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 Table of Contents  
ORIGINAL ARTICLE
Year : 2021  |  Volume : 5  |  Issue : 3  |  Page : 320-326

In silico analysis and structural prediction of a hypothetical protein from Leishmania major


Department of Biotechnology, RV College of Engineering, Bengaluru, Karnataka, India

Date of Submission24-Jun-2021
Date of Acceptance12-Aug-2021
Date of Web Publication7-Sep-2021

Correspondence Address:
Achisha Saikia
Flat No. 201, Lakhimi Apartment, Lakhimi Nagar, Hatigaon, Guwahati - 781 038, Assam
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/bbrj.bbrj_126_21

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  Abstract 


Introduction: Leishmania major causes mucocutaneous leishmaniasis which is characterized by chronic skin sores. In L. major, some proteins are classified as hypothetical proteins (HPs). These proteins are chains of amino acids whose existence is predicted by sequencing organisms, but their functions remain unknown. They could further be analyzed, functionally annotated, and structurally predicted to open the doors to various applications. Methods: In this study, the HP AKK31191.1 from L. major was selected from the National Center for Biotechnology Information database. Various tools were used for one-dimensional (1D), 2D structural prediction followed by predicting the 3D protein structure via ab initio and homology modeling. The structure was analyzed and validated using various in silico tools. Results: A detailed information on the physicochemical analysis of the protein was achieved. It was found that this particular HP could be located in the cytoplasm. 2D structural analysis showed that the protein consisted of random coils at a higher amount succeeded by extended strands and alpha-helix. These data were validated through a Ramachandran plot. Subsequently, the 3D structure of the protein was visualized in UCSF Chimera which portrayed the random coils, extended strands, and the alpha-helix in different colors. Conclusions: This study focused on finding the characteristic features of the HP, predicting the 3D structure, functionally annotating the protein, and finding another similarity sequence. Through structural prediction, disease-associated mutations can be identified, and other functionally significant sites can be facilitated by determining the spatial positions of active sites and other critical residues.

Keywords: National Center for Biotechnology Information, hypothetical protein, in silico methods, Leishmania major, protein structure prediction, sequencing, structure validation


How to cite this article:
Saikia A, Palherkar DA, Hiremath L. In silico analysis and structural prediction of a hypothetical protein from Leishmania major. Biomed Biotechnol Res J 2021;5:320-6

How to cite this URL:
Saikia A, Palherkar DA, Hiremath L. In silico analysis and structural prediction of a hypothetical protein from Leishmania major. Biomed Biotechnol Res J [serial online] 2021 [cited 2021 Dec 6];5:320-6. Available from: https://www.bmbtrj.org/text.asp?2021/5/3/320/325609




  Introduction Top


The advent of genome sequencing and associated research in the 1990s has largely assisted in the association of functions to the sequenced genes and domains.[1] As of 2020, more than 10,000 species belonging to the archaeal, bacterial, and eukaryotic super kingdoms have been partially or completely sequenced, and it has been aimed to sequence about 1.5 million genomes within the next 30 years (https://www.ncbi.nlm.nih.gov/genome/browse/#!/prokaryotes/).[2] This “era of genomics” has seen tremendous data gathering related to the sequencing projects produced each year, serving many purposes.[3] These gathered data are large and explanatory but ambiguity still exists while annotating linked genes or proteins. This ambiguity on an average amount up to 70% of the total genome length.[2],[4]

The investigations of omics have employed sequences of genes and/or proteins from public databanks, and some have been successful in characterization and assignment of function.[1],[3] Nonetheless, there have been many instances where the genes or proteins have not been annotated, which may have arisen as a result of an outdated knowledge base or very low similarity to nucleic acid/amino acid sequences of known function.[2],[3] As their functions are unknown, and it is still unclear whether they constitute actual proteins, they have been termed as “hypothetical” proteins (HPs) or “uncharacterized” proteins.[1],[3] A few HPs have been observed to be conserved across several phylogenetic lineages.[5] Making up quite a large portion of the genome, the study of these proteins can offer several benefits, such as the recognition of their role in secretory and biosynthetic pathways.[6] The detection and legitimate annotations of such HPs can lead to uncovering novel conformational orientations of 3D structures, which might lead to the emergence of cascades and additional synthetic pathways.[5],[7],[8]

