fell into two categories: one or more spots with lower than expected molecular weights and often different pI values, indicating modification by proteolytic processing (Table 3A), and multiple spots with the same apparent molecular weights but different pI values, indicating modification by covalent addition of a small adduct (Table 3B). In the former case, the full-length protein was often, but not always, detected. A similar fraction of proteins of unknown function and annotated as ‘hypo-thetical’ or ‘conserved hypothetical’ was subject to post-translational modification, but these will be described in a later publication and are not considered in Table 3. Evidence in the peptide mass fingerprint data for the specific nature of the modification was obtained in 10 out of the 88 proteins listed (Table 4). Figure 2 illustrates the characterization of an example of each type of modification by interpretation of the MALDI-TOF data.

Table 3. .  Proteins identified at pI and molecular weight co-ordinates on the 2D-PAGE maps that are inconsistent with their predicted amino acid sequences, indicating possible post-translational modification either by proteolytic processing (A) or by covalent addition of a low-molecular-weight adduct (B).
ORF no.Gene productSpot no. in Fig. 1
  1. Some proteins appear to be subject to both types of modification and are present in both parts. Only proteins from primary or secondary metabolic pathways, or with identified modifications, are assigned to spots in Fig. 1.

SCO0498Probable peptide monooxygenase33
SCO1230Putative tripeptidylaminopeptidase 
SCO164420S proteasome beta subunit precursor35
SCO18153-Oxoacyl-[acyl carrier protein] reductase 
SCO1860Putative secreted protein 
SCO1947Glyceraldehyde-3-phosphate dehydrogenase 9
SCO199830S ribosomal protein S1 
SCO2619ATP-dependent Clp protease subunit13
SCO2920Probable secreted proteinase 
SCO3236Probable oxygenase34
SCO3671Heat shock protein DNAK 
SCO390950S ribosomal protein L930
SCO4296Chaperonin GroEL2 
SCO4439 d -Alanyl- d -alanine carboxypeptidase  
SCO4643UDP-N-acetylanoylpyruvoylglucosamine reductase 
SCO4662Elongation factor Tu1 
SCO4762Chaperonin GroEL1 
SCO4808Succinyl-CoA synthetase beta chain 8
SCO4824Bifunctional protein methylene tetra-hydrofolate dehydrogenase/cyclohydrolase 
SCO5073Putative oxidoreductase ActVI-ORF229
SCO5074Putative dehydratase ActVI-ORF331
SCO5088Ketoacyl synthase beta subunit ActI-ORF224
SCO5113Oligopeptide ABC transport protein BldK-ORFB 
SCO5254Superoxide dismutase precursor32
SCO5371ATP synthase alpha chain 
SCO5477Probable oligopeptide-binding lipoprotein 
SCO562430S ribosomal protein S2 
SCO5737Guanosine pentaphosphate synthetase 
SCO5769Recombinase RecA 
SCO5892Polyketide synthase RedL18
SCO62823-Oxoacyl-[acyl carrier protein] reductase11
SCO1074Putative peptidase 
SCO1476S-adenosylmethionine synthetase 
SCO1594Phenyl-tRNA synthetase beta chain 
SCO164320S proteasome alpha subunit12
SCO1814Enoyl-[acyl carrier protein] reductase 
SCO1935Transketolase 3
SCO1947Glyceraldehyde-3-phosphate dehydrogenase 9
SCO2180Dihydrolipoamide dehydrogenase21
SCO2181Probable dihydrolipoamide S-succinyltransferase14
SCO2198Glutamine synthetase I 6
SCO23903-Oxoacyl-[acyl carrier protein] synthase II 
SCO2478Putative reductase 
SCO2618ATP-dependent Clp protease subunit 2 
SCO2822Putative decarboxylase 
SCO32294-Hydroxyphenylpyruvic acid dioxygenase 
SCO3337Probable pyrroline-5-carboxylate reductase28
SCO3409Inorganic pyrophosphatase 
SCO3549Antisigma factor antagonist BldG 
SCO3563Acetyl-CoA synthase 
SCO3671Heat shock protein DNAK 
SCO3878DNA polymerase III beta chain 
SCO3890Thioredoxin reductase 
SCO390630S ribosomal protein S6 
SCO3970XAA-Pro aminopeptidase I 
SCO4164Putative thiosulphate sulphurtransferase 
SCO4209Phosphoglycerate mutase19
SCO4240ABC transport ATP binding protein MsiK 
SCO4296Chaperonin GroEL2 
SCO4645Aspartate aminotransferase 7
SCO465250S ribosomal protein L10 
SCO4661Elongation factor G 
SCO4662Elongation factor Tu1 
SCO4677Putative regulatory protein 
SCO4729DNA-directed RNA polymerase alpha chain 
SCO4762Chaperonin GroEL1 
SCO4770Inosine-5′-monophosphate dehydrogenase25
SCO4792LuxR family two-component regulator 
SCO4808Succinyl-CoA synthetase beta chain 8
SCO4809Succinyl-CoA synthetase alpha chain22
SCO4921Probable acyl-CoA carboxylase complex A chain 
SCO5071Hydroxylacyl-CoA dehydrogenase ActVI-ORFA16
SCO5079Hypothetical protein ActVA-ORF420
SCO5080Possible hydrolase ActVA-ORF526
SCO5115Probable ABC transporter intracellular ATPase chain BldK-ORFD17
SCO5371ATP synthase alpha chain 
SCO5396Putative cellulose-binding protein10
SCO5419Putative thioredoxin 
SCO5515Probable d-3-phosphoglycerate dehydrogenase 4
SCO5535Putative carboxyl transferase 
SCO5584Nitrogen regulatory protein GlnK15
SCO5625Elongation factor Ts 
SCO5699Prolyl-tRNA synthetase 
SCO5737Guanosine pentaphosphate synthetase 
SCO5777Glutamate uptake system ATP-binding protein 
SCO5803SOS regulatory protein LexA 
SCO5999Aconitase 1
SCO6198Probable secreted protein 
SCO62823-Oxoacyl-[acyl carrier protein] reductase11
SCO6636Bacteriophage φ-C31 resistance protein PglZ 
SCO7000Isocitrate dehydrogenase 2
SCO7036Argininosuccinate synthase 5
SCO7510Peptidyl-prolyl cis-trans isomerase 
Table 4. . Co- and post-translational modification of proteins suggested by MALDI-TOF peptide mass fingerprint analysis.
Protein Modification
SCO164320S proteasome alpha subunitRemoval of initiator Met residue and acetylation of N-terminal Ser
SCO5115ABC transporter intracellular
  ATPase BldK-ORFD
Removal of initiator Met residue and acetylation of N-terminal Thr
SCO5396Putative cellulose-binding proteinRemoval of initiator Met residue and acetylation of N-terminal Ser
SCO2198Glutamine synthetase IAdenylylation of peptide 395–420
SCO5584Nitrogen regulatory protein GlnKAdenylylation of peptide 48–58
SCO164420S proteasome β-subunit precursorProteolytic processing between amino acid residues 53/54
SCO2619ATP-dependent Clp protease
  proteolytic subunit I
Proteolytic processing between amino acid residues 22/23
SCO390950S ribosomal protein L9Proteolytic processing between amino acid residues 3/4
SCO5074Putative dehydratase ActVI-ORF3Proteolytic processing between amino acid residues 31/32 and 34/35
SCO5254Superoxide dismutase precursorProteolytic processing between amino acid residues 14/15
Figure 2.

