Comparative Docking Studies on Neurological and Psychological disorders Genes (BDNF and CLCN2) using Cheminformatics Protocols.

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1 Available online at ISSN: INTERNATIONAL JOURNAL OF NOVEL TRENDS IN PHARMACEUTICAL SCIENCES RESEARCH ARTICLE Comparative Docking Studies on Neurological and Psychological disorders Genes (BDNF and CLCN2) using Cheminformatics Protocols. M.Balaji 1*, Radha.T 2 and Indhumathi.S.P 2 1 Akshaya Neuroinformatics Research center, Chennai TN 2 Dept of Advanced zoology and Biotechnology, Ethiraj College for Women (Autonomous), Chennai TN Article Info Article history Received 10 May 2012 Revised 15 May 2012 Accepted 10 Jun 2012 Available online 30 Jun 2012 Keywords Epilepsy Disorders, Psychological Disorders, Molecular Dynamics, Protein Modeling, Drug Docking. Abstract Aim: The current work performed is a target based approach for drug therapy. This strategic approach was tested for the selected diseases such as Psychology and Epilepsy disorder. Methods: The advanced software tools and database are used to perform molecular modeling and drug docking studies. Results/Discussions: In the present investigation, our focus was primarily on drug molecular properties like thermostats, Vander walls interaction etc., in protein-ligand docking interactions energy plays a vital role in binding affinities. The Molecular drug docking results are validated based on the Vaderwall s interaction and free binding energies between the de novo Ligand and protein receptors. Conclusion: The results obtained from the investigation would have a scope for drug designing in the future. We suggest that the (targets) is the best drug candidate for Psychological Disorder. In this project, we strongly conclude that the above results would be very useful in Drug Designing, Pharmacology and Pediatric Neurology studies. INTRODUCTION Epilepsy is one of the most common of the serious neurological disorders. 1 About 3% of people will be diagnosed with epilepsy at some time in their lives. 2 Genetic, congenital, and developmental conditions are mostly associated with it among younger patients; tumors are more likely over age 40; head trauma and central nervous system infections may occur at any age. The prevalence of active epilepsy is roughly in the range 5 10 per 1000 people. Up to 5% of people experience non febrile seizures at some point in life; epilepsy's lifetime prevalence is relatively high because most patients either stop having seizures or (less commonly) die of it. Epilepsy's approximate annual incidence rate is per 100,000 in industrialized countries and per 100,000 in resource-poor countries; socioeconomically deprived people are at higher risk. In industrialized countries the incidence rate decreased in children but increased among the elderly during the three decades prior to 2003, for reasons not fully understood. 3 Beyond symptoms of the underlying diseases that can be a part of certain epilepsies, people with epilepsy are at risk for death from four main problems: status epilepticus (most often associated with anticonvulsant noncompliance), suicide associated with depression, trauma from seizures, and sudden unexpected death in epilepsy (SUDEP) [4][5][6] Those at highest risk for epilepsy-related deaths usually have underlying neurological impairment or poorly controlled seizures; generally those with more benign epilepsy syndromes a lower risk for epilepsy-related death. The NICE National Sentinel Audit of Epilepsy- Related Deaths, 7 led by "Epilepsy Bereaved" drew attention to this important problem. The Audit revealed; "1,000 deaths occur every year in the UK as a result of epilepsy" and most of them are associated with seizures and 42% of deaths were potentially avoidable". [8] Certain disorders also seem to occur in higher than expected rates in people with epilepsy, and the risk of these "comorbidities" often varies with the epilepsy syndrome. These disorders include depression and anxiety disorders, migraine and other headaches, infertility and low sexual libido. Attention-deficit/hyperactivity disorder (ADHD) affects three to five times more children with epilepsy than children in the general population. [9] ADHD and epilepsy have significant consequences on a child's behavioral, learning, and social development. [10] Epilepsy is prevalent in autism. [11] To whom correspondence should be addressed: Mr. M. Balaji address: bioinfobalaji@gmail.com VOLUME 2 NUMBER 2 JUN

