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Genomics Inform > Volume 20(1); 2022 > Article
Kader, Ahammed, Khan, Ashik, Islam, and Hossain: Hypothetical protein predicted to be tumor suppressor: a protein functional analysis


Litorilituus sediminis is a Gram-negative, aerobic, novel bacterium under the family of Colwelliaceae, has a stunning hypothetical protein containing domain called von Hippel-Lindau that has significant tumor suppressor activity. Therefore, this study was designed to elucidate the structure and function of the biologically important hypothetical protein EMK97_00595 (QBG34344.1) using several bioinformatics tools. The functional annotation exposed that the hypothetical protein is an extracellular secretory soluble signal peptide and contains the von Hippel-Lindau (VHL; VHL beta) domain that has a significant role in tumor suppression. This domain is conserved throughout evolution, as its homologs are available in various types of the organism like mammals, insects, and nematode. The gene product of VHL has a critical regulatory activity in the ubiquitous oxygen-sensing pathway. This domain has a significant role in inhibiting cell proliferation, angiogenesis progression, kidney cancer, breast cancer, and colon cancer. At last, the current study depicts that the annotated hypothetical protein is linked with tumor suppressor activity which might be of great interest to future research in the higher organism.


Bacteria possess tremendous compatibility that can be used to the necessity of human welfare and Litorilituus sediminis can be one of them. L. sediminis is a Gram-negative, aerobic, curved-rod shaped, non-spore-forming, catalase, and oxidase-positive bacterium with the polar or sub-polar flagellum. It was isolated from a sediment sample that was collected from the coastal region of Qingdao, China [1]. This organism grew optimally at 37°C, pH 8–9. This type of bacterium was novel among the other genera under the family of Colwelliaceae. The characteristics like phenotypic, chemotaxonomic, and well-confirmed phylogenetic evidence of Litorilitus belonging to the family Colwelliaceae was distinctive that implied as a novel genus. This novel bacterium has a prominent concentration of cellular constituents compared with other genera and these are C16:0 and C16:1 ω7c fatty acids, phosphatidylethanolamine, phosphatidylglycerol, aminophospholipid, and two amino lipids (AL1, AL2) as well as isoprenoid quinone 8 [1]. Along with bacterial cellular components, a profuse number of proteins exist where approximately 2% of the genes code for proteins as well as the remaining are non-coding or still functionally unknown [2].
The number of genes having unknown functions referred to as hypothetical proteins is present in each organism’s genome [3] and these are a category of the protein whose existence is not confirmed by any experimental evidence but can be predicted to be expressed from an open reading frame [4]. The hypothetical proteins can be classified as uncharacterized protein families which are experimentally verified to exist but have not been identified or linked to a known gene, and the other type is the domain of unknown functions [5] that is experimentally characterized proteins in the absences of known functional or structural domains [6,7]. Despite the lack of functional characterization, they play a significant role in understanding biochemical and physiological pathways like exploring new structures and functions [8], pharmacological targets and markers [9], and early detection and benefits for proteomic and genomic research [10]. With the advancement of Computational Biology, it has become easier to analyze hypothetical proteins using bioinformatics tools that provide various advantages like the determination of 3D structural conformation, identification of new domains and motifs, assessment of new cascades and pathways, phylogenetic profiling, and functional annotation [11]. A recent study showed that the annotated hypothetical protein is linked with hydrolase activity which might be of great interest to further research in bacterial genetics [12].
However, due to novel genera under the family of Colwelliaceae, this study intended to characterize the protein EMK97_00595 (Litorilituus sediminis), a family of von Hippel-Lindau (VHL) that have an overwhelming function as a tumor suppressor in higher organisms. The main feature of VHL is that it is a critical regulator of the ubiquitous oxygen-sensing pathway and can act as a substrate recognition component of an E3 ubiquitin ligase complex [13], also promote the degradation of epidermal growth factor receptor, pro-angiogenesis factors, remodeling of the extracellular matrix, and helps in apoptosis resulting tumor suppression [14].
In the higher organism during cellular normoxia when oxygen is available, the cellular hypoxia-inducible factor 1α (HIFα) is hydroxylated by prolyl hydroxylase and works as a felicitous substrate for von Hippel-Lindau tumor suppressor protein (pVHL) which is a constitutive active site of E3 ubiquitin ligase. The hydroxyproline of hydroxylated HIFα provides a binding signal for pVHL, which leads to efficient ubiquitylation and proteasomal degradation of HIFα protein. On the other hand, in hypoxia condition HIFα is not prolyl hydroxylated and may escape pVHL recognition, resulting in accumulation of HIFα and formation of a complex with HIF1β, goes into the nucleus and activates a transcriptional program to cope with the short-term, long-term effects of oxygen deprivation, several signaling pathways as well as angiogenesis factor for leading cell proliferation or tumor [14,15]. So the function of the hypothetical protein that exists in the L. sediminis is considerable.
Therefore, this study manifests a reliable interpretation of this hypothetical protein EMK97_00595 (QBG34344.1) by adopting an integrated workflow that can be a potential research interest in the field of tumor suppression study.


Sequence retrieval and similarity identification

The hypothetical protein EMK97_00595 (Litorilituus sediminis) was chosen by exploring the NCBI database which can act as a significant research interest in numerous cancer research fields in the near future (Supplementary Table 1). The sequence of the hypothetical protein (GenBank accession: QBG34344.1 and NCBI reference sequence: WP_130598461.1) that may contain a tumor suppressor domain was retrieved and collected as a FASTA format and submitted to several prediction servers for the in-silico characterization. Initially, a similarity search was performed using the NCBI BLASTp program [16] against the non-redundant and Swissprot database [17], for predicting the function of the hypothetical protein.

