Abstract
Next-generation DNA sequencing (NGS) has made it feasible to sequence large number of microbial genomes and advancements in computational biology have opened enormous opportunities to mine genome sequence data for novel genes and enzymes or their sources. In the present communication in silico mining of microbial genomes has been carried out to find novel sources of nitrilases. The sequences selected were analyzed for homology and considered for designing motifs. The manually designed motifs based on amino acid sequences of nitrilases were used to screen 2000 microbial genomes (translated to proteomes). This resulted in identification of one hundred thirty-eight putative/hypothetical sequences which could potentially code for nitrilase activity. In vitro validation of nine predicted sources of nitrilases was done for nitrile/cyanide hydrolyzing activity. Out of nine predicted nitrilases, Gluconacetobacter diazotrophicus, Sphingopyxis alaskensis, Saccharomonospora viridis, and Shimwellia blattae were specific for aliphatic nitriles, whereas nitrilases from Geodermatophilus obscurus, Nocardiopsis dassonvillei, Runella slithyformis, and Streptomyces albus possessed activity for aromatic nitriles. Flavobacterium indicum was specific towards potassium cyanide (KCN) which revealed the presence of nitrilase homolog, that is, cyanide dihydratase with no activity for either aliphatic, aromatic, or aryl nitriles. The present study reports the novel sources of nitrilases and cyanide dihydratase which were not reported hitherto by in silico or in vitro studies.
1. Introduction
Advancement in the DNA sequencing technologies has led to sequencing of large number of genomes and the enormous sequence data are available in the public domain. The fourth-generation DNA sequencing has made it possible to sequence a bacterial genome within a few hours at a reasonably low cost [1–4]. As of today 5293 prokaryotic and 22 eukaryotic genomes have been completely sequenced and the sequence data are easily accessible in databases such as NCBI, GOLD, and IMG/ER. It is evident from previous studies that not all the gene/protein sequences in the databases are functionally characterized, which make these repositories a rich source for the discovery of novel genes and proteins [5, 6]. Genome mining has emerged as an alternate approach to find novel sources of desired genes/proteins as the conventional screening methods which involve isolation of microbes and their screening for desired products are time consuming, tedious, and cost intensive [7, 8].
Microbial nitrilases are considered to be the most important enzymes in the nitrilase superfamily that find application in the synthesis of fine chemicals, production of some important acids, and drug intermediates and in green chemistry [9–13]. Besides their wide applications nitrilases are prone to certain limitations, for example, their inactivation or inhibition by the acidic product, extremes of pH, temperature, and organic solvent [14, 15]. These limitations are being addressed either by the isolation of microorganisms from the extreme habitat or by enrichment techniques for specific substrate using conventional microbiological procedures [6] prone to limitation as mentioned above. The present communication focuses on in silico screening of publicly available bacterial genomes for nitrilase genes and in vitro validation of the predicted novel sources of nitrilases.
2. Material and Methods
2.1. Genome Screening Using Homology and Motif Based Approach
Primary screening of microbial genomes (data given as supplementary material in Supplementary Material available online at https://doi.org/10.1155/2017/7039245) was done using homology based approach. Tblastn and blastp were used to screen the sequenced genomes with query sequence to identify the presence and position of similar genes in the genome. Computationally predicted proteins from the bacterial genomes with keyword “nitrilase/cyanide dihydratase” were also downloaded using advanced search options in the IMG/ER database. Sequences with low (30%) and high similarity (80%) were discarded. Nitrilase gene in contigs showing the presence of nitrilase homologs was downloaded from IMG/ER. GenMark S tool was used to predict the ORFs in each contig, and the output was downloaded selecting protein sequence as output option. Amino acid sequences less than 100 amino acids were considered to be as false positive (FP) and were discarded. Small amino acid sequence database was created which was further subjected to local blast, to confirm the presence of nitrilase homolog in the contigs of the individual genome.
On the other hand, protein based manually designed motifs (MDMs) were used to screen the bacterial genome to search for the presence of conserved motifs using MAST (Motif Alignment and Search Tool) at MEME (Multiple Em for Motif Elicitation) suite. The motifs used are already described in our previous communication [12]. Motifs identified in sequences less than hundred amino acids were rejected, considered to be false positive (FP). Sequences above 100 amino acids were taken to be as true positive (TP).
2.2. Study of Physiochemical Properties and Phylogenetic Analysis of Predicted Nitrilases
Physiochemical data of the in silico predicted nitrilases were generated from the ProtParam software using ExPASy server and compared to the values deduced from the previous nitrilase study [16]. Some important physiochemical properties such as number of amino acids, molecular weight (kda), isoelectric point (pI), computing pI/Mw and the atomic compositions, values of instability index, aliphatic index, and grand average of hydropathicity (GRAVY) were calculated. A comparative chart was drawn between previously characterized and predicted nitrilases.
An output file of multiple aligned sequences using Clustal W for both previously characterized and predicted nitrilases was used to generate the Neighbor Joining (NJ) tree using MEGA 6 version. Phylogenetic tree was generated in order to predict the sequences as aliphatic or aromatic with previously characterized nitrilases.
2.3. Nitrilase Activity Assay
Culture of some of the bacteria predicted to have nitrilase gene (Shimwellia blattae, Runella slithyformis, Geodermatophilus obscurus, Nocardiopsis dassonvillei, Streptomyces albus, Flavobacterium indicum, Saccharomonospora viridis, Sphingopyxis alaskensis, and Gluconacetobacter diazotrophicus) was procured from Microbial Type Culture Collection (MTCC); Chandigarh Escherichia coli BL21 (DE3) from Invitrogen was used as negative control as this organism does not have nitrilase gene. These cultures were grown in the laboratory using different media (Table 1) for the production of nitrilase activity following the procedures described earlier [17–19]. Nitrilase activity was assayed in 1.0 mL reaction mixture containing nitrile as substrate (1–10 mM) and 0.1 mL resting cells. After 30 min of incubation at 30°C the reaction was quenched with 0.1 M HCl and the amount of ammonia released was estimated using nitrilase assay, that is, modified phenate-hypochlorite method described by Dennett and Blamey [20]. One unit of nitrilase activity was defined as the amount of enzyme required to release 1 μmole of ammonia per min under the assay conditions.
Table 1.