Examples of MALDI-TOF peptide mass fingerprint data identifying post-translational modification of proteins by (A) acetylation (predicted mass increase of 42.0 Da), (B) adenylylation (predicted mass increase of 329.0 Da) and (C) proteolytic processing (predicted mass loss for Ile of 113.08 Da). In each fingerprint, the x -axis is mass/charge ratio (m/z), and the y -axis is absolute intensity. The observed mass accuracies in the MALDI-TOF fingerprints are: (A) 60 p.p.m.; (B) 20 p.p.m.; and (C) 20 p.p.m.


The putative cellulose-binding protein SCO5396 was identified in two positions on the proteome map (spots labelled 10 in Fig. 1) with the same apparent molecular weight but significantly different pI values. The more basic spot gave a tryptic peptide at 1370.76 Da corresponding to the N-terminal peptide 2–13, indicating that the initiator Met residue had been co-translationally removed (Fig. 2A, top). In the more acidic spot (i.e. on the left in Fig. 1), this peptide had increased in mass by 42.0 Da, diagnostic of the addition of an acetyl group, presumably on the N-terminal Ser residue (Fig. 2A, bottom). A similar modification was identified for the 20S proteasome alpha subunit (spots 12 in Fig. 1) and for the putative ABC transport protein BldK-ORFD (spots 17 in Fig. 1), although the presumptive N-terminal acetylated residue is Thr in the latter (data not shown).


By analogy with the nitrogen regulatory system in E. coli, glutamine synthetase I (GSI) was already believed to be modified by adenylylation on a conserved Tyr-397 residue, and biochemical evidence for this has been reported previously (Fink et al., 1999). The MALDI-TOF data showed the tryptic peptide 395–420 at the expected mass of 2807.4 Da for the larger, more basic spot 6 in Fig. 1, but this increased by 329.0 Da, corresponding to the addition of an adenylyl group, in the smaller spot on the left (Fig. 2B). Adenylylation of the nitrogen regulatory protein GlnK (spot 15 in Fig. 1) was identified similarly, but this is described in detail elsewhere (Hesketh et al., 2002).

Proteolytic processing.

The 50S ribosomal protein L9 was identified as two spots that differed in both pI and molecular weight (spots 30 in Fig. 1), suggesting modification by proteolytic processing. The spot with the higher molecular weight showed the tryptic peptide corresponding to amino acids 3–22 at the expected mass of 1993.10 Da, in agreement with this being the unprocessed form of the protein (Fig. 2C, top). In the lower of the two spots, the peptide decreased in mass by 113.12 Da (Fig. 2C, bottom), corresponding to the loss of the isoleucine residue at its N-terminus (expected mass loss 113.08 Da). This indicates removal of the N-terminal MKI residues from ribosomal protein L9. The theoretical isoelectric point (9.97) and molecular weight (15.6 kDa) of L9 protein N-terminally truncated in this way are in good agreement with the position of the lower spot in Fig. 1 (predicted values for the unprocessed protein are pH 9.60 and 15.9 kDa). MALDI-TOF data indicating proteolytic processing of the superoxide dismutase precursor protein (SCO5254; spot 32 in Fig. 1) and the 20S proteasome beta subunit precursor (SCO1644; spot 35 in Fig. 1) agreed with previous reports (Kim et al., 1998; Nagy et al., 1998). Evidence for the proteolysis of S. coelicolor ClpP1 (SCO2619; spot 13 in Fig. 1) has also been reported, although the processing site was not defined (de Crecy-Lagard et al., 1999).

