Genomics Inform Search


Genomics Inform > Volume 21(1); 2023 > Article
Tasneem, Gupta, Momin, Hossain, Osman, and Rabbi: In silico annotation of a hypothetical protein from Listeria monocytogenes EGD-e unfolds a toxin protein of the type II secretion system


The gram-positive bacterium Listeria monocytogenes is an important foodborne intracellular pathogen that is widespread in the environment. The functions of hypothetical proteins (HP) from various pathogenic bacteria have been successfully annotated using a variety of bioinformatics strategies. In this study, a HP Imo0888 (NP_464414.1) from the Listeria monocytogenes EGD-e strain was annotated using several bioinformatics tools. Various techniques, including CELLO, PSORTb, and SOSUIGramN, identified the candidate protein as cytoplasmic. Domain and motif analysis revealed that the target protein is a PemK/MazF-like toxin protein of the type II toxin-antitoxin system (TAS) which was consistent with BLASTp analysis. Through secondary structure analysis, we found the random coil to be the most frequent. The Alpha Fold 2 Protein Structure Prediction Database was used to determine the three-dimensional (3D) structure of the HP using the template structure of a type II TAS PemK/MazF family toxin protein (DB ID_AFDB: A0A4B9HQB9) with 99.1% sequence identity. Various quality evaluation tools, such as PROCHECK, ERRAT, Verify 3D, and QMEAN were used to validate the 3D structure. Following the YASARA energy minimization method, the target protein's 3D structure became more stable. The active site of the developed 3D structure was determined by the CASTp server. Most pathogens that harbor TAS create a crucial risk to human health. Our aim to annotate the HP Imo088 found in Listeria could offer a chance to understand bacterial pathogenicity and identify a number of potential targets for drug development.


Listeria monocytogenes is a pathogenic facultative, intracytosolic, gram-positive bacterium for humans and several animal species which is responsible for the infection-listeriosis [1]. Listeric infections are often associated with gastrointestinal (GI) listeriosis (non-invasive) in immunocompetent persons or listeriosis (invasive) in immune-compromised individuals [2,3]. Invasive listeriosis is responsible for meningitis in immunocompromised people and miscarriage in pregnant women [4]. Patients with chronic renal failure and cirrhosis, as well as those on drugs to reduce gastric acidity, are at greater risk of listeriosis [5]. Moreover, urinary tract infections by L. monocytogenes were also recorded, in an instance after detecting this bacterium in urine samples [6]. One of the prime reasons for listeriosis outbreaks is inadequate hygiene standards and insufficient implementation of standard sanitation operating procedures in the food manufacturing industry [7]. Multidrug-resistant L. monocytogenes has been found in irrigation waters and agricultural soils, and can spread to agronomical fresh product risking food safety [8]. There is compelling evidence that contaminated food, mostly ready-to-eat meals, is the main route of transmission of this bacterium into humans. The ability of L. monocytogenes to traverse host barriers (such as the intestinal barrier, and the materno-fetal barrier), is responsible for causing listeriosis. L. monotocytogenes can also be detectable in the bloodstream during an infection [9]. After successfully evading the GI tract environment, L. monocytogenes can invade and persist in mammalian host cells due to presence of multiple virulence factors [10]. Because of the clinical significance of listeriosis, several genomes of L. monocytogenes strains have already been sequenced to have a deeper understanding of the species' lifestyle and pathogenicity, as well as the possible impact of strain variability on virulence. L. monocytogenes could be divided into four evolutionary lineages and four serogroups based on polymerase chain reaction testing in the two genes of hlyA and iap [11,12]. The major portion of L. monocytogenes isolates are from lineage I, including serotypes 1/2b, 3b, and 4b. On the other hand, lineage II comprises serotypes 1/2a, 1/2c, 3a, and 3c. The bacteria L. monocytogenes EGD-e (serovar 1/2a) is responsible for various listeriosis outbreaks. The EGD-e strain has a circular chromosome consisting of 2,944,528 bp with an estimated 39% of G + C content [13]. The EGD-e strain of L. monocytogenes genome has been predicted to contain a sum of 2,853 genes that code for proteins. A large number of genes that regulate the pathogenicity, development, and survival of EGD-e strain have already been characterized by researchers and annotated in the published genome sequence [14-17]. Nonetheless, numerous loci with putative genes that codes for protein are designated as "Hypothetical proteins (HP)" because the relationship between those proteins and listeria life cycle are poorly understood. An HP is predicted to be encoded by a recognized open reading frame but has no experimental evidence to support its putative function [18]. In most genomes, around 50% of the protein-coding genes are designated as HPs. Thereby, HPs are likely to have their own significance in an organism's overall proteomic platform. An appropriate annotation of the HPs found in a pathogen helps researcher not only to gain an improved knowledge of the pathogen's virulent actions but also to discover new structures of proteins, metabolic pathways, and functions [19]. HPs may likely to act an important role in organisms’ growth, survival, and disease progression. They can also serve as potential pharmacological targets and genetic markers for the development of novel antimicrobial medicines and medications [20]. In recent times, bioinformatics has improved our knowledge of protein function-structure interactions. Bioinformatics analysis has the advantage of being less expensive and time-saving than conventional in vitro procedures. Functional annotation of HPs utilizing different structural and sequence-specific bioinformatics softwares can aid in the classification of these proteins into which several functional groups, providing more information about their structures, activities, and contribution in metabolism [21]. Because L. monocytogenes is prevalent in surroundings, constant focus by risk managers is much needed to control Listeria in food production facilities. Therefore, to curtail the prevalence and to develop effective control measures against listeriosis, a better understanding of the microorganism's characteristics, environmental influence, and host-virulent factors interactions are required [22]. Thus, the main objective of this study is to ascribe a potential biological function and predictive structure to the HP Imo088 (accession No. NP_464414.1) of L. monocytogenes EGD-e. The protein sequences were analyzed utilizing latest bioinformatics software and tools for homology search against functionally characterized proteins, determination of domain and physicochemical properties, prediction of subcellular localization, and determination of active site. We believe that this interpretation will strengthen our knowledge about the functional activities of the HP Imo088 found in listeria and provide a platform to discover potential pharmacological targets.