Composition of various media used to cultivate procured strains for nitrilase production.
| Name of the organism | MTCC number | Composition (gL−1) | pH | Growth temperature |
|---|---|---|---|---|
|
Shimwellia blattae ATCC 29907 |
4155 | Beef extract: 1.0 g Yeast extract: 2.0 g Peptone: 5.0 g NaCl: 5.0 g Agar: 15.0 g |
7.0–7.5 | 37°C |
|
| ||||
|
Runella slithyformis ATCC 29530 |
9504 | Glucose: 1.0 g Peptone: 1.0 g Yeast extract: 1.0 g Agar: 15.0 g Glucose: 4.0 g |
7.0–7.5 | 26°C |
|
| ||||
|
Geodermatophilus obscurus DSM 43160 |
4040 | Yeast extract: 4.0 g Malt extract: 10.0 g CaCO3: 2.0 g Agar: 12.0 g |
7.2–7.5 | 28°C |
|
| ||||
|
Nocardiopsis dassonvillei DSM 43111 |
1411 | Yeast extract: 4.0 g Malt extract: 1.0 Glucose: 4.0 g Agar: 20.0 g |
7.2–7.4 | 28°C |
|
| ||||
|
Streptomyces albus J1074 |
1138 | Yeast extract: 4.0 g Malt extract: 1.0 g Glucose: 4.0 g Agar: 20.0 g |
7.2–7.4 | 25°C |
|
| ||||
|
Flavobacterium indicum DSM 17447 |
6936 | Tryptic soy broth with agar (TSBA-100) |
7.3–7.5 | 30°C |
|
| ||||
|
Saccharomonospora viridis ATCC 15386 |
320 | Yeast extract: 4.0 g Malt extract: 1.0 g Glucose: 4.0 g Agar: 20.0 g |
7.2–7.4 | 45°C |
|
| ||||
|
Sphingopyxis alaskensis DSM 13593 |
7504 | Beef extract: 1.0 g Yeast extract: 2.0 g Peptone: 5.0 g NaCl: 5.0 g Agar: 15.0 g |
7.0–7.5 | 30°C |
|
| ||||
|
Gluconacetobacter diazotrophicus ATCC 49037 |
1224 | Yeast extract: 5.0 g Peptone: 3.0 g Mannitol: 25.0 g Agar: 15.0 g |
7.0–7.3 | 28°C |
|
| ||||
|
Escherichia coli BL21 (DE3)∗ |
— | Yeast extract: 5.0 g NaCl: 10.0 g Casein enzymatic hydrolysate: 10.0 g |
7.0–7.5 | 37°C |
∗Negative control.
3. Results
3.1. Genome Screening Using Conserved Motifs and Homology Search
As many as 138 candidate sequences were identified using tblastn and blastp at IMG/ER on both gene and protein level. Identification of potentially coding nitrilase genes was done using homology based approach (blastp and tblastn) allowing the identification of nitrilase sequences. To identify newer sources of nitrilases, candidate sequences bearing unassigned functions (hypothetical, uncharacterized, or putative) were selected from the translated genomes (Table 2). The identified sequences shared 30–50% sequence identity to biochemically characterized Rhodococcus rhodochrous J1 nitrilase which was taken as query sequence. Catalytic residues were found to be conserved in all the predicted proteins. Nine predicted and translated sequences were further chosen for their in silico and in vitro validation based on the manually designed motifs (MDMs) (Tables 3 and 4) identified from previous study [12].
Table 2.
Prediction of ORFs length in the individual scaffold for prediction of coding sequence for nitrilase using IMG/ER.
| Name of organism | Scaffold or genome length (bp) with accession number | Total number of ORF's predicted in scaffold of complete genome | Predicted coding region for nitrilase | Number of base-pairs |
|---|---|---|---|---|
|
Acaryochloris marina MBIC11017 |
NC_009925 (6503724 bp) |
152 | 200001–200999 | 999 |
|
| ||||
|
Acetobacter pasteurianus IFO 3283-32 |
AP011157 (191443 bp) |
120 | 174107–173133 | 974 |
|
| ||||
|
Achromobacter xylosoxidans A8 |
NC_014640 (7013095 bp) |
406 | 200001–200960 | 960 |
|
| ||||
|
Acidovorax avenae avenae ATCC 19860 |
NC_015138 (5482170 bp) |
188 | 201035–200001 | 1035 |
|
| ||||
|
Acidothermus cellulolyticus 11B |
NC_008578 (2443540 bp) |
403 | 200001–201131 | 1131 |
|
| ||||
|
Acidaminococcus fermentans VR4 |
NC_013740 (2329769 bp) |
293 | 200924–200001 | 924 |
|
| ||||
|
Alcanivorax dieselolei B5 |
CP003466 (4928223 bp) |
343 | 200001–200981 | 981 |
|
| ||||
|
Arthrobacter aurescens TC1 |
NC_008711 (4597686 bp) |
385 | 200001–200930 | 930 |
|
| ||||
|
Azorhizobium caulinodans ORS 571 |
NC_009937 (5369772 bp) |
262 | 89665–88580 | 1083 |
|
| ||||
|
Azospirillum sp. B510 |
NC_013854 (3311395 bp) |
402 | 200001–200921 | 921 |
|
| ||||
|
Bacillus pumilus SAFR-032 |
NC_009848 (3704465 bp) |
73 | 201026–200001 | 1026 |
|
| ||||
|
Bradyrhizobium japonicum USDA 110 |
NC_004463 (9105828 bp) |
387 | 200001–200966 | 966 |
|
| ||||
|