Co-translational modification by removal of the N-terminal initiator Met occurred in 149 out of 190 (78%) cases in which the N-terminal peptide was detected. Processing by the N-methionylaminopeptidase was observed if the second amino acid was Ser (45 out of 149 examples), Thr (40), Ala (32), Pro (27), Gly (4) or Val (1). On only one occasion was any of these six amino acids found as the second residue if the N-terminal methionine was retained. This is in broad agreement with observations in B. subtilis in which two-thirds of proteins were reported to be similarly processed (Buttner et al., 2001)

Representation of primary and secondary metabolic pathways

Streptomycetes produce ≈ 60% of all commercially useful antibiotics (Berdy, 1984; Miyodah, 1993). Most antibiotics are the product of complex biosynthetic pathways, often encoded by 20–30 clustered genes. Production of the pigmented antibiotics actinorhodin (Act) and undecylprodigiosin (Red) has been studied extensively in S. coelicolor (for reviews, see Chater and Bibb, 1997; Champness, 2000) and involves clusters of approximately 22 genes each. A third cluster producing the calcium-dependent antibiotic (CDA) and consisting of 40 genes has also been studied (Chong et al., 1998; Huang et al., 2001). In the extensive spot identification studies for the preparation of a proteome map of S. coelicolor (see above), more than one-third of the proteins from each of these pathways was identified (Table 5). Proteins from three clusters that specify metabolic products that have yet to be identified experimentally (Bentley et al., 2002) were also extensively represented: a type I polyketide synthase cluster (SCO6273–6288), a deoxysugar/glycosyltransferase cluster (SCO0381–0401) and a non-ribosomal peptide synthetase cluster predicted to encode a novel siderophore, coelichelin (SCO0489–0499). Several clusters were unrepresented.

Table 5. .  Summary of proteins identified on the proteome map from secondary metabolic pathways during growth in SMM.
Biosynthetic pathway/gene clusterPathwaygenesannotatedPathwaygene productsidentifiedPercentagepathwaydetected
  1. Chromosomal locations and extent of gene clusters are taken from Bentley et al. (2002). No proteins were detected from the remaining predicted secondary metabolic clusters, i.e. siderophore (coelibactin), SCO7681–7691 (11 ORFs); siderophore, SCO5799–5801 (three ORFs); non-ribosomal peptide synthases, SCO6429–6438 (10 ORFs); type II fatty acid synthases, SCO1265–1273 (nine ORFs); WhiE spore pigment cluster, SCO5314–5320 (seven ORFs); isorenieratine, SCO0185–0191 (seven ORFs); eicosapentaenoic acid, SCO0124–0129 (six ORFs); chalcone synthases, SCO1206–1208 (three ORFs); chalcone synthases, SCO7669–7671 and SCO7222 (three ORFs); sesquiterpene cyclase, SCO5222–5223 (two ORFs); type I polyketide synthases, SCO6826–6827 (two ORFs); geosmin, SCO6703 (one ORF).

Calcium-dependent antibiotic CDA, SCO3210–32494016 40
Actinorhodin, SCO5071–50922215 68
Undecylprodigiosin, SCO5877–589822 8 36
Deoxysugar synthase/glycosyltransferase cluster, SCO0381–040121 7 33
Type I polyketide synthase, SCO6273–628816 6 38
Hopanoids, SCO6759–677113 1  7
Siderophore (coelichelin), SCO0489–049911 5 45
Siderophore (desferrioxamine), SCO2783–2785 4 4100

The primary metabolic pathways were somewhat more highly represented, with ≈ 60% or more of proteins annotated as enzymes of glycolysis, the TCA cycle, the pentose phosphate pathway, purine ribonucleotide biosynthesis and pyrimidine nucleotide biosynthesis being mapped (Table 6). About 50% of amino acid biosynthetic proteins were identified. Lists of individual members of all pathways considered here, including those that were and were not identified during proteome mapping, can be found in the Supplementary material.

Table 6. . Summary of proteins identified on the proteome map from primary metabolic pathways during growth in SMM.
Biosynthetic pathwayPathway genesannotatedaPathway geneproducts identifiedPercentage pathway detected
  • a . Derived from interpretation of the annotated S. coelicolor genome sequence at .

  • b

    . See Supplementary material for details of pathway members.

  • c

    . See Supplementary material for details of individual pathways.

Glycolysisb 241355
Pentose phosphate pathwayb 141071
TCA cycleb 211257
Amino acid biosynthesisc1155548
Purine ribonucleotide biosynthesisb 181267
Pyrimidine ribonucleotide biosynthesisb 8 562