Multiple sequence alignment and phylogeny analysis

A multiple sequence alignment is a tool used to explore closely related genes or proteins to find the evolutionary relationships between genes and to identify shared patterns among functionally or structurally related genes. Sequence alignment was performed by the MUSCLE server of EBI [18], and an evolutionary relationship was accomplished by Jalview 2.11 software [19], between the hypothetical protein EMK97_00595 and the proteins that had structural similarity with the protein of interest.

Analysis of physicochemical properties

ProtParam [5] is a tool that computes various physical and chemical parameters of protein sequences. The physicochemical properties of the hypothetical protein were predicted using the ProtParam tool in the ExPASy server [20], which predicts all the relative properties including molecular weight, theoretical pI, amino acid composition, the total number of positive and negative residues, instability index, aliphatic index and grand average of hydropathicity (GRAVY) [21-23].

Analysis of the secondary structure

The servers that were utilized to predict protein secondary structure were SOPMA [24] and PSIPRED [25]. SOPMA is a general secondary structure prediction tool, on the other hand, PSIPRED is a server for comprehensive analysis of protein. The server SOPMA was initially employed to predict the secondary structure and then the result derived from the SOPMA server was validated by exploiting PSIPRED.

3D structure modeling and quality assessment

HHpred server [26] that works based on the pairwise comparison profile of hidden Markov models, was used to build the 3-dimensional structure using the best scoring template. The confidence of the predicted structure was also visualized by SWISS-MODEL [27]. Several quality assessment tools of the SAVES and ProFunc [28] server were applied to estimate the reliability of the predicted 3D structure model of the hypothetical protein. The Ramachandran plot for the model was built using the PROCHECK program [29] to visualize the backbone dihedral angles of amino acid residues. The quality of the protein 3D structure was assessed with the help of the ERRAT server [30] and Varify 3D server was used to determine the compatibility of an atomic model (3D) with its amino acid sequence as well as comparing the results to standard structures [31,32].

Active site determination

Computed Atlas of Surface Topography (CASTp) is an online active site determination server [33] that calculates the location, delineation, and concave surface regions on 3D structures of proteins. CASTp predicted the active site of the selected hypothetical protein that showed the binding sites, amino acid binding regions with area and volume.

Identification of protein subcellular localization and topology

The subcellular location of the following protein was predicted by using the BUSCA web server [34]. BUSCA amalgamates different tools—DeepSig, TPpred3, PredGPI, BetAware, ENSEMBLE3.0, BaCelLo, MemLoci, and SChloro to predict protein features related to localization. The result was further checked by Cello [35], PsortB [36], Gneg-mPLoc [37], SOSUIGramN [38], and PSLpred [39]. Prediction of signal peptide was done by using PrediSi [40] and SignalP-5.0 Server [41]. The solubility of the hypothetical protein was evaluated by Protein-sol [42] and SOSUI [43] webserver. Protein transmembrane helices were assessed by HMMTOP [44], TMHMM [45], and Sable [46] webserver. The topology of hypothetical protein was predicted by the ProFunc server [14].

Prediction of protein domain, superfamily, family, coil, and folding pattern

Domain/superfamily/family of the following hypothetical protein was analyzed by using the servers—CDD (conserved domain database) from NCBI [47], Pfam [48], SMART [49], Interpro [50], SCOP [51,52], Supfam [53], Motif, ProFunc [28], Phyre [54], and CATH-Gene3D [55]. Among them, CDD, Pfam, SMART, Interpro, SCOP, Supfam, MotifFinder were employed to predict function from the sequence of the hypothetical protein, and ProFunc, Phyre 2, and CATH-Gene3D servers were used to predict the function from the 3-dimensional structure of the hypothetical protein. Only the lowest e-value was considered to determine protein classification, which indicates good similarity. The protein folding pattern was determined by using Phyre 2 and PFP-FunDSeqE [56] servers where protein coil nature was determined by using PCoils [57] from the Bioinformatics toolkit server.

Generation of protein-protein interaction network

As the proposed investigation seeking a tumor suppressor protein from microorganisms, STRING [58] has been used to summarize the network information of VHL tumor suppressor protein. Because of being a novel microorganism, there is no specific network is available. Here the VHL protein from humans has been used as a supposition model that might give an intellectual knowledge about VHL protein if it may apply to the human.


Identification of sequence homology

The overall workflow of this study has been shown in Fig. 1. The BLASTp result of the FASTA sequence of the selected protein shows the sequence homology with other identical proteins (Tables 1 and 2). Construction of phylogenetic tree using multiple sequence alignment generated from BLASTp result shows the evolutionary relationship of the selected hypothetical protein (WP_130598461.1) (Fig. 2).

Analysis of physicochemical properties

The physicochemical properties of a protein can be characterized by an analysis of the analogous properties of the amino acids (Supplementary Table 2). The hypothetical protein is negatively charged as the theoretical pI: 4.22 and the total number of positively (Arg + Lys) and negatively charged residues (Asp + Glu) were found to be 10 and 27, respectively. The computed instability index was 32.71 classifying the protein as a stable one. The aliphatic index was 77.37 which gives an indication of proteins’ stability over a wide temperature range and all the other properties have been summarized (Supplementary Table 2).

Secondary structure analysis

The secondary structure of a protein can be able to provide some worthy information about the function. The query hypothetical protein shows the percentages of alpha-helix, beta-turn, extended strand, and the random coil of protein 21.13%, 9.91%, 33.33%, and 36.15%, respectively from SOPMA (Supplementary Figs. 1 and 2, Supplementary Table 3). The results of the secondary structure were also cross-checked by the PRISPRED server which shows a summary of similar results (Supplementary Fig. 3). The representative secondary structure of the hypothetical protein (WP_130598461.1) has been shown (Fig. 3).
Secondary structure predicted from SOPMA server directed (Fig. 3A); having maximum portion of random coil (36.15%), extended strand (33.33%) and alpha-helix (21.13%) and others information displayed in Supplementary Fig. 1 and Table 3. Here, alpha-helix, beta-turn, extended strand and the random coil is indicated as blue, green, red and orange, respectively (Fig. 3A). Simultaneous analyses of secondary structure from the PSIPRED server was presented (Fig. 3B, Supplementary Fig. 3), where the helix, strand and coil sections were indicated by specified color code. Other information is available in Supplementary Figs. 26.

