INTRODUCTION:

Alkaptonuria is an autosomal recessive, congenital disorder which is an inborn error of metabolism of phenylalanine and tyrosine characterized by the inability to metabolize HGA¹.

It has a characteristic mutation of homogentisate 1,2 dioxygenase (HGD) resulting in the accumulation of HGA, mapped the gene to the chromosome 3q21-q23 which splits the aromatic ring homogentisic acid to maleyl acetoacetic acid paved by the tyrosine catabolic pathway^2-3. The gene usually gets transferred to the offspring from both the parents which regulates the HGD levels.⁴

Homogentisic acid oxidized to benzoquinone acetate which will polymerize forming melanin like polymers and it is deposited in the fibrous tissue as well as cartilage responsible for causing ochronotic pigmentation⁵. The excess HGA passes through urine giving the black color and leads to degenerative premature arthritis and cardiac valve deterioration⁶. In previous study, the presence of HGA in dark urine was 55% while 45% upon chronic joint pain diagnosed by gas chromatography – mass spectro photometry as a part of urinary organic acid analysis^7-8. It has a great impact on the quality of life, where many effected individuals have pain, sleeping abnormality and respiratory problems⁴.

Earlier scientists analyzed the secondary structure which contain a high amount of alpha helix and beta sheet⁹. They have also analyzed the total intrinsic disorder and protein disorder propensity which showed the protein maintains the ordered structure except for the one where iron coordinated with His335, His371, and Glu341 indicating the active site residues and it can be used as target for various drug molecules^10-11. HGD accommodates 14 exons which wrap about 60 kb of genomic DNA and encrypts 69,973 daltons, 445 amino acids form a dimer of two trimers to form a functional hexamer. The crystalline structure has been resolved depicting the allelic heterogeneity^12-13.

The carriers of the disease express 50 % or more of normal enzymatic activity with high incidence rate at about 1 in every 25,000 live births being reported worldwide which is considered far lower. The recent reports showed distribution of this disease found to be equal in males as well as females indicating it is an autosomal recessive or autosomal dominant trait but onset of arthritic symptoms in males found to be reported earlier and with greater degree of severity as compared to females¹⁴.

Based upon the recent research, we had adopted the In-silico approaches by introducing various computational analysis which aids us in diagnosis and treatment of Alkaptonuria. This study was based upon its mortality rate in India as well as globally that aims to identify the most potential deleterious ns-SNPs in order to design new effective drugs for Alkaptonuria in near future.

METHODOLOGY:

Retrieval of ns-SNPs

The National Centre for Biotechnology Information https://www.ncbi.nlm.nih.gov/ is an open-source database. This database was utilized to gather adequate information of missense ns-SNPs within homosapiens HGD gene and sequence of protein in FASTA format were retrieved. Furtherly, the retrieved ns-SNPs were subjected to various in-silico tools to analyse the deleterious missense ns-SNPs of HGD gene.

Computational tools were executed to envision non-synonymous SNPs using preliminary tools and trajectory tools.

Preliminary Tools:

SIFT (Sorting Intolerant From Tolerant):

This tool https://sift.bii.a-star.edu.sg/ was used to estimate the amino acid substitution that affects the protein function. It is extensively used to speculate the deleterious ns-SNPs¹⁵. SIFT uses protein sequence homology and takes a query sequence as an input to speculate permissible (tolerated) and deleterious substitutions for each and every position of query sequence¹⁶. Finally, the output identified and segregated the deleterious and tolerant mutations based on the probability score¹⁷.

PolyPhen2 (Polymorphism Phenotyping 2.0):

Based on structural analysis, PolyPhen http://genetics.bwh.harvard.edu/pph2/index.shtml is performed by using several parameters¹⁸ of amino acids substitution and homologous sequence¹⁹. The input query is in FASTA format. To predict the ns-SNPs it uses empirically derived rules¹⁸ and computes the position- specific independent count (PSIC) score, sensitivity and specificity of both mutations. The substitution effect of an amino acid is considered primarily when the PSIC score is high predicting whether they are “probably damaging,” or “possibly damaging,” or “benign”¹⁹.

I-Mutant3.0:

It http://gpcr2.biocomp.unibo.it/cgi/predictors/I-Mutant3.0/I-Mutant3.0.cgi is a stability forecasting software which measures the destabilisation of protein²⁰. It is a web- based online tool to estimate the variations in Single Point Mutations²¹ which was tested and trained from ProTherm¹⁶. I-Mutant provides the expected free energy change (DDG) value and the prediction is a sign of increase or decrease. The output file is calculated from the difference between unfolding Gibbs free energy values of native and mutant types²¹.

