In silico functional annotation of hypothetical proteins from the Bacillus paralicheniformis strain Bac84 reveals proteins with biotechnological potentials and adaptational functions to extreme environments.

Members of the Bacillus genus are industrial cell factories due to their capacity to secrete significant quantities of biomolecules with industrial applications. The Bacillus paralicheniformis strain Bac84 was isolated from the Red Sea and it shares a close evolutionary relationship with Bacillus licheniformis. However, a significant number of proteins in its genome are annotated as functionally uncharacterized hypothetical proteins. Investigating these proteins' functions may help us better understand how bacteria survive extreme environmental conditions and to find novel targets for biotechnological applications. Therefore, the purpose of our research was to functionally annotate the hypothetical proteins from the genome of B. paralicheniformis strain Bac84. We employed a structured in-silico approach incorporating numerous bioinformatics tools and databases for functional annotation, physicochemical characterization, subcellular localization, protein-protein interactions, and three-dimensional structure determination. Sequences of 414 hypothetical proteins were evaluated and we were able to successfully attribute a function to 37 hypothetical proteins. Moreover, we performed receiver operating characteristic analysis to assess the performance of various tools used in this present study. We identified 12 proteins having significant adaptational roles to unfavorable environments such as sporulation, formation of biofilm, motility, regulation of transcription, etc. Additionally, 8 proteins were predicted with biotechnological potentials such as coenzyme A biosynthesis, phenylalanine biosynthesis, rare-sugars biosynthesis, antibiotic biosynthesis, bioremediation, and others. Evaluation of the performance of the tools showed an accuracy of 98% which represented the rationality of the tools used. This work shows that this annotation strategy will make the functional characterization of unknown proteins easier and can find the target for further investigation. The knowledge of these hypothetical proteins' potential functions aids B. paralicheniformis strain Bac84 in effectively creating a new biotechnological target. In addition, the results may also facilitate a better understanding of the survival mechanisms in harsh environmental conditions.

Introduction

Bacillus paralicheniformis is a newly discovered species in the Bacillus genus [1]. It is phylogenetically closely related to B. licheniformis [1, 2]. In the biotechnology sector, B. licheniformis has already been employed to produce biochemicals, enzymes, antibiotics, and other products [1, 3]. Several current investigations have indicated that B. paralicheniformis species have a strong potential for the biosynthesis of antimicrobial compounds [4, 5]. One of the strains can also inhibit plant pathogenic microbes [6]. In this way, B. paralicheniformis may be of biotechnological relevance but still, it has remained largely unexplored.

B. paralicheniformis is a gram-positive, facultatively anaerobic, rod-shaped, motile, and endospore-forming Bacillus species [1]. The B. paralicheniformis strains are found in a variety of habitats, including soil, freshwater, marine, and niches associated with food [1, 4, 6]. This strain is adapted to survive in extreme conditions such as high osmolarity which provides it with metabolic capabilities similar to industrial strains [4]. The B. paralicheniformis strain Bac84 was isolated from the Red Sea which is an ecosystem of harsh, extremely saline, and high temperature [4]. Hence, this strain may be a potential microbial cell factory to produce both thermo-tolerant and osmotolerant enzymes that may be more suitable for use in industry as well as able to survive frequent exposure to these extreme conditions [7]. This particular strain showed promising antibacterial activity against three-indicator pathogens: Salmonella typhimurium, Staphylococcus aureus, and Pseudomonas syringae [8]. Additionally, one very closely related strain (B. paralicheniformis Strain GSFE7- 95% genome sequence similarity) has been reported to be involved in the promotion of halotolerant plant growth [9]. Besides, another closely related strain (B. paralicheniformis Strain CCMM B940 which shares 98.94% identity with B. paralicheniformis strain Bac84) can break down complex polysaccharides [10].

The genome of B. paralicheniformis strain Bac84 has been fully sequenced and published [4]. According to the National Center for Biotechnology Information database—NCBI repository, it encodes 4,237 proteins (CP023665.1). However, 414 coding sequences have been anticipated to encode for proteins without any expression and function-associated data. These sequences have been assigned as “hypothetical”. These hypothetical proteins (HPs) have constituted a considerable portion (9.8% of the total number of proteins) of the genome. Functional annotation is necessary for these HPs to find the possible roles in the cell which can lead to an understanding of new structures, and functions in this bacterium. Several studies have revealed the expression of HPs [11-13]. Homology-based gene annotation has been assigned previously to predict the unknown functions of numerous HPs in several organisms [14-18]. Additionally, numerous bioinformatics tools are available to determine the functions of the HPs such as Pfam, InterPro, CATH, SUPERFAMILY, SMART, CDD-BLAST SCANPROSITE, and many more [17-23]. Moreover, the STRING database is also an essential way of protein-protein interaction (PPI) determination to understand the protein functions in a biological network [24-26]. Hence, the PPI study of these HPs can lead to inferences about their biological functions [27]. Furthermore, the tertiary structure modeling through homology searches utilizing the SWISS-MODEL server is important to find the function of unknown proteins [28].

In this study, we aimed to determine the functional roles of the HPs from the B. paralicheniformis strain Bac84. We utilized an annotation-based workflow to determine the functions of the HPs for the identification of new biotechnologically important proteins as well as novel proteins contributing to the survival of this bacterium in extreme environments. We successfully identified potential target proteins in the B. paralicheniformis strain Bac84. It may eventually be possible to develop new biotechnological applications based on further experimental validation of these identified proteins.

Materials and methods

Sequence retrieval

The genome of B. paralicheniformis strain Bac84 was used (CP023665.1). It has 4,376,831 bp in length containing 4413 genes. It encodes 4,237 proteins and 414 are HPs among those (https://www.ncbi.nlm.nih.gov/genome/). The HPs’ sequences were obtained in FASTA format for the analyses (S1 Table).