Sequence retrieval with FASTA format

The FASTA sequence of the HP (NP_464414.1) were obtained from the NCBI (National Center for Biotechnology Information; [23] database. The protein sequence was then submitted on to numerous prediction servers for the in-silico annotation [24].

Physicochemical properties analysis

The ExPASy ProtParam ( tool was used to characterize HPs in terms of their physicochemical features which includes molecular weight, aliphatic index, extinction coefficients, amino acid composition, grand average of hydropathy (GRAVY), isoelectric point (pI), and estimated half-life [25].

Prediction of protein subcellular localization

The putative subcellular localization of the HP was determined by CELLO v.2.5 ( [26], an analysis based on a two-level support vector prediction system. Subcellular localization predicted by CELLO was further correlated with the result of PSORTb ( [27], SOSUI ( [28], and PSLpred ( [29]. SOSUI discriminates between soluble and transmembrane proteins by calculating the average hydrophobicity of protein. In contrast, PSORTb and PSLpred predict subcellular localization of prokaryotic proteins on the basis of various features e.g., amino acid and dipeptide composition, composition of 33 physicochemical properties, and evolutionary information of PSI-BLAST.

Identification of protein domain and motif

For protein domain analysis, NCBI CD-Search ( [30], Pfam 34.0 ( [31], InterProScan5 ( To determine the protein sequence motif, MOTIF Search ( tool was used [32]. Conserved domain (CD) search compares a query sequence with the CD alignments which was found in the Conserved Domain Database (CDD). The functional analysis of the protein was carried out by using the InterProscan tool. Pfam is a protein family database that uses hidden Markov models (HMMs) in order to generate annotations and multiple sequence alignments.

Protein family and phylogenetic tree analysis

The homologs of the HP (NP_464414.1), a protein-BLAST (BLASTp) ( [33] from NCBI (National Center for Biotechnology Information) against the non-redundant database with default parameters was performed. This approach is based on the local alignment of protein sequence to find similar proteins. CLC Sequence Viewer version 8.0 was used for multiple sequence alignment and to create phylogenetic tree for few selected sequences.

Secondary structure prediction

Two-dimensional structure of the NP_464414.1 protein was determined using SOPMA (Self-optimized prediction method with alignment) ( [34] and PSIPRED (Position Specific Iterated – BLAST) ( [35]. Result from SOPMA analysis was correlated with the result of PSIPRED.

Homology modeling

The Alpha Fold 2 Protein Structure Prediction Database ( [36] was used to determine the 3D structure of our putative HP and the performance of this determination was based on the pairwise comparison profile of HMMs. The template protein of the type II toxin-antitoxin system PemK/MazF family toxin protein (DB ID_AFDB: A0A4B9HQB9) was retrieved from the query result for homology-based modeling. UCSF Chimera 1.16 was employed to visualize the 3D model structure.