Bradyrhizobium sp. BTAi1 |
NC_009485 (8264687 bp) |
392 | 201146–200001 | 1146 |
|
| ||||
|
Bradyrhizobium sp. ORS278 |
NC_009445 (7456587 bp) |
395 | 201041–200001 | 1041 |
|
| ||||
|
Brevibacillus brevis NBRC 100599 |
NC_012491 (6296436 bp) |
182 | 200001–200960 | 960 |
|
| ||||
|
Flavobacterium indicum GPTSA100-9 |
HE774682 (2993089 bp) |
317 | 200001–200981 | 981 |
|
| ||||
|
Saccharomonospora viridis P101 |
NC_013159 (4308349 bp) |
315 | 200001–200996 | 996 |
|
| ||||
|
Sphingopyxis alaskensis DSM13593 |
NC_008048 (3345170 bp) |
387 | 200001–201017 | 1017 |
|
| ||||
|
Burkholderia cenocepacia J2315 |
NC_011000 (3870082 bp) |
393 | 199944–201050 | 1050 |
|
| ||||
|
Burkholderia glumae BGR1 |
NC_012720 (141067 bp) |
154 | 47491–48477 | 1017 |
|
| ||||
|
Burkholderia gladioli BSR3 |
NC_015376 (3700833 bp) |
338 | 200001–201014 | 1014 |
|
| ||||
| Burkholderia phymatum |
NC_010623 (2697374 bp) |
375 | 199971–201023 | 1023 |
|
| ||||
| Burkholderia phytofirmans |
NC_010681 (4467537 bp) |
357 | 200001–201035 | 1035 |
|
| ||||
|
Burkholderia sp. CCGE1002 |
NC_014119 (1282816 bp) |
280 | 72013–73041 | 1020 |
|
| ||||
|
Burkholderia sp. CCGE1003 |
NC_014540 (2966498 bp) |
344 | 200019–201041 | 1022 |
|
| ||||
| Burkholderia vietnamiensis G4 |
NC_009254 (1241007 bp) |
436 | 199986–201023 | 1037 |
|
| ||||
|
Burkholderia xenovorans LB400 |
NC_007951 (4895836 bp) |
396 | 200001–200996 | 996 |
|
| ||||
| Caulobacter sp. K31 |
NC_010335 (233649 bp) |
219 | 180936–181871 | 935 |
|
| ||||
|
Chlorobium phaeobacteroides BS1 |
NC_010831 (2736403 bp) |
382 | 200001–200936 | 936 |
|
| ||||
| Clostridium difficile 630 |
NC_009089 (4290252 bp) |
364 | 200001–200927 | 927 |
|
| ||||
|
Clostridium difficile CD196 |
NC_013315 (4110554 bp) |
308 | 200001–200927 | 927 |
|
| ||||
|
Clostridium difficile R20291 |
NC_013316 (4191339 bp) |
329 | 200001–200927 | 927 |
|
| ||||
|
Clostridium kluyveri NBRC 12016 |
NC_011837 (3896121 bp) |
442 | 200001–200930 | 957 |
|
| ||||
|
Clostridium kluyveri ATCC 8527 |
NC_009706 (3964618 bp) |
491 | 200001–200930 | 930 |
|
| ||||
|
Conexibacter woesei DSM 14684 |
NC_013739 (6359369 bp) |
388 | 200001–200942 | 942 |
|
| ||||
|
Cupriavidus necator ATCC 17699 |
NC_008313 (4052032 bp) |
318 | 200001–201017 | 1017 |
|
| ||||
|
Cupriavidus necator ATCC 43291 |
NC_015726 (3872936 bp) |
318 | 200001–201017 | 1017 |
|
| ||||
|
Cyanobium gracile ATCC 27147 |
Cyagr_Contig81 (3342364 bp) |
405 | 200001–200999 | 999 |
|
| ||||
|
Deinococcus deserti (strain VCD115) |
NC_012529 (314317 bp) |
269 | 200001–200951 | 951 |
|
| ||||
|
Deinococcus peraridilitoris DSM 19664 |
Deipe_Contig72.1 (3881839 bp) |
412 | 200001–200951 | 951 |
|
| ||||
|
Desulfomonile tiedjei ATCC 49306 |
Desti_Contig107.1 (6500104 bp) |
379 | 200001–201029 | 1029 |
|
| ||||
| Dickeya zeae Ech1591 |
NC_012912 (4813854 bp) |
194 | 200001–200927 | 927 |
|
| ||||
| Erwinia billingiae Eb661 |
NC_014305 (169778 bp) |
194 | 87964–88965 | 1001 |
|
| ||||
|
Erythrobacter litoralis HTCC2594 |
NC_007722 (3052398 bp) |
411 | 200001–200969 | 969 |
|
| ||||
|
Flavobacterium indicum DSM 17447 |
HE774682 (2993089 bp) |
317 | 200001–200981 | 981 |
|
| ||||
|
Frateuria aurantia ATCC 33424 |
Fraau_Contig24.1 (3603458 bp) |
366 | 200001–200924 | 924 |
|
| ||||
|
Geobacillus sp. Y4.1MC1 |
NC_014650 (3840330 bp) |
434 | 200001–200966 | 966 |
|
| ||||
|
Geobacillus thermoglucosidasius C56-YS93 |
NC_015660 (3893306 bp) |
446 | 200001–200966 | 966 |
|
| ||||
|
Geodermatophilus obscurus DSM 43160 |
NC_013757 (5322497 bp) |
244 | 54102–54884 | 783 |
|
| ||||
|
Gluconacetobacter diazotrophicus ATCC 49037 |
NC_010125 (3944163 bp) |
333 | 200001–200960 | 960 |
|
| ||||
|
Haliangium ochraceum DSM 14365 |
CP002175 (2309262 bp) |
377 | 200001–200957 | 957 |
|
| ||||
|
Halanaerobium praevalens ATCC 33744 |
NC_013440 (9446314 bp) |
262 | 200001–200999 | 999 |
|
| ||||
|
Hyphomicrobium sp. MC1 |
NC_015717 (4757528 bp) |
392 | 200001–200984 | 984 |
|
| ||||
|
Janthinobacterium sp. Marseille |
NC_009659 (4110251 bp) |
398 | 200001–201068 | 1068 |
|
| ||||
|
Jannaschia sp. CCS1 |
NC_007802 (4317977 bp) |
382 | 200001–201026 | 1026 |