Functional annotation of hypothetical proteins

Functional annotation was applied to the HPs to reveal their functions (Fig 1). Firstly, several publicly available tools and databases (Pfam, InterPro, CATH, SUPERFAMILY, SMART, SCANPROSITE, and CDD-BLAST) are listed in the S2 Table were used. These bioinformatics tools and databases assist to find the conserved domains and afterward categorize the proteins. Pfam [29], InterPro [30], SUPERFAMILY [20], and SCANPROSITE [31] were employed to interpret the functional roles of the HPs based on similarity. Additionally, SMART and CATH were used to search for functions of our HPs based on the domain architecture and to categorize the domains within the structural hierarchy respectively [32, 33]. Conserved Domain Database (CDD) was utilized to search conserved domains [34]. All these analyses were performed in the default parameters and the results are given in detail in the S3 Table. These web tools showed distinctive results and to perform downstream analyses, 37 HPs were filtered as these HPs exhibited functional domains or motifs in at least three of the bioinformatic tools (S4 Table).

Fig 1

Workflow representing the overall design of the study.

The tasks listed in the green outlined boxes were applied only after the analyzed HPs showed the same function in at least three different bioinformatics tools.

We also have predicted the gene ontology of all the HPs using Argot2.5 (Annotation Retrieval of Genel Ontology Terms) [35] (S5 Table) and the findings are illustrated in Fig 2.

Fig 2

The gene ontology of all the 414 HPs.

(A) The distribution of the HPs among the three gene ontology categories. (B) Graph of the cellular components. (C) Graph of the biological processes. (D) Graph of the molecular functions. Here, the distribution of GO terms is presented on the Y axis and the area of the bubbles is relative to the number of proteins found in each category.

We further used the FASTA sequences of the selected 37 HPs for manual annotation utilizing the Basic Local Alignment Search Tool (BLAST) [36]. Here, the NCBI nonredundant database and hits with an identity ≥ 90% were employed (S6 Table).

In addition, we used BPROM (in the default settings) to perform the promoter analysis of the 37 proteins [37]. All the DNA sequences were downloaded from the NCBI database. The Shine Dalgarno (SD) sequence was manually assigned in this case.

The DEG database was utilized to detect the essential genes with the screened 37 HPs [38]. The search was performed against the available genomes of Bacillus subtilis 168, and Bacillus thuringiensis BMB171 in the default parameters (S7 Table).

Prediction of physicochemical parameters and the Sub-cellular localization

The physicochemical parameters of the selected 37 HPs were theoretically measured using Expasy’s Protparam server [39]. The predicted properties such as molecular mass, isoelectric point (pI), extinction coefficient, the total number of +/- residues, extinction coefficient, instability index, aliphatic index, and grand average of hydropathicity (GRAVY) were determined.

Determination of the protein cellular localization helps to estimate its function. In this study, PSORTb [40] and CELLO [41] were used to identify the proteins’ location in the cell. PSORTb includes both lab experimental data sets as well as in silico predictions. In contrast, CELLO employs a two-level support vector machine (SVM) based system.

Furthermore, SOSUI [42], HMMTOP [43], TMHMM [44], and SignalP [45] were utilized to predict the transmembrane helices as well as determine the presence of signal peptide cleavage sites. All the results of these characterization analyses were listed in the S8 Table.

Protein-protein interaction analysis

In this study, STRING software [24, 26] was used to predict interactive partners using a confidence score above 0.7 for ensuring the dependability of the predictions (S9 Table). We had to use the Bacillus licheniformis DSM 13 reference genome to generate the interaction networks as the dataset for any strain of B. paralicheniformis has not been available yet. Both the physical and functional associations were applied to compute the networks. The Cytoscape was used to visualize the interaction networks (S1 Fig).

Tertiary structure prediction

Tertiary protein structures give significant insights into the molecular basis of protein function [46]. We used the SWISS-MODEL server [28] for homology modeling of the target proteins where only templates with an identity ≥ 30% were considered (S10 Table). The UCSF Chimera-1.16 was used to visualize the 3D structures as well as to perform the structural alignments (Fig 3A & 3B). Additionally, several predicted structures were also compared with the AlphaFold models to validate the structures.

Fig 3

A & B. Tertiary structures analysis. Three-dimensional structures were modeled by the SWISS-MODEL server reliably using the templates with higher coverage, more than 30% of identity, and higher GMQE scores along with Ramachandran Favored percentages ≥90%. Only the templates determined by the X-ray crystallography with high resolution were used. The known proteins and the modeled structures are indicated in red and blue colors respectively. The proteins are orientated using the Chimera MatchMaker according to the optimal superposition of the matching residues.

Performance assessment

We performed a ROC- receiver operating characteristic analysis with 100 functionally characterized proteins (S11 Table) from the genome of the B. paralicheniformis strain Bac84 to check the accuracy of the anticipated functions of our studied HPs [47]. These proteins were functionally checked using the seven databases used for our studied HPs.

For the interpretation, the binary numerals “1” and “0” were applied as the true positive and true negative respectively. The integers ‘2’, ‘3’, ‘4’, and ‘5’ were used to assess the prediction efficacy. After that, these datasets were submitted to the Web-based Calculator and calculated the specificity, sensitivity, accuracy, and the ROC area of each tool employed earlier for functional prediction of the HPs.

Results and discussion

Analysis of The hypothetical proteins from the B. Paralicheniformis strain Bac84 genome

DNA sequencing technologies are advancing, and high throughput sequencing technologies have allowed a significant number of bacterial genome sequencing. Sequence homology techniques are commonly used for the annotation of genes [48]. Nevertheless, these homology techniques alone are not always able to predict functions accurately and lead to false annotations [49]. Hence, multiple bioinformatic tools must be employed to assign functional annotations of HPs. In this study, we applied a number of effective tools and databases to do the annotation of HPs from the B. paralicheniformis strain Bac84.