PANTHER (Protein Analysis Through Evolutionary Relationships):

This tool http://www.pantherdb.org/tools/csnpScoreForm.jsp was used to analyse the protein and related functional impact on them. It uses a conversation on evolutionary relationships of amino acid to predict²² specific ns-SNPs to cause functional changes on the protein. The result or score is obtained by using a symmetric algorithm which takes a absolute value to adhere the convention of the substitution matrix, according to which high negative scores correspond to severity of the substitutions. Based on the arrangement and positioning of evolutionary related proteins, it can estimate the substitution position and specific evolutionary conservation score¹⁶.

SNP and GO (Single Nucleotide Polymorphism: Database and Gene Ontology):

SNPs and GO https://snps.biofold.org/snps-and-go/snps-and-go.html are web servers that accurately predict the single point mutation which is related to the disease of protein sequence²³and collects the unique frame work information²⁴. FASTA format is taken as an input and employee the output as a probability score greater than 0.5. This server indicates that the disease-related impacts are observed as an effect of ns-SNPs on the functions of native protein²³.

FASTA format of the HGD gene was given as an input for those above five Preliminary tools, and the ns-SNPs which were predicted as deleterious by combined prediction using preliminary tools were considered for further analysis.

Energy Minimization and RMSD Calculation:

The three – Dimensional structure plays an important role in studying the conduit of protein. Since the HGD protein has no structure available, So by utilizing SWISS-MODEL expasy (http://swissmodel.expasy.org/) a 3D structure was modelled, validated through PROCHECK and ProSA web server (https://prosa.services.came.sbg.ac.at/prosa.php). In due time, by Swiss-PDB viewer (https://spdbv.vital-it.ch/) all mutant structures were generated and prepared for GROMACS by deleting all ligands, water molecules, hetero atoms and optimized by minimization of energy²⁵.Then GROMACS (http://www.gromacs.org) was used for energy minimization of proteins both native and mutant which works on L-BFGS, (Limited-memory Broyden-Fletcher-Goldfarb-Shannoquasi-Newtonian minimizer). Structures collected from GROMACS were exploited for RMSD (Root Mean Square Deviation) calculation by using SWISS PDB viewer.

Trajectory Tools:

SRide:

This tool http://sride.enzim.hu/ identifies stability residues of both native and mutant proteins. If a residue is confidential as stabilization centre (SC) then we accurate more values for vanderwaals atom radii. The input of protein sequence is uploaded directly in PDB format to analyse the structures which are obtained by computational approaches or homology modelling. Calculations are performed on the selected protein chain and to calculate Long Range Order ( LRO), Hydrophobicity (HP), Stabilization centre (SC) inter-chain interactions are not taken into account. The output of protein sequence is used to calculate the conservation score conjointly with HP, LRP²⁶.

Consurf:

This tool https://consurf.tau.ac.il/ConSurf_Or_ConSeq.php helps in estimating the evolutionary conservation of residue position on protein. Based on algorithm²¹ the tool consurf spurt the analysis of homologous sequence by virtue of BLAST to spawn the conservation scores²⁷. Depend upon the phylogenetic relation between homologous sequence the consurf calculates conservation score of amino acid²⁸. The score ranges from 1 to 9 grade and estimate the conservation of amino acid all over the evolution²¹.

SPPIDER (Solvent accessibility based on Protein-Protein Interface iDEntification and Recognition): This http://cmb.bnu.edu.cn/SPIDer/ is a database employed for the prediction of protein – protein interactions. By evaluating single protein chain with considered 3D structure the Sppider server is used to analyse the protein-protein complex and identify the reputated protein interface. The input is given in PDB format then the predicted results can be pursued easily in 3D structure with several molecular modelling software. The output in Sppider can be viewed by JMol java-applet.

PSIPRED:

This http://bioinf.cs.ucl.ac.uk/psipred/ is a method used to investigate protein structure. This server permit users to capitulate (submit) the protein sequences in FASTA format and perceive the output results of prediction mutations in the form of textually by virtue of e-mail and graphically via the web²⁹.

FLEXPRED:

It was http://flexpred.rit.albany.edu/ a web server utilised for predicting residue positions involved in conformational switches on protein. Flexpred quickly predicts the absolute residual fluctuation of 3D protein structure³⁰ and upload FASTA format based on amino acid sequence and solvent accessibility residue in PDB format as outputs. The output shows the predicted labels for individual residue position³¹ by comparing the predicted fluctuations with observed fluctuations in MD simulations³⁰. The obtained labels shown as “R” (rigid) and “F” (flexible)³¹.

For these five trajectory tools, Either as a sequence or as a structure file of HGD was submitted in tools. ns-SNPs which were earlier predicted as deleterious in the maximum number of tools were listed out for further and extended analysis.