Quality assessment

To assess the quality of the predicted 3D structure, various evaluation tools were used. These include PROCHECK (, Verify3D ( [37], and QMEAN ( [38] programs of ExPASy server of SWISS-MODEL Workspace.

Active site analysis

CASTp Computed Atlas of Surface Topography of proteins; server was applied to predict and locate the protein’s active site. CASTp not only predict the active pockets residing in protein surfaces, but also the key residues and the regions of protein that interact with ligands in the inner region of the three-dimensional structure.

Energy minimization of the model structure

The energy of the 3D model structure was minimized using YASARA ( [39] force field minimizer. After refining through YASARA, a more stable and reliable 3D structure of the target protein was gained.


Physicochemical properties

ProtParam tool was used to determine some crucial physiochemical features. The protein was predicted to contain 115 amino acids, an isoelectric point (PI) of 6.73, and a molecular weight of 12,759.85 Da. The calculated value of GRAVY of the protein was −0.057. The protein was classified as stable, because the instability index of the desired protein was computed to be 36.52. The most abundant amino acids were valine (11.3%), isoleucine (9.6%), leucine (8.7%), lysine (7.8%), asparagine (7.8%), alanine (7%), glutamate (6.1%), glycine (6.1%), threonine (6.1%), arginine (5.2%), glutamine (4.3%), tyrosine (1.7%), and histidine (1.7%). Surprisingly, tryptophan and cysteine were found to be completely absent in the protein sequence. The protein contains a total of 16 negatively charged (Asp + Glu) and 16 positively charged (Arg + Lys) amino acids (Table 1). The report on atomic composition showed that the protein comprises of 1839 atoms having molecular formula of protein C568H943 N155O170S3.

Subcellular localization

A protein's function is significantly influenced by where it is located within the cell. For this reason, understanding a protein's location in the cellular setting is beneficial to discover proteins with undetermined function. Determination of subcellular localization of the target protein was done by CELLO and further confirmed by PSORTb, SOSUIGramN, and PSLpred server. It was found by all of these methods that the protein would be cytoplasmic (Table 2). This knowledge might be helpful for interpreting the functional role as well as for designing a drug against the target protein.

Domain and motif identification

The specific hit explored by CD search tool predicted that the query protein belongs to PemK toxin superfamily with an E-value of 2.87e−43. This domain covers 5 to 110 amino acid residues of our protein sequence (Table 3). The result of the CD search analysis was found to be comparable with the outcome of InterProScan and Pfam. The Pfam tool predicted the PemK toxin superfamily domain at 4 to109 amino acid residues. The InterProScan server predicted PemK-like domain in the range of 1 to 112 amino acid residues of the HP. Similarly, the MOTIF server predicted the PemK domain at the position of 4 to 109 amino acid residues having an E-value of 2.6e-33. PemK is a growth inhibitor that is found in Escherichia coli and it auto regulating synthesis by binding to the promoter region of Pem operon. A typical bacterial toxin-antitoxin system contains the toxin molecule of this family. A number of different toxins, such as MazF, Kid, PemK, ChpA, ChpB, and ChpAK are also members of this family [40].

Protein family and phylogeny analysis

The BLASTp search was carried out against the non-redundant database which showed sequence similarities (up to 96 %) with other known PemK/MazF family toxin proteins of type II toxin-antitoxin system from different Listeriaceae (Table 4). Multiple sequence alignments of few selected proteins retrieved from BLASTp results were done to observe the conserved and dissimilar residues among the homologs (Fig. 1). A phylogenic tree was built using the same information (Fig. 2). The target protein, as well as two other Listeria monocytogenes proteins, appear to have a common ancestor with the WP_185340554.1 protein of Listeria seerigeli. The scale bar estimates sequence divergence, and amount of genetic change is represented by the line segment with the number (0.013).

Secondary structure prediction

To analyze the protein’s secondary structure, a server named SOPMA was used to estimate the proportions of extended strand (21.74%), alpha helix (33.91%), beta turn (3.48%), and random coil (40.87%). Similar outcomes were also found during PSIPRED analysis (Fig. 3).