We first identified the domains of the HPs which are structural, functional, and evolutionary parts of a protein, therefore providing the functional role of a protein [50]. We extensively analyzed all the 414 HPs sequences using Pfam, InterPro, CATH, SUPERFAMILY, SMART, SCANPROSITE, and CDD-BLAST (S3 Table). The results were evaluated aiming to assign functions to HPs and it revealed 37 HPs which demonstrated similar functions from three or more programs listed in Table 1. In this way, functional annotations were assigned with strong confidence to the HPs. For the rest HPs (n = 377), domains were recognized from less than three mentioned bioinformatic tools which are needed further assessments.

Table 1

Hypothetical proteins functionally annotated from the B. paralicheniformis strain Bac84.

No.	HP ID	Inferred function
1	WP_158700706.1	Metal-dependent hydrolase
2	WP_230368348.1	Catalytic core DNA breaking-rejoining enzymes
3	WP_095290960.1	RNA polymerase sporulation sigma factor SigK
4	WP_026579962.1	YhzD-like protein
5	WP_224146215.1	Response regulator aspartate phosphatase
6	WP_095291534.1	The YqzH-like protein family
7	WP_003179940.1	The YgaB-like protein family
8	WP_020449960.1	Inner membrane protein YiaA-like
9	WP_105981192.1	YqaH-like protein
10	WP_020453622.1	Bacteriophage A118-like, holin
11	WP_006638778.1	Metal-responsive transcriptional regulator
12	WP_003180123.1	Sigma-M inhibitor protein YhdK
13	WP_025810847.1	Streptogramin lyase
14	WP_020450411.1	RlpA-like domain superfamily
15	WP_105980832.1	Phenylalanyl-tRNA synthetase
16	WP_009328837.1	Flavin-phosphopantothenoylcysteine decarboxylase/Flavin prenyltransferase
17	WP_003180732.1	Pathogenicity locus—Putative mitomycin resistance proteins
18	WP_199792123.1	YetA-like protein
19	WP_020451108.1	ESAT-6-like superfamily
20	WP_020451191.1	YkyB-like protein
21	WP_026579751.1	Transcription regulator DksA-related
22	WP_105980957.1	Nudix_Hydrolase super family
23	WP_023857538.1	YhzD-like protein
24	WP_020451915.1	Heat Shock protein (Hsp20 proteins)
25	WP_020452052.1	HesB-like domain superfamily
26	WP_026579290.1	YqfQ-like protein
27	WP_020452371.1	RmlC-like cupin superfamily
28	WP_234026546.1	Chromosome segregation protein SMC
29	WP_023855527.1	Response regulator aspartate phosphatase
30	WP_105981186.1	Putative phage metallopeptidase
31	WP_105981199.1	Alpha/Beta hydrolase fold
32	WP_003185659.1	Swarming motility protein SwrA
33	WP_023857076.1	Acyl-CoA N-acyltransferase
34	WP_023856950.1	BslA (Biofilm surface layer A)
35	WP_026580354.1	Immunity protein WapI-like/YxiJ super family
36	WP_023856884.1	Six-hairpin glycosidase superfamily
37	WP_020453535.1	Prephenate dehydratase

Further, the GO terms were determined using the ARGOT2.5 server [35] that provides results based on the confidence scores. 133 HPs have GO term predictions among the 414 targets and the distribution among the GO categories was depicted in Fig 2. The rest of the HPs with no GO terms can be found in the S5 Table. Among the three categories, the largest cluster was cellular components followed by molecular functions and biological processes. We found seven different GO terminologies in the cellular component category including 45 having membrane function (Fig 2B). Although studying membrane proteins is difficult, it is well known that many membrane proteins play important roles in gram-positive bacteria’s physiology [51, 52]. The membrane proteins come first in the interaction among cells and the environmental stresses [53]. These membrane HPs need to be analyzed as these may have considerable roles in the survival mechanism of the B. paralicheniformis strain Bac84 in extreme environments. For biological processes, twenty-five different GO terminologies were identified, mostly associated with transcription and DNA-related processes (Fig 2C). Transcriptional regulation is a crucial process for a living organism. The cell can respond to intracellular and external signals such as environmental cues or nutritional insufficiency through this transcription-controlling process. According to the GO annotation, the molecular function category showed twenty-one GO terminologies; mostly indicated to several enzymatic functions, and the others related to protein binding (Fig 2D). Here, the DNA and protein interactions (sequence-specific and sequence non-specific binding) are involved in many biological processes including regulation of transcription, DNA repair, DNA modification, etc. [54]. Additionally, the proteins with enzymatic functions have potential biotechnological applications [55, 56].

Additionally, 15 HPs carried homologous sequences with described functions were found in BlastP analysis whereas the remaining HPs were matched to uncharacterized family proteins and/or hypothetical proteins (S6 Table). All the 15 HPs that matched with functional proteins in the BlastP analysis were functionally similar to the anticipated functions. We also analyzed the promoter regions of all 37 proteins. Promoter segments are required for the start of transcription at a certain genomic site. Several conserved regions such as the Pribnow box and -35 box were determined along with the SD sequence (S2 Fig). These conserved sequences are vital for the binding of RNA polymerase and ribosome [57, 58]. The SD-sequence initiates the translation process and has a huge influence on protein expression levels [59, 60]. It was found that all 37 proteins have SD sequences. The findings from the promoter analysis of the 37 proteins indicate that further experimental validation is worth pursuing. We did not find any study regarding the experimental transcription data sets of the organism.