Several proteins from both primary and secondary metabolic pathways were identified at more than one position on the proteome map, and may therefore be subject to regulation by post-translational modification (see Table 3 and Fig. 1). Primary metabolic proteins of this type (yellow spots on Fig. 1) were usually detected as chains of two or more spots with the same apparent molecular weights but different pI values, suggesting covalent modification by low-molecular-weight adducts. Apart from adenylylation of GSI (see above), the nature of the modification of these proteins was not revealed by MALDI-TOF peptide mass fingerprint data. Both covalent modification and proteolytic processing were also found in secondary metabolic proteins (red spots in Fig. 1). For example, the Act pathway members SCO5071 (spot 16) and SCO5075 (spot 20) and the putative 3-oxoacyl[acyl carrier protein] reductase from the type I polyketide cluster (SCO6282; spot 11) appear to be covalently modified, whereas proteolytic processing was detected for RedL (SCO5892; spot 18) from the undecylprodigiosin cluster, the putative dehydratase ActVI-ORF3 (SCO5074; spot 31) from the Act cluster, the putative peptide monooxygenase from coelichelin biosynthesis (SCO0498; spot 34) and the putative oxygenase from the CDA pathway (SCO3236; spot 33). Two Act biosynthetic proteins, SCO5088 (spot 24) and SCO5073 (spot 29), were detected both at their predicted pI and molecular weight co-ordinates and at positions with similar pI values but significantly higher molecular weights. This may indicate that these proteins form stable multimers that are not completely denatured under the conditions used. Only the modification of ActVI-ORF3 (SCO5074; spot 31) could be characterized further from the mass spectrometry data, which showed that it involves proteolytic processing at two sites between amino acid residues 31/32 and 34/35 (data not shown). Analysis of a sample obtained by externally extracting proteins from intact washed mycelium with a solution containing 2% SDS and 50 mM dithiothreitol (DTT) revealed that, surprisingly, the truncated ActVI-ORF3 is located extracellularly, presumably non-covalently associated with the cell wall matrix (Fig. 4).

Figure 4.

Processed ActVI-ORF3 is present in the cell wall matrix. 2D-gel separations of protein extracts prepared (A) from whole cells or (B) by externally washing cells in a 2% SDS extraction buffer. The arrow indicates ActVI-ORF3 protein. The experiment was performed on duplicate biological samples, and both showed similar protein expression profiles.

Expression analysis of certain primary and secondary metabolic proteins

Production of the pigmented antibiotics Act and Red in liquid minimal medium supplemented with casamino acids (SMM) begins in the transition phase between exponential growth and stationary phase. Figure 3 shows a comparison of the expression of certain primary and secondary metabolic proteins between exponentially growing and stationary phase cultures. Two proteins from the Act cluster (ActVA-ORF5 and ActI-ORF2) and two from the Red (RedI and RedR) were present in the stationary phase cultures but were not detected during exponential growth. Similarly, the putative 3-oxoacyl-[acyl carrier protein] reductase (SCO6282) and epoxide hydrolase (SCO6277) enzymes from a type I polyketide secondary metabolic cluster showed a large increase in abundance between the exponential and stationary phase samples. Triose phosphate isomerase (SCO1945), fructose bisphosphate aldolase (SCO3649) and succinyl-CoA synthetase alpha chain (SCO4809), all enzymes from central carbon metabolism, were present in approximately constant amounts in both samples. Interestingly, two isoforms (SCO7511 and SCO1947) of glyceraldehyde-3-phosphate dehydrogenase from the glycolytic pathway showed different patterns of expression. SCO1947 levels were the same in both exponential and stationary phase, but SCO7511, barely detectable in exponentially growing cultures, increased markedly in stationary phase.

Figure 3.

A comparison of the expression of certain primary and secondary metabolic proteins between exponentially growing and stationary phase cultures of S. coelicolor M145. Larger arrows represent higher protein level in one gel than in the other. The experiment was performed on duplicate biological samples, and both showed similar protein expression profiles.

The Streptomyces coelicolor online proteome map

The 2D gel images in Fig. 1, together with the spot identification results, are being made available over the World Wide Web to provide a reference map of the S. coelicolor proteome. At the time of writing, the reference gel for the pH 4–7 isoelectric point range was fully accessible, whereas those for pH 4.5–5.5, pH 5.5–6.7 and pH 6–11 were under construction ( A tool for comparing two or more gel images with each other is also accessible from the same website at


Silver staining is one of the most sensitive methods for detecting proteins on polyacrylamide gels, allowing as little as 0.5–2 ng of an individual protein to be detected (Berggren et al., 2000; Gorg et al., 2000). The proportion of the S. coelicolor proteome that can be analysed using silver staining of 2D-PAGE-separated proteins depends, at least in part, on the pH range of the IPG strip used for the first-dimension separation (see Table 1). Using the intermediate-range IPG strip covering pH 4–7, about 20–25% of the proteins in the theoretical proteome predicted to be observable in the resulting 2D-PAGE gel were actually detected. This figure was reduced to 10–12% when analysing the more basic pI range using the pH 6–11 IPG strip. Attempts to increase the percentage of the proteome detectable using the pH 4–7 and pH 6–11 IPG strips by loading more protein sample resulted in poorer separations (data not shown). This was especially true of the pH 6–11 strips, which were generally the most difficult to use successfully. However, good separation of higher protein loads was achieved using the narrow-range IPG strips pH 4.5–5.5 and 5.5–6.7, significantly increasing the proportion of theoretical proteins detected to over 40%. Clearly, and more pertinently, the limited conditions used for culturing S. coelicolor in this study will also have restricted the number of proteins detected, as not all the proteome will be expressed. In particular, S. coelicolor does not sporulate in shaken liquid-grown cultures, so proteins involved in this differentiation process are unlikely to be present. Nevertheless, it is striking that the combined data for the pH 4.5–5.5 and 5.5–6.7 isoelectric point ranges indicate that 2468 out of a total of predicted 4932 protein spots can be detected (see Table 1), suggesting that transition phase mycelium in this supplemented minimal medium is expressing 50% of its genes (i.e. by extrapolation, some 3912 of the 7825 in the genome).