Assessment and validation of protein 3-dimensional structure

PROCHECK program was used for the validation of predicted tertiary structure, where the distribution of φ and ψ angle in the model within the limits are shown (Table 4, Fig. 4). The model was presumed to be a good one according to the Ramachandran Plot Statistics, with 91.1% residues in the most favored regions. Finally, the structure validation server Verifiy3D and ERRAT was implicated in verifying the established model of 3D structure for the target sequence. In the Verify3D graph, 93.75% of the residues have averaged a 3D-1D score ≥ of 0.2 which indicates that the environmental profile of the model is good (Fig. 5)and the overall quality factor predicted by the ERRAT server was 60.7143 indicates a quality model (Supplementary Fig. 7). From ProFunc, the average G-factors of the hypothetical protein is calculated to be ‒0.20, which indicates a usual protein model. Overall quality factor of the structure has been also depicted (Supplementary Fig. 5).

Active site calculation

The active site of the selected hypothetical protein constituted by 11 amino acids of an area with 52.957 and a volume of 22.609. Chain X of the hypothetical protein shows the amino acids involved in the active site (F, V, Y, Y, T, L, E, V, T, Q, W) (Fig. 6A and 6B).
The selected hypothetical protein has 11 active sites with variable size and is constituted by 64 amino acids demonstrated (Fig. 6A and 6B). Different binding pockets of the hypothetical protein were indicated as red, blue, green, purple, orange, and pink region, and where the amino acids contributing to the beta-bridge, beta-strand, bend, turn, and coiled regions were specified by colored bars. The largest active site (red spheres) with the contributing amino acids was directed (Fig. 6C and 6D).

Assessment of protein subcellular localization and topology

The subcellular localization of the hypothetical protein seems to be an extracellular secretory signal peptide. Protein-sol and SOSUI both predict the hypothetical protein as a soluble protein. HMMTOP, TMHMM predicted the protein as a non-transmembrane protein (Table 5). The predicted topology of the protein has shown here from N-terminal to the C-terminal.
Topology of the hypothetical protein EMK97_00595. The topological orientation of the respective strands depicted (pink arrow) from the amino terminal (N) to the carboxyl terminal (C) end exposed in Fig. 7.

Functional annotation of the hypothetical protein

The initial protein domain was achieved from the CDD of NCBI. The region of the domain, superfamily, and family classifications have been determined by the servers—CDD, Pfam, SMART, Interpro, SCOP, Supfam, MotifFinder, ProFunc, Phyre 2, and CATH-Gene3D. The domain, superfamily, and family were selected based on the lowest e-value of the following domain. The higher e-value has been filtered out from the selection procedure. The e-value 9.11e-05 of VHL beta domain from ProFunc, 2.71e-09 of VHL superfamily from SCOP, 8.1e-03 of VHL family from Supfam indicate extremely good protein alignment, respectively. The overall alignment range of the VHL beta domain was 133-212, VHL superfamily and family were 144‒200, respectively. Protein coil nature was determined by using PCoils from the Bioinformatics toolkit server. According to Phyre 2, the folding pattern of the following hypothetical protein is pre-albumin-like. On the other hand, PEF-FunSeqE is called the protein immunoglobulin-like. Both are secreted protein as well as soluble protein and hence provide a properly defined similarity indication of VHL protein (Table 6, Supplementary Fig. 4 and 7-9).

Analysis of protein network

The STRING interaction of VHL protein from Homo sapiens has been shown in Fig. 8 as a model. VHL interacts with various proteins based on their combined score (Table 7). The network has 11 nodes, 40 edges, average node degree 7.27, local clustering coefficient 0.819, expected number of edges 18, and the p-value of protein-protein interaction enrichment 7.07e-06 indicates the network has significantly more interactions than expected.
Because of being a noble microorganism that produces hypothetical VHL protein, the VHL protein from humans has been used as a supposition model that likely to be similar to VHL protein found from microorganisms. The model VHL protein interacts with 10 other proteins such as AKT1, AKT2, CUL2, EGLN1, EPAS1, HIF1A, PPP2CA, RBX1, TCEB, and TCEB2.

Similarity analysis between query (Litorilituus sediminis, EMK97_00595) and target (Homo sapiens, AAB64200.1) pVHL proteins

The mentioned L. sediminis (EMK97_00595) and target (Homo sapiens, AAB64200.1) pVHL proteins (Table 8) molecular weight, aliphatic index, and pI value bolster the confidence value between these two pVHL proteins to be more congruous for their almost resemble value [59].
The other properties like helix, coil, and beta sheet contents are also comparable whereas the beta sheet contents were massive in the query protein rather than target protein which implies that the bacterial query pVHL proteins have higher potentiality to drive role as a tumor suppressor protein comparing with human pVHL proteins. Because the beta domain in the pVHL protein provide the binding site for HIFα degradation. The most intriguing matter from the comparisons, the query protein is highly stable rather than the human protein which implicate to substitute this protein in human is considerable [60].
Even though the helix content is a bit more in the human pVHL protein the consequence of it, in overall amino acid sequences alignment and structure formation are demonstrated following in Fig. 9 and Supplementary Fig. 10.
The human pVHL protein has a greater instability index than the novel bacterial protein, indicating that the bacterial pVHL protein will be very effective as an anti-proliferative drug to substitute in humans, which necessitates additional research (Fig. 10).