Furthermore, the DEG database was utilized to predict fundamental genes (S7 Table). This database adapts both in vitro and in vivo experiments to detect fundamental genes which are essential for cellular machinery [38]. Though different challenging lab experiments were used to detect the essential genes such as RNA interference, gene knockouts, and transposon mutagenesis [61], this DEG database offers an alternative for predicting essential genes. In our analysis, we did not find any essential genes among the targeted 37 HPs.

Physicochemical characterization and subcellular localization

To evaluate the physicochemical characteristics and their cellular distribution the sequences of the screened 37 HPs were used (S8 Table). Most of the studied proteins had molecular weight (MW) values over 10000 Da. Proteins with a lower MW (< 10000 Da) need special modifications for analysis in the SDS-PAGE system [62]. Hence, the first few HPs with lower MW require special attention to perform further lab experiments. The pH value of a protein at which it carries no net electrical charge is known as isoelectric point pI. For our selected HPs, it ranged from 4.4 to 10.48 and 11 proteins have acidic nature (pI < 7), whereas others were found to be basic. Along with the MW, the pI also helps in the laboratory analysis of proteins [63].

The aliphatic index (AI) is used to evaluate the protein thermostability and our HPs were in the range of 55.19–145.1. The range of temperatures at which a protein will be stable increases with increasing AI values [64]. Protein WP_003180123.1, associated with growth and survival after salt stress showed the highest value of 145.1. The instability index (II) was applied to get the idea regarding in vitro protein stability. 15 HPs were considered to be unstable, and 22 HPs were stable. The cut-off values >40 and <40 were used to categorize stable and unstable proteins, respectively [65]. The GRAVY indicates the interactive nature of a protein with water [66]. Among these 37 HPs, only four (WP_158700706.1; WP_003180123.1; WP_023857538.1 and WP_020453535.1) showed positive values which indicates that these might be hydrophobic.

Moreover, the cellular localization of proteins is vital for their biological functions in a specific environment [67-69]. Among the 37 HPs, most of the proteins were determined as cytoplasmic. Several cytoplasmic proteins are in the regulation of several functional processes including biosynthesis, regulatory activities, and transport which may help environmental bacteria to compete with the neighboring organisms in the same ecological niche [70]. Additionally, we only found 4 proteins to have signal peptides that are critically related to protein secretion [71].

Protein-protein interactions

To determine the interaction partners of the HPs, we performed a protein-protein interaction analysis [72]. In this study, protein WP_095290960.1, RNA polymerase sporulation sigma factor SigK showed a very strong interaction (score 0.930) with the sporulation stage IV protein A (SpoIVA) which is involved in sporulation [73]. WP_006638778.1 interacted with EndoA–a putative RNase (score 0.988) with functional endoribonuclease activity [74]. WP_009328837.1 was found to interact with the YacB (score 0.987) which catalyzes the phosphorylation of pantothenate [75]. The protein WP_023855527.1 showed interaction with the Raca protein which is required for the formation of axial filaments [76]. All these findings along with the other predictions (S9 Table and S2 Fig) strengthened our functional predictions.

Tertiary structure predictions

X-ray crystallography has become a robust approach to determining novel protein structures [77]. The functional annotation methods in combination with the protein structure analysis are evident to lead to the interpretation of uncharacterized proteins [78, 79]. In this study, we employed the protein structure homology-modeling server SWISS-MODEL to have the tertiary structures and used the UCSF Chimera software to visualize the models. Next, we compared the structures of known proteins with the modeled structures to check the degree of similarity (Fig 3A & 3B).

We successfully build the three-dimensional models for 9 HPs with identity above 30% and the details were listed in the S10 Table. We also checked the quality of the models with the Ramachandran plots and scores (S10 Table and S3 Fig). Structural comparisons were performed based on the Needleman-Wunsch algorithm [80]. We observed different percentages of structural similarities between the models and known proteins (S10 Table). The alignment results from the structural comparisons were shown in S4 Fig. The structural data collected for several HPs has validated the precise functional annotation. For instance, WP_105981199.1 and WP_023856950.1 showed high identities and resolutions which were functionally annotated as Alpha/Beta hydrolase and BslA (Biofilm surface layer A) respectively. The structures built for these two proteins were determined by X-ray crystallography from two Bacillus sp. and those two template proteins have similar functions as we predicted in this study. In this way, proteins with similar sequences usually exhibit similar functions. Proteins dissimilar to current PDB entries may correspond to novel functions. In addition, several final protein models were visualized using the Chimera 1.16 and compared to the predicted models suggested by AlphaFold (S5 Fig). We used the AlphaFold since Alpha-Fold has been demonstrated to be more accurate than Nuclear magnetic resonance spectroscopy (NMR) [81]. The findings showed similarities among the predicted models by Swiss-Model vs AlphaFold.

ROC performance measurement

The availability of genome sequences is increasing which is also allowing more scope to do the computational protein analysis. As these analysis methods are solely dependent on autonomic computing, the accuracy of these methods should be high. The ROC analysis is a broadly applied technique for evaluating the tool’s accuracy. The employed pipeline had an average accuracy of 98 percent (Table 2), and the ROC analysis’s findings supported the strong dependability of the tools used.

Table 2

ROC results of the tools used in this study.

Software	Accuracy (%)	Sensitivity (%)	Specificity (%)	ROC area
Pfam	99.0	98.0	100	0.99
InterPro	100.0	100.0	100.0	1
CATH	100.0	100.0	100.0	1
SUPERFAMILY	96.0	94.7	100.0	0.99
SCANPROSITE	97.0	93.8	100.0	0.99
SMART	98.0	97.0	100.0	1
CDD-BLAST	96.0	65.9	100.0	0.985

Proteins with biotechnological potentials

We found several proteins that can be used for biotechnological applications.