Analysis of post-translational regulation

Although the regulation of cellular processes and adaptive responses in bacteria has been studied extensively at the transcriptional level, post-translational regulatory mechanisms are much less understood. Proteomic analysis using 2D-PAGE coupled with mass spectrometry offers the opportunity to observe and identify modification of proteins at a general cellular level. The functions of proteins can be altered by specific covalent attachment of adducts (e.g. phosphorylation, glycosylation, nucleotidylation, acetylation) or by proteolytic processing. Both events alter the pI and/or mass of the protein and so can usually be observed on 2D-PAGE. In the S. coelicolor proteome map, ≈ 110 out of a total of 770 gene products were identified in more than one position, or at pI and apparent mass co-ordinates significantly different from predicted values, indicating extensive regulation at the post-translational level (88 are listed in Table 3): a proportion similar to that reported for other prokaryotes (Buttner et al., 2001; Tonella et al., 2001). The initial peptide mass fingerprint analysis identified the modification of only 10 of these proteins, probably partly because fingerprint data typically cover ≈ 30–60% of a protein's sequence (data not shown), so modification at positions not represented will be missed. Protein digestion with endopeptidases other than trypsin before mass spectrometry may help in this situation, but some covalently attached groups are labile and cannot be detected routinely. Phosphorylation of His occurs on nitrogen, producing a high-energy phosphoramidate bond that is easily hydrolysed in acidic conditions, whereas phospho-Asp is the most labile phosphorylated residue, with a half-life of a few hours under physiological conditions (reviewed by Robinson et al., 2000). These modifications, the basis of bacterial two-component regulatory systems, are therefore unlikely to survive staining of the 2D gels, which involved the use of a fixing solution containing 10% acetic acid, and mass spectrometry. Identification of the more stable phosphoester phosphorylation that occurs on Ser, Thr or Tyr residues using PAGE followed by mass spectrometry has been reported (Godovac-Zimmermann et al., 1999; Buttner et al., 2001), but no examples were detected in this work. Of the modifications identified in this study, three were by acetylation at the N-terminus after removal of the initiator Met residue, two involved adenylylation, and five proteins had been N-terminally truncated. Modification of the 20S proteasome by N-acetylation in yeast involves removal of the Met residue by N-methionylaminopeptidase, followed by acetylation with an Nα-acetyltransferase (Kimura et al., 2000). Homologues of these enzymes are predicted from the S. coelicolor genome and are presumably responsible for the observed modification of the 20S proteasome alpha subunit, and of BldK-ORFD and the putative cellulose-binding protein. The functional significance of Nα-acetylation of these proteins is unclear.

Analysis of the biosynthesis of antibiotics and other secondary metabolites

Post-translational modification.

The proteome mapping results indicate that at least six enzymes in the Act cluster are subject to post-translational modifications, and one each from the undecylprodigiosin (SCO5877–5898), ‘coelichelin’ (SCO0489–0499) and type I polyketide synthase (SCO6273–6288) clusters (see Table 3). These metabolic pathways may therefore be significantly regulated at the post-translational level. The actVI-ORF3 gene (SCO5074) encodes a putative dehydratase enzyme that is part of the biosynthetic pathway for Act, a blue member of the benzoisochromanequinone (BIQ) family of pigmented antibiotics. Mutation in actVI-ORF3 causes a 50% decrease in the accumulation of Act (and a reddish brown phenotype), and it is believed to be responsible for pyran ring closure, an intermediate step in Act biosynthesis (that may also occur spontaneously), leading to the formation of the BIQ chromophore (Fernandez-Moreno et al., 1994; Ichinose et al., 1998a; Taguchi et al., 2000). In this study, ActVI-ORF3 protein was detected in two forms: a major spot corresponding to ActVI-ORF3 truncated by removal of the N-terminal 31 amino acid residues, and a minor one in which the first 34 amino acids were missing (spots 31 in Fig. 1). The full-length protein was not detected. Analysis of the protein sequence using the signalp server at predicted a signal peptide cleavage site between amino acids 31 and 32, in agreement with the observed results.

The protein extract analysed was prepared from mycelium that had been collected by centrifugation and subjected to two washes, indicating that, if ActVI-ORF3 is exported, it is effectively retained within the cell wall matrix. This was confirmed by analysis of a preparation obtained by externally extracting proteins from intact washed mycelium (Fig. 4). Export of an enzyme required for the efficient biosynthesis of an antibiotic that is thought to be produced intracellularly appears to be anomalous, particularly as it is involved in an intermediate biosynthetic event. Database analysis of the other members of the Act cluster revealed only one other protein with a predicted signal peptide sequence, the putative integral membrane transport-related protein ActII-ORF3, and it seems unlikely that all the Act biosynthetic steps following BIQ chromophore formation are extracellular. It is possible that full-length ActVI-ORF3 catalyses the intermediate biosynthetic event intracellularly, and the exported processed protein is responsible for a different extracellular activity, perhaps later in the pathway. However, no full-length protein was detected, so the exported enzyme may perform the pyran ring closure on a secreted metabolite that is then reabsorbed by the cell for completion of the subsequent biosynthetic steps. Only two homologues of actVI-ORF3 are known, and both are also in BIQ antibiotic biosynthetic clusters: gra-ORF18 from the granaticin cluster (Ichinose et al., 1998b), and med-ORF5 from medermycin biosynthesis (K. Ichinose, personal communication). Interestingly, the encoded proteins of both sequences are also predicted to contain N-terminal signal peptides. The observation that ActVI-ORF3 is exported to the cell wall matrix may therefore be of general significance to the BIQ family of antibiotics, and provide a new insight into their biosynthetic pathways.