The sequence information as well as the structural information contributes to understanding the function of a hypothetical protein (Tables 1 and 2, Fig. 2, Supplementary Table 1). This study aims to characterize a hypothetical protein, which showed strong homology with VHL superfamily, involved in tumor suppressor. Therefore, the amino acid sequence of the hypothetical protein EMK97_00595 (Litorilituus sediminis) was retrieved (Supplementary Table 2), and initially, the physicochemical properties were obtained by ExPASy’s ProtParam tool and the prediction results are the deciding factors for the hydrophilicity, stability, and function of the protein [61]. The protein was considered as a stable one even in a wide temperature range as the instability index and the aliphatic index were 32.71 and 77.37, respectively. And the query protein seems to be hydrophilic as the GRAVY was ‒0.261 (Table 3).
Protein structure is closely associated with its function. The secondary structure, viz. helix, sheet, turn and therefore the coil of any protein has an excellent association with the structure, function, and interaction of the protein (Fig. 3). The query hypothetical protein contains the percentages of alpha-helix, beta-turn, extended strand, and the random coil 21.13%, 9.91%, 33.33%, and 36.15%, respectively (Supplementary Table 3, Supplementary Figs. 1-4). Findings from SOPMA revealed that the protein has an abundance of coiled regions that contributes to higher stability and conservation of the protein structure (Fig. 3) [61]. Moreover, the protein features a reliable helices percentage in its structure, which may facilitate folding by providing more flexibility to the structure; thus, protein interactions could be increased [62].
For the prediction of the protein 3D model, HHpred was employed, where the highest identical template was selected for getting an acceptable model. The query protein WP_012259469.1 showed the highest template identity of 25% with von Hippel-Lindau disease tumor suppressor; E3 ubiquitin ligase, transcription factor, hypoxic signaling, transcription; (Homo sapiens) with lowest E-value: 1.1e-11. Ramachandran plot analysis revealed that 91.1% of residues were located in the most favored regions. Moreover, residues in additional allowed regions and generously allowed regions were 7.1% and 0.0%, respectively, which evaluated the quality of the model to be good and reliable as it is generally accepted that if 90% of residues are in the most favored regions, it is likely to be a reliable model [63], shown in Fig. 4B. The model is compatible with its sequence as Verify 3D analysis implies that 93.75% of the residues had an average 3D–1D score of ≥0.2 (Fig. 5).“Overall quality factor” was estimated by ERRAT, which is used to evaluate the amino acid environment for non-bonded atomic interactions. Higher scores indicate higher quality, and the query protein’s quality factor was 60.7143, which is greater than the generally accepted range (>50) for a high-quality model [64]. The average G-factor of the query protein is ‒0.20 obtained from ProFunc analysis, which indicates a usual protein model.
Protein’s active site was determined by CASTp, containing 11 amino acids (F, V, Y, Y, T, L, E, V, T, Q, W) of an area with 52.957 and a volume of 22.609, shown in Fig. 6A and 6B. The subcellular localization obtained from CELLO, BUSCA, and other similar servers, seems to be an extracellular secretory signal peptide (Supplementary Fig. 6) and non-transmembrane (Table 5). As the functions of secreted proteins are diverse, the query hypothetical protein may work like paracrine, autocrine, endocrine, or neuroendocrine depending on the target [65]. Solubility is the most important factor and an excellent index for protein functionality (Supplementary Fig. 5). Protein-sol and SOSUI both predict the hypothetical protein as a soluble one, so it may possess good dispersibility and lead to the formation of finely dispersed colloidal systems.
The superfamily, family, and domain information have been determined by a combinational sequence and structural informative approach based on the e-value of different sequence and structure analysis servers. These servers suggested the following hypothetical protein EMK97_00595 from the organism L. sediminis to be a VHL beta domain from the VHL superfamily (Table 6, Supplementary Figs. 8 and 9). VHL tumor suppressor protein can play a role in tumor suppression in multiple ways and the most common of them is targeting the HIF that mediated tumor suppression activity through polyubiquitylation and proteasomal degradation [66]. The major contribution of pVHL is to suppress clear-cell renal cell carcinoma in kidney cancer [66,67] and phosphodiesterase 9A gene as novel biomarker in human colorectal cancer [68].
L. sediminis is a novel species and the investigated protein EMK97_00595 is also novel so there is no specific STRING derived protein-protein network is available for this organism. The protein-protein interaction network analysis shown here from H. sapiens is just for a supposition model to evaluate how the protein interacted in humans (Fig. 8). The protein-protein interaction of VHL-HIF1A with a combined score of 0.999 indicated a strong relationship between these two proteins. The interaction between VHL and HIF1A indicating the involvement of the same pathway to suppress tumor activity (Table 7, Supplementary Fig. 11) [13].
Overall, the combinational strategy of computing physicochemical properties, evaluating the secondary structure and tertiary structure information, and domain information analysis denoted the protein as VHL tumor suppressor protein that is associated with VHL disease (Table 8, Supplementary Figs. 10, 11).
Protein is the building block of life that serves both biological processes and molecular functions in living organisms. Hence, this study investigated the functional role of a hypothetical protein from a novel bacterium, L. sediminis that possesses a significant tumor suppression activity. The employment of highly recommended bioinformatics tools to analyze the combinational sequence and structural information revealed the underlying molecular function of the examined hypothetical protein. The current investigation suggested that the hypothetical protein may exhibit a VHL beta domain that is similar to the human VHL beta domain and is also a part of pVHL (Figs. 9 and 10). Therefore, this finding with the aid of bioinformatics tools can soften our viewpoint for further investigation and experimental validation of this hypothetical protein containing VHL beta domain, and the use of this hypothetical protein with the aid of modern biotechnology might be utilized to suppress tumor progression in higher organisms such as human as an alternative to human defective or mutated VHL protein in the near future.


Authors’ Contribution

Conceptualization: MAK, SAAA, MUH. Data curation: AA, MSK. Formal analysis: MAK, SAAA, AA, MSK. Methodology: SAAA, MSK, MUH, MSI. Writing - original draft: MAK, AA, SAAA, MSK. Writing - review & editing: MUH, MSI.