2 M. Balaji et al., Comparative docking studies on neurological and psychological disorders genes (BDNF and Objectives of the present investigation: To find out the potential gene candidates present in Psychological and Neurological disorders. To predict the 3D structure of the BDNF and CCLN4 proteins. To design the suitable drug structure, drug candidate and validate the advanced Cheminformatics software. Materials and Methods Protein Receptor Identification: The Psychology and Epilepsy disorder protein sequences for Human is searched in the NCBI search options and retrieved in FASTA format. Three Dimensional Structure Predictions: Tertiary structure prediction was performed using an automated knowledge based homology modeling server PHYRE to model the 3D structure of the (BDNF and CCLN4) protein. Molecular Visualization Tools: Molecular visualization tools were used to view the model BDNF and CCLN4 protein structure. (Discovery studio: Drug Designing: The suitable Phenytonin, and lithium were selected using NCBI-PUBCHEM chemical databases and designed the ligands using MOLINSPIRATION Molecular Docking: Patch Dock Server - server was used to analyze the docking mechanisms of the BDNF and CCLN2 protein and designed ligand molecules. Drug Interaction Prediction: Ligand interaction studies were done using Molegro molecular viewer, molsoft and Discovery studio and RESULTS AND DISCUSSION NAME GENE Table: 1 Gene Sequence Information OMIM ID GENE ID CHR LOC EXPRESSION BDNF Brain-derived neurotrophic factor CLCN Chloride channel 2 Figure: 1 3Dimensional structure of BDNF Protein Red helix Blue sheets, green truns and white - coils VOLUME 2 NUMBER 2 JUN

3 M. Balaji et al., Comparative docking studiess on neurological and psychological disorders genes (BDNF and Figure: 2 3Dimensional structure of CLCN2 Protein Grey carbon,red oxygen, blue nitrogen and yellow sulphur Table 2: Cheminformatics-Drug Designing: Mol inspiration software Drug name : Phenytoin Pub chem ID: 1775 SMILES: C1=CC=C(C=C1)C2(C(=O)NC(= =O)N2)C3=CC=CC=C3 Mol. weight IUPAC name Mol. Formula C 15 H 12 N 2 O 2 2D STRUCTURE 3D STRUCTURE Drug name : lithium Pubchem ID: SMILES: [Li+] Mol. Weight [g/mol] Table: 3ligand Information [g/mol] 5,5- diphenylimidazoli dine-2,4-dione IUPAC name 5,5- diphenylimidazolidine- 2,4-dione Mol.Formula C 15 H 12 N 2 O 2 2D STRUCTURE VOLUME 2 NUMBER 2 JUN

4 M. Balaji et al., Comparative docking studiess on neurological and psychological disorders genes (BDNF and Figure: 3 Calculation of Molecular properties Figure: 4 Calculation of Bioactivity scores Figure: 5 Molecular Docking Patch Dock Results 60 VOLUME 2 NUMBER 2 JUN 2012

5 M. Balaji et al., Comparative docking studies on neurological and psychological disorders genes (BDNF and Figure: 6 Protein BDNF and Phenytoin sodium complex higher binding affinities is Protein line model with labels and green color stick model Ligand Table 4: Drug binding studies Drugs De novo drug (phen+ Li) Protein Receptors BDNF CLCN Molecular docking: In the present investigation we used advanced automated molecular docking server called PATCHDOCK. 12 PatchDock algorithm is inspired by object recognition and image segmentation techniques used in Computer Vision. Docking can be compared to assembling a jigsaw puzzle. When solving the puzzle we try to match two pieces by picking one piece and searching for the complementary one. We concentrate on the patterns that are unique for the puzzle element and look for the matching patterns in the rest of the pieces. PatchDock employs a similar technique. Given two molecules, their surfaces are divided into patches according to the surface shape.fig (2) shows the results page of de novo drug docking (CLCN2 with ligands). Tertiary structure prediction: The CLCN2 s and BDNF FASTA sequences were applied into CPH server to model the 3D structure. The three dimensional structure prediction of protein is one of the basic foundation for structure based drug designing. In this present investigation we used CPH model server. Here we discussed the technical information and significance of the three dimensional structure prediction and tool information. The protein structure prediction remains an extremely difficult and unresolved undertaking. The two main problems are calculation of protein free energy and finding the global minimum of this energy. A protein structure prediction method must explore the space of possible protein structures which is astronomically large. These problems can be partially bypassed in "comparative" or homology modelling and fold recognition methods, in which the search space is pruned by the assumption that the protein in question adopts a structure that is close to the experimentally determined structure of another homologous protein. On the other hand, the de novo or ab initio protein structure prediction methods must explicitly resolve these problems. The progress and challenges in protein structure prediction have been reviewed in Zhang 2008 (Zhang Y (2008). In these results, the 3D structure shows the alpha helix (beta sheet) and coiled regions of the CLCN2 protein structure. This is one of the primary uses for introducing new ligands to the target CLCN2 protein. Drug designing: The drugs (Sodium Volporate and Phenobarbitol) have anti convulsant properties and the lithium ion reduces the Psychological problems. Molinspiration 13 offers broad VOLUME 2 NUMBER 2 JUN