To determine the structures of HPs, the sequence is retrieved from National Center for Biotechnology Information (NCBI). NCBI (https://www.ncbi.nlm.nih.gov/) maintains the GenBank (R) nucleic acid sequence database and provides various data retrieval techniques and computing resources to enable researchers to perform on-demand analyses of the biological data from GenBank.[9] Along with nucleotides, the data could also be retrieved in the form of amino acid sequences from protein database. To delve into the functionality of the protein, universal protein resource (UniProt) is explored. UniProt (https://www.uniprot.org/) is a large collection of protein databases that let researchers traverse through all the significant information available on proteins and their associated functional annotation.[10] To learn about the physicochemical properties of the sequence, tools such as EXPASY ProtParam (https://web.expasy.org/protparam/) are used. Properties of the amino acids and their interactions determine the physicochemical properties of the protein. An essential part of functional annotation is prediction of subcellular locations.[11] In bioinformatics-based prediction of protein function and genome annotation, prediction of protein subcellular localization can improve the discovery of therapeutic targets. There are a multitude of bioinformatic tools used for the protein identification in eukaryotes, prokaryotes, fungi, and for some specific model organisms such as Arabidopsis thaliana (SLocX) and Oryza sativa (RSLpred). Subcellular localization locates whether the query protein resides in the cytoplasm [Figure 1], mitochondrion, nucleus, secretory organelle, or chloroplast in case of plants, if the protein is a signal peptide or some predicted features of the proteins such as ER retention signal and apoptosis.[12] For one-dimensional (1D), 2D, and 3D protein model prediction, tools such as CRNPRED [Figure 2], Self-Optimized Prediction Method with Alignment, Profile neural network systems from HeiDelberg (PHD), Protein Homology/AnalogY Recognition Engine 2 (Phyre2), and Swiss model can be employed which have been shown to have high efficacy.[2],[6],[13] For homology modeling, tools such as SWISS-MODEL, MODELLER, MaxMod, PyMod, and PRIMO can be used for predicting 3D structure.[14] Furthermore, there is a requirement for a high-quality assessment of protein tertiary structure in order to verify the structure determination approach, regulate the model's viability, and ascertain its prospective applications.[15],[16] SAVES v6.0 server (https://saves.mbi.ucla.edu/) is a verification server that holds ERRAT, Verify3D, PROVE, PROCHECK, and WHATCHECK.
Figure 1: Results received by CELLO2GO (first one) and iLoc-Animal (second one) showcasing that the protein is present in the cytoplasm

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Figure 2: CRN5000 results illustrating the 318 amino acids, its secondary structure where H is (alpha) helix, E is (beta) strand, and C is coil (all others), and contact numbers are encoded in two states where B indicates buried and E means exposed