Conflicts of Interest

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


We are very grateful to the book of Gobeshonay Bioinformatics-1st Part.

Supplementary Materials

Supplementary data can be found with this article online at
Supplementary Table 1.
Information of query hypothetical protein
Supplementary Table 2.
Total number of amino acid composition and percentages
Supplementary Table 3.
Components of secondary structure of query hypothetical protein
Supplementary Fig. 1.
Amino acid sequence from SOPMA.
Supplementary Fig. 2.
Secondary structure plot of VHL domain. VHL, von Hippel-Lindau.
Supplementary Fig. 3.
PSIPRED shows the helix (red) and strand (green) positions in sequence.
Supplementary Fig. 4.
PCoils represents the position of Window 14 (green), Window 21 (blue), and Window 28 (purple) in the sequence.
Supplementary Fig. 5.
ERRAT generated overall quality factor bar diagram.
Supplementary Fig. 6.
Conserved domain of VHL superfamily. VHL, von Hippel-Lindau.
Supplementary Fig. 7.
Result of Motif finder.
Supplementary Fig. 8.
Polarity prediction result of PSIPRED.
Supplementary Fig. 9.
Signal peptide prediction of WP_130598461.1 through SignalP-5.0.
Supplementary Fig. 10.
Amino acid and secondary structure comparison between hypothetical and human pVHL protein. pVHL, von Hippel-Lindau tumor suppressor protein.
Supplementary Fig. 11.
Mechanism of pVHL mediated tumor suppression activity. The pVHL comprises a small α and a large β subunit. The α-domain serves as a binding site, whereas the β-domain plays important role in substrate recognition. During normoxia (oxygen available condition), HIFα binds with VHL beta domain and VHL alpha domain associates with E3 ubiquitin ligase via elongin BC complex which leads to efficient ubiquitylation and proteasomal degradation of HIFα that suppress tumor proliferation activity. On the other hand, During Hypoxia or when pVHL is defective, HIFα unable to recognize pVHL and so promotes tumor progression process. pVHL, von Hippel-Lindau tumor suppressor protein; HIFα, hypoxia inducing factor-α.

