Protection of Primary Dopaminergic Midbrain Neurons Through Impact of Small Molecules Using Virtual Screening of GPR139 Supported by Molecular Dynamic Simulation and Systems Biology

IntroductionGPCR share a common structural feature, i.e., the presence of seven trans-membrane helices having three intracellular and three extracellular loops. The carboxyl terminal is intracellular whereas amino terminal is extracellular. Various conformational changes are observed in structure of GPCR during the binding with ligand, coupling with G protein and interaction with other proteins. In Rhodopsin class of GPCR the basic structure of GPCR is resolved by X-ray crystallography. Ligand acts as an extracellular stimulus for GPCRs to bring physiological changes in organisms. GPR139 has been found to have effective physiological role in primary dopaminergic midbrain neurons and in central nervous system. Recent reports suggested that the ligand of GPR139 protein inhibits the growth of primary dopaminergic midbrain neurons in central nervous system. These discoveries indicated the potential involvement and influence of GPR139 protein in central nervous systemMethodTherefore, we used multi-approach analysis to investigate the role of GPR139 in the molecular mechanisms of central nervous system. In silico screening was performed to study compound 1 binding with GPR139 protein in their predicted three-dimensional structures. Compound 1 was subjected to molecular dynamics (MD) simulation and stability analysis.ResultsThe results of MD analysis suggested that the loop region in GPR139 protein structure could affect its binding with drugs. Finally, we cross-validated the predicted compound 1 through systems biology approach. Our results suggested that GPR139 might play an important role in primary dopaminergic midbrain neurons therapy.

[1]  Aman Chandra Kaushik,et al.  Boolean network model for GPR142 against Type 2 diabetes and relative dynamic change ratio analysis using systems and biological circuits approach , 2015, Systems and Synthetic Biology.

[2]  S. Mitaku,et al.  Identification of G protein‐coupled receptor genes from the human genome sequence , 2002, FEBS letters.

[3]  W. L. Jorgensen,et al.  The OPLS [optimized potentials for liquid simulations] potential functions for proteins, energy minimizations for crystals of cyclic peptides and crambin. , 1988, Journal of the American Chemical Society.

[4]  Brian K. Shoichet,et al.  ZINC - A Free Database of Commercially Available Compounds for Virtual Screening , 2005, J. Chem. Inf. Model..

[5]  Aman Chandra Kaushik,et al.  Molecular modeling and molecular dynamics simulation-based structural analysis of GPR3 , 2017, Network Modeling Analysis in Health Informatics and Bioinformatics.

[6]  J. C. Phillips Icosahedral ordering in quasicrystals and metallic glasses , 1986 .

[7]  Susheel Kumar,et al.  Deciphering the Biochemical Pathway and Pharmacokinetic Study of Amyloid βeta-42 with Superparamagnetic Iron Oxide Nanoparticles (SPIONs) Using Systems Biology Approach , 2018, Molecular Neurobiology.

[8]  C. Berger,et al.  Novel human g-protein coupled receptor , 2003 .

[9]  T. V. Suchithra,et al.  Network Analysis of MPO and Other Relevant Proteins Involved in Diabetic Foot Ulcer and Other Diabetic Complications , 2017, Interdisciplinary Sciences: Computational Life Sciences.

[10]  M. Nagano,et al.  Molecular cloning and characterization of a novel Gq-coupled orphan receptor GPRg1 exclusively expressed in the central nervous system. , 2005, Biochemical and biophysical research communications.

[11]  A. Kaushik,et al.  In Silico Analysis of Sequence–Structure–Function Relationship of the Escherichia coli Methionine Synthase , 2015, Interdisciplinary Sciences: Computational Life Sciences.

[12]  R. Purohit,et al.  Structure based energy calculation to determine the regulation of G protein signalling by RGS and RGS-G protein interaction specificity , 2012, Interdisciplinary Sciences: Computational Life Sciences.

[13]  Wei Kong,et al.  Construction of Transcriptional Regulatory Network of Alzheimer’s Disease Based on PANDA Algorithm , 2018, Interdisciplinary Sciences: Computational Life Sciences.

[14]  Hege S. Beard,et al.  Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. , 2004, Journal of medicinal chemistry.

