Drug designing and validation studies on human asthma disease gene NPSR1 using Cheminformatics tools

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1 Available online at wwwijntpsorg ISSN: INTERNATIONAL JOURNAL OF NOVEL TRENDS IN PHARMACEUTICAL SCIENCES Drug designing and validation studies on human asthma disease gene NPSR1 using Cheminformatics tools RESEARCH ARTICLE Cardilya Grace C *, Hepzibah W and John William S Department of Advanced Zoology and Biotechnology, (SECNARM), Loyola College, Chennai-34 Tamil Nadu Article Info Article history Received 15 May 2012 Revised 18 May 2012 Accepted 14 Jun 2012 Available online 30 Jun 2012 Keywords Protein Modeling, Drug Designing, Drug Docking, Clinical immunology, Pharmacoinformatics Abstract Asthma is defined by the Global Initiative for Asthma as a chronic inflammatory disorder of the airways in which many cells and cellular elements play a role The chronic inflammation is associated with airway hyper responsiveness that leads to recurrent episodes of wheezing, breathlessness, chest tightness and coughing particularly at night or in the early morning These episodes are usually associated with widespread, but variable airflow obstruction within the lung that is often reversible either spontaneously or with treatment" The primary aim of this study is to identify the mutational gene responsible for asthma and the potential drug candidate for the target We finally conclude that the designed drug candidates would prove to be useful for patients affected by chronic inflammatory disease and general inflammatory disease This work would be great use in the field of Clinical immunology and Pharmacoinformatics INTRODUCTION As of 2009, 300 million people worldwide were affected by asthma leading to approximately 250,000 deaths per year [1-4] It is estimated that asthma has a 7-10% prevalence worldwide [5] As of 1998, there was a great disparity in the prevalence of asthma across the world, with a trend toward more developed and westernized countries having higher rates of asthma, [6] with as high as a 20 to 60- fold difference Westernization however does not explain the entire difference in asthma prevalence between countries, and the disparities may also be affected by differences in genetic, social and environmental risk factors [61] Mortality however is most common in low to middle income countries, [7] while symptoms were most prevalent (as much as 20%) in the United Kingdom, Australia, New Zealand, and Republic of Ireland; they were lowest (as low as 2 3%) in Eastern Europe, Indonesia, Greece, Uzbekistan, India, and Ethiopia [8] Asthma affects approximately 7% of the population of the United States [9] and 5% of people in the United Kingdom [10] Asthma causes 4,210 deaths per year in the United States [11][12] In 2005 in the United States asthma affected more than 22 million people including 6 million children [13] It accounted for nearly 1/2 million hospitalizations that same year [14] More boys have asthma than girls, but more women have it than men [15] In England, an estimated 261,400 people were newly diagnosed with asthma in 2005; 57 million people had an asthma diagnosis and were prescribed 326 million asthma-related prescriptions Aim and objectives To find out the mutational genes involved in Human Asthma disease To model the 3D structure and identify the potential drug candidates using Cheminformatics protocols Methodology Tools and database are used: NCBI, BLAST, Ensembl genome browser, CPH server, Chemaxon,Patchdock server, Discovery studio software, Molegro molecular viewer Sequence Retrieval System: The potential gene candidate (NPSR1) was retrieved from NCBI protein database and converted into FASTA format Molecular Modeling: The retrieved protein is applied into CPH 30 server (Advanced Protein modeling server) in order to predict the three dimensional structure and the predicted structure was viewed using RASMOL, MOLSOFT, MOLEGRO MOLECULAR VIEWER and DISCOVERY STUDIO SOFTWARE To whom correspondence should be addressed: Cardilya Grace C address: cardilya@gmailcom VOLUME 2 NUMBER 2 JUN