|
| ||||
|
Maricaulis maris MCS10 |
NC_008347 (3368780 bp) |
392 | 200001–200933 | 933 |
|
| ||||
| Methylobacterium extorquens CM4 |
NC_011758 (380207 bp) |
211 | 7191–8267 | 1077 |
|
| ||||
|
Methylobacterium extorquens ATCC 14718 |
NC_012811 (1261460 bp) |
436 | 200001–201077 | 1077 |
|
| ||||
| Methylobacterium extorquens DM4 |
NC_012988 (5943768 bp) |
378 | 200001–200918 | 918 |
|
| ||||
| Methylobacterium extorquens PA1 |
NC_010172 (5471154 bp) |
354 | 200001–201110 | 1110 |
|
| ||||
|
Methylomonas methanica MC09 |
Contig38 (5051681 bp) |
402 | 200001–200996 | 996 |
|
| ||||
| Methylobacterium nodulans ORS2060 |
NC_011892 (487734 bp) |
425 | 200001–201116 | 1116 |
|
| ||||
|
Methylobacterium populi ATCC BAA-705 |
NC_010725 (5800441 bp) |
193 | 61617–62693 | 1077 |
|
| ||||
| Methylibium petroleiphilum PM1 |
NC_008825 (4044195 bp) |
364 | 200001–201074 | 1074 |
|
| ||||
|
Methylobacterium radiotolerans ATCC 27329 |
NC_010505 (6077833 bp) |
377 | 200001–201077 | 1077 |
|
| ||||
|
Methylocella silvestris BL2 |
NC_011666 (4305430 bp) |
439 | 199971–201029 | 1029 |
|
| ||||
|
Mycobacterium intracellulare ATCC 13950 |
CP003322 (5402402 bp) |
383 | 199938–200897 | 897 |
|
| ||||
|
Mycobacterium liflandii 128FXT |
CP003899 (6208955 bp) |
405 | 200001–201059 | 1059 |
|
| ||||
| Mycobacterium rhodesiae NBB3 | MycrhN_Contig54.1 (6415739 bp) |
267 | 200001–200957 | 957 |
|
| ||||
|
Mycobacterium smegmatis ATCC 700084 |
CP001663 (6988208 bp) |
377 | 200001–200978 | 978 |
|
| ||||
|
Natranaerobius thermophilus ATCC BAA-1301 |
NC_010718 (3165557 bp) |
387 | 200001–200930 | 930 |
|
| ||||
|
Nocardia farcinica IFM 10152 |
NC_006361 (6021225 bp) |
390 | 198993–199811 | 818 |
|
| ||||
|
Nocardiopsis dassonvillei DSM 43111 |
NC_014211 (775354 bp) |
353 | 201134–200001 | 843 |
|
| ||||
|
Oligotropha carboxidovorans ATCC 49405 |
CP002826 (3595748 bp) |
372 | 200001–201065 | 1065 |
|
| ||||
| Pantoea sp. At-9b |
NC_014839 (394054 bp) |
349 | 114577–115581 | 1005 |
|
| ||||
|
Peptoniphilus duerdenii ATCC BAA-1640 |
NZ_AEEH01000050 (96694 bp) |
80 | 52942–53863 | 921 |
|
| ||||
|
Photorhabdus asymbiotica ATCC 43949 |
NC_012962 (5064808 bp) |
338 | 200001–201050 | 1050 |
|
| ||||
|
Pirellula staleyi ATCC 27377 |
NC_013720 (6196199 bp) |
338 | 200001–200909 | 909 |
|
| ||||
|
Polaromonas naphthalenivorans CJ2 |
NC_008781 (4410291 bp) |
389 | 200001–201041 | 1062 |
|
| ||||
| Polaromonas sp. JS666 |
NC_007948 (5200264 bp) |
398 | 200001–200942 | 942 |
|
| ||||
|
Pseudomonas syringae pv. lachrymans M302278PT |
Lac106_115287.20 (115287 bp) |
107 | 47704–48747 | 1043 |
|
| ||||
|
Pseudoalteromonas atlantica ATCC BAA-1087 |
NC_008228 (5187005 bp) |
397 | 200001–200921 | 921 |
|
| ||||
|
Pseudomonas aeruginosa P7-L633/96 |
Ga0060317_132 (369634 bp) |
270 | 91986–92801 | 816 |
|
| ||||
|
Pseudomonas brassicacearum NFM421 |
NC_015379 (6843248 bp) |
377 | 200001–201026 | 1026 |
|
| ||||
| Pseudomonas sp. TJI-51 |
AEWE01000051 (6502 bp) |
05 | 1482–2498 | 1017 |
|
| ||||
|
Pseudomonas fluorescens Pf-5 |
NC_007492 (6438405 bp) |
349 | 200001–200924 | 924 |
|
| ||||
|
Pseudomonas fluorescens SBW25 |
NC_012660 (6722539 bp) |
376 | 200043–200930 | 888 |
|
| ||||
|
Pseudomonas mendocina NK-01 |
NC_015410 (5434353 bp) |
376 | 200001–200883 | 883 |
|
| ||||
|
Pseudomonas syringae pv. tomato DC 3000 |
PSPTOimg_DC3000 (6397126 bp) |
377 | 200001–201011 | 1011 |
|
| ||||
|
Pseudomonas syringae pv. syringae B728a |
NC_007005 (6093698 bp) |
196 | 8233–9231 | 999 |
|
| ||||
| Pseudoxanthomonas suwonensis11-1 |
NC_014924 (3419049 bp) |
362 | 200001–200885 | 885 |
|
| ||||
|
Pseudonocardia dioxanivorans ATCC 55486 |
CP002593 (7096571 bp) |
386 | 200001–201008 | 1008 |
|
| ||||
|
Ralstonia solanacearum GMI1000 |
NC_003295 (3716413 bp) |
343 | 200001–201032 | 1032 |
|
| ||||
|
Rhizobium hainanense CCBAU 57015 |
Ga0061100_113 (148344 bp) |
146 | 61240–62280 | 1040 |
|
| ||||
| Rhizobium leguminosarum bv. Viciae 3841 |
NC_008380 (5057142 bp) |
397 | 200001–201047 | 1047 |
|
| ||||
|
Rhizobium leguminosarum bv. trifolii WSM1325 |
NC_012850 (4767043 bp) |
210 | 18450–19442 | 993 |