Fig. 1.
A schematic representation of the overall experimental design.
Fig. 2.
Evolutionary analysis of different von Hippel-Lindau (VHL) proteins with the target protein shown in the blue box (WP_130598461.1). Evolutionary analysis of different VHL proteins with the target protein shown in the blue box (WP_130598461.1) having maximum query cover, score and identity with its close relative Colwellia sp. RSH04 (WP_118961164.1) and other organisms. The BLASTp result against non-redundant and SwissProt database showed homology with other von Hippel-Landau (pVHL) domain-containing proteins. Multiple sequence alignment was considered the FASTA sequences of the hypothetical protein (QBG34344.1) and the homologous annotated proteins. Phylogenetic analysis was performed to confirm homology assessment between the proteins, down to the complex and subunit level. The tree was constructed based on the alignment where distances between branches were also included and the BLASTp result gives a similar concept about the protein.
Fig. 3.
Model of secondary structure. (A) Secondary structure information from SOPMA server. (B) Sequential organization and graphical visualization of secondary structure from PSIPRED.
Fig. 4.
Graphical representation and assessment of protein 3D structure. Predicted 3-dimensional structure from SAVES server (Pymol view) (A), from SWISS-MODEL (B), and Ramachandran plot analysis of 3D modeled structure validated by PROCHECK program (C).
Fig. 5.
3D-structure validation by Verifiy3D.
Fig. 6.
Active site of the hypothetical protein, binding site of the hypothetical protein indicated by red region (A, C), and amino acids involved in the active site (B, D).
Fig. 7.
Topology of hypothetical protein.
Fig. 8.
Protein-protein interaction network of the hypothetical VHL protein. VHL, von Hippel-Lindau.
Fig. 9.
The amino acid sequence alignment between query and target pVHL protein. The black legends below the two amino acid sequences alignment indicate the consensus amino acid of the protein (from Jalview analysis). pVHL, von Hippel-Lindau tumor suppressor protein.
Fig. 10.
The structural similarity prediction between query and target pVHL protein. (A, B) pVHL proteins contain the beta domain that actually paly role as a tumor suppressor protein is superimposed (using PyMOL) to infer how much structural similarity they have, the superimposed result (C) is absolutely congruous each other in the β domain region which dictate the human pVHL proteins can play magnificent role as a tumor suppressor protein even though it contain α domain. pVHL, von Hippel-Lindau tumor suppressor protein.
Table 1.
Similar proteins obtained from the non-redundant database
Accession No. Description Scientific name Total score Query cover (%) E-value Identity (%)
WP_118961164.1 Hypothetical protein (Colwellia sp. RSH04) Colwellia sp. RSH04 349 100 5.00E-120 74.18
WP_033081725.1 Hypothetical protein (Colwellia psychrerythraea) Colwellia psychrerythraea 235 100 4.00E-75 51.17
WP_142932219.1 Hypothetical protein (Aliikangiella sp. M105) Aliikangiella sp. M105 108 94 2.00E-25 34.78
WP_155746905.1 Hypothetical protein (Scytonema sp UIC 10036) Scytonema sp. UIC 10036 61.2 45 3.00E-08 34.02
BAZ36602.1 Hypothetical protein NIES4101_25210 (Calothrix sp NIES-4101) Calothrix sp. NIES-4101 57.8 27 5.00E-07 44.83
Table 2.
Similar proteins obtained from Swissprot database
Entry Protein names Identity (%) Score E-value
A0A396TZK2 Uncharacterized protein (Colwellia sp. RSH04) 74.2 894 1.3e-120
A0A545UCJ6 VHL domain-containing protein (Aliikangiella sp. M105) 34.3 81 8.3e-28
A0A1Z4R2C0 VHL domain-containing protein (Calothrix sp. NIES-4101) 36.6 150 1.5e-9
A0A1I6H391 Por secretion system C-terminal sorting domain-containing protein (Robiginitalea myxolifaciens) 37.1 133 7e-6
A0A2S7JPT4 VHL domain-containing protein (Limnohabitans sp. TS-CS-82) 35.1 124 2e-5
Table 3.
Physicochemical properties of the hypothetical protein (WP_130598461.1)
Property Value
Molecular weight 23,229.44
Theoretical pI 4.22
Total No. of negatively charged residues (Asp + Glu) 27
Total No. of positively charged residues (Arg + Lys) 10
The instability index (II) is computed to be 32.71
Formula C1024H1552N262O346S5
Total No. of atoms 3189
Aliphatic index 77.37
Grand average of hydropathicity (GRAVY) ‒0.261
Table 4.
Ramachandran plot statistics of the predicted 3D model for the target protein EMK97_00595 (WP_130598461.1)
Plot statistics No. of amino acid residues (%)
Residues in the most favored regions [A, B, L] 51 (91.1)
Residues in additional allowed regions [a, b, l, p] 4 (7.1)
Residues in generously allowed regions [~a, ~b, ~l, ~p] 0
Residues in disallowed regions 1 (1.8)
No. of non-glycine and non-proline residues 56 (100)
No. of end-residues (excl. Gly and Pro) 2
No. of glycine residues (shown as triangles) 4
No. of proline residues 2
Total No. of residues 64
Table 5.
Assessment of subcellular localization
Prediction Servers Results
Prediction of subcellular localization BUSCA Extracellular space, signal peptide
Cello Extracellular
PsortB Unknown, signal peptide
Cell-PLoc Extracellular
PSLpred Extracellular protein
SOSUIgramN Outer membrane
Signal peptide prediction Predisi Secreted protein, signal peptide
SignalP-5.0 Server Signal Peptide
Prediction of protein solubility SOSUI Soluble protein
Protein-sol Soluble protein
Prediction of transmembrane helices HMMTOP None
Sable No transmembrane domain
Table 6.
Function annotation of hypothetical protein through the analysis of protein domain/superfamily/family
Server Domain/Superfamily/Family e-value/Confidence Region/Alignment
Functional annotation from sequence
 Conserved domain database (CDD) Superfamily: pVHL 6.22e-05 146‒197
 Pfam Family: VHL (VHL beta domain) 1.3e-02 144‒200
 SMART VHL 1.2e-02 133‒205
 Interpro VHL superfamily - 144‒199
VHL beta domain - 131‒212
 Superfamily 1.75 (SCOP) Superfamily: VHL 2.71e-09 144‒199
Family: VHL 8.1e-03
 Supfam Superfamily: VHL 1.54e-09 144‒199
Family: VHL 8.1e-03
 Motif (From Pfam) VHL beta domain 8.1e-03 146‒200
Functional annotation from the 3D structure
 ProFunc VHL beta domain 9.11e-05 131‒191
 Phyre 2 Superfamily: VHL 99.8% (confidence) 135‒212
Family: VHL
 CATH-Gene3D (from Interpro) VHL beta domain - 131‒212
Table 7.
Interacting proteins and their combined score from STRING 11.0 server
Interacted protein Combined score
AKT1 (RAC-alpha serine/threonine-protein kinase) 0.997
AKT2 (RAC-beta serine/threonine-protein kinase) 0.994
CUL2 (cullin-2; core component of multiple cullin-RING-based ECS E3 ubiquitin-protein ligase complexes) 0.999
EGLN1 (Egl nine homolog 1) 0.989
EPAS1 (endothelial PAS domain-containing protein 1) 0.994
HIF1A (hypoxia-inducible factor 1-alpha) 0.999
PPP2CA (serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform) 0.993
RBX1 (E3 ubiquitin-protein ligase RBX1) 0.982
TCEB1 (elongin-C) 0.999
TCEB2 (elongin-B) 0.998
Table 8.
Comparison between query and target pVHL protein properties
Characteristics of pVHL protein Litorilituus sediminis Homo sapiens
No. of residues 213 213
Molecular weight 23,229.44 24,152.78
Theoretical pI 4.22 4.68
Aliphatic index 77.37 75.45
Overall confidence value (%) 75.4 78.2
Predicted % helix content 11 (24 residues) 28 (60 residues)
Predicted % beta sheet content 43 (91 residues) 12 (26 residues)
Predicted % voil content 46 (98 residues) 60 (127 residues)
Instability index 32.71 68.65

pVHL, von Hippel-Lindau tumor suppressor protein.