WP_158700706.1 was predicted as a Metallo-dependent hydrolase (the amidohydrolase superfamily). This group includes numerous hydrolytic enzymes with a varied spectrum of substrates and reactions. The microbial obtained amidohydrolase possesses extensive biotechnological applications that include cosmetics, food, and therapeutics, especially as an anticancer/anti-proliferative agent [82, 83]. This hydrolase group also contains amylases and α-amylase derived from B. licheniformis, B. amyloliquefaciens and B. stearothermophilus which has been commercially used in fermentation, paper, and textiles industries [84, 85].

Protein WP_020453622.1 is a Bacteriophage A118-like, holin that involves the lysis of bacterial membrane [86]. These holins can be utilized for controlled pore formation and can promote the release of the desired products. Microorganisms are used and improved for the industrial manufacture of a wide range of substances, including pharmaceuticals and biofuels. These target compounds can be sequestered inside the cell causing toxic effects to the chassis without an efficient active efflux system. In this case, Holin-mediated cell lysis offers an efficient releasing mechanism [87]. One of the rate-limiting steps is releasing products from the microbial host for biotechnology-based chemical production on an industrial scale. Holins can provide an affordable and effective method of product release in many instances where the use of mechanical disruption or solvent extraction increases the cost of production [88]. Liu and Curtiss applied phage holin/endolysin cassettes containing a nickel-inducible signal transduction system into the chromosome of Synechocystis sp. strain PCC6803 which is being developed for biofuel production [89]. They successfully eliminated the chemical or mechanical removal step by just adding nickel to the culture medium resulting in cell lysis. Another group utilized a light-inducible lytic mechanism in the same cyanobacterium for similar purposes [90].

The protein WP_009328837.1 was predicted as Flavin-containing phosphopantothenoylcysteine decarboxylase which is involved in coenzyme A (CoA) biosynthesis [91]. CoA is a crucial cofactor involved in many metabolic processes including secondary metabolites production. These distinctive features make CoA an economically significant chemical compound in the cosmetic, and therapeutic industries [92]. Hence, the catalytic abilities of this enzyme make it of immense biotechnological significance.

The protein WP_020452371.1 is in the RmlC-like cupin superfamily and RmlC is a dTDP-sugar isomerase enzyme (dTDP—deoxythymidine diphosphates). This enzyme is involved in the L-rhamnose synthesis, commonly found in bacteria and plants [93, 94]. This sugar getting more interest due to its wide range of substrate specificity and its excellent potential for various unique sugars syntheses such as D-allose, D-cellulose, L-mannose, L rhamnulose, L-spotose, and L-talose [95]. Besides, rhamnose is combined with lipids to form rhamnolipids that can be used as potential biosurfactants [94].

The protein WP_105981199.1 contains an α/β-hydrolase fold that includes proteases, lipases, peroxidases, esterase, epoxide hydrolases, dehalogenases, and many others [96]. Therefore, this protein can be studied further to uncover its actual functionality as several hydrolases are being used in industrial processes [56]. Additionally, an α/β-hydrolase fold protein was also studied which is involved in the cyclic oligopeptide antibiotic ‘thiostrepton’ biosynthesis [97].

The protein WP_023857076.1 carries a structural domain found in numerous acyl-CoA acyltransferases including the N-acetyl transferase (NAT) [98]. Several NATs from Bacillus sp. Have shown the capability to metabolize xenobiotic compounds that are highly toxic contaminants of groundwater and soils [99]. This study showed that a class of industrial contaminants or by-products of agrochemicals named “Arylamines” can be converted into less toxic states by Bacillus NATs. Hence, our WP_023857076.1 protein should be studied further to find out its bioremediation potential. Additionally, a synthetic N-acetyltransferase (MAT—methionine sulfone N-acetyltransferase) from a bacterial source was utilized to successfully design herbicide “Phosphinothricin” -resistant rice and Arabidopsis [100].

Different glycosyltransferases transfer sugar parts from donor molecules to acceptors to form glycosidic bonds and involve in disaccharides, oligosaccharides, and polysaccharides biosynthesis. Several microbial glycosyltransferases are frequently applied in food processes such as in the shelf-life improvement of bakeries, production of glucose, fructose, or dextrins, lactose hydrolysis, food pectins modification, and many others [101, 102]. In our study, protein WP_023856884.1 has the catalytic domain of the Six-hairpin glycosidase superfamily. To use this class of enzymes in different industrial conditions several enzymes functional in alkaline/acidic pH and/or at high temperatures have been discovered from various microorganisms [103-105]. In several studies, bacterial glycosidases were characterized to improve human health and the treatment of different diseases [106, 107].

The WP_020453535.1 was anticipated to be a prephenate dehydratase that is involved in the biosynthesis of phenylalanine and phenylalanine is an essential amino acid for animals. Recently, the interest in microbial production of L- phenylalanine has increased [108]. It has been widely used in food and feeds as a taste and aroma enhancer, in pharmaceuticals as the drug’s building block, as well as used in cosmetics as an ingredient [109, 110].

Proteins with adaptational functions to extreme environments

In this study, we identified 12 HPs that may have a significant role for B. paralicheniformis in the adaptation to extreme environments.

Sporulation aids bacterial survival in extreme environments by limiting active growth [111]. We found protein WP_095290960.1 as RNA polymerase sporulation sigma factor SigK which is involved in the gene expression controlling during sporulation [112]. Two HPs (WP_224146215.1 and WP_023855527.1) were identified to be the aspartate phosphatase, which regulates the phosphorelay for sporulation initiation by dephosphorylating Spo0F-P [113]. In this way, these HPs can be predicted to play crucial roles in adaption, and survival in extreme environments.