Pathway representation.

Of the 20 putative secondary metabolite gene clusters identified in the S. coelicolor genome sequence (Bentley et al., 2002), eight had at least one protein each identified on the proteome map (see Table 5). The biosynthetic clusters for the antibiotics Act, Red and CDA had 68%, 36% and 40% of their proteins represented. There are several possible explanations for failure to detect all the proteins for each pathway. Some are putative integral membrane proteins, so cannot readily be detected using conventional 2D-PAGE. Some Red and CDA proteins have molecular weights> 200 kDa, outside the range of the 12.5% gels used (for cluster members, see Supplementary material). The pathway-specific regulatory proteins for Act and Red biosynthesis were not detected despite a detailed search of their predicted gel locations, even though the relevant genes are known to be transcribed in equivalent cultures of the same strain (Hesketh et al., 2001). The proteins are presumably present but at levels below the limit of detection. However, the transcriptional regulator for CDA production, CdaR, was identified, as well as the two-component system response regulator AbsA2, which is also encoded by a gene in the CDA cluster (Anderson et al., 2001). Interestingly, proteins from three putative secondary meta-bolite clusters producing chemicals of as yet unknown structure were also identified on the map, corresponding to the deoxysugar synthase/glycosyltransferase cluster SCO0381–0401, the ‘coelichelin’ cluster for a predicted (but not yet confirmed) oligopeptide siderophore (SCO0489–0499) and the type I polyketide cluster SCO6273–6288. The 3-oxoacyl-[acyl carrier protein] reductase (SCO6282) from the latter pathway is ex-pressed in a growth phase-dependent manner (Fig. 3) and, in early stationary phase cultures, is perhaps the most abundant protein spot in the entire detectable proteome. Presumably the metabolites produced by these three pathways are being synthesized under the conditions used. In addition to coelichelin, three other siderophores are postulated for S. coelicolor. All the proteins from one of these, desferrioxamine (SCO2782–2785) were identified, but none was seen from either of the other two pathways. The WhiE polyketide spore pigment is associated with sporulation of surface-grown cultures (Davis and Chater, 1990), and the expression of the whiE genes depends on a series of sporulation-specific regulatory genes (Kelemen et al., 1998), no corresponding proteins of which were detected here. It is therefore not surprising that WhiE proteins were not detected in samples prepared from liquid-grown vegetative mycelium.

Analysis of primary metabolism

Proteins annotated as functioning in primary metabolic pathways were highly represented on the proteome map with ≈ 60% or more of the enzymes assigned to glycolysis, the TCA cycle, the pentose phosphate pathway and purine and pyrimidine ribonucleotide biosynthesis being identified (see Table 6). This figure may be deceptively low because these pathways in S. coelicolor often appear to have more than one enzyme isoform for some of the metabolic steps, potentially offering additional levels of control or developmental responsiveness at these points. For example, only two steps in glycolysis have just a single gene product assigned to them; the remainder typically have two or three each (Fig. 5). At least one isoform was present for each step, so the pathway is in theory 100% functionally represented. Understanding the regulation of these pathways will require analysis of the expression of these isoenzymes under different nutritional conditions or different developmental stages. Figure 5 shows that, of the three predicted glyceraldehyde-3-phosphate dehydrogenase isoforms from glycolysis, only two were detected in this study (see also Supplementary material). Of these, SCO1947 appears to be constitutive, whereas SCO7511 is growth phase dependent (see Fig. 3). Thus, even the limited conditions in this study yielded useful information about isoenzyme expression patterns (see Supplementary material). In Streptomyces aureofaciens, two genes encoding glyceraldehyde-3-phosphate dehydrogenase activity also appear to be differently regulated: transcription of one, highly homologous to SCO7511, is activated by the regulator GapR, whereas the other is GapR independent (Spusansky et al., 2001).

Figure 5.

The glycolytic pathway in S. coelicolor illustrating the assignment of multiple gene products to individual metabolic steps, based on the annotation of the genome sequence at . Gene products identified in the proteome map are underlined.

Hood et al. (1992 ) reported that most amino acid biosynthetic enzymes are expressed at very low constitutive levels in S. coelicolor , so it is reassuring to note that ≈ 50% could be detected and identified using proteomics. These include TrpB and TrpC1 from the tryptophan biosynthetic pathway, the corresponding transcripts of which could barely be detected in previously reported S1 nuclease protection experiments ( Hu et al., 1999 ).


No attempt was made in the work reported here to simplify the protein separations by subdividing the sample into soluble and membrane fractions, and this is reflected in the 10 lipoproteins and 16 ABC transport system ATP-binding subunits identified on the proteome map (see Table 2). No integral membrane proteins were detected and, even when a membrane protein sample was prepared and analysed, no proteins identified possessed more than three predicted transmembrane helices (data not shown). This is consistent with previous studies in other bacteria (Molloy et al., 1999; Nilsson and Davidsson, 2000). Another group of proteins also strikingly absent from the list of those identified is the sigma factors, which determine promoter selection by RNA polymerase. There are an extraordinarily high number (65) of sigma factors encoded in the S. coelicolor genome, as well as at least seven antisigma factors and 15 antisigma factor antagonists (some of each of which were detected). Sigma factor heterogeneity in bacteria is an important way of regulating expression of specific sets of genes under different conditions and in different cell types (for a review, see Gross et al., 1998). Although no sigma factors were identified, about half (31 out of 65) have predicted pI values> pH 7 and so can only be detected using the pH 6–11 IPG strips that give the poorest results (see Table 1). However, the principal vegetative sigma factor, HrdB, which is expected to be moderately abundant, has a predicted pI of 6.13, and its absence from the pH 5.5–6.7 proteome map may indicate that the sample preparation method, which does not remove DNA, is inappropriate. Among other regulatory proteins, 11 out of a possible 80 two-component system response regulators plus a further 36 proteins annotated as ‘regulators’ were detected. Only one Ser/Thr kinase and no two-component system sensor kinases were detected, possibly reflecting the difficulty in analysing proteins with several transmembrane domains. Interestingly, 30 secreted proteins were identified on the proteome map, at least 10 of which appear, from comparison of their observed pI and molecular weight positions with predicted values, to have been processed already and therefore exported. Presumably, these proteins do not end up exclusively in the culture medium.