|
| ||||
|
Rhodopseudomonaspalustris TIE-1 |
NC_011004 (5744041 bp) |
387 | 199980–201050 | 1070 |
|
| ||||
|
Rhodopseudomonas palustris DX-1 |
NC_014834 (5404117 bp) |
390 | 200001–200954 | 954 |
|
| ||||
|
Rubrobacter xylanophilus DSM 9941 |
NC_008148 (3225748 bp) |
385 | 200001–201080 | 1080 |
|
| ||||
|
Ruegeria pomeroyi ATCC 700808 |
NC_006569 (491611 bp) |
308 | 118859–119893 | 1035 |
|
| ||||
|
Runella slithyformis ATCC 29530 |
Unknown (6568739 bp) |
362 | 200001–200933 | 933 |
|
| ||||
|
Saccharothrix espanaensis ATCC 51144 |
HE804045 (9360653 bp) |
347 | 200001–201020 | 1020 |
|
| ||||
| Saccharomonospora viridis ATCC 15386 |
NC_013159 (4308349 bp) |
315 | 200001–200996 | 996 |
|
| ||||
|
Shewanella halifaxensis HAW-EB4 |
NC_010334 (5226917 bp) |
337 | 200001–200945 | 945 |
|
| ||||
|
Shewanella pealeana ATCC 700345 |
NC_009901 (5174581 bp) |
333 | 200001–200945 | 945 |
|
| ||||
| Shewanella sediminis HAW-EB3 |
NC_009831 (5517674 bp) |
337 | 200001–200954 | 954 |
|
| ||||
|
Shewanella violacea JCM 1017 |
NC_014012 (4962103 bp) |
307 | 200001–200936 | 936 |
|
| ||||
|
Shewanella woodyi ATCC 51908 |
NC_010506 (5935403 bp) |
327 | 200001–201005 | 1005 |
|
| ||||
|
Shimwellia blattae ATCC 29907 |
EBLc (4158725 bp) | 376 | 200001–201029 | 1029 |
|
| ||||
|
Singulisphaera acidiphila ATCC 1392 |
Sinac_Contig49.1 (9629675 bp) |
337 | 200001–201014 | 1014 |
|
| ||||
| Sorangium cellulosum Soce56 |
NC_010162 (13033779 bp) |
329 | 200001–201029 | 1029 |
|
| ||||
|
Sphingopyxis alaskensis DSM 13593 |
NC_008048 (3345170 bp) |
387 | 200001–201017 | 1016 |
|
| ||||
|
Sphaerobacter thermophilus DSM 20745 |
NC_013524 (1252731 bp) |
335 | 200097–201092 | 995 |
|
| ||||
|
Sphingomonas wittichii RW1 |
NC_009511 (5382261 bp) |
354 | 200001–201026 | 1026 |
|
| ||||
|
Spirosoma linguale ATCC 33905 |
NC_013730 (8078757 bp) |
339 | 200001–200906 | 906 |
|
| ||||
|
Starkeya novella ATCC 8093 |
NC_007604 (2695903 bp) |
402 | 200001–201005 | 1005 |
|
| ||||
| Streptomyces albus J1074 |
CP004370 (6841649 bp) |
252 | 1635309–1636256 | 948 |
|
| ||||
|
Synechococcus elongatus PCC 7942 |
NC_007604 (2695903 bp) |
402 | 200001–201005 | 1005 |
|
| ||||
|
Syntrophobacter fumaroxidans DSM 10017 |
NC_008554 (4990251 bp) |
337 | 200001–200987 | 987 |
|
| ||||
|
Synechococcus sp. ATCC 27264 |
NC_010475 (3008047 bp) |
431 | 200001–201008 | 1008 |
|
| ||||
|
Synechococcus elongatus PCC 6301 |
NC_006576 (2696255 bp) |
402 | 200001–201005 | 1005 |
|
| ||||
|
Synechococcus sp. PCC 7002 |
NC_010475 (3008047 bp) |
431 | 200001–201008 | 1008 |
|
| ||||
|
Synechococcus sp. WH8102 |
NC_005070 (2434428 bp) |
537 | 200001–201017 | 1017 |
|
| ||||
| Synechocystis sp. |
CP003265 (3569561 bp) |
371 | 200001–201026 | 1026 |
|
| ||||
|
Synechocystis sp. PCC 6803 |
NC_017052 (3570103 bp) |
374 | 200001–201026 | 1026 |
|
| ||||
|
Terriglobus roseus KBS 63 |
Terro_Contig51.1 (5227858 bp) |
354 | 200001–200873 | 873 |
|
| ||||
|
Tistrella mobilis KA081020-065 |
CP003239 (1126962 bp) |
379 | 200001–201077 | 1077 |
|
| ||||
| Variovorax paradoxus (strain EPS) |
NC_014931 (6550056 bp) |
360 | 200001–201035 | 1035 |
|
| ||||
|
Variovorax paradoxus S110 |
NC_012791 (5626353 bp) |
420 | 200001–201053 | 1053 |
|
| ||||
| Verminephrobacter eiseniae EF01-2 |
NC_008786 (5566749 bp) |
337 | 200001–200987 | 1020 |
|
| ||||
|
Zobellia galactanivorans DSM 12802 |
FG20DRAFT (5340688 bp) |
331 | 200001–200951 | 951 |
|
| ||||
|
Zymomonas mobilis subsp. Mobilis ATCC 10988 |
NZ_ACQU01000006 (113352 bp) |
113 | 82520–83509 | 990 |
Table 3.
Manually designed motifs (MDMs) for aliphatic and aromatic nitrilases showing the presence of essential catalytic triad (E, K, and C).
| Nitrilases | Manually designed motif |
|---|---|
| Aliphatic | [FL]-[ILV]-[AV]-F-P-E-[VT]-[FW]-[IL]-P-[GY]-Y-P-[WY] |
| R-R-K-[LI]-[KRI]-[PA]-T-[HY]-[VAH]-E-R | |
| C-W-E-H-[FLX]-[NQ]-[PT]-L | |
| [VA]-A-X-[AV]-Q-[AI]-X-P-[VA]-X-[LF]-[SD] | |
|
| |
| Aromatic | [ALV]-[LV]-[FLM]-P-E-[AS]-[FLV]-[LV]-[AGP]-[AG]-Y-P |
| [AGN]-[KR]-H-R-K-L-[MK]-P-T-[AGN]-X-E-R | |
| C-W-E-N-[HY]-M-P-[LM]-[AL]-R-X-X-[ML]-Y | |
| A-X-E-G-R-C-[FW]-V-[LIV] | |
Table 4.