The protein WP_006638778.1 is a metal-responsive transcriptional regulator which can be engaged in the homeostasis and metabolism of any specific metal. These metal-responsive transcriptional regulators allow mechanisms for selective metal ion accumulation and utilization as well as tightly regulate intracellular metal trafficking mechanisms [114]. Metals can be limited in the environment or can be in high amounts that cause toxicity in extreme environments. Hence, a metal-responsive transcriptional regulator protein might be essential to the microorganism for the evolution and adaptation in that specific extreme environment [115]. Likewise, WP_026579751.1 is related to the transcription regulator DksA. It is an RNA polymerase-binding transcription factor and is involved in different stress conditions, including nitrosative stress, nutritional shortage, and other environmental stresses [116, 117]. So, this HP can be taken part in extreme environmental adaptations.

We detected a sigma-M inhibitor protein (WP_003180123.1). The sigma-M (YhdM) gene is essential for growth and survival in salt stress conditions [118]. Our predicted Sigma-M inhibitor WP_003180123.1 might play role in salt stress adaptation similarly to a previous study [119].

Protein WP_105980957.1 contains a Nudix hydrolase domain that hydrolyzes intracellular nucleotides, regulates their levels, and removes potentially toxic derivatives [120]. Some superfamily members can degrade mutagenic, oxidized, and damaged nucleotides that may occur due to exposure to extreme environments [121].

As mentioned earlier, WP_023857076.1 carries a structural domain found in numerous acyl-CoA acyltransferases including- GCN5-related N-acetyltransferases (GNAT) and Glycine N-acyltransferase [122]. The proteins from these classes were studied and found to be involved in the adaptation to diverse environmental stress conditions including high salinity, pH tolerance, nutrient stress, etc. [123, 124].

Small Heat shock proteins are abundant molecular chaperones that counteract the aggregation of protein upon stress-induced unfolding [125]. We identified protein WP_020451915.1 as a heat shock protein (Hsp20). Several studies showed that Hsp20 responds to different environmental stresses including severe heat, hydrogen peroxide, desiccation, and osmotic shocks [126-129]. Therefore, WP_020451915.1 might have adaptational functions to extreme environments.

The HesB-like domain is observed in several microbial nitrogen fixation proteins that are associated with FeS-cluster assembly [130]. Previous studies found that proteins having a HesB-like domain are involved in different metal resistance and thermal stress conditions [131, 132]. HesB-like domain-containing protein WP_020452052.1 might also play role in survival in the extreme environment specifically in metal-rich or metal deficient conditions.

The WP_003185659.1 protein was identified as a swarming motility protein SwrA which is a transcription factor. It drives the fla/che operon, which encodes the components of the flagella, and causes swarming motility [133]. Another study showed that SwrA is involved in bacterial motility [134] and bacterial motility might be significant in extreme temperatures [135].

The WP_023856950.1 protein was predicted as a biofilm surface layer A (BslA) protein which acts as a hydrophobin and participates in biofilm assembly [136]. Certain microorganisms have great resistance to environmental challenges because of biofilm development [137-139]. Therefore, this protein might be crucial for adaptation to harsh environments.

Conclusions

Protein macromolecules are involved in numerous biological processes. Hence, functional annotation of proteins is crucial. An in silico approach was employed in this study to attribute functional annotation of HPs from the B. paralicheniformis strain Bac84 genome. We predicted the functions of 37 HPs from this bacterium. The determination of physicochemical parameters and subcellular localization were effective to understand the specific properties of the annotated proteins. The PPI and tertiary structures of these proteins were also explored which assisted to obtain more understanding of the annotated proteins. Several protein structures were also validated by the AlphaFold protein modeling. We identified several proteins with biotechnological potentials as well as proteins having the possibility to be involved in extreme environmental adaptation of the B. paralicheniformis strain Bac84. Moreover, the findings of this strategy suggested that it can be utilized to perform the predictive annotations of unknown proteins. The combination of such in-silico analysis with the proper lab experiments was successful to obtain functional annotations of HPs from different organisms [140-142]. Furthermore, the results also open prospects for further research of this bacterium for biotechnological applications.

Protein-protein interaction networks obtained from STRING analysis.

Networks are visualized using Cytoscape.

(PDF)

Click here for additional data file.

Promoter analysis of the 37 proteins using BPROM.

(PDF)

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Ramachandran plots for the 3D models of the 9 proteins by the SWISS-MODEL serve.

(PDF)

Click here for additional data file.

Alignment results from the superposition analysis.

(PDF)

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Comparison of the structures predicted by AlphaFold and Swiss-Model.

(PDF)

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All the hypothetical proteins from the B. paralicheniformis strain Bac84.

(XLSX)

Click here for additional data file.

List of bioinformatics tools and databases used.

(XLSX)

Click here for additional data file.

Annotation dataset results for the 414 hypothetical proteins submitted to the workflow with Pfam, InterPro, CATH, SUPERFAMILY, SCANPROSITE, SMART, and CDD-Blast.

(XLSX)

Click here for additional data file.

List of selected HPs from the B. paralicheniformis strain Bac84.

(XLSX)

Click here for additional data file.

GO terms by Argot2.5 for all the HPs.

(XLSX)

Click here for additional data file.

Results of the BlastP search for similar sequences against the non-redundant (nr) database.

(XLSX)

Click here for additional data file.

Result of essential gene prediction using DEG database.

(XLSX)

Click here for additional data file.

List of predicted physicochemical parameters, sub-cellular localization, and prediction of transmembrane helices for the selected 37 HPs.

(XLSX)

Click here for additional data file.

Protein-protein interactions analyses of the 37 HPs.

(XLSX)

Click here for additional data file.

Tertiary structural information of HPs from B. Paralicheniformis strain Bac84.