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ERRAT program is used to validate the protein structures that were established by X-ray crystallography. For error values, the position of a sliding 9-residue window is depicted. Nonbonded atom–atom interactions are reported in the structure and form the basis of the error function (https://servicesn.mbi.ucla.edu/ERRAT/help). Another example of a verification program is Verify3D, which classifies each residue in the protein according to the environment around the residue in the input model. The final score for the protein structure is a total of the propensities of the various residues, derived using statistical data from structures entered in a PDB format [Figure 3] and [Figure 4].[15] Moreover, the protein quality of a predicted protein structure after employing an ab initio approach or homology modeling is often analyzed through a Ramachandran plot. Every polypeptide amino acid residue has a distinct set of phi (ϕ) and psi (Ψ) angles; hence, each residue can be plotted on a Ramachandran plot with the x and y coordinates representing the ϕ and Ψ angles [Figure 3]. Polypeptides will adopt a certain torsional angle every time it transitions from a polypeptide chain to a secondary structure, forming a structure such as an alpha-helix or beta-sheet [Figure 6]. The use of this method, therefore, enables us to identify and verify the secondary structure from a specific polypeptide within a constrained region on the plot. The right-handed alpha-helix falls on the lower left quadrant with backbone torsional angled phi (ϕ) = –57° and psi (Ψ) = –47°, thus occupying a smaller space. On the other hand, the extended strands that mostly make up the beta-sheets are positioned on the upper left quadrant of the Ramachandran plot. This positioning occurs due to the ϕ and Ψ angles.[17] Bioinformatic tools such as RAMPAGE and MolProbity could be used for plotting a Ramachandran plot. These respective tools give an analysis of favored regions, allowed regions, and the outliers. The favored regions are fully allowed regions where an amino acid could be positioned in any of the four quadrants, such as glycine which has no side chains (https://swissmodel.expasy.org/course/text/chapter1.htm). When generating the Ramachandran plot, the allowed regions indicate the possible values of the ϕ/Ψ angles for an amino acid.[18] Amino acids that have nonfavorable dihedral angles are known as the outliers in a Ramachandran plot [Figure 9], sometimes the outliers also occur due to errors during data processing.[19]
Figure 3: Profile neural network systems from HeiDelberg results showing random coils (orange), extended strands (red), and alpha-helix (blue)

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Figure 4: Results validated by PredictProtein tool

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Figure 5: Results showed by SWISS-MODEL

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Figure 6: Visualization done in UCSF Chimera. Color codes: Blue – extended strands, green – alpha-helix, and gray – loops

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Figure 7: ERRAT program result showing an overall quality factor of 71.429

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Figure 8: Verify3D program results illustrating a PASS compatibility score of 91.19%

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Figure 9: Ramachandran plot. The outlier regions were five outliers (phi, psi): 6 Thr (−77.8, −114.0), 160 Pro (−111.5, 163.1), 191 Asn (−58.9, −175.8), 192 Val (−75.7, 53.8), and 263 Ser (−37.1, −96.2)

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Databases such as NCBI conserve domain database (NCBI CDD) (https://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi) document functional sites (such as active sites or binding sites for cofactors) and distinctive sites (such as signature motifs) across domain families, and release information about the conserved domain matches. Hence, detailed information on a protein family and their subsequent functions could be unraveled.[20] Similarly, another database specializing in protein called protein family database (Pfam) includes protein families and domains that are frequently utilized to annotate novel genomes, metagenomes, and aid in experimental investigations that target specific proteins and systems. Pfam builds a hidden Markov model (HMM) through HMMER software to check the entered sequence against a nonredundant database composed of proteomes called Pfamseq.[21]

This paper focuses on structural prediction of a HP (GenBank: AKK31191.1) in Leishmania major. In NCBI protein database, the 4 available L. major HP sequences had 142, 174, 318, and 360 amino acids. When cross-checked with UniProt, it was revealed that out of these 4 aforementioned sequences, the one with 142 amino acids had recently been characterized as rhodanese domain-containing protein. The selected protein possessing gene DB LMJF_10_1250 has a total of 318 amino acids. The protein region encompassing the residues 13-307 is a member of the pfam08950 family, and the overall protein has been designated as a protein with unknown function, designated as domain of unknown function (DUF) 1861 (https://www.ncbi.nlm.nih.gov/protein/828277917). To predict the structure of this HP, both ab initio modeling (Phyre2) and homology modeling (SWISS-PROT) have been carried out. Basic local alignment search tool (for proteins) (BLAST P) has been used to find a similar protein sequence. Various in silico tools were used to investigate the physicochemical, structural, and functional properties of the uncharacterized protein.[22] The final model had been submitted to Protein Model Database (PMDB) and PMID: PM0084121 was assigned [Figure 6], [Figure 7], [Figure 8], [Figure 9], [Figure 10], [Figure 11], [Figure 12].
Figure 10: National Center for Biotechnology Information conserved domain database showcasing the family where the hypothetical protein belongs

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Figure 11: Protein family database confirming the domain of unknown function

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Figure 12: Basic local alignment search tool for protein results depicting similarity with another Leishmania major hypothetical protein

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  Methods Top


Hypothetical protein selection and retrieval

NCBI was used to find a HP. In the search bar, HP L. major was typed. HP with the Accession ID AKK31191.1 and GI: 828277917 was selected. UniProt was used to confirm whether the HP AKK31191.1 is still uncharacterized.

