Comprehensive characterization of high-risk coding and non-coding single nucleotide polymorphisms of human CXCR4 gene

Abstract

CXCR4, a chemokine receptor known as Fusin or CD184, spans the outer membrane of various human cells, including leukocytes. This receptor is essential for HIV infection as well as for many vital cellular processes and is implicated to be associated with multiple pathologies, including cancers. This study employs various computational tools to investigate the molecular effects of disease-vulnerable germ-line missense and non-coding SNPs of the CXCR4 gene. In this investigation, the tools SIFT, PROVEAN, PolyPhen-2, PANTHER, SNAP 2.0, PhD-SNP, and SNPs&GO were used to predict potentially harmful and disease-causing nsSNPs in CXCR4. Additionally, their impact on protein stability was examined by I-mutant 3.0, MUpro, Consurf, and Netsurf 2.0, combined with conservation and solvent accessibility analyses. Structural analysis with normal and mutant residues of the protein harboring these disease-associated functional SNPs was conducted using TM-align and SWIS MODEL, with visualization aided by PyMOL and the BIOVINA Discovery Studio Visualizer. The functional impact of wild-type and mutated CXCR4 variants was evaluated through molecular docking with its natural ligand CXCR4-modulator 1, using the PyRx tool. Non-coding SNPs in the 3' -UTR were investigated for their regulatory effects on miRNA binding sites using PolymiRTS. Five non-coding SNPs were identified in the 3'-UTR that can disrupt existing miRNA binding sites or create new ones. Non-coding SNPs in the 5' and 3'-UTRs, as well as in intronic regions, were also examined for their potential roles in gene expression regulation. Furthermore, RegulomeDB databases were employed to assess the regulatory potential of these non-coding SNPs based on chromatin state and protein binding regulation. In the mostly annotated variant (ENSP00000241393) of the CXCR4 gene, we found 23 highly deleterious and pathogenic nsSNPs and these were selected for in-depth analysis. Among the 23 nsSNPs, five (G55V, H79P, L80P, H113P, and P299L) displayed notable structural alternation, with elevated RMSD values and reduced TM (TM-score) values. A molecular docking study revealed the significant impact of the H113P variant on the protein-ligand binding affinity, supported by MD simulation over 100 nanoseconds, which highlighted substantial stability differences between wild-type and H113P mutated proteins during ligand binding. This comprehensive analysis shed light on the potential functional consequences of genetic variation in the CXCR genes, offering valuable insights into the implications of disease susceptibility and may pave the way for future therapeutic interventions.

Fig 1. Flowchart of the methodology used in this study.

All the coding SNPs obtained from the NCBI and ENSEMBL databases were filtered to find out the most deleterious and disease-causing SNPs using seven different bioinformatics tools. Then the filtered nsSNPs were assessed for their effect on the secondary and tertiary structure of the protein. The effect of a drug was predicted against nsSNPs that have a massive effect on the protein structure. The consequences of the non-coding SNPs were also investigated.

Fig 2. Most deleterious and disease-causing nsSNPs estimated by different in silico tools.

Five in silico tools–SIFT, Polyphen2.0, PROVEAN, PANTHER, and SNAP2.0 were used to predict the most deleterious nsSNPs, and two tools–PhD-SNP and SNPs & GO–were used to predict disease-causing nsSNPs. Among the deleterious and disease-causing nsSNPs, 23 were common.

Fig 3. Schematic representation of the relative surface accessibility and conservation of the high-risk SNPs predicted by NetSurfP-2.0 and Consurf web tool.

In NetSurfP2.0, a threshold of 25% was utilized, meaning SNPs with more than 25% RSA are expected to be exposed on the protein surface.

Fig 4. Effects of the filtered nsSNPs within CXCR4 on post-translational modification.

(a) Within the wild-type of CXCR4 protein, K38, and Q314 do not exhibit acetylation or Pyrrolidone carboxylic acid modification, while (b) in the context of mutated protein with F36C and P299L substitution, K38 becomes acetylated and Q314 undergoes pyrrolidone carboxylic acid modification.

Fig 5. Superimposed structures of wild and mutant variants of CXCR4.

(a) G55V, (b) H79P, (c) L80P, (d) H113P, and (e) P299L. Here, the blue-colored residues represent the native amino acids, and the red residues represent mutated residues. The superimposed position of the native and mutated amino acids shows that the distinguished structure of the side chain of these amino acids likely have impact on the alteration of the 3D structure of the protein.

Fig 6. Structural analysis of wild type and mutated variants of CXCR4.

(a) G55 in the wild-type protein and Val55 in mutated protein forms 2 H-bonds with Thr51 and Val59; (b) His79 in wild type protein engages in 4 H-bonds with Lys75, Tyr76, Val82, and Ala83, while the mutated Pro79 forms only 1 H-bond with Ala83; (c) H-bond number again changed with Leu80 (with Tyr76, Ala83, and Asn84) mutation from 3 to 2 with Pro80 (with Ala83 and Ans84); meanwhile, (d) H-bond number decreased from 4 to 2 in the mutated version of H113 (Cys109, Cys109, Thr117, Asp171) to P113 (Tyr116, Thr117); (e) P299 and L299 both create 2 H-bond but with different amino acids, P299 with Tyr302, Cys295, and L299 with Tyr302, Ala303, respectively. The changes in H-bond in the 3D structure of the CXCR4 protein likely have a significant impact on the structure functions of the protein.

Fig 7. The structure of the protein shows the docking-related and mutation-prone residues.

Docking-related residues (denoted with red) and the mutation-prone residues (denoted with blue) from both (a) side view and (b) top view. In both images, the residue H113, positioned in the docking site and susceptible to mutation is indicated by a red circle.

Fig 8. Molecular docking results of CXCR4-modulator 1 with CXCR4 protein using PyRx software.

All the mutant variants of the ligand showed greater affinity with their receptor during the simulated docking process. However, a significant decrease in the affinity was observed for the H113P mutant variant.

Fig 9. Molecular dynamics simulation.

The MD simulation results show key structural properties for both the wild type (depicted in green) and the mutant (depicted in blue). (a) RMSD of the wild type and the mutant, with time in nanoseconds (ns) on the X-axis and the RMSD values in nanometers (nm) on the Y-axis represent. (b) RMSF of the wild type and the mutant are compared, using amino acid residues on the X-axis and the RMSF values in nm on the Y-axis. (c) The radius of Gyration of the wild type and the mutant, representing the time in picoseconds (ps) on the X-axis and the area in squire nanometer (nm²) on the Y-axis. (d) The SASA of the wild type and the mutant, with time in picoseconds (ps) on the and the SASA values in nm on the Y-axis.