Aliphatic and aromatic nitrilase motif patterns with bold letter depicting catalytic center (E, K, and C) in predicted nitrilases.
| Nitrilases | Manually Designed motif | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
|---|---|---|---|---|---|---|---|---|---|---|
| Aliphatic | [FL]-[ILV]-[AV]-F-P-E-[VT]-[FW]-[IL]-P-[GY]-Y-P-[WY] | A-F-P-E-V-F-V-P-A-Y-P-Y | F-P-E-L-W-L-P-G-Y-P-I-F | F-P-E-V-F-I-S-G-Y-P-Y-W-N-W | F-P-E-V-F-I-A-G-Y | F-P-E-T-F-V-P-Y-Y-P-Y | ||||
| R-R-K-[LI]-[KRI]-[PA]-T-[HY]-[VAH]-E-R | L-R-R-K-L-V-P-T-W | R-R-K-L-K-P-T-H-V-E-R | R-K-L-V-P-T-W-A-E-K-L-T | R-H-R-K-L-V-P-T-W-A-E-R | R-R-K-I-T-P-T-Y-H-E-R | |||||
| C-W-E-H-[FLX]-[NQ]-[PT]-L | C-G-E-N-T-N-T-L-A | C-A-E-N-M-Q-P-L | C-G-E-N-T-N-T-L-A | C-G-E-N-T-N-T-L-A-R-F-S | C-W-E-H-Y-N-P-L- | |||||
| [VA]-A-X-[AV]-Q-[AI]- X- P-[VA]-X-[LF]-[SD] | V-A-A-V-Q-A-A-P-V-F-L-D-P | V-A-S-V-Q-A-E | V-Q-T-A-P-V-F-L-N-V-E | A-A-V-Q-A-A-P-V-F-L | A-A-V-Q-I-S-P-V-L- | |||||
|
| ||||||||||
| Aromatic | [ALV]-[LV]-[FLM]-P-E-[AS]-[FLV]-[LV]-[AGP]-[AG]-Y-P | F-Q-E-V-F-N-A | P-E-S-F-I-P-C-Y-P-R-G | F-P-E-A-F-L-G-T-Y-P | S-E-T-F-S-T-G | |||||
| [AGN]-[KR]-H-R-K-L-[MK]-P-T-[AGN]-X-E-R | R-K-H-H-I-P-Q-V | H-R-K-L-K-P-T-G-L-E-R | H-R-K-V-M-P-T-G-A-E-R | R-K-L-H-P-F-T | ||||||
| C-W-E-N-[HY]-M-P-[LM]-[AL]-R-X-X-[ML]-Y | C-Y-D-R-H | C-W-E-N-Y-M-P-L-A-R-M | C-W-E-N-Y-M-P-L-L-R-A | C-Y-D-L-R-F-A | ||||||
| A-X-E-G-R-C-[FW]-V-[LIV] | A-H-L-W-K-L-E | A-L-E-G-R-C-F-V-L-A | A-L-E-G-R-C-W-V | A-I-E-N-Q-A-Y-V | ||||||
3.2. Physiochemical Parameters and Phylogenetic Analysis
In silico identified nitrilases were analyzed for their physiochemical properties using ProtParam, an online tool at the ExPASy proteomic server. The selected candidates values for various properties were found to be very much similar to those with earlier published data by Sharma and Bhalla [16] as mentioned in Table 5. Average values deduced for aliphatic and aromatic nitrilases from earlier characterized proteins were taken as standard for the comparison of a predicted set of nitrilase. The values of the same were found to be very much similar to those with earlier published data by Sharma and Bhalla [16] as mentioned in Table 5. The total number of amino acids ranged from 260 amino acids (Nocardiopsis dassonvillei) to 342 amino acids (Shimwellia blattae) with different molecular weight. Isoelectric point ranged between 4.8 and 5.8 which is found to be closer to the consensus value, that is, the average data value from previously characterized aliphatic or aromatic nitrilases.
Table 5.
Comparison of physiochemical properties of aliphatic, aromatic, and predicted nitrilase from the average consensus values reported by Sharma and Bhalla [16].
| Parameters | Average value for aliphatic | Average value for aromatic | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Number of amino acids | 352.2 | 309.8 | 338.0 | 331.0 | 326.0 | 260.0 | 280.0 | 310.0 | 315.0 | 342.0 | 319.0 |
|
| |||||||||||
| Molecular weight (Da) | 38274.0 | 33693.5 | 36154.9 | 36491.2 | 36364.7 | 27903.3 | 31464.1 | 34938.1 | 33821.5 | 37472.7 | 34678.7 |
|
| |||||||||||
| Theoretical pI |
5.5 | 5.5 | 5.0 | 4.9 | 6.2 | 5.2 | 5.6 | 5.4 | 4.8 | 5.4 | 5.8 |
|
| |||||||||||
| NCR∗ | 41.7 | 35.8 | 41.0 | 44.0 | 40.0 | 32.0 | 36.0 | 43.0 | 43.0 | 41.0 | 39.0 |
|
| |||||||||||
| PCR∗ | 30.3 | 29.2 | 26.0 | 25.0 | 37.0 | 21.0 | 27.0 | 34.0 | 29.0 | 30.0 | 32.0 |
|
| |||||||||||
| Extinction coefficients (M−1 cm−1) at 280 nm |
50213.3 | 43975.0 | 45295.0 | 33015.0 | 43890.0 | 35200.0 | 62465.0 | 53400.0 | 47900.0 | 38305.0 | 31775.0 |
|
| |||||||||||
| Instability index | 41.2 | 38.5 | 30.1 | 52.5 | 27.0 | 27.7 | 28.6 | 39.6 | 46.6 | 36.6 | 38.5 |
|
| |||||||||||
| Aliphatic index | 89.40 | 89.90 | 94.1 | 87.9 | 93.6 | 81.1 | 76.0 | 90.9 | 86.2 | 92.8 | 89.3 |
|
| |||||||||||
| Grand average of hydropathicity (GRAVY) | 00.10 | 00.01 | 0.027 | −0.17 | −0.14 | −0.051 | −0.283 | −0.109 | 0.045 | −0.052 | −0.002 |
NCR∗: negatively charged residues; PCR∗: positively charged residues.
Neighbor Joining (NJ) tree using MEGA 6 shows the phylogenetic analysis with in silico predicted sequences from completely sequenced microbial genomes with that of previously characterized nitrilase sequences. They were distinguished either as aliphatic or aromatic according to their position in the phylogenetic tree (Figure 1).
Figure 1.
Neighbor Joining (NJ) method differentiating characterized and in silico predicted as aliphatic and aromatic nitrilases.