Three-dimensional structure determination and model quality assessment

The template structure of the type II toxin-antitoxin system PemK/MazF family toxin protein (DB ID_AFDB: A0A4B9HQB9) was used to determine the 3D structure of our target protein, which showed 99.1% identities with our desired protein in the Alpha Fold 2 Protein Structure Prediction Database. The template protein is a toxin MazF protein from Listeria monocytogenes. The structure was visualized by UCSF Chimera 1.16 (Fig. 4). PROCHECK was used to evaluate the projected 3D structure of target protein through Ramachandran plot analysis. As reported by PROCHECK, 92.2% amino acid residues covered the most favored regions in “Ramachandran plot” that is regarded as a valid model quality (Table 5, Fig. 5A). By Verify 3D plot, we concluded that 93.04% of the residues had an averaged 3D-1D score ≥ 0.2. The overall quality factor of the predicted protein came out to be 95.1923 through ERRAT program. The predicted model reliability is reflected through the QMEAN4 score which compares the model structure with already determined experimental structure of similar size. The QMEAN4 global score of our target protein is 0.40 which indicates as good (Fig. 5B).

Active site analysis

By using the CASTp server, the active site of the developed 3D structure was assessed (Fig. 6). The most active site was discovered in one of the largest pockets with 74.975 solvent-accessible (SA) surface area and a total volume of 24.651 amino acids, respectively. Key active residues predicted from pocket are TYR10, ILE24, ILE47, THR48, ALA49, PHE68, ARG70, SER72, and ILE91. The main step when designing a medication or inhibitor is the identification of active site of amino acids.

Energy minimization result

The energy of the protein’s three-dimensional structure was minimized by YASARA force field minimizer. The energy was reduced to –51,862.6 kJ/mol from –65,533.5 kJ/mol after energy minimization. The initial value was –0.04 kJ/mol; however, after the minimization process, the end value was 0.57 which indicates the structure as stable one.


Listeria is an intrinsic pathogen that is gram-positive, rod-shaped, non-spore producing and catalase positive. The Listeria genus contains 17 species, six of which L. monocytogenes, L. ivanovii, L. seeligeri, L innocua, L. welshimeri, and L. grayi are most frequent [41]. Among these species, only L. monocytogenes is responsible for serious complications in both human and animals. L. monocytogenes is a prominent cause of foodborne disease worldwide, with a high hospitalization and fatality rate. Characterization of HPs NP_464414.1 of L. monocytogenes EGD-e can aid in understanding bacterial metabolic regulations, formulating disease control strategies, and developing effective therapeutics. Various computational resources were employed in this study to characterize the HP NP_464414.1 of L. monocytogenes EGD-e from structural and functional aspects. The physiochemical properties’ analysis revealed that the protein consists of 115 amino acid sequence, have a molecular weight of 12,759.85, the GRAVY score of –0.057, and a theoretical PI of 6.37 (Table 1). In our investigation, we used CELLO for the prediction of subcellular location which revealed the query protein to be a cytoplasmic one. The analysis of the protein’s secondary structure reveals the prevalence of extended strand, beta turn, alpha helix, and random coil. Domain and motif study indicates that our target HP