2 Cheminformatics: Drug Designing and Development: Ligand selection: The suitable chemical compound was identified using NCBI Pubchem compound database The combination and modification of ligand was done using Molinspiration software Molecular Docking: The molecular docking studies were performed using PATCH DOCK server in order to predict the Binding affinities of the de novo ligand and Asthma protein receptors Results Table: 1 NCBI gene database and OMIM Gene name NPSR1 Protein product Neuropeptide S receptor 1 Chr loc Summary 7 This gene is a member of the G protein-coupled receptor 1 family and encodes a plasma membrane protein Increased expression of this gene in ciliated cells of the respiratory epithelium and in bronchial smooth muscle cells is associated with asthma Mutations in this gene have also been associated with this disease Alternatively spliced variants which encode different protein isoforms have been described; however, not all variants have been fully characterized Protein modeling: Nucleotide Sequence (1116 nt): ATGCCAGCCAACTTCACAGAGGGCAGCTTCGATTCCAGTGGGACCGGGCAGACGCTGGATTCTTCCCCAGTG GCTTGCACTGAAACAGTGACTTTTACTGAAGTGGTGGAAGGAAAGGAATGGGGTTCCTTCTACTACTCCTTTA AGACTGAGCAATTGATAACTCTGTGGGTCCTCTTTGTTTTTACCATTGTTGGAAACTCCGTTGTGCTTTTTTCCA CATGGAGGAGAAAGAAGAAGTCAAGAATGACCTTCTTTGTGACTCAGCTGGCCATCACAGATTCTTTCACAG GACTGGTCAACATCTTGACAGATATTAATTGGCGATTCACTGGAGACTTCACGGCACCTGACCTGGTTTGCCG AGTGGTCCGCTATTTGCAGGTTGTGCTGCTCTACGCCTCTACCTACGTCCTGGTGTCCCTCAGCATAGACAGA TACCATGCCATCGTCTACCCCATGAAGTTCCTTCAAGGAGAAAAGCAAGCCAGGGTCCTCATTGTGATCGCCT GGAGCCTGTCTTTTCTGTTCTCCATTCCCACCCTGATCATATTTGGGAAGAGGACACTGTCCAACGGTGAAGT GCAGTGCTGGGCCCTGTGGCCTGACGACTCCTACTGGACCCCATACATGACCATCGTGGCCTTCCTGGTGTA CTTCATCCCTCTGACAATCATCAGCATCATGTATGGCATTGTGATCCGAACTATTTGGATTAAAAGCAAAACCT ACGAAACAGTGATTTCCAACTGCTCAGATGGGAAACTGTGCAGCAGCTATAACCGAGGACTCATCTCAAAGG CAAAAATCAAGGCTATCAAGTATAGCATCATCATCATTCTTGCCTTCATCTGCTGTTGGAGTCCATACTTCCTG TTTGACATTTTGGACAATTTCAACCTCCTTCCAGACACCCAGGAGCGTTTCTATGCCTCTGTGATCATTCAGAA CCTGCCAGCATTGAATAGTGCCATCAACCCCCTCATCTACTGTGTCTTCAGCAGCTCCATCTCTTTCCCCTGCA GGGAGCAAAGATCACAGGATTCCAGAATGACGTTCCGGGAGAGAACTGAGAGGCATGAGATGCAGATTCTG TCCAAGCCAGAATTCATCTAG Translation (371 aa): MPANFTEGSFDSSGTGQTLDSSPVACTETVTFTEVVEGKEWGSFYYSFKTEQLITLWVLFVFTIVGNSVVLFSTWRR KKKSRMTFFVTQLAITDSFTGLVNILTDINWRFTGDFTAPDLVCRVVRYLQVVLLYASTYVLVSLSIDRYHAIVYPMK FLQGEKQARVLIVIAWSLSFLFSIPTLIIFGKRTLSNGEVQCWALWPDDSYWTPYMTIVAFLVYFIPLTIISIMYGIVIRTI WIKSKTYETVISNCSDGKLCSSYNRGLISKAKIKAIKYSIIIILAFICCWSPYFLFDILDNFNLLPDTQERFYASVIIQNLPA LNSAINPLIYCVFSSSISFPCREQRSQDSRMTFRERTERHEMQILSKPEFI 70 VOLUME 2 NUMBER 2 JUN 2012

3 Figure: 1Protein modeling- Discovery studio software 3 D structure of NPSR1 protein Solid ribbon The above picture shows the secondary structure of NPSR1 protein and red indicates helix, Blue indicates sheets, green indicates turns and white indicates the coils regions present in the target Figure: 2Protein modeling- Discovery studio software 3 D structure of NPSR1 protein CPK model VOLUME 2 NUMBER 2 JUN

4 Red Oxygen, White Hydrogen, Grey Carbon, Blue Nitrogen and Yellow Sulphur Figure: 22Cheminformatics Drug designing and development SMILES Epinephrine - Compound Summary (CID 5816) CNCC(C1=CC(=C(C=C1)O)O)O Aspirin - Compound Summary (CID 2244) CC(=O)OC1=CC=CC=C1C(=O)O Hydrogen 2-acetylphenyl 2-(3,4-dihydroxyphenyl)-3-(methylamino)propyl carbonate Figure: 23 MOLINSPIRATION SOFTWARE Calculation of Molecular Properties of de novo ligand Figure: 3 Protein modeling- Discovery studio software 3 D structure of NPSR1 protein Stick model with labels 72 VOLUME 2 NUMBER 2 JUN 2012

5 Red Oxygen, White Hydrogen, Grey Carbon and Blue Nitrogen Figure: 4 Molecular Docking : Patch Dock Results Binding affinities of NPSR1 and Epinephrine (-16322) Figure: 4 1Molecular Docking: Patch Dock Results Binding affinities of NPSR1 and Epinephrine + Aspirin (De Novo) ( ) VOLUME 2 NUMBER 2 JUN

6 Figure: 5Drug Docking - Discovery studio software NPSR1 protein and Epinephrine Ligand protein complex: line model NPSR1 protein and green Stick model Epinephrine Figure: 6Drug Designing - Discovery studio software 3D structure of De novo Ligand Epinephrine and Aspirin in Ball and Stick model with labels 74 VOLUME 2 NUMBER 2 JUN 2012