6 M. Balaji et al., Comparative docking studies on neurological and psychological disorders genes (BDNF and range of cheminformatics software tools supporting molecule manipulation and processing, including SMILES and SDfile conversion, normalization of molecules, generation of tautomers, molecule fragmentation, calculation of various molecular properties needed in QSAR, molecular modelling and drug design, high quality molecule depiction, molecular database tools supporting substructure and similarity searches. Molinspiration supports internet chemistry community by offering free online services for calculation of important molecular properties (logp, polar surface area, number of hydrogen bond donors and acceptors and others), as well as prediction of bioactivity score for the most important drug targets (GPCR ligands, kinase inhibitors, ion channel modulators, nuclear receptors). Fig shows the molecular drug properties and drug likeness scores of de novo ligands. Conclusion The primary aim of the research investigation is to find out the best drug for epilepsy as well as for the anxiety problems related to it. Here we suggest the best existing drug candidate Phenotoin sodium and we introduce the lithium ion to combine with the existing drug. The present investigation of the project work clearly shows that the designed drug candidates are best candidates for drug docking. The Phenytoin sodium and Lithium ion ligand shows a better binding affinity than the protein target CLCN2 and BDNF2.We conclude that the designed de novo ligands are best inhibitors for CLCN2 proteins which also reduce the rate of the Childhood Epilepsy along with Anxiety problems. Reference 1. Hirtz D, Thurman DJ, Gwinn-Hardy K, Mohamed M, Chaudhuri AR, Zalutsky R "How common are the 'common' neurologic disorders?". Neurology 2007; 68 (5): DOI: /01.wnl a3. 2. Hauser, WA, Kurland, LT (1975). "The epidemiology of epilepsy in Rochester, Minnesota, 1935 through 1967".Epilepsia 16 (1): DOI: /j tb04721.x. 3. Sander JW "The epidemiology of epilepsy revisited".curr Opin Neurol (2003) 16 (2): DOI: / Walczak TS, Leppik IE, D'Amelio M, Rarick J, So E, Ahman P, Ruggles K, Cascino GD, Annegers JF, Hauser WA (2001). "SIncidence and risk factors in sudden unexpected death in epilepsy: a prospective cohort study". Neurology. 2001; 56 (4): Lathers, C. and P. Schraeder. Epilepsy and Sudden Death. Dekker, NY, NY Hitiris, N., R. Mohanraj, J. Norrie and M. J. Brodie (2007). "Mortality in epilepsy". Epilepsy Behavior. 2007; 10 (3): DOI: /j.yebeh Hanna et al Hanna et al, The National Sentinel Audit of Epilepsy Related Death, The Stationary Office, London Plioplys, S., Dunn, DW., Caplan, R. "10-year research update review: psychiatric problems in children with epilepsy". J Am Acad Child Adolesc Psychiatry. 2007; 46 (11): DOI: /chi.0b013e fc. 10. Reilly, Colin, J. "Attention Deficit Hyperactivity Disorder (ADHD) in Childhood Epilepsy". Research in Developmental Disabilities: A Multidisciplinary Journal. Retrieved 24 October Levisohn PM. "The autism-epilepsy connection".epilepsia. 2007; 48(9): DOI: /j x 12. bioinfo3d.cs.tau.ac.il/patchdock/ 62 VOLUME 2 NUMBER 2 JUN 2012