belongs to PemK toxin superfamily. A typical bacterial toxin-antitoxin system contains the toxin molecule of this family (Table 3). We used other bioinformatics resources to confirm that the prediction was highly accurate. BLASTp against the non-redundant database revealed up to 96 % sequence similarity with other type II toxin-antitoxin system PemK/MazF family toxin (L. monocytogenes) (Table 4). TASs are small genetic components composed of toxic protein and its antitoxin protein, with the latter counteracting the former's toxicity. Through in-silico analysis, two toxin-antitoxin systems (TASs) (lmo0113-0114 and lmo0887-0888) are found in L. monocytogenes EGD-e using TADB2. Only a few studies on TASs of L. monocytogenes have been conducted so far and those were also limited to few strains and few TAS pairs [42,43]. The strain ATCC19117 was studied using in silico approach where few TASs pair were found (lmo0887-0888, lmo0113-0114, and Imo1301-1302) with subsequent 3D structure and possible inhibitory peptide analysis [44]. As an endoribonuclease, the toxin PemK selectively identifies and cut the tetrad sequence UAUU in a target mRNA without the need for ribosomes. It is suggested that the antitoxin Pemk acts as both a transcription factor and a toxin activity neutralizer, enabling bacterial survival [45]. It's interesting to note that, unlike the cell translation machinery, the target sequence (UAUU) of PemK is present in a sizable percentage of mRNA transcripts that encode virulence related protein [46,47]. The regulated proteins were implicated in a variety of processes, including, cytoskeleton function, protein and lipid synthesis, heat shock and stress response, ATP synthesis, innate immunological defense, muscle construction, and others. The overexpression of the pemK gene severely inhibited bacterial growth in the case of other dangerous bacteria, such as Mycobacterium tuberculosis, Klebsiella pneumonia, and Bacillus anthracis [48]. PemK toxins coordinate the modulation of particular gene pools in the bacterial transcriptome, but their experimental characterization is challenging. The tertiary structure of the protein was developed from Alpha Fold 2 Protein Structure server and the quality of the model was assessed by evaluation software like Verify 3D, PROCHECK, ERRAT, and QMEAN. 92.2 % amino acid residues covered the most favored region in Ramachandran plot, which depicts the model quality as valid (Fig. 5A). The result of QMEAN4 server (Fig. 5B) revealed that the Z score of the anticipated model was 0.40, which also denotes a good quality model. After YASARA energy minimization process, the 3D structure of target protein became more stable which turned to be 0.57. In CASTp analysis, one largest pocket was found as active sites with SA surface area of 74.975 and volume of 24.651 amino acids. The majority of viruses with toxin-antitoxin in their system pose a significant threat to individual’s health [44]. Although tremendous progress has been made in investigating the roles of toxin-antitoxins in recent years, many functional and structural aspects of toxin-antitoxins and their effectors remain elusive. Our aim of the study was to identify the structural and biological function of the HP NP_464416.1 of L. monocytogenes through an in silico approach. This annotation of the HP is fundamental to strengthen the basic knowledge on L. monocytogenes which may aid in understanding the mechanism of bacterial pathogenicity and virulence.