1. Wang Y, Zhao R, Ji S, Li Z, Yu T, Li B, et al. Litorilituus sediminis gen. nov. sp. nov., isolated from coastal sediment of an amphioxus breeding zone in Qingdao, China. Antonie Van Leeuwenhoek 2013;104:423–430.
crossref pmid
2. Uhlen M, Oksvold P, Fagerberg L, Lundberg E, Jonasson K, Forsberg M, et al. Towards a knowledge-based Human Protein Atlas. Nat Biotechnol 2010;28:1248–1250.
crossref pmid
3. Naveed M, Tehreem S, Usman M, Chaudhry Z, Abbas G. Structural and functional annotation of hypothetical proteins of human adenovirus: prioritizing the novel drug targets. BMC Res Notes 2017;10:706.
crossref pmid pmc
4. Bashir Z, Rizwan M, Mushtaq K, Munir A, Ali I. In silico structural and functional prediction of Phaseolus vulgaris hypothetical protein PHA VU_004G136400g. J Proteomics Bioinform 2017;10:207–211.
5. Jaroszewski L, Li Z, Krishna SS, Bakolitsa C, Wooley J, Deacon AM, et al. Exploration of uncharted regions of the protein universe. PLoS Biol 2009;7:e1000205.
crossref pmid pmc
6. Bharat Siva Varma P, Adimulam YB, Kodukula S. In silico functional annotation of a hypothetical protein from Staphylococcus aureus. J Infect Public Health 2015;8:526–532.
crossref pmid
7. Mudgal R, Sandhya S, Chandra N, Srinivasan N. De-DUFing the DUFs: deciphering distant evolutionary relationships of domains of unknown function using sensitive homology detection methods. Biol Direct 2015;10:38.
crossref pmid pmc
8. 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
9. Shahbaaz M, Hassan MI, Ahmad F. Functional annotation of conserved hypothetical proteins from Haemophilus influenzae Rd KW20. PLoS One 2013;8:e84263.
crossref pmid pmc
10. Mohan R, Venugopal S. Computational structural and functional analysis of hypothetical proteins of Staphylococcus aureus. Bioinformation 2012;8:722–728.
crossref pmid pmc
11. Mills CL, Beuning PJ, Ondrechen MJ. Biochemical functional predictions for protein structures of unknown or uncertain function. Comput Struct Biotechnol J 2015;13:182–191.
crossref pmid pmc
12. Ferdous N, Reza MN, Emon MTH, Islam MS, Mohiuddin AKM, Hossain MU. Molecular characterization and functional annotation of a hypothetical protein (SCO0618) of Streptomyces coelicolor A3(2). Genomics Inform 2020;18:e28.
crossref pmid pmc
13. Haase VH. The VHL/HIF oxygen-sensing pathway and its relevance to kidney disease. Kidney Int 2006;69:1302–1307.
crossref pmid
14. Zhang Q, Yang H. The roles of VHL-dependent ubiquitination in signaling and cancer. Front Oncol 2012;2:35.
crossref pmid pmc
15. Blankenship C, Naglich JG, Whaley JM, Seizinger B, Kley N. Alternate choice of initiation codon produces a biologically active product of the von Hippel Lindau gene with tumor suppressor activity. Oncogene 1999;18:1529–1535.
crossref pmid
16. Johnson M, Zaretskaya I, Raytselis Y, Merezhuk Y, McGinnis S, Madden TL. NCBI BLAST: a better web interface. Nucleic Acids Res 2008;36:W5–W9.
crossref pmid pmc
17. Boeckmann B, Bairoch A, Apweiler R, Blatter MC, Estreicher A, Gasteiger E, et al. The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003. Nucleic Acids Res 2003;31:365–370.
crossref pmid pmc
18. Madeira F, Park YM, Lee J, Buso N, Gur T, Madhusoodanan N, et al. The EMBL-EBI search and sequence analysis tools APIs in 2019. Nucleic Acids Res 2019;47:W636–W641.
crossref pmid pmc
19. Waterhouse AM, Procter JB, Martin DM, Clamp M, Barton GJ. Jalview Version 2--a multiple sequence alignment editor and analysis workbench. Bioinformatics 2009;25:1189–1191.
crossref pmid pmc
20. 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.

21. Ikai A. Thermostability and aliphatic index of globular proteins. J Biochem 1980;88:1895–1898.
22. Kyte J, Doolittle RF. A simple method for displaying the hydropathic character of a protein. J Mol Biol 1982;157:105–132.
crossref pmid
23. Guruprasad K, Reddy BV, Pandit MW. Correlation between stability of a protein and its dipeptide composition: a novel approach for predicting in vivo stability of a protein from its primary sequence. Protein Eng 1990;4:155–161.
crossref pmid
24. Geourjon C, Deleage G. SOPMA: significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments. Comput Appl Biosci 1995;11:681–684.
crossref pmid
25. McGuffin LJ, Bryson K, Jones DT. The PSIPRED protein structure prediction server. Bioinformatics 2000;16:404–405.
crossref pmid
26. Zimmermann L, Stephens A, Nam SZ, Rau D, Kubler J, Lozajic M, et al. A completely reimplemented MPI bioinformatics toolkit with a new HHpred server at its core. J Mol Biol 2018;430:2237–2243.
crossref pmid
27. Schwede T, Kopp J, Guex N, Peitsch MC. SWISS-MODEL: an automated protein homology-modeling server. Nucleic Acids Res 2003;31:3381–3385.
crossref pmid pmc
28. Laskowski RA, Watson JD, Thornton JM. ProFunc: a server for predicting protein function from 3D structure. Nucleic Acids Res 2005;33:W89–W93.
crossref pmid pmc
29. Laskowski RA, MacArthur MW, Moss DS, Thornton JM. PROCHECK: a program to check the stereochemical quality of protein structures. J Appl Crystallogr 1993;26:283–291.
30. Colovos C, Yeates TO. Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci 1993;2:1511–1519.
crossref pmid pmc
31. Kihara D, Chen H, Yang YD. Quality assessment of protein structure models. Curr Protein Pept Sci 2009;10:216–228.
crossref pmid
32. Eisenberg D, Luthy R, Bowie JU. VERIFY3D: assessment of protein models with three-dimensional profiles. Methods Enzymol 1997;277:396–404.
33. Tian W, Chen C, Lei X, Zhao J, Liang J. CASTp 3.0: computed atlas of surface topography of proteins. Nucleic Acids Res 2018;46:W363–W367.
crossref pmid pmc
34. Savojardo C, Martelli PL, Fariselli P, Profiti G, Casadio R. BUSCA: an integrative web server to predict subcellular localization of proteins. Nucleic Acids Res 2018;46:W459–W466.
crossref pmid pmc
35. Yu CS, Lin CJ, Hwang JK. Predicting subcellular localization of proteins for Gram-negative bacteria by support vector machines based on n-peptide compositions. Protein Sci 2004;13:1402–1406.
crossref pmid pmc
36. Yu NY, Wagner JR, Laird MR, Melli G, Rey S, Lo R, et al. PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinformatics 2010;26:1608–1615.
crossref pmid pmc
37. Shen HB, Chou KC. Gneg-mPLoc: a top-down strategy to enhance the quality of predicting subcellular localization of Gram-negative bacterial proteins. J Theor Biol 2010;264:326–333.
crossref pmid
38. Imai K, Asakawa N, Tsuji T, Akazawa F, Ino A, Sonoyama M, et al. SOSUI-GramN: high performance prediction for sub-cellular localization of proteins in gram-negative bacteria. Bioinformation 2008;2:417–421.
crossref pmid pmc
39. Bhasin M, Garg A, Raghava GP. PSLpred: prediction of subcellular localization of bacterial proteins. Bioinformatics 2005;21:2522–2524.
crossref pmid
40. Hiller K, Grote A, Scheer M, Munch R, Jahn D. PrediSi: prediction of signal peptides and their cleavage positions. Nucleic Acids Res 2004;32:W375–W379.
crossref pmid pmc
41. Almagro Armenteros JJ, Tsirigos KD, Sonderby CK, Petersen TN, Winther O, Brunak S, et al. SignalP 5.0 improves signal peptide predictions using deep neural networks. Nat Biotechnol 2019;37:420–423.
crossref pmid
42. Hebditch M, Carballo-Amador MA, Charonis S, Curtis R, Warwicker J. Protein-Sol: a web tool for predicting protein solubility from sequence. Bioinformatics 2017;33:3098–3100.
crossref pmid pmc
43. Mitaku S, Hirokawa T. Physicochemical factors for discriminating between soluble and membrane proteins: hydrophobicity of helical segments and protein length. Protein Eng 1999;12:953–957.
crossref pmid
44. Tusnady GE, Simon I. The HMMTOP transmembrane topology prediction server. Bioinformatics 2001;17:849–850.
crossref pmid
45. Moller S, Croning MD, Apweiler R. Evaluation of methods for the prediction of membrane spanning regions. Bioinformatics 2001;17:646–653.
crossref pmid
46. Wagner M, Adamczak R, Porollo A, Meller J. Linear regression models for solvent accessibility prediction in proteins. J Comput Biol 2005;12:355–369.
crossref pmid
47. 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
48. 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
49. Letunic I, Khedkar S, Bork P. SMART: recent updates, new developments and status in 2020. Nucleic Acids Res 2021;49:D458–D460.
crossref pmid
50. Blum M, Chang HY, Chuguransky S, Grego T, Kandasaamy S, Mitchell A, et al. The InterPro protein families and domains database: 20 years on. Nucleic Acids Res 2021;49:D344–D354.
crossref pmid
51. Gough J, Karplus K, Hughey R, Chothia C. Assignment of homology to genome sequences using a library of hidden Markov models that represent all proteins of known structure. J Mol Biol 2001;313:903–919.
crossref pmid
52. Pandurangan AP, Stahlhacke J, Oates ME, Smithers B, Gough J. The SUPERFAMILY 2. 0 database: a significant proteome update and a new webserver. Nucleic Acids Res 2019;47:D490–D494.