(XLSX)

Click here for additional data file.

Dataset of functional annotation for 100 functionally known proteins from B. paralicheniformis strain Bac84 using the same pipeline used for the HP prediction.

(XLSX)

Click here for additional data file.

7 Sep 2022

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Reviewer #1: The work presented by Rahman and colleagues provides an interesting approach for annotating hypothetical proteins in the genome of Bacillus paralicheniformis strain bac84. The use of well-established tools is interesting and relevant. The findings, this reviewer agrees that they have both basic biology and biotechnology interests. The manuscript has some issues in the writing that a thorough revision can fix. Overall, this reviewer thinks that the approach is sound and the results interesting. However, in the following lines, I respectfully provide some concerns regarding how the results are shown and described in the manuscript. I hope they are useful for the authors and may help assess important aspects of the study.

Overall, the approach reported here is sound, using well-established tools that are valid and relevant for assessing sequence features that may stand out, and thus, assessing the functional role of proteins of unknown function is important. The main concern of this reviewer regarding the manuscript is that the discussion is highly speculative regarding some hypothetical proteins annotated in this study. The discussion can be focused on searching which genes contain a clearer homolog along with good structural comparisons (like structural alignments, authors can either generate the model or use those available in the AlphaFold2 database at EBI) to assess the extent of both homology and relevant residues/domains for each hypothetical protein. This strengthens the workflow used in this manuscript. This reviewer suggests using the metabolic enzymes found, for example, WP_020453535.1, and comparing it with other similar enzymes. Using structural alignments, authors may confirm the fold and critical catalytic residues, thus confirming the success of the analysis.

Additionally, with my previous comment, I suggest adding in the introduction or in the discussion some features of this particular strain, especially those regarding the known phenotypes it displays, such as biofilm formation, resistance or sensitivity to antibiotics or heavy metals and all that is known about its physiology. Please, check all the available literature supporting the possible role of these hypothetical proteins in the physiology of this strain.

By checking the hypothetical proteins in Supplementary Table 1, why authors did not discard very short sequences? Can these be annotation artifacts in this and other genomes of close species? Is there any suggestion that these proteins may be real ORFs such as promoter or regulatory sequences? This study will greatly benefit from including the promoter analysis of the 37proteins with a final annotation and support that further experimental validation is worth pursuing. This reviewer also thinks lines 201-202 are unnecessary; why these small proteins should gain focused attention?

In lines 217-220, the authors make a statement that is hard to follow in the figures presented in this manuscript. I kindly request to provide a figure with the genes involved in response to environmental cues. Also, I recommend toning this statement down since no experimental validation is provided for the hypothetical proteins analyzed here.

Several hits (Table S6) seem to be related to phage integrases. Are these proteins in integrons or remanent phages? If they are, this reviewer thinks that additional analysis should be conducted, such as G+C bias with the rest of the genome and codon usage (CAI) analysis to verify if these genes are horizontally acquired and perhaps further analyze them in specific phage databases for enzymatic or structural features more associated with phages.

In the conclusion, I also think that the statement that "… this strategy provided us with excellent results…" is too optimistic. Of 414 proteins, 37 are just a fraction of the unknown proteins. I kindly invite the authors to tone down the manuscript.

In the tertiary structure prediction section, Swiss-model is a powerful tool, but for some time now, there have been open, collaborative notebooks for using AlphaFold2. In this reviewer's opinion, small proteins should be de novo modelled with AlphaFold2 since recently has been shown to be as effective as NMR structural determinations (Structure. 2022 Jul 7;30(7):925-933.e2. doi: 10.1016/j.str.2022.04.005.). The use of template-based models is very useful for the objective of this manuscript. This recommendation is to validate the structure of at least some of these proteins to prevent bias towards known structures. If the two models are very similar, then confidence in the putative function is stronger. Otherwise, this may result from a forceful prediction of a known template.

Regarding table 2, the average is not advised here since each tool uses different algorithms to predict and assess conserved domains or features in these proteins. Each tool has its own performance; therefore, the table is informative, but I suggest removing the average, which may be misleading.

The general quality of the main figures is low; they look a bit fuzzy; I recommend using higher resolution images. I think this may have been the result of the generation of the review pdf, but I kindly suggest revising this.

Figure 2 legend is poorly described. I recommend indicating the neighborhood of the found proteins and exploiting the data shown here in more detail. Also, Figure 3 provides little information. This reviewer suggests using structural comparisons (alignments) to support their findings further, as mentioned above.

Minor comments:

This reviewer suggests modifying the use of "functional annotation" throughout the manuscript. I think the authors should tone down the manuscript since no experimental evidence of these genes is provided. As stated above, I believe this is a good starting point for characterizing these genes. My humble suggestion is to use "predictive annotation."

Line 2, capitalize Bacillus, please.

Line 42-43, please italicize B. licheniformis

Line 44, please change "things" to "products"

Line 66, please change expressional for expression and function associated data.

In line 61, please change "considerable" with the corresponding percentage of the total coding capacity of this strain.

Line 96, please change to Table S3. Also, please revise the numbering of the Supplementary Tables; S11 is S10.

Line 102, please capitalize fasta

Line 114, please correct Line 114, "of a helps to" is not correct.

Line 127, please correct to Fig S1. Also, I suggest changing the labels for supplementary Tables throughout the manuscript.

Please clarify line 190; RNA interference usually is an experimental approach for eukaryotic organisms.

Line 226, I suggest the following modification "…putative RNase (score 0.987) with functional endoribonuclease activity"

Please check gene and protein names to use the correct format; some lack capital letters at the beginning of the name, and genes lack italics.

Line 257, in other places, authors use this statement. I suggest changing to "can be used for biotechnological applications" in the manuscript.