Authors’ Contribution

Conceptualization: SDG, MFR

Data curation: MT, SDG, MBM, KMH, TBO

Formal analysis: MFR, MT

Methodology: MBM, KMH, TBO

Writing - original draft: MT, SDG, MBM

Writing - review & editing: MT, SDG, MFR.

Conflicts of Interest

No potential conflict of interest relevant to this article was reported.


The authors acknowledge the Department of Biotechnology and Genetic Engineering, Noakhali Science and Technology University for providing support to conduct the research work.

Fig. 1.
Multiple Sequence Alignment among different type II toxin-antitoxin system proteins with the target protein at the top row (sources for the sequences: Row 2, 3 and 9, Listeria monocytogenes; Row 4 to 6, Listeria seeligeri; Row 7 to 8, Listeria ivanovii, Row 10; Listeria immobilis). The figure was generated by CLC Sequence Viewer version 8.
Fig. 2.
Phylogenetic tree with true distance from target protein (NP_464414.1). The tree was generated using CLC Sequence Viewer version 8. Here, the scale bar estimates sequence divergence, and amount of genetic change is represented by the line segment with the number (0.013).
Fig. 3.
Predicted secondary structure of the target protein using PSI-PRED server. This graphical representation has four different sections. First section consists of bars with different heights. The length of the bar height is proportional to confidence score. In the second section, the pink color denotes the alpha helix, the yellow color denotes beta sheets or strands, and the gray color depicts coils; the coil connects a particular alpha helix with the particular beta sheets. The third section contains an alphabetic representation, which denotes the secondary structure of a protein; Here E, H, C are used for beta sheets, alpha helixes and coils, respectively. In the last section, the arrangement of amino acids is presented in alphabetic form.
Fig. 4.
Predicted three-dimensional structure of the target protein (visualized by UCSF Chimera 1.16).
Fig. 5.
Model Quality Assessment. (A) Ramachandran plot of the model structure validated by PROCHECK server. Here, 92.2% amino acid residues covered the most favored regions [A, B, L]. (B) Graphical representation of QMEAN result of the model structure. Here, Z score of the anticipated model was 0.40 (indicates good agreement between the model structure and experimental structure of similar size).
Fig. 6.
Determination of active site using CASTp server. The largest active site was found in the areas with 74.975 and volume of 24.651 amino acids.
Table 1.
The physicochemical properties of the NP_464414.1 protein estimated by ProtParam
Descriptions Value
No. of amino acids 115
Molecular weight (Da) 12,759.85
Theoretical PI 6.37
No. of positively charged residues 16
No. of negatively charged residues 16
No. of atoms 1839
Instability Index 36.52
Aliphatic Index 110.96
Grand average of hydropathicity –0.057
Table 2.
The subcellular localization prediction of the query protein NP_464414.1
Server Final prediction
CELLO v.2.5 Cytoplasmic localization
PSORTb Cytoplasmic localization
SOUSIGramN Cytoplasmic localization
PSLpred Cytoplasmic protein
Table 3.
Result of CDD of NP_464414.1
Name Acession Description Interval E-value
PemK toxin pfam02452 PemK-like, MazF-like toxin of type II toxin-antitoxin system 5–110 2.87e-43
Table 4.
BLASTp result showing similarity between proteins
Accession No. Organism Protein name Score Protein identity (%) E-value
NP_464414.1 Listeria monocytogenes Hypothetical Protein
WP_010989608.1 Listeria monocytogenes Type II toxin-antitoxin system PemK/MazF family toxin 232 100 7.00E-77
WP_070005577.1 Listeria monocytogenes Type II toxin-antitoxin system PemK/MazF family toxin 230 98.26 4.00E-76
WP_185340554.1 Listeria seeligeri Type II toxin-antitoxin system PemK/MazF family toxin 229 97.39 2.00E-75
WP_046326403.1 Listeria seeligeri Type II toxin-antitoxin system PemK/MazF family toxin 228 97.39 4.00E-75
EAF6615127.1 Listeria monocytogenes Type II toxin-antitoxin system PemK/MazF family toxin 227 96.52 8.00E-75
WP_025279828.1 Listeria ivanovii Type II toxin-antitoxin system PemK/MazF family toxin 226 96.52 2.00E-74
WP_003761304.1 Listeria immobilis Type II toxin-antitoxin system PemK/MazF family toxin 226 97.37 2.00E-74
EFS00786.1 Listeria seeligeri FSL N1-067 Type II toxin-antitoxin system PemK/MazF family toxin 226 97.37 2.00E-74
EFR97581.1 Listeria ivanovii FSL F6-596 Type II toxin-antitoxin system PemK/MazF family toxin 224 96.49 1.00E-73
Table 5.
Ramachandran plot statistics of the target protein
Statistics No. of AA residues (%)
Residues in the most favored regions [A,B,L] 94 (92.2)
Residues in additional allowed regions [a,b,I,p] 7 (6.9)
Residues in generously allowed regions [⁓a, ⁓b,⁓l,⁓p] 1 (1)
Residues in disallowed regions 0 (0)
Total (100)
No. of non-glycine and non-proline residues 102
No. of end residues (excl. Gly and Pro) 2
No. of glycine residues (shown in triangles) 7
No. of proline residues 4
Total No. of residues 115