Clearly, this approach to proteomic analysis of S. coelicolor has some important deficiencies, not least the absence of certain groups of proteins and the relatively poor initial success rate for specifically characterizing post-translational modifications. Nevertheless, it offers unique insights into the molecular biology of the organism: the expression of many proteins from both primary and secondary metabolic pathways can be analysed; post-translational regulation of proteins can be investigated on a general cellular level; and the physical locations for some proteins can be determined. Approximately 35% of the S. coelicolor genome is annotated as encoding ‘hypothetical’ proteins of unknown function, and 182 proteins of this type were identified on the proteome map. No examples were found of overt disagreements between predicted and observed proteins, providing experimental support for the general accuracy of the S. coelicolor genome sequence and its annotation. Information about the expression, post-translational modification and physical location of these (no longer!) hypothetical proteins from proteomic analyses is going to be important for assigning future functions. As many of the hypothetical proteins have apparent orthologues in pathogenic mycobacteria, some of the information may have an immediate application in the context of pathogenesis.

Experimental procedures


Immobilized pH gradient (IPG) strips and IPG buffers were purchased from Amersham Biosciences. Acrylamide, duracryl and chemicals for silver staining were obtained from Genomic Solutions. All other chemicals were from Sigma unless stated in the text.

Strain and growth conditions

Streptomyces coelicolor M145 was cultivated with vigorous agitation at 30°C in minimal medium supplemented with 0.2% casamino acids (SMM) as described previously ( Kieser et al., 2000 ). Briefly, a high-density spore preparation [about 10 10 colony-forming units (cfu) ml −1 ] was pregerminated in 2× YT media for 7 h at 30°C. Germlings were harvested by centrifugation (5 min at 4000  g ), resuspended in SMM and sonicated briefly to disperse any aggregates before inoculation into 50 ml of SMM in 250 ml siliconized flasks containing coiled stainless steel springs. Each flask received the equivalent of 5 × 10 7 spore cfu.

Protein extraction and 2D-gel electrophoresis

The preparation of the protein sample to be separated was kept as simple as possible, disrupting harvested mycelia directly into denaturing isoelectric focusing (IEF) buffer containing Pefabloc SC (Roche) protease inhibitor. This method minimized unwanted protein degradation that was observed in initial studies using a Tris buffer containing Pefabloc SC for cell lysis (data not shown). Removal of DNA, RNA, salts and polysaccharides from the sample was therefore not performed (other than the shearing of viscous DNA and RNA by sonication), and the protein extracts were sometimes difficult to focus when the sample was loaded by in-gel rehydration. Loading the sample, soaked in paper strips, at the anodic end of IPG strips that had already been rehydrated improved the reproducibility of separations significantly.

Mycelium for protein extraction was harvested from cultures by brief centrifugation (30 s at 4000 g) at room temperature and immediately frozen in liquid nitrogen. Typically, mycelium from 10 ml culture aliquots was collected, and the transfer time from culture flask to frozen sample was 1.5 min. Frozen cells were thawed on ice in 5 ml of washing buffer (40 mM Tris, pH 9.0, 1 mM EGTA, 1 mM EDTA), then pelleted by centrifugation (5 min at 3000 g) at 4°C. Washed cells were resuspended in 400 µl of denaturing IEF buffer UTCHAPS [7 M urea, 2 M thiourea, 4% (w/v) CHAPS, 40 mM Tris, pH 9.0, 1 mM EDTA, 50 mM DTT, 4 mM Pefabloc SC protease inhibitor (Roche)] and disrupted by sonication (Sanyo Soniprep 150; 10 × 2 s bursts at amplitude 7.5 µm) while cooling in an ethanol–ice bath. Cell debris was removed by centrifugation (15 min, 10 000 g, 4°C), and the protein extract was stored frozen in aliquots at −80°C until use. For the preparation of extracellular protein extracts, washed cells were resuspended in extraction buffer consisting of 40 mM Tris, pH 9.0, 1 mM EDTA, 50 mM DTT, 2% SDS and 4 mM Pefabloc SC protease inhibitor. Cells were vortexed for 2 min before being pelleted by centrifugation (5 min, 10 000 g, 4°C). The supernatant was precipitated using the Amersham 2D Clean-Up kit according to the manufacturer's instructions, and the precipitated proteins were redissolved in UTCHAPS and stored at −80°C until use.