[15]  Sanjay Kumar,et al.  Structure Based Virtual Screening Studies to Identify Novel Potential Compounds for GPR142 and Their Relative Dynamic Analysis for Study of Type 2 Diabetes , 2018, Front. Chem..

[16]  Jiong Wu,et al.  Co-expression Network Analysis Revealing the Potential Regulatory Roles of lncRNAs in Alzheimer’s Disease , 2019, Interdisciplinary Sciences: Computational Life Sciences.

[17]  Jingwei Weng,et al.  Molecular dynamics simulation of membrane proteins. , 2014, Advances in experimental medicine and biology.

[18]  John L. Klepeis,et al.  Molecular dynamics - Scalable algorithms for molecular dynamics simulations on commodity clusters , 2006, SC.

[19]  Dhiraj Kumar,et al.  An In-Silico Investigation of Key Lysine Residues and Their Selection for Clearing off Aβ and Holo-AβPP Through Ubiquitination , 2018, Interdisciplinary Sciences: Computational Life Sciences.

[20]  GPR139, an Orphan Receptor Highly Enriched in the Habenula and Septum, Is Activated by the Essential Amino Acids l-Tryptophan and l-Phenylalanine , 2015, Molecular Pharmacology.

[21]  Aman Chandra Kaushik,et al.  Modelling and receptor-based virtual screening studies of GPR139 , 2017, Int. J. Bioinform. Res. Appl..

[22]  A. Anbarasu,et al.  In silico study of Alzheimer’s disease in relation to FYN gene , 2012, Interdisciplinary Sciences: Computational Life Sciences.

[23]  M. Mortrud,et al.  The G protein-coupled receptor repertoires of human and mouse , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[24]  Liaoyuan A. Hu,et al.  Identification of Surrogate Agonists and Antagonists for Orphan G-Protein-Coupled Receptor GPR139 , 2009, Journal of biomolecular screening.

[25]  M. Fellous,et al.  Novel paralogy relations among human chromosomes support a link between the phylogeny of doublesex-related genes and the evolution of sex determination. , 2002, Genomics.

[26]  Joseph A. Hill Nicotinic receptor-associated 43K protein and progressive stabilization of the postsynaptic membrane , 2008, Molecular Neurobiology.

[27]  W. Southerland,et al.  Computational modeling study of human nicotinic acetylcholine receptor for developing new drugs in the treatment of alcoholism , 2009, Interdisciplinary Sciences: Computational Life Sciences.

[28]  Duncan Ayers,et al.  Systems Medicine: The Application of Systems Biology Approaches for Modern Medical Research and Drug Development , 2015, Molecular biology international.

[29]  A. Vaidya,et al.  TGF-β signaling and its role in the regulation of hematopoietic stem cells , 2015, Systems and Synthetic Biology.

[30]  M. Yadav,et al.  Isolation and characterization of a novel chlorpyrifos degrading flavobacterium species EMBS0145 by 16S rRNA gene sequencing , 2014, Interdisciplinary Sciences: Computational Life Sciences.

[31]  A. Kaushik,et al.  3D structure prediction and molecular dynamics simulation studies of GPR139 , 2016, 2016 International Conference on Bioinformatics and Systems Biology (BSB).

[32]  Thomas A. Halgren,et al.  Identifying and Characterizing Binding Sites and Assessing Druggability , 2009, J. Chem. Inf. Model..

[33]  S. Rasmussen,et al.  The structure and function of G-protein-coupled receptors , 2009, Nature.

[34]  Kirsten B. Andersen,et al.  The GPR139 reference agonists 1a and 7c, and tryptophan and phenylalanine share a common binding site , 2017, Scientific Reports.

[35]  Jonathan Y. Mane,et al.  Computer assisted design of second-generation colchicine derivatives , 2010, Interdisciplinary Sciences: Computational Life Sciences.

[36]  David E. Gloriam,et al.  Computer-Aided Discovery of Aromatic l-α-Amino Acids as Agonists of the Orphan G Protein-Coupled Receptor GPR139 , 2014, J. Chem. Inf. Model..