1. Matereke LT, Okoh AI. Listeria monocytogenes virulence, antimicrobial resistance and environmental persistence: a review. Pathogens 2020;9:528.
crossref pmid pmc
2. Allen KJ, Walecka-Zacharska E, Chen JC, Katarzyna KP, Devlieghere F, Van Meervenne E, et al. Listeria monocytogenes: an examination of food chain factors potentially contributing to antimicrobial resistance. Food Microbiol 2016;54:178–89.
3. Iwu CD, Okoh AI. Characterization of antibiogram fingerprints in Listeria monocytogenes recovered from irrigation water and agricultural soil samples. PLoS One 2020;15:e0228956.
crossref pmid pmc
4. Cossart P, Archambaud C. The bacterial pathogen Listeria monocytogenes: an emerging model in prokaryotic transcriptomics. J Biol 2009;8:107.
crossref pmid pmc
5. Ramaswamy V, Cresence VM, Rejitha JS, Lekshmi MU, Dharsana KS, Prasad SP, et al. Listeria: review of epidemiology and pathogenesis. J Microbiol Immunol Infect 2007;40:4–13.
6. Danion F, Maury MM, Leclercq A, Moura A, Perronne V, Leotard S, et al. Listeria monocytogenes isolation from urine: a series of 15 cases and review. Clin Microbiol Infect 2017;23:583–585.
crossref pmid
7. Dufour C. Application of EC regulation no. 2073/2005 regarding Listeria monocytogenes in ready-to-eat foods in retail and catering sectors in Europe. Food Control 2011;22:1491–1494.
8. Iwu CD, Okoh AI. Preharvest transmission routes of fresh produce associated bacterial pathogens with outbreak potentials: a review. Int J Environ Res Public Health 2019;16:4407.
crossref pmid pmc
9. Cossart P, Toledo-Arana A. Listeria monocytogenes, a unique model in infection biology: an overview. Microbes Infect 2008;10:1041–1050.
crossref pmid
10. Gozel B, Monney C, Aguilar-Bultet L, Rupp S, Frey J, Oevermann A. Hyperinvasiveness of Listeria monocytogenes sequence type 1 is independent of lineage I-specific genes encoding internalin-like proteins. Microbiologyopen 2019;8:e00790.
crossref pmid pmc pdf
11. Doumith M, Buchrieser C, Glaser P, Jacquet C, Martin P. Differentiation of the major Listeria monocytogenes serovars by multiplex PCR. J Clin Microbiol 2004;42:3819–3822.
crossref pmid pmc pdf
12. Orsi RH, den Bakker HC, Wiedmann M. Listeria monocytogenes lineages: genomics, evolution, ecology, and phenotypic characteristics. Int J Med Microbiol 2011;301:79–96.
crossref pmid
13. Hain T, Ghai R, Billion A, Kuenne CT, Steinweg C, Izar B, et al. Comparative genomics and transcriptomics of lineages I, II, and III strains of Listeria monocytogenes. BMC Genomics 2012;13:144.
crossref pmid pmc pdf
14. Glaser P, Frangeul L, Buchrieser C, Rusniok C, Amend A, Baquero F, et al. Comparative genomics of Listeria species. Science 2001;294:849–852.
crossref pmid
15. Hurley D, Luque-Sastre L, Parker CT, Huynh S, Eshwar AK, Nguyen SV, et al. Whole-genome sequencing-based characterization of 100 Listeria monocytogenes isolates collected from food processing environments over a four-year period. mSphere 2019;4:e00252–19.
crossref pmid pmc pdf
16. Johansson J, Freitag NE. Regulation of Listeria monocytogenes virulence. Microbiol Spectr 2019;7:7.4.27.
crossref pdf
17. Travier L, Lecuit M. Listeria monocytogenes ActA: a new function for a 'classic' virulence factor. Curr Opin Microbiol 2014;17:53–60.
crossref pmid
18. Galperin MY, Koonin EV. 'Conserved hypothetical' proteins: prioritization of targets for experimental study. Nucleic Acids Res 2004;32:5452–5463.
crossref pmid pmc
19. Hawkins T, Kihara D. Function prediction of uncharacterized proteins. J Bioinform Comput Biol 2007;5:1–30.
crossref pmid
20. Cui JJ, Tran-Dube M, Shen H, Nambu M, Kung PP, Pairish M, et al. Structure based drug design of crizotinib (PF-02341066), a potent and selective dual inhibitor of mesenchymal-epithelial transition factor (c-MET) kinase and anaplastic lymphoma kinase (ALK). J Med Chem 2011;54:6342–6363.
crossref pmid
21. Nimrod G, Schushan M, Steinberg DM, Ben-Tal N. Detection of functionally important regions in "hypothetical proteins" of known structure. Structure 2008;16:1755–1763.
crossref pmid
22. Buchanan RL, Gorris LG, Hayman MM, Jackson TC, Whiting RC. A review of Listeria monocytogenes: an update on outbreaks, virulence, dose-response, ecology, and risk assessments. Food Control 2017;75:1–13.
23. Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Wheeler DL. GenBank. Nucleic Acids Res 2003;31:23–27.
crossref pmid pmc
24. Rabbi MF, Akter SA, Hasan MJ, Amin A. In silico characterization of a hypothetical protein from Shigella dysenteriae ATCC 12039 reveals a pathogenesis-related protein of the type-VI secretion system. Bioinform Biol Insights 2021;15:11779322211011140.
crossref pmid pmc pdf
25. Gasteiger E, Hoogland C, Gattiker A, Duvaud S, Wilkins MR, Appel RD, et al. Protein identification and analysis tools on the ExPASy server. In: The Proteomics Protocols Handbook (Walker JM, ed.). Totowa: Humana Press, 2005. pp. 571–607.