Physicochemical analysis of the hypothetical protein

EXPASY ProtParam tool was used for physicochemical analysis.

EXPASY ProtParam gives a detailed result about the molecular weight, theoretical isoelectric point (PI), the total number of positively and negatively charged residues, molecular formula, total number of atoms, the instability index, aliphatic index, and grand average of hydropathicity (GRAVY).[23]

Subcellular localization

CeLL02Go was used to find the subcellular localization. WOLF PSORT and iLoc-Animal were used to validate the result [Figure 1].[24],[25],[26]

CELL02Go, WOLF PSORT, and iLoc-Animal are tools used for subcellular localization in eukaryotes [Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6].

One-dimensional structure analysis

CRNPRED was used for predicting the primary structure of the HP [Figure 2].[27]

Two-dimensional structure analysis

PHD program was used to predict the 2D structure. JPRED4 and Predict Protein were used to validate the results.[28],[29],[30]

Homology modeling

SWISS-MODEL [Figure 5] was used for homology modeling. Robetta, IntFOLD6, and RaptorX were also used for validation.[6],[31],[32],[33],[34]

Three-dimensional structure prediction

Phyre2 was used for predicting the 3D structure of the HP. The result was later validated with I-TASSER.[35],[36]

Quality assessment and structure validation

ERRAT and Verify3D which belong to the verification server SAVES were used to test the model for recognition of error in the 3D structure.

During the model creation and refining process, defects might be incorporated into the protein model that may vary in their severity and importance.[37] A Ramachandran plot [Figure 9] was created by MolProbity to check for the defects and ascertain the fraction of residues that can be in the favored, allowed, and outlier region.

Functional annotation

To establish whether the HP had conserved domains and to find out any potential function of the HP, the NCBI Conserved Domain Database was used. To verify the results received from NCBI CDD, Pfam database was used for cross-checking. A BLAST sequence analysis was carried out to find the similarity score of this particular protein with other proteins.

Submission of model

The HP model of AKK31191.1 was submitted to PMDB (http://srv00.recas.ba.infn.it/PMDB/) with PMID: PM0084121.


  Results Top


Physicochemical characteristics of AKK31191.1

EXPASY ProtParam tool analyzed the physicochemical characteristics of AKK31191.1. The predicted protein consists of 318 amino acids and has a molecular weight of 36212.98 Dalton. The theoretical PI is shown to be 5.42. The total number of negatively charged residues (aspartic acid + glutamic acid) is 44 and the total number of positively charged residues (arginine + lysine) is 33.

The molecular formula of the HP is C1626H2472N426O484S15 and the total number of atoms is estimated to be 5023. The instability index was low at 27.53. The GRAVY is −0.326, indicating that the protein is hydrophilic. The aliphatic index is a little high and seen to be around 77.20 which indicates that the protein is thermostable.[38]

Subcellular localization

Protein subcellular localization indicates where the protein resides inside a cell. CELLO2GO characterized the HP to be present in the cytoplasm. iLoc-Animal and YLoc validated the result received by CELLO2GO.

One-dimensional structure analysis

CRNPRED was kept at CRN5000 which has 5000D state vectors and gives the most accurate prediction.

Two-dimensional structure analysis

PHD was used for secondary structure analysis which was later validated by PredictProtein and JPRED4.

Random coil was seen at the highest amount occupying 51.26% of the protein. It was followed by extended strand at 41.19% and then alpha-helix was seen at 7.55%.