3.3. In Vitro Validation of Some In Silico Predicted Nitrilases
To validate for nitrile transforming activity of nine predicted novel sources of nitrilases, these were tested against common aliphatic, aromatic, and aryl nitriles and potassium cyanide (KCN). Gluconacetobacter diazotrophicus, Sphingopyxis alaskensis, Saccharomonospora viridis, and Shimwellia blattae were found to be more specific for aliphatic nitriles. On the other hand, Geodermatophilus obscurus, Nocardiopsis dassonvillei, Runella slithyformis, and Streptomyces albus exhibited nitrilase activity for aromatic nitriles. Flavobacterium indicum was the only organism which showed no activity for either aliphatic, aromatic, or aryl nitriles but was specific towards the degradation of the potassium cyanide (KCN) (Table 6). On the other hand, negative control, that is, Escherichia coli BL21 (DE3), showed no activity for any of the nitriles/substrates tested.
Table 6.
Nitrilase activity∗ of in silico predicted microbial sources of nitrilases assayed using common aliphatic, aromatic, aryl aliphatic, and KCN as substrate.
| Organisms | Substrates | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Valeronitrile | Benzonitrile | Mandelonitrile | Isobutyronitrile | Adiponitrile | 2-Cyanopyridine | Propionitrile | Acrylonitrile | KCN | |
|
Streptomyces albus J1074 |
0.0015 | 0.0027 | ND | 0.0014 | ND | ND | ND | ND | ND |
|
| |||||||||
|
Nocardiopsis dassonvillei DSM 43111 |
ND | 0.0040 | 0.0024 | ND | ND | ND | ND | ND | ND |
|
| |||||||||
|
Geodermatophilus obscurus DSM 43160 |
ND | 0.0043 | 0.0021 | ND | ND | ND | ND | ND | ND |
|
| |||||||||
|
Shimwellia blattae ATCC 29907 |
0.0028 | ND | 0.0016 | 0.0019 | ND | ND | ND | ND | ND |
|
| |||||||||
|
Runella slithyformis ATCC 29530 |
ND | 0.0297 | 0.0152 | 0.0095 | ND | ND | 0.0169 | ND | ND |
|
| |||||||||
|
Gluconacetobacter diazotrophicus ATCC 49037 |
ND | 0.0016 | 0.0020 | 0.0051 | 0.0048 | ND | ND | ND | ND |
|
| |||||||||
|
Sphingopyxis alaskensis DSM 13593 |
ND | 0.00073 | ND | 0.0024 | 0.00075 | ND | ND | ND | ND |
|
| |||||||||
|
Saccharomonospora viridis ATCC 15386 |
ND | ND | ND | 0.0030 | ND | ND | ND | ND | ND |
|
| |||||||||
|
Flavobacterium indicum DSM 17447 |
ND | ND | ND | ND | ND | ND | ND | ND | 0.25 |
|
| |||||||||
|
Escherichia coli BL21 (DE3)∗∗ |
ND | ND | ND | ND | ND | ND | ND | ND | ND |
∗Expressed as µmole of ammonia released/min/mg dcw under the assay conditions; ND = not detected; ∗∗negative control.
4. Discussion
Annotation of sequenced genomes to identify new genes has become integral part of the research in bioinformatics [21–24]. The present investigation has revealed some novel sources of nitrilases. Homology and conserved motif approach screened microbial genomes and proteins predicted as nitrilase or cyanide dihydratase or carbon-nitrogen hydrolase in 138 prokaryotic bacterial genomes. Manually designed motifs (MDMs) also differentiated the in silico predicted nitrilases as aliphatic or aromatic [12] as the designed motifs are class specific. All the four motifs identified were uniformly conserved throughout the two sets of aliphatic and aromatic nitrilases as mentioned in Table 4.
The sequences belonged to the nitrilase superfamily, showing the presence of the catalytic triad Glu (E), Lys (K), and Cys (C) to be conserved throughout. Phylogenetic analysis using the MEGA 6.0 version for the aliphatic and aromatic set of protein sequences revealed two major clusters. Neighbor Joining (NJ) tree used for phylogenetic analysis revealed that in silico predicted proteins (this study) and previously identified nitrilases as aliphatic and aromatic [16] were found to be grouped in their respective clusters (Figure 1).
Aliphaticity and aromaticity of in silico predicted and characterized nitrilases were differentiated based on their physiochemical properties. The physicochemical properties of the predicted set of nitrilase were deduced using the ProtParam subroutine of Expert Protein Analysis System (ExPASy) from the proteomic server of the Swiss Institute of Bioinformatics (SIB), in order to predict aromaticity or aliphaticity. Several of the parameters (number of amino acids, molecular weight, number of negatively charged residues, extinction coefficients, and grand average of hydropathicity) listed in Table 5 are closer to the consensus values reported for aromatic and aliphatic nitrilases, supporting that the predicted set of nitrilase has aromatic or aliphatic substrate specificity (Table 5).
In silico predictions were verified by in vitro validation of the predicted proteins. Common nitriles (aliphatic, aromatic, and aryl nitriles) and potassium cyanide (KCN) were tested to check for the nitrile/cyanide transforming ability of the predicted proteins. Out of nine predicted proteins eight were found active for different nitriles, whereas Flavobacterium indicum was found to hydrolyze toxic cyanide (KCN) into nontoxic form (Table 6). The present approach contributed to finding novel sources of desired nitrilase from microbial genome database.
5. Conclusion
Genome mining for novel sources of nitrilases has predicted 138 sources for nitrilases. In vitro validation of the selected nine predicted sources of nitrilases for nitrile/cyanide hydrolyzing activity has furthered the scope of genome mining approaches for the discovery of novel sources of enzymes.
Supplementary Material
List of organisms with completely sequenced genomes avaliable at NCBI and IMG/ER.
Acknowledgments
The authors are thankful to the Department of Biotechnology (DBT), New Delhi, for the continuous support to the Bioinformatics Centre, Himachal Pradesh University, Summer Hill, Shimla, India.
Conflicts of Interest
The authors declare that they have no conflicts of interest.