Molecular Docking and Discovery Studio Visualizer:

Molecular docking is one of the most widely used methods in structure-based drug design because it can predict the formation of ligand bonds of small molecules to the appropriate target binding site³². The Molecular Docking analysis of native and mutant protein were demonstrated by Autodock vina. Autodock Vina uses interaction maps for docking and evaluate the binding affinity of ligand³³ to predict the predominant known 3D structure of protein³⁴. Additionally autodock tool aids in structure completion by computing the mixing hydrogen and side chain atoms³⁵. Therefore, before performing docking, polar hydrogens were computed to the macromolecules and assign atomic charges by using Autodocking Vina³³. Using this, the exact locations of target proteins and ligand binding were obtained by PyMol and clearly visualised by using BIOVIA Discovery Studio Visualize. Discovery studio visual image program is used to envision the receptors, ligand structures, element bonding network, to calculate length of the bonds and to render images³⁶.

Flowchart disclose the unified process of Alkaptonuria using peculiar bioinformatics tools as shown in Figure 1.

Figure 1: Flowchart of whole study

RESULTS:

Retrieval of ns-SNPs:

This study is useful to analyse non-synonymous SNPs in human HGD genes. In the HGD gene there are 13,343 SNPs. Out of these SNPs, only 337 are non-synonymous missense mutations which were extracted and collected from the National Centre Biotechnology Information(NCBI) database.

Combined Analysis using Preliminary Tools:

For studying the structural and functional effect five different software’s were used (SIFT, POLYPHEN 2.0, I-MUTANT 3.0, PANTHER, SNPs and GO). After analysis with these five tools, 98 SNPs of non-synonymous missense mutations were predicted to be highly damaged. SIFT and PolyPhen are among the evocative for empirical rules-based methods. Since the Preliminary tools involve different algorithms, a combined approach may increase the meticulousness in identifying the deleterious ns-SNPs. Only those 98 possibly deleterious ns-SNPs were considered for the subsequent analysis.

Energy Minimization and RMSD Calculations:

Energy minimization study helps in gathering the information about the stability of the protein structure. From the results obtained, it induces us to further emphasize on the dynamic structures of native and mutant HGD genes. The 3D structure of the HGD gene was modelled by using SWISS-MODEL. The RMSD results of all mutants did not show much variations and are almost in similar terms. If the RMSD value obtained is high, then the deviation between the native and mutant will be more.

Table 1: List of ns-SNPs predicted as deleterious through RMSD calculation through Gromacs.

S. No	Residual Change	RMSD(Å)
1	P32S	2.078
2	L39H	1.849
3	S45P	1.769
4	G46E	1.956
5	R53W	1.807
6	L61P	1.976
7	R63S	1.729
8	C120W	1.875
9	C120Y	1.863
10	G121R	1.982
11	G123R	2.356
12	D151V	3.526
13	P230S	3.581
14	G270R	3.921
15	T328P	1.747
16	R330S	1.867
17	H335R	1.983
18	L345P	2.013
19	I346T	2.107
20	G360R	2.271
21	G360V	1.912
22	G361R	2.215
23	H371R	2.133
24	H371N	2.252
25	P373L	2.213
26	V408G	1.954
27	L430H	1.76

Energy minimization is done for all structures of mutants and also for native which was obtained from the SWISS-PDB viewer by using GROMACS which was shown in Figure 2. After energy minimization, The two-dimensional (2D) structure of native swap in to three-dimensional structure as shown in Figure 3. Therefore, RMSD was calculated by super-imposing native and mutant structures in SWISS-PDB viewer. Based on the RMSD value 27 SNPs are obtained, the threshold value was fixed as above 1.7Å. The higher the RMSD value, the degree of deleterious effect will also be higher. Based on that 27 SNPs were highly deleterious as shown in Table1

Figure 2: Visualization of Native Protein (Left side: Minimized Structure, Right side: Non minimized Structure)

Trajectory Analysis:

After initial preliminary analysis, the possible deleterious 27 ns-SNPs were analysed with trajectory analysis. Trajectory analysis revealed that five ns-SNPs (R53W, L61P, G121R, G361R and L430H) are highly deleterious based on transition in polarity.

Later an inhibitor molecule NITISINONE is analysed which showed the binding sites with residues are selected as ligand molecules. The structure of ligand molecules is derived from the PubChem. pyMOL is performed on active sites residues which is an open source molecular visualization tool which produces high quality three dimensional images of small molecules.

Molecular Docking and Discovery Studio Visualizer:

The molecular simulation predicted the binding affinity of ligand molecule with native as well as mutant structure of protein for identifying the most potential deleterious single candidate ns-SNP. Autodocking vina is an open source tool to perform autodock. The structure was produced by Auto Dock Vina visualized by the tool, Discovery Biovia, which depicts the active site of protein along with the ligand. Literature survey identified H70, F73, S150, N247, T249, F254, I391, E389, R330, P331, A392, R321, S67, E42, V68, I216, I297, G214, S296, I297, P211, T299, P99, K98, A415, E101, W411, and P215 as active sites of residues. From the results of docking, the binding energy differs drastically for all residues except L61P as shown in Table 2. In Figure 4 it shows the visualization of mutant protein L61P in the form of two-dimensional (2D) and three-dimensional (3D). The mutation could also be the rationale for these variations and it supports the conclusion obtained from all the previous analyses.