Homology modeling

According to SWISS-MODEL, a BLAST query was carried out and the template similarity of the HP AKK31191.1 was found to be 60.84. Global Model Quality Estimation (GMQE) was found to be 0.84 which depicts that the quaternary structure is reliable to be followed in the modeling process.

Three-dimensional structural analysis

The predicted model's alignment and detection rates are improved due to Phyre2 as it uses the alignment of HMM. Phyre2 has given a confidence level of 98%. The results were later validated with I-TASSER. The PDB models were viewed in UCSF Chimera, as shown in [Figure 6].

Quality assessment and structure validation

ERRAT evaluated the nonbonded interaction between several types of atoms using the atomic interaction to determine the model's reliability and precision. The overall quality factor showed by ERRAT is 71.429 showing that the model has a good quality and may be applied in many fields such as target identification and drug designing. Verify3D showed that 91.19% of the residues have an average of 3D-1D greater than equal to 0.2, illustrating that the structure is quite compatible.

The Ramachandran plot created by MolProbity showed that 93.7% of all residues were in favored regions, 98.4% of all residues were in allowed regions, and 5 were in outlier regions. The extended strands are seen as dense dots on the plot named “general case” on the upper left corner.

Functional annotation

NCBI conserved database shows that the HP AKK31191.1 belongs to DUF1861 family protein. The Pfam database confirmed that this HP found in L. major has no known function yet [Figure 11].

Basic local alignment search tool sequence analysis

The BLAST protein results show that the HP AKK31191.1 has 100% sequence similarity with another L. major HP with strain Friedlin [Figure 12].


  Discussion Top


Our work had been designed to generate the first 3D model of a HP of L. major with accession ID AKK 31191.1. A physicochemical analysis of the protein reveals its molecular weight to be approximately 36 kDa with a PI of 5.42. The presence of larger quantities of negatively charged residues such as aspartic acid and glutamic acid lowered the PI of the protein. High aliphatic index indicates the thermal stability of the HP. A 2D structural analysis discloses the composition of the protein largely occupied by random coils at about 51.3% followed by extended strand at 41.2% and α-helix at 7.55%. Quaternary structure of the protein has been deemed fit for modeling process by GMQE.

With a very high confidence level of the Phyre2 tool, which was based on alignments using HMM, the predictive model's detection rate and alignments are improved and further validated by I-TASSER. A high overall quality factor and 3D-1D score that indicates the model's reliability and precision has illustrated that the model can be employed for further experiments such as drug designing and target identification rendering the structure fairly compatible. As of now, the HP belongs to a Pfam family DUF1861, indicating that the domain has no annotated function. A comparative analysis through BLAST P shows that another HP from L. major matches with a 100% identity.


  Conclusion Top


This study illustrates that currently the HP AKK31191.1 is not associated with a family of protein with known functions. With the course of time, when it becomes a characterized protein, future studies of this protein might unveil some of their role in partially known or unknown pathways and mechanisms or may lead to the investigation of related proteins that might be involved in central systems.

Acknowledgment

The authors listed in this paper wish to express their appreciation to the RSST Trust, Bangalore, and the Department of Biotechnology for their continuous support and encouragement.

Ethical statement

This material is the authors' own original work, which has not been previously published elsewhere.

The paper is not currently being considered for publication elsewhere.

The paper reflects the authors' own research and analysis in a truthful and complete manner.

The paper properly credits the meaningful contributions of co-authors and co-researchers.

The results are appropriately placed in the context of prior and existing research.

All sources used are properly disclosed (correct citation). Literally copying of text must be indicated as such by using quotation marks and giving proper reference.

Human/animal rights

This article does not contain any studies with human or animal subjects performed by the any of the authors.

Financial support and sponsorship

Nil.

Conflicts of interest

The authors declare that none of the authors have any competing interest.



 
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    Figures

  [Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6], [Figure 7], [Figure 8], [Figure 9], [Figure 10], [Figure 11], [Figure 12]



 

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