The electronic version of this article is the complete one and can be found online at: http://www.tbiomed.com/content/10/1/3
| Received: | 17 December 2012 |
| Accepted: | 2 January 2013 |
| Published: | 4 January 2013 |
© 2013 Sehgal et al.; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Background
Schizophrenia is a neurodegenerative disorder that occurs worldwide and can be difficult to diagnose. It is the foremost neurological disorder leading to suicide among patients in both developed and underdeveloped countries. D-amino acid oxidase activator (DAOA), also known as G72, is directly implicated in the glutamateric hypothesis of schizophrenia. It activates D-amino acid oxidase, which oxidizes D-serine, leading to modulation of the N-methyl-D-aspartate receptor.
Methods
MODELLER (9v10) was utilized to generate three dimensional structures of the DAOA candidate gene. The HOPE server was used for mutational analysis. The Molecular Evolutionary Genetics Analysis (MEGA5) tool was utilized to reconstruct the evolutionary history of the candidate gene DAOA. AutoDock was used for protein-ligand docking and Gramm-X and PatchDock for protein-protein docking.
Results
A suitable template (1ZCA) was selected by employing BLASTp on the basis of 33% query coverage, 27% identity and E-value 4.9. The Rampage evaluation tool showed 91.1% favored region, 4.9% allowed region and 4.1% outlier region in DAOA. ERRAT demonstrated that the predicted model had a 50.909% quality factor. Mutational analysis of DAOA revealed significant effects on hydrogen bonding and correct folding of the DAOA protein, which in turn affect protein conformation. Ciona was inferred as the outgroup. Tetrapods were in their appropriate clusters with bifurcations. Human amino acid sequences are conserved, with chimpanzee and gorilla showing more than 80% homology and bootstrap value based on 1000 replications. Molecular docking analysis was employed to elucidate the binding mode of the reported ligand complex for DAOA. The docking experiment demonstrated that DAOA is involved in major amino acid interactions: the residues that interact most strongly with the ligand C28H28N3O5PS2 are polar but uncharged (Gln36, Asn38, Thr 122) and non-polar hydrophobic (Ile119, Ser171, Ser21, Ala31). Protein-protein docking simulation demonstrated two ionic bonds and one hydrogen bond involving DAOA. Lys-7 of the receptor protein interacted with Lys-163 and Asp-2037. Tyr-03 interacted with Arg-286 of the ligand protein and formed a hydrogen bond.
Conclusion
The predicted interactions might serve to inhibit the disease-related allele. It is assumed that current bioinformatics methods will contribute significantly to identifying, analyzing and curing schizophrenia. There is an urgent need to develop effective drugs for schizophrenia, and tools for examining candidate genes more accurately and efficiently are required.
Keywords:
Schizophrenia; Bioinformatics; Modeling; Docking; DAOA; Phylogenetic analysisBackground
The nature of a human medical disorder is often elucidated through biological markers and behavioral studies. Diagnosis of mental disorders is very difficult because it primarily relies on behavioral markers. An example of a complex mental disorder is schizophrenia (SZ), diagnosis of which depends on abnormal behavior such as paranoia, dampening of emotions and auditory hallucinations. Genome-wide studies have attained a major role in SZ research because high-throughput technologies are valuable for discovering relevant genes. SZ is a psychiatric disorder with severe manifestations - abnormal behavior, disorganized speech and figments of the imagination - and an estimated heritability of about 80% [1]. Negative symptoms can also include affective flattening, avolition, and alogia. Approximately 1% of the population is affected during the course of life. The effects of SZ usually start during the patient’s late teens to early twenties; females have an age of onset five years later than males [2]. A recent meta-data analysis estimated the risk of SZ in males to be about 40% higher than in females [3]. Epidemiological studies of SZ have shown that it occurs in all populations with a prevalence of approximately 1.5-4.5 per thousand and an incidence of 0.17-0.43 per thousand [4].
According to analysis of gene linkage data and meta-analysis of genome scans [5], highly vulnerable genes on chromosomes 1q, 3p, 5q, 6p, 8p, 11q, 14p, 20q and 22q [6,7] contribute to SZ. Both functional and positional candidate SZ genes have been studied and various promising candidates that might be involved in risk for the disease have been identified.