26. Yu CS, Chen YC, Lu CH, Hwang JK. Prediction of protein subcellular localization. Proteins 2006;64:643–651.
crossref pmid
27. Gardy JL, Laird MR, Chen F, Rey S, Walsh CJ, Ester M, et al. PSORTb v.2.0: expanded prediction of bacterial protein subcellular localization and insights gained from comparative proteome analysis. Bioinformatics 2005;21:617–623.
crossref pmid pdf
28. Hirokawa T, Boon-Chieng S, Mitaku S. SOSUI: classification and secondary structure prediction system for membrane proteins. Bioinformatics 1998;14:378–379.
crossref pmid pdf
29. Bhasin M, Garg A, Raghava GP. PSLpred: prediction of subcellular localization of bacterial proteins. Bioinformatics 2005;21:2522–2524.
crossref pmid
30. Lu S, Wang J, Chitsaz F, Derbyshire MK, Geer RC, Gonzales NR, et al. CDD/SPARCLE: the conserved domain database in 2020. Nucleic Acids Res 2020;48:D265–D268.
crossref pmid pdf
31. El-Gebali S, Mistry J, Bateman A, Eddy SR, Luciani A, Potter SC, et al. The Pfam protein families database in 2019. Nucleic Acids Res 2019;47:D427–D432.
crossref pmid
32. Smoly IY, Lerman E, Ziv-Ukelson M, Yeger-Lotem E. MotifNet: a web-server for network motif analysis. Bioinformatics 2017;33:1907–1909.
crossref pmid pdf
33. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol 1990;215:403–410.
crossref pmid
34. Combet C, Blanchet C, Geourjon C, Deleage G. NPS@: network protein sequence analysis. Trends Biochem Sci 2000;25:147–150.
crossref pmid
35. Buchan DW, Jones DT. The PSIPRED Protein Analysis Workbench: 20 years on. Nucleic Acids Res 2019;47:W402–W407.
crossref pmid pmc pdf
36. Varadi M, Anyango S, Deshpande M, Nair S, Natassia C, Yordanova G, et al. AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res 2022;50:D439–D444.
crossref pmid pdf
37. Eisenberg D, Luthy R, Bowie JU. VERIFY3D: assessment of protein models with three-dimensional profiles. Methods Enzymol 1997;277:396–404.
38. Benkert P, Biasini M, Schwede T. Toward the estimation of the absolute quality of individual protein structure models. Bioinformatics 2011;27:343–350.
crossref pmid pdf
39. Krieger E, Joo K, Lee J, Lee J, Raman S, Thompson J, et al. Improving physical realism, stereochemistry, and side-chain accuracy in homology modeling: four approaches that performed well in CASP8. Proteins 2009;77 Suppl 9:114–122.
crossref pmid pdf
40. Masuda Y, Miyakawa K, Nishimura Y, Ohtsubo E. chpA and chpB, Escherichia coli chromosomal homologs of the pem locus responsible for stable maintenance of plasmid R100. J Bacteriol 1993;175:6850–6856.
crossref pmid pmc pdf
41. Gasanov U, Hughes D, Hansbro PM. Methods for the isolation and identification of Listeria spp. and Listeria monocytogenes: a review. FEMS Microbiol Rev 2005;29:851–875.
crossref pmid
42. Xie Y, Wei Y, Shen Y, Li X, Zhou H, Tai C, et al. TADB 2.0: an updated database of bacterial type II toxin-antitoxin loci. Nucleic Acids Res 2018;46:D749–D753.
crossref pmid
43. Curtis TD, Takeuchi I, Gram L, Knudsen GM. The influence of the toxin/antitoxin mazEF on growth and survival of Listeria monocytogenes under stress. Toxins (Basel) 2017;9:31.
crossref pmid pmc
44. Aguero JA, Akarsu H, Aguilar-Bultet L, Oevermann A, Falquet L. Large-scale comparison of toxin and antitoxins in Listeria monocytogenes. Toxins (Basel) 2020;12:29.
crossref pmid pmc
45. Zhang J, Zhang Y, Zhu L, Suzuki M, Inouye M. Interference of mRNA function by sequence-specific endoribonuclease PemK. J Biol Chem 2004;279:20678–20684.
crossref pmid
46. DeLeo FR, Diep BA, Otto M. Host defense and pathogenesis in Staphylococcus aureus infections. Infect Dis Clin North Am 2009;23:17–34.
crossref pmid pmc
47. Bukowski M, Lyzen R, Helbin WM, Bonar E, Szalewska-Palasz A, Wegrzyn G, et al. A regulatory role for Staphylococcus aureus toxin-antitoxin system PemIKSa. Nat Commun 2013;4:2012.
crossref pmid pdf
48. Bleriot I, Blasco L, Delgado-Valverde M, Gual de Torella A, Ambroa A, Fernandez-Garcia L, et al. Mechanisms of tolerance and resistance to chlorhexidine in clinical strains of Klebsiella pneumoniae producers of carbapenemase: role of new type II toxin-antitoxin system, PemIK. Toxins (Basel) 2020;12:566.
crossref pmid pmc
Share :
Facebook Twitter Linked In Google+
METRICS Graph View
  • 0 Crossref
  • 0 Scopus
  • 2,473 View
  • 100 Download
Related articles in GNI


Browse all articles >

Editorial Office
Room No. 806, 193 Mallijae-ro, Jung-gu, Seoul 04501, Korea
Tel: +82-2-558-9394    Fax: +82-2-558-9434    E-mail:                

Copyright © 2024 by Korea Genome Organization.

Developed in M2PI

Close layer
prev next