Table 2: Binding energies in kcal/mol of protein and ligand interactions

	Native	R53W	L61P	G121R	G361R	L430H
Binding Affinity	-6.1	-4.8	-4.3	-5.1	-4.9	-4.8

These predictions are in accordance with the deviation in RMSD values, as predicted earlier. The analogue which acquires the highest fitness score is having the highest binding affinity³⁵.

Figure 3: Visualization of Native Protein (Left side: 2D Visualization, Right side: 3D Visualization)

Figure 4: Visualization of Mutant Protein L61P (Left side: 2D Visualization, Right side: 3D Visualization)

DISCUSSION:

Modern research studies advocated that³²In-silico analyses was used to contribute more factual prediction of disease-related mutations in HGD genes³⁸. In order to identify the most destructive non-synonymous SNPs (ns-SNPs), we were able to identify harmful ns SNPs in candidate target genes on the computer. In this study, we used several computer methods to identify the most likely pathogenic mutations. Various algorithms are often used as powerful tools for prioritizing malicious SNPs from a given data set³⁹. Therefore, screening of polymorphism is used to identify the missense SNPs which affect the function of protein that are associated with the disease is a crucial work⁴⁰. Computational tools play a major role in identifying and predicting the most deleterious SNPs which are highly stable to be damaged⁴¹. Considerable information is found in NCBI databases. From NCBI database 337 missense mutations were retrieved from the human HGD gene to identify the deleterious missense mutations by using various informatics tools⁴⁰. To know the possible effects of ns-SNPs preliminary tools like SIFT, PolyPhen2, I-mutant 3.0, Panther and SNPs and GO were used. When compared to the other in-silico tools PolyPhen and SNPs and GO have more greater algorithms for predicting the functional nsSNPs⁴². Some mutations were sorted out from those missense ns-SNPs which are highly deleterious⁴³.

The maximum aligned protein in SWISS-PDB was chosen as the template. The template structure was retrieved from PDB. The final template structure was downloaded and used for further to predict the query structure. The constructed models were reduced to a minimum with SWISS PDB viewer. By minimizing energy, incorrect angles or lengths between amino acids can be corrected⁴⁴. The crystal structure of the HGD gene was found in the PDB database. Later, SWISS-MODEL is performed to predict the possible three dimensional structures of native and mutant HGD gene. Energy minimization is executed in SWISS-PDB viewer by using GROMACS. The RMSD values were assumed by superimposing the mutant models over the native model under a SWISS-PDB viewer⁴¹. Trajectory analysis were performed by using different parameters and predict the most deleterious ns-SNPs which are highly pathogenic that affects the stability of HGD gene⁴⁵. In docking analysis, protein-ligand interactions were analysed using Autodock Vina. Before docking analysis, evaluation is done to the binding sites of ligand NITISINONE in pyMol. To pre- calculate grid maps the interaction energies of various atoms with protein or DNA or RNA. AutoDock then uses these grid plots to perform binding calculations, and then limit the interaction energy of the ligand with the protein⁴⁶. Active site prediction was done to predict the protein-ligand binding packages which can play a significant role in drug development⁴⁷. Among all the missense mutations only one mutation is revealed as the most damaging ns-SNP by reducing the binding affinity and causing the structural deviation of HGD gene. Hence, these findings follow the computational tools which might be helpful for subjecting the laboratory confirmatory analysis⁴⁸.

CONCLUSION:

This study embraces more on the effect of functional SNPs in HGD genes associated with disease Alkaptonuria. Based on computational analysis, it was stated that out of 337 SNPs missense mutations only five SNPs R53W (rs759435977), L61P (rs1324654414), G121R (rs866380660), G361R (rs765219004) and L430H (rs779866537) were responsible for diagnosing disease Alkaptonuria. From those 5SNPs only one SNP i.e. L61P (rs1324654414) is identified as highly deleterious in the ligand-binding region of HGD gene. To differentiate polymorphism from damaging missense mutations some ns-SNPs were predicted in human HGD genes which would be useful in drug metabolism and for clinical response.

ACKNOWLEDGMENT:

We the authors thank the Council of Scientific and Industrial Research (Govt. of India) - NEIST, Jorhat for creating a platform to conduct this research work in Summer Research Training Program (SRTP) 2020.

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Received on 03.07.2021 Modified on 05.09.2021

Research J. Pharm. and Tech 2022; 15(9):3898-3904.

DOI: 10.52711/0974-360X.2022.00653