The symptoms of SZ have different dimensions that usually occur together and can reflect substantial variation among patient phenotypes [8-10]. Different researchers have formulated various models of these dimensions but the most widely appreciated 3D models were first proposed by Bilder et al. and Liddle [9,11]. These authors concluded that the main symptoms are poverty of speech, formal thought disorder, decreased voluntary movement, psychomotor impairment, bizarre behavior, hallucinations, abnormal acts, inappropriate affects, flat affects, flattening, avolition, and alogia.
A genome-wide association study (GWAS) for SZ was conducted in 2008 but no significant loci were reported, though 7000 samples were used [12,13].
The gene DAOA, located on chromosome 13q3, encodes the D-amino acid oxidase activator protein, as shown by functional and expression studies. It is significantly associated with SZ and is also known as G72. The D-amino acid oxidase activator (DAOA) is directly implicated in the glutamateric hypothesis of SZ [14]. When D-amino acid oxidase is activated, D-serine is oxidized and the product modulates the N-methyl-D-aspartate receptor. Modulation of this receptor leads to the cause of SZ; glutamate signaling is involved in important pathways directly linked to SZ [15].
DAOA is also involved in other psychotic disorders and can modify the cognitive and negative symptoms of mood. It could be the primary genetic cause of the observed overlap of phenotypes between bipolar disorder and SZ [16].
Bioinformatics has been used for in silico analysis of biological queries using mathematical and statistical techniques. X-ray and NMR techniques are expensive and time-consuming for structural modeling of proteins. Screening of small chemical compounds against target receptors by high throughput screening (HTS) is very expensive.
In this work, we predicted the 3D structure and the protein-ligand and protein-protein docking of DAOA using different bioinformatics strategies. The main aim of our research was to predict the 3D structure and docking. The objective of the present study was to elucidate the interactions of DAOA protein with ligands and other proteins and to identify the connection of DAOA to SZ. Protein-protein docking and interaction simulations reveal hydrogen and ionic bonds. The present work was conducted to provide molecular insights into the structure of the protein and to find its most plausible function.
Results
This paper describes the implementation of an in silico technique to recruit and analyze DAOA, the most likely candidate gene for SZ. The direct involvement of DAOA in disease pathogencity has already been reported in several research studies on SZ.
Initially, a literature search was conducted to explore the most likely candidate gene involved in SZ. A comparative modeling technique (MODELER 9v10) was adopted to predict the three dimensional structure of the protein encoded by the selected gene. The protein data bank (PDB) was checked for the 3D structure of the selected protein, and it was confirmed that no 3D structure had been predicted to date. To check the quality and reliability of the predicted model, the evaluation tools ERRAT and Rampage were used.
Protein-ligand and protein-protein docking of DAOA were simulated. The ZINC and PubChem databases were used to retrieve the ligand and STRING was used to identify protein interactions [17].
DAOA has been mapped on chromosome 13, with starting and ending base pairs 06118216 and 10143383 respectively. Homology modeling was implemented to generate the 3D structure of the encoded protein. MODELER 9v10 was used to construct the protein model. A basic local alignment technique (BLAST) was utilized to identify the homology between the target protein and its template.
The lowest energy minimization value for the predicted structure was selected for further analysis.
The 3D structure or modeling of DAOA is not known and no structural information can be found for the templates. The amino acid sequence of DAOA in FASTA format was retrieved from Uniprot with accession number A2T115. Table 1 lists the three templates 1ZCA, 1V30 and 2E5K with optimal alignment of the first template and good alignment for the others, sorted by overall quality, query coverage, similarity and E-values.
The structure predicted by MODELLER 9v10 with the alpha helices and beta-pleated sheets visualized by Chimera 1.6 is illustrated in Figure 1(A). Figure 1(B) demonstrates a superimposition of structure and template. The predicted structure is evaluated in Figures 2 and 3.
Theoretical Biology and Medical Modelling 2013, 10:3 doi:10.1186/1742-4682-10-3
- Corresponding author: Asif Mir asif.mir@iiu.edu.pk
1 Department of Bioinformatics and Biotechnology, International Islamic University, H-10 Sector, Islamabad, Pakistan
2 Institute of Biochemistry and Biotechnology, Department of Biochemistry, Arid Agriculture University Rawalpindi Pakistan, Rawalpindi, Pakistan
2 Institute of Biochemistry and Biotechnology, Department of Biochemistry, Arid Agriculture University Rawalpindi Pakistan, Rawalpindi, Pakistan