For first-dimension isoelectric focusing, 18 cm IPG strips pH 4–7, pH 6–11, pH 4.5–5.5 or pH 5.5–6.7 (Amersham Biosciences) were rehydrated overnight in IEF buffer containing 1% ampholytes according to the manufacturer's instructions using a Phaser isoelectric focusing unit (Genomic Solutions) set at 20 V. Protein samples to be separated were applied to the rehydrated strips at the anodic end, soaked in a 5–10 mm section of IEF electrode strip (Amersham Biosciences). Separation was performed for 120 000 V-h with a maximum voltage of 5000 V. After isoelectric focusing, IPG strips were equilibrated for the second dimension for 15 min in IPG equilibration buffer (50 mM Tris, pH 6.8, 6 M urea, 30% glycerol, 1% SDS and 0.01% bromophenol blue) plus 80 mM DTT, then for 10 min in IPG equilibration buffer plus 135 mM iodoacetamide. Approximately 1 cm was removed from the anodic end of each equilibrated strip, before applying the strip to the top of a vertical 12.5% SDS-PAGE gel for second-dimension separation using the Investigator 5000 system from Genomic Solutions.

Large-format gels (24 cm wide × 22 cm high × 1 mm thick) for the second-dimension separation were cast in-house using the system supplied by Genomic Solutions. 12.5% SDS-PAGE gels, prepared using 0.65% N,N-methylenebisacrylamide as cross-linking agent, were used for pH 4–7 and pH 4.5–5.5 IPG strips, whereas pH 5.5–6.7 and pH 6–11 strips were applied to similar gels made with 0.8% cross-linker. Electrophoresis was performed while cooling using the maximum setting (≈ 4–10°C) at 20 000 mW per gel constant power and a maximum voltage of 500 V. Gels were then stained with either colloidal Coomassie G-250 (Neuhoff et al., 1988) or silver nitrate (Rabilloud, 1992) and scanned in a ProXPRESS proteomic imaging system (Perkin-Elmer). Image analysis was performed using phoretix 2D version 5.1 (NonLinear Dynamics): spot detection was optimized automatically using the ‘Spot Detection Wizard’ and then edited manually; background subtraction was performed automatically using the ‘Mode of Non-Spot’ setting; images were then normalized to the total spot volume for quantification. Spot filtering was not used.

Protein identification using mass spectrometry

Protein spots of interest were excised from colloidal Coomassie-stained gels, in-gel digested with trypsin and identified by MALDI-TOF peptide mass fingerprint analysis. Excised gel pieces (≈ 1-mm-diameter circles cut from 1-mm-thick gels) were washed twice with 100 µl of 50 mM ammonium bicarbonate for 15 min, once with 100 µl of 20% acetonitrile−40 mM ammonium bicarbonate for 15 min and once with 100% acetonitrile. Washed gel pieces were allowed to air-dry for 10 min before being rehydrated in 5 µl of 10 µg ml−1 trypsin (sequencing grade modified; Promega) in 10 mM ammonium bicarbonate. Digestion (37°C for 4 h) was stopped by the addition of 5 µl of 5% formic acid. Extraction of peptides into this solution was encouraged by a sonic water bath treatment for 20 min. Peptide extract (0.5 µl) was spotted on to a thin layer of α-cyano-4-hydroxycinnamic acid applied to a MALDI-TOF sample template and analysed by MALDI-TOF mass spectrometry (Bruker Reflex III) using an accelerating voltage of 25 kV. Samples were externally calibrated using a standard mixture of six peptides ranging in mass from 1046.5423 Da to 3494.6500 Da, and the mass accuracy obtained was 60 p.p.m. or better. Identification of proteins from MALDI-TOF peptide mass fingerprint data was performed using the ‘Mascot’ search engine at and was based on a positive result using their ‘Probability Based MOWSE Score’ algorithm. A MOWSE score of 60 or higher is significant at the 5% level or better, and proteins in this work typically gave scores> 80 (frequently considerably so). In addition, no identification was accepted unless at least five peptides representing at least 20% of the protein sequence were detected in the MALDI-TOF peptide mass fingerprint.

Assignment of proteins to metabolic pathways

Proteins were assigned to primary metabolic pathways using the ‘Protein Classification Scheme’ web page of the annotated Streptomyces coelicolor genome sequence at, as it ap-peared in May 2002. The annotation of the genome, and therefore the Protein Classification Scheme, is likely to be modified as more experimental data are obtained about the function of genes that have currently been assigned on the basis of sequence information alone. It is therefore possible that proteins assigned to metabolic pathways in this study may in future be shown to perform different functions in the cell.

Assignments to gene clusters encoding secondary metabolites are based on an analysis of the genome sequence reported by Bentley et al. (2002). In some cases, these assignments are based on extensive experimental data (act, red: reviewed by Chater and Bibb, 1997; Champness, 2000; cda: Chong et al., 1998; Huang et al., 2001; whiE: Davis and Chater, 1990).


This work was funded by grants (2/FGT11406, 208/FGT11408 and 208/IGF12432) under the BBSRC's Technologies for Functional Genomics and Investigating Gene Function initiatives, and by a competitive strategic grant from the BBSRC to the John Innes Centre. We thank Mike Naldrett for his extensive contribution in establishing technology for proteome analysis at the JIC.

Supplementary material

The following material is available from http:www.blackwell-science.comproductsjournalssuppmatmolemole3219mmi3219sm.htm

Section 1. Detection of gene products assigned to individual metabolic steps in primary metabolic pathways in S. coelicolor.

Section 2. Identification of proteins from individual amino acid biosynthetic pathways.

Section 3. Detection of gene products assigned to individual metabolic steps in secondary metabolic pathways in S. coelicolor.