Computational analysis of deleterious single nucleotide polymorphisms in catechol O-Methyltransferase conferring risk to post-traumatic stress disorder

Abstract

Post-traumatic stress disorder (PTSD) is one of the prevalent neurological disorder which is drawing increased attention over the past few decades. Major risk factors for PTSD can be categorized into environmental and genetic factors. Among the genetic risk factors, polymorphisms in the catechol-O-methyltransferase (COMT) gene is known to be associated with the risk for PTSD. In the present study, we analysed the impact of deleterious single nucleotide polymorphisms (SNPs) in the COMT gene conferring risk to PTSD using computational based approaches followed by molecular dynamic simulations. The data on COMT gene associated with PTSD were collected from several databases including Online Mendelian Inheritance in Man (OMIM) search. Datasets related to SNP were downloaded from the dbSNP database. To study the structural and dynamic effects of COMT wild type and mutant forms, we performed molecular dynamics simulations (MD simulations) at a time scale of 300 ns. Results from screening the SNPs using the computational tools SIFT and Polyphen-2 demonstrated that the SNP rs4680 (V158M) in COMT has a deleterious effect with phenotype in PTSD. Results from the MD simulations showed that there is some major fluctuations in the structural features including root mean square deviation (RMSD), radius of gyration (Rg), root mean square fluctuation (RMSF) and secondary structural elements including α-helices, sheets and turns between wild-type (WT) and mutant forms of COMT protein. In conclusion, our study provides novel insights into the deleterious effects and impact of V158M mutation on COMT protein structure which plays a key role in PTSD.

Keywords: Catechol-O-Methyltransferase (COMT); Molecular dynamic simulations (MD simulations); Molecular modelling; Mutation; Single nucleotide polymorphism (SNP).

Fig. 1.

Distribution of SNPs in COMT gene. Represents the distribution of coding synonymous (2.04%), missense (3.79%), nonsense (0.23%), 3′ UTR (7.45%), 5′ UTR (4.32%), 3′ splice site (0.05%), 5′ splice site (0.06%), frame shift (0.36%), stop gained (0.23%) and intronic (40.59%) SNPs in COMT gene.

Fig. 2.

Structure of COMT. a) Cartoon structure of the human COMT structure (PDB ID: 3BWM). Helices shown in deep orange color, flat ribbons, strands shown in yellow, and coils shown in deep gray color. Ligand S-adenosyl-L-methionine co-substrate (SAM) shown in cyan, Potassium shown in purple ball, 3,5-Dinitrocatechol (DNC) is shown in green, Magnesium shown in green ball. Visualization of these structures was performed using UCSF Chimera. b) Upper panel represents the WT COMT whereas lower panel represents the mutant V158M COMT protein with helices shown in red color, strands shown in yellow and coils shown in green color cartoon. Visualization of these structures was performed using pymol. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 3.

RMSDs and Rg are shown for native and V158M mutant COMT. a) RMSD of backbone atoms during total 300 ns b) RMSD of Cα atoms during total 300 ns. RMSD of backbone atoms during c) 0–50 ns d) 50–100 ns e) 100–150 ns f) 150–200 ns g) 200–250 ns h) 250–300 ns molecular dynamic simulation i) Rg of backbone atoms during 300 ns molecular dynamic simulation.

Fig. 4.

RMSF of the Cα atoms of native and mutant COMT vs. time. Native is shown in green; V158M mutant form of COMT is shown in red color. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 5.

A) Solvent accessible surface area during total 300 ns B) Average number of protein-solvent intermolecular H-bonds during total 300 ns. Native is shown in green; V158M mutant form of COMT is shown in red color. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 6.

Rotational correlation function (RCF) of the NH bond vector and Principal component analysis of COMT. a) The RCF was calculated for the last 100 ns of the trajectory using the second order Legendre polynomial (P2) of the NH bond vector (a) and a nonlinear curve fitted with the two parameters in a model-free approach b) The eigenvalues plotted against the corresponding eigenvector indices obtained from the Cα covariance matrix constructed from the total MD trajectory c) Projection of the motion of the COMT WT and mutant in the phase space along the first two principal eigenvectors.

Fig. 7.

Contact map for residues in WT and mutant COMT. Distance matrices consisting of the smallest distance between residue pairs and the number of different atomic contacts between residues over the whole trajectory for a) wild type COMT and b) V158M mutant COMT.

Fig. 8.

Number density plot of COMT during 300 ns MD Simulations. a) wild type COMT and b) V158M mutant COMT.