7 Figure: 7Drug Docking - Discovery studio software NPSR1 protein and Epinephrine +Aspirin Protein surface (yellow) and Ligand Stick model (green) Figure: 8 Drug Docking Molegro molecular viewer software NPSR1 protein and Epinephrine + Aspirin Protein secondary structure view and Ligand spacefill model (green) VOLUME 2 NUMBER 2 JUN

8 Table: 2Molecular Drug Docking Studies NPSR1 Epinephrine 3D 3D docking Epinephrine + Aspirin De novo drug 3D 3D docking Discussion The present investigation focuses on leading human ASTHMA diseases The mutation gene was identified using NCBI gene database and OMIM (Table: 1) The identified gene coded protein sequences were retrieved from NCBI protein database The selected protein sequences are converted into FASTA format in order to predict the protein structural analysis and docking studies 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 modeling 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 3 D prediction of target protein NPSR1 can be done using CPH 30 server The submitted protein sequences are automatically converted into 3D structures and best models are downloaded and viewed with help of molecular visualization tools Figure ( 1 and 2) shows the 3D structure of the target cancer proteins Pubchem is a database of chemical molecules and their activities against biological assays The system is maintained by the National Center for Biotechnology Information (NCBI), a component of the National Library of Medicine, which is part of the United States National Institutes of Health (NIH) Using Pubchem compound databases the ligand 1 (Epinephrine) and ligand 2 (Aspirin) are collected (Figure: 3 and 6) Molinspiration offers broad 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 (Figure: 22 and 23 ) Docking is frequently used to predict the binding orientation of small molecule drug candidates to their protein targets in order to predict the affinity and activity of the small molecule Hence docking plays an important role in the rational design of drugs Given the biological and pharmaceutical significance of molecular docking, considerable efforts have been directed towards improving the methods used to predict docking 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 These patches correspond to patterns that visually distinguish between puzzle pieces Once the patches are identified, they can be superimposed using shape matching algorithms Filtering and Scoring - the candidate complexes from the previous step are examined We discard all complexes with unacceptable penetrations of the atoms of the receptor to the atoms of the ligand Finally, the remaining candidates are ranked according to a geometric shape complementarity score Figure (5, 7 and 8) represents the ligand receptor complex form, Table : 2 shows the Patch dock results Conclusion Computer-assisted drug design uses computational chemistry to discover, enhance, or study drugs and related biologically active molecules The most fundamental goal is to predict whether a given molecule will bind to a target and if so how strongly Molecular mechanics or molecular dynamics are most often used to predict the conformation of the small molecule and to model conformational 76 VOLUME 2 NUMBER 2 JUN 2012

9 changes in the biological target that may occur when the small molecule binds to it There are two major findings in this research investigation The first finding is that the potential protein target was identified in different cancers and, the second finding is that the best drug candidate was identified Ligand 1 (Epinephrine) has anti asthma properties and 2 (Aspirin) has anti inflammatory property We conclude that designed de novo drug candidate rapidly reduces the effect of asthma and inflammatory Finally, we conclude that these two ligands are combined and designed which form potential inhibitors of Asthma and inflammatory diseases References 1 GINA 2009; 2 2 Fanta, CH "Asthma" New England Journal of Medicine 2009; 360 (10): DOI:101056/NEJMra PMID "World Health Organization Fact Sheet Fact sheet No 307: Asthma" 2009 Retrieved Braman SS The global burden of asthma Chest 2006; 130: 4S 12S DOI:101378/chest1301_suppl4SPMID Lazarus SC "Clinical practice Emergency treatment of asthma" N Engl J Med 2010; 363(8): DOI:101056/NEJMcp PMID "Worldwide variation in prevalence of symptoms of asthma, allergic rhino conjunctivitis, and atopic eczema: ISAAC The International Study of Asthma and Allergies in Childhood (ISAAC) Steering Committee" Lancet 351 (9111): 1998; DOI:101016/S (97) Gold, DR, Wright R "Population disparities in asthma" Annul Rev Public Health 2005; 26: 89 13DOI:101146/annurevpublhealth PMID World Health Organization "WHO: Asthma" Archived from the original on 15 December 2007 Retrieved Worldwide variation in prevalence of symptoms of asthma, allergic rhino conjunctivitis, and atopic eczema: ISAAC The International Study of Asthma and Allergies in Childhood (ISAAC) Steering Committee" Lancet 351 (9111): April 1998 DOI:101016/S (97) PMID Anderson, HR; Gupta R, Strachan DP, Limb ES "50 years of asthma: UK trends from 1955 to 2004"Thorax 2007; 62 (1): DOI:101136/thx PMC PMID Lazarus SC "Clinical practice Emergency treatment of asthma" N Engl J Med 2010; 363(8): DOI:101056/NEJMcp Getahun D, Demissie K, Rhoads GG "Recent trends in asthma hospitalization and mortality in the United States"Journal of asthma 2005; 42(5): DOI:101081/JAS NHLBI Guideline 2007; 1 14 Hayden, Merrill "Asthma Guide" Simpson CR, Sheikh A "Trends in the epidemiology of asthma in England: a national study of 333,294 patients" J R Soc Med 2010; 103(3): DOI:101258/jrsm PMID VOLUME 2 NUMBER 2 JUN