I suggest removing line 280, which is repetitive with this paragraph.

In line 281, I think "anticipated" is not a correct word here, perhaps predicted.

Please check the grammar in line 328; this line is confusing.

Please add italics in line 367

I hope these comments are useful for the authors.

Best regards

Reviewer #2: This manuscript employed a structured in-silico approach incorporating numerous bioinformatics tools and databases for functional annotation of hypothetical proteins from Bacillus paralicheniformis Bac84 reveals proteins with biotechnological potentials and adaptational functions to extreme environments. The knowledge of these hypothetical proteins' potential functions aids B. paralicheniformis Bac84 in effectively creating a new biotechnological target and also facilitate a better understanding of the survival mechanisms in harsh environmental conditions. The findings are meaningful and my comments are as follows:

1. Functional mining of these hypothetical proteins is interesting in B. paralicheniformis Bac84. Are all these 37 hypothetical proteins existing in the species B. paralicheniformis? Which of these hypothetical proteins are specific in strain Bac84? Are these 37 hypothetical proteins can be used as indicators for the classification and identification of the two species B. paralicheniformis and B. licheniformis? I think the authors could add these contents into the Results or Discussion parts of this manuscript if possible.

2. I wonder if the authors considered whether these hypothetical proteins can actually be expressed? RNA-seq or RT-PCR technologies could be applied to further understand the expression patterns of these hypothetical proteins.

3. The English writing of this manuscript could be further improved. Some small errors should be changed, for example, in line 2 and 6, “bacillus” should be changed to “Bacillus”; in line 37, the word “strain” should be standardized; in line 42 to line 43, “B. licheniformis” should be changed to “B. licheniformis”.

Reviewer #3: Atikur Rahman et al., analyzed in silico 414 sequences of hypothetical proteins from Bacillus paralicheniformis strain bac84 with the aim of attributing, at least in a predictive form, its functional role based on sequence and structure homology with well-known proteins. 37 hypothetical proteins resulted with an associated function before the bioinformatic analyses using several online tools. The authors also mentioned in this manuscript the potential of their findings to possibly use some of these proteins in biotechnological applications. This draft could be recommended for publication in PLoS ONE not without first addressing several points of concern detected.

A) In figure 1, outlined green boxes are depicted to indicate several steps performed as part of the general protocol applied to all sequences, however, it is not clear why some boxes were green outlined and not others. I assume that tasks described in the green outlined boxes were only applied if the HPs analyzed fulfilled the same predicted function in at least three bioinformatics tools. This is an elementary observation but it could be confusing when interpreting the figure. I suggest that a figure caption with information that allows understanding of the content of the figure be added since the one described in line 99 is only limited to giving the figure a name but does not explain its graphic content.

B) Results of gene ontology shown in figure 2 are confusing to my eyes. What is the rationale to use the “bubbles” representation? What X axis indicated? If the distances between the bubbles do not indicate something significant and it is only to increase the area of the bubble based on the number of proteins that have a certain predicted function, I suggest that the data be represented in another way, for example, in a bar graph (as in section A), a pie chart or even in tables. This applies to items B and C of this figure.

C) Figure 3 is pretty poor. It is not very informative as it is only limited to showing the structures of the eleven modeled proteins and does not provide relevant information about the findings. If the reader wishes to know what theoretical function each protein has, and the whose predicted structure is shown, it is necessary to check the supplementary table S10. The latter is not at all practical when reading the manuscript. As in the other captions of figures 1 and 2, the caption of figure 3 is very insignificant because it does not provide information on the structures, how they were modeled, the selection of colors, and the reliability of the models presented, among others. Please consider these aspects to improve this figure.

D) Lines 180-181: Please name at least three types of DNA-Protein interactions in the biological context.

E) Line 230: S2 Fig must be S1.

F) In lines 244-245 the authors claim: “…proteins with similar sequences usually exhibit similar functions. Proteins dissimilar to current PDB entries may correspond to novel functions.” Can the authors do a more detailed analysis by comparing the structures of known (crystallized) proteins with the structures they modeled? Thus, it would be possible to have a clearer idea of how similar the three-dimensional structures are, as it is mentioned that a part of the modeled protein may resemble the already known one, but another part may not.

G) Lines 246-247: Can the authors be more specific about what they consider an "excellent degree of reliability? Table S10 shows the percentage of a favored Ramachandran structure, however, it gives little information on specific Ramachandran conformations for helices and B-strands. A supplemental figure showing each Ramachandran plot for at least the 11 modeled protein structures of Figure 3 might be beneficial.

H) Lines 248-254: “ROC” is not defined in the manuscript. Please define it. PLoS One is not a bioinformatic specialized journal, therefore, some terms may be unusual for different readers.

I) Line 263: a connector is necessary at the end of the line.

J) Do the authors consider it really relevant for the study to present the information in Table 2? Bioinformatic approaches used are all previously verified and properly referenced here. I suggest eliminating or including it as supplementary material.

K) Figure S1 shows the protein-protein interactions that resulted in the analysis performed on STRING, however, it is not specified what type of hypothetical or experimentally verified interaction was found. STRING provides information in a color code depending on the type of interaction (hypothetical, known, direct interactions, expression, operon array, among others), however, this information was omitted for the results shown using Cytoscape. I encourage authors to consider mentioning this information.

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20 Sep 2022

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28 Sep 2022

In silico functional annotation of hypothetical proteins from the Bacillus paralicheniformis strain bac84 reveals proteins with biotechnological potentials and adaptational functions to extreme environments

PONE-D-22-21684R1

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4 Oct 2022

PONE-D-22-21684R1

In silico functional annotation of hypothetical proteins from the Bacillus paralicheniformis strain Bac84 reveals proteins with biotechnological potentials and adaptational functions to extreme environments

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