53. Pandit SB, Bhadra R, Gowri VS, Balaji S, Anand B, Srinivasan N. SUPFAM: a database of sequence superfamilies of protein domains. BMC Bioinformatics 2004;5:28.
crossref pmid pmc
54. Kelley LA, Mezulis S, Yates CM, Wass MN, Sternberg MJ. The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protoc 2016;10:845–858.
55. Sillitoe I, Dawson N, Lewis TE, Das S, Lees JG, Ashford P, et al. CATH: expanding the horizons of structure-based functional annotations for genome sequences. Nucleic Acids Res 2019;47:D280–D284.
crossref pmid
56. Shen HB, Chou KC. Predicting protein fold pattern with functional domain and sequential evolution information. J Theor Biol 2009;256:441–446.
crossref pmid
57. Gruber M, Soding J, Lupas AN. Comparative analysis of coiled-coil prediction methods. J Struct Biol 2006;155:140–145.
crossref pmid
58. Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 2019;47:D607–D613.
crossref pmid
59. 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.

60. Montgomerie S, Cruz JA, Shrivastava S, Arndt D, Berjanskii M, Wishart DS. PROTEUS2: a web server for comprehensive protein structure prediction and structure-based annotation. Nucleic Acids Res 2008;36:W202–W209.
crossref pmid pmc
61. Hasan MA, Mazumder MH, Chowdhury AS, Datta A, Khan MA. Molecular-docking study of malaria drug target enzyme transketolase in Plasmodium falciparum 3D7 portends the novel approach to its treatment. Source Code Biol Med 2015;10:7.
crossref pmid pmc
62. Butt AM, Batool M, Tong Y. Homology modeling, comparative genomics and functional annotation of Mycoplasma genitalium hypothetical protein MG_237. Bioinformation 2011;7:299–303.
crossref pmid pmc
63. Hooda V, Gundala PB, Chinthala P. Sequence analysis and homology modeling of peroxidase from Medicago sativa. Bioinformation 2012;8:974–979.
crossref pmid pmc
64. Messaoudi A, Belguith H, Ben Hamida J. Three-dimensional structure of Arabidopsis thaliana lipase predicted by homology modeling method. Evol Bioinform Online 2011;7:99–105.
crossref pmid pmc pdf
65. Farhan H, Rabouille C. Signalling to and from the secretory pathway. J Cell Sci 2011;124:171–180.
crossref pmid
66. Kaelin WG Jr. Treatment of kidney cancer. Cancer 2009;115:2262–2272.
crossref pmid
67. Clark PE. The role of VHL in clear-cell renal cell carcinoma and its relation to targeted therapy. Kidney Int 2009;76:939–945.
crossref pmid pmc
68. Susmi TF, Rahman A, Khan MMR, Yasmin F, Islam MS, Nasif O, et al. Prognostic and clinicopathological insights of phosphodiesterase 9A gene as novel biomarker in human colorectal cancer. BMC Cancer 2021;21:577.
crossref pmid pmc


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