[37]  T. F. Blessia,et al.  Unwinding the Novel Genes Involved in the Differentiation of Embryonic Stem Cells into Insulin-Producing Cells: A Network-Based Approach , 2016, Interdisciplinary Sciences: Computational Life Sciences.

[38]  H. Schaller,et al.  Characterisation and differential expression of two very closely related G-protein-coupled receptors, GPR139 and GPR142, in mouse tissue and during mouse development , 2006, Neuropharmacology.

[39]  Jeremy R. Greenwood,et al.  Epik: a software program for pKa prediction and protonation state generation for drug-like molecules , 2007, J. Comput. Aided Mol. Des..

[40]  Dongqing Wei,et al.  Deciphering G-Protein-Coupled Receptor 119 Agonists as Promising Strategy against Type 2 Diabetes Using Systems Biology Approach , 2018, ACS Omega.

[41]  Federico D. Sacerdoti,et al.  Scalable Algorithms for Molecular Dynamics Simulations on Commodity Clusters , 2006, ACM/IEEE SC 2006 Conference (SC'06).

[42]  Peter B. McGarvey,et al.  Infrastructure for the life sciences: design and implementation of the UniProt website , 2009, BMC Bioinformatics.

[43]  Dongqing Wei,et al.  G-protein-coupled receptors function as logic gates for nanoparticle binding using systems and synthetic biology approach , 2019, Journal of Materials Research.

[44]  Zhiyong Pei,et al.  Risk-Predicting Model for Incident of Essential Hypertension Based on Environmental and Genetic Factors with Support Vector Machine , 2018, Interdisciplinary Sciences: Computational Life Sciences.

[45]  Ying Zuo,et al.  Establishment of the method for screening the potential targets and effective components of huatuo reconstruction pill , 2014, Interdisciplinary Sciences: Computational Life Sciences.

[46]  W. L. Jorgensen,et al.  Development and Testing of the OPLS All-Atom Force Field on Conformational Energetics and Properties of Organic Liquids , 1996 .

[47]  W. Sherman,et al.  Prediction of Absolute Solvation Free Energies using Molecular Dynamics Free Energy Perturbation and the OPLS Force Field. , 2010, Journal of chemical theory and computation.

[48]  David Calkins,et al.  Towards the comprehensive, rapid, and accurate prediction of the favorable tautomeric states of drug-like molecules in aqueous solution , 2010, J. Comput. Aided Mol. Des..

[49]  Devanshi D. Dave,et al.  Portraying the Effect of Calcium-Binding Proteins on Cytosolic Calcium Concentration Distribution Fractionally in Nerve Cells , 2018, Interdisciplinary Sciences: Computational Life Sciences.

[50]  Jakub Pas,et al.  Ligand.Info small-molecule Meta-Database. , 2004, Combinatorial chemistry & high throughput screening.

[51]  B. O'dowd,et al.  Novel human G-protein-coupled receptors. , 2003, Biochemical and biophysical research communications.

[52]  A. Kaushik,et al.  Insights into unbound–bound states of GPR142 receptor in a membrane-aqueous system using molecular dynamics simulations , 2018, Journal of biomolecular structure & dynamics.

[53]  Ming Zheng,et al.  Inferring Gene Regulatory Networks Based on a Hybrid Parallel Genetic Algorithm and the Threshold Restriction Method , 2017, Interdisciplinary Sciences: Computational Life Sciences.

[54]  Sanjay Kumar,et al.  Nano-particle mediated inhibition of Parkinson’s disease using computational biology approach , 2018, Scientific Reports.

[55]  Robert Fredriksson,et al.  Nine new human Rhodopsin family G-protein coupled receptors: identification, sequence characterisation and evolutionary relationship. , 2005, Biochimica et biophysica acta.

[56]  Dongqing Wei,et al.  Evaluation and validation of synergistic effects of amyloid-beta inhibitor–gold nanoparticles complex on Alzheimer’s disease using deep neural network approach , 2019, Journal of Materials Research.

[57]  Tiratha Raj Singh,et al.  A New Decision Tree to Solve the Puzzle of Alzheimer’s Disease Pathogenesis Through Standard Diagnosis Scoring System , 2017, Interdisciplinary Sciences: Computational Life Sciences.