References
- 1.Seffernick J. L., Samanta S. K., Louie T. M., Wackett L. P., Subramanian M. Investigative mining of sequence data for novel enzymes: a case study with nitrilases. Journal of Biotechnology. 2009;143(1):17–26. doi: 10.1016/j.jbiotec.2009.06.004. [DOI] [PubMed] [Google Scholar]
- 2.Land M., Hauser L., Jun S., et al. Insights from 20 years of bacterial genome sequencing. Functional & Integrative Genomics. 2015;15(2):141–161. doi: 10.1007/s10142-015-0433-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Yadav D. Relevance of bioinformatics in the era of omics driven research. Journal of Next Generation Sequencing & Applications. 2015;2(1, article e102) doi: 10.4172/2469-9853.1000e102. [DOI] [Google Scholar]
- 4.Feng Y., Zhang Y., Ying C., Wang D., Du C. Nanopore-based fourth-generation DNA sequencing technology. Genomics, Proteomics & Bioinformatics. 2015;13(1):4–16. doi: 10.1016/j.gpb.2015.01.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Van Lanen S. G., Shen B. Microbial genomics for the improvement of natural product discovery. Current Opinion in Microbiology. 2006;9(3):252–260. doi: 10.1016/j.mib.2006.04.002. [DOI] [PubMed] [Google Scholar]
- 6.Luo X. J., Yu H. L., Xu J. H. Genomic data mining: an efficient way to find new and better enzymes. Enzyme Engineering. 2012;1, article 104 doi: 10.4172/2329-6674.1000104. [DOI] [Google Scholar]
- 7.De Sousa-Pereira P., Amado F., Abrantes J., Ferreira R., Esteves P. J., Vitorino R. An evolutionary perspective of mammal salivary peptide families: cystatins, histatins, statherin and PRPs. Archives of Oral Biology. 2013;58(5):451–458. doi: 10.1016/j.archoralbio.2012.12.011. [DOI] [PubMed] [Google Scholar]
- 8.Adrio J. L., Demain A. L. Microbial enzymes: tools for biotechnological processes. Biomolecules. 2014;4(1):117–139. doi: 10.3390/biom4010117. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Raj J., Singh N., Prasad S., Seth A., Bhalla T. C. Bioconversion of benzonitrile to benzoic acid using free and agar entrapped cells of Nocardia globerula NHB-2. Acta Microbiologica et Immunologica Hungarica. 2007;54(1):79–88. doi: 10.1556/amicr.54.2007.1.8. [DOI] [PubMed] [Google Scholar]
- 10.Rao M. A., Scelza R., Scotti R., Gianfreda L. Role of enzymes in the remediation of polluted environments. Journal of Soil Science and Plant Nutrition. 2010;10(3):333–353. [Google Scholar]
- 11.Kaul P., Asano Y. Strategies for discovery and improvement of enzyme function: state of the art and opportunities. Microbial Biotechnology. 2012;5(1):18–33. doi: 10.1111/j.1751-7915.2011.00280.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Sharma N. N., Sharma M., Bhalla T. C. Nocardia globerula NHB-2 nitrilase catalysed biotransformation of 4-cyanopyridine to isonicotinic acid. AMB Express. 2012;2, article 25 doi: 10.1186/2191-0855-2-25. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Bhatia S. K., Mehta P. K., Bhatia R. K., Bhalla T. C. Optimization of arylacetonitrilase production from Alcaligenes sp. MTCC 10675 and its application in mandelic acid synthesis. Applied Microbiology and Biotechnology. 2014;98(1):83–94. doi: 10.1007/s00253-013-5288-9. [DOI] [PubMed] [Google Scholar]
- 14.Gong J.-S., Lu Z.-M., Li H., Shi J.-S., Zhou Z.-M., Xu Z.-H. Nitrilases in nitrile biocatalysis: recent progress and forthcoming research. Microbial Cell Factories. 2012;11, article 142 doi: 10.1186/1475-2859-11-142. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Ramteke P. W., Maurice N. G., Joseph B., Wadher B. J. Nitrile-converting enzymes: an eco-friendly tool for industrial biocatalysis. Biotechnology and Applied Biochemistry. 2013;60(5):459–481. doi: 10.1002/bab.1139. [DOI] [PubMed] [Google Scholar]
- 16.Sharma N., Bhalla T. C. Motif design for nitrilases. Journal of Data Mining in Genomics & Proteomics. 2012;3, article 119 doi: 10.4172/2153-0602.1000119. [DOI] [Google Scholar]
- 17.Sharma N., Kushwaha R., Sodhi J. S., Bhalla T. C. In silico analysis of amino acid sequences in relation to specificity and physiochemical properties of some microbial nitrilases. Journal of Proteomics and Bioinformatics. 2009;2(4):185–192. doi: 10.4172/jpb.1000076. [DOI] [Google Scholar]
- 18.Bhalla T. C., Miura A., Wakamoto A., Ohba Y., Furuhashi K. Asymmetric hydrolysis of α-aminonitriles to optically active amino acids by a nitrilase of Rhodococcus rhodochrous PA-34. Applied Microbiology and Biotechnology. 1992;37(2):184–190. doi: 10.1007/bf00178168. [DOI] [Google Scholar]
- 19.Vejvoda V., Kubáč D., Davidová A., et al. Purification and characterization of nitrilase from Fusarium solani IMI196840. Process Biochemistry. 2010;45(7):1115–1120. doi: 10.1016/j.procbio.2010.03.033. [DOI] [Google Scholar]
- 20.Dennett G. V., Blamey J. M. A new thermophilic nitrilase from an antarctic hyperthermophilic microorganism. Frontiers in Bioengineering and Biotechnology. 2016;4, article 5 doi: 10.3389/fbioe.2016.00005. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Fawcett J. K., Scott J. E. A rapid and precise method for the determination of urea. Journal of Clinical Pathology. 1960;13:156–159. doi: 10.1136/jcp.13.2.156. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Friedberg I. Automated protein function prediction—the genomic challenge. Briefings in Bioinformatics. 2006;7(3):225–242. doi: 10.1093/bib/bbl004. [DOI] [PubMed] [Google Scholar]
- 23.Armengaud J. A perfect genome annotation is within reach with the proteomics and genomics alliance. Current Opinion in Microbiology. 2009;12(3):292–300. doi: 10.1016/j.mib.2009.03.005. [DOI] [PubMed] [Google Scholar]
- 24.Poptsova M. S., Gogarten J. P. Using comparative genome analysis to identify problems in annotated microbial genomes. Microbiology. 2010;156(7):1909–1917. doi: 10.1099/mic.0.033811-0. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
List of organisms with completely sequenced genomes avaliable at NCBI and IMG/ER.