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RESEARCH ARTICLE Network Biology Approaches to Identify the Drug Lead Molecule for Neurodevelopmental Disorders in Human

Archana Verma1, Shweta Singh Chauhan1, Vaishali Pankaj1, Neha Srivastva1,2 and Prachi Srivastava1,*

1AMITY Institute of Biotechnology, AMITY University Uttar Pradesh Lucknow Campus, UP, India 2Dr. A.P.J. Abdul Kalam Technical University, Lucknow, UP, India

Abstract: Aims: To identify most novel drug target and lead molecule for neurodevelopmental disorder Autism, Intellectual Disability (ID) and Attention Deficit Hyperactivity Disorder (ADHD) diseases through system biology approaches

Background: Neurodevelopmental disorders (NNDs) are disabilities associated chiefly with the functioning of the neurological system and brain. Children with neurodevelopmental disorders have difficulties with speech, behaviour, learning and other neurological functions. Systems biology is a holistic approach to enciphering the complexity of biological systems and their interactions. It opens the way to a more successful discovery of novel therapeutics.

Objective: To identify most novel drug target and lead molecule for neurodevelopmental disorder Autism, Intellectual Disability (ID) and Attention Deficit Hyperactivity Disorder (ADHD) diseases through system biology approaches.

Methods: A list of genes was collected from NCBI database for Autism, Intellectual Disability (ID) and Attention Deficit Hyperactivity Disorder (ADHD) diseases. STRING database and Cytoscape software was used for construction and interpreting molecular interaction in the network. 3D structure of target protein, was build and validated.The phytochemicals were identified through various research articles and filtered out by virtual screening through Molinspiration. Molecular docking analyses of known phytochemical with target proteins were performed usingAutoDock tool.

Result: AKT1 for Autism, SNAP25 for Intellectual Disability (ID) and DRD4 for Attention Deficit Hyperactivity Disorder (ADHD) were identified as most potential drug target through network study. further the modelled structure of obtained target were undergo molecular docking study with kown phytochemicals. Based on lowest binding energy, Huperzine A for Autism and ID, Valerenic acid for ADHD found to be the most potential therapeutic molecules.

Conclusion: Huperzine A against Autism and ID, Valerenic acid against ADHD found to be the most potential therapeutic molecules and expected to be effective in the treatment of NNDs. Phytochemicals do not have side effects so extract of these can be taken in preventive form too as these disorders occur during developmental stages of the child. Further the obtained molecule if experimentally validated would play promising role for the treatment of NDDs in human.

Keywords: Neurodevelopmental disorder, Autism, Intellectual Disability, Attention Deficit Hyperactivity Disorder, Docking, STRING Database.

Article History Received: October 14, 2019 Revised: January 27, 2020 Accepted: February 03, 2020

1. INTRODUCTION circumstances with onset in the developmental stage [1, 2]. The disorders usually clear early in development, often before the The neurodevelopmental disorders are a group of child enters grade school, and are marked by developmental

DOI: 10.2174/1875036202013010015, 2020, 13, 15-24 16 The Open Bioinformatics Journal, 2020, Volume 13 Verma et al. deficits that produce problems of personal, social, academic, or (ADHD) and to identify the most potential target for diseases. occupational functioning [3, 4]. Systems biology approaches Further drug discovery approach was used to identify novel can be examined as a new area of study that aims to find out lead molecules for the disease. biology at the system level, entailing the functional study of the structure and dynamics of cells and organisms [5]. 2. MATERIALS AND METHODS Systems biology approaches applied to healthcare would 2.1. Data Collection try to identify the systems that, when altered, shift the body from a “healthy” to a “disease” state. The working hypothesis For Autism 494 genes, for Intellectual Disability (ID) 399 is that the elements of the biological system that are involved genes, and 137 genes for Attention Deficit Hyperactivity in the observed switch between states are specific to the disease Disorder (ADHD) were reported from NCBI. and may be candidate targets for treatment to restore the system to its original healthy state [5, 6]. From a medical 2.2. Construction of PPI Network approach, systems biology offers a primal input to conduct STRING [15] allows a detailed study of network functional analyses of genomic functions that could be interaction by providing several features. PPI network commonly used in finding of the gene, detection of biomarker, constructed using of STRING 10.0 (Search Tool for the disease categorization, drug discovery, therapy strategies and, Retrieval of Interacting Genes) [16] by 494 genes for Autism, in the last instance, predictive and preventive medicine [6, 7]. 399 genes for Intellectual Disability (ID), and 137 genes for Autism is the highest expanding neurodevelopmental Attention Deficit Hyperactivity Disorder (ADHD) as input and disorder in the world today. In the US, the rates of autism have based on highest confidence score 0.7 obtained from increased from 1 in 2500 in 1970 to 1 in 88 today [8, 9]. A experimental data [17]. Further PPI network was analyzed and triad of deficits in the areas of social communication, language visualized by Cytoscape 3.3.0 [18, 19]. advancement, repetitive behavior, and limited range of interests describe the core symptoms used in the diagnosis of autism 2.3. Analysis of PPI Network [10]. The growth of autism seems to be a complex interaction Analysis of the protein-protein interaction (PPI) network between genes and environmental factors. Autism influences was done by using Cytoscape 3.3.0. Betweenness Centrality many systems in the body, which include metabolic, mitochon- (BC) and node degree are the parameters included in drial, immunological, gastrointestinal, and neurological [8]. Cytoscape Plug-in Network Analyzer. Node is represented by Attention deficit hyperactivity disorder (ADHD) has been gene and interaction is represented by edges. Edges' number characterized by the core symptoms of inattention, specifies node degree [20]. Betweenness centrality explains the hyperactivity, and impulsivity. ADHD presents in childhood significance of the node primarily based on the quantity of the but persists into adulthood in almost 60 percent of cases, with shortest ways that undergo every node. In the study, the PPI an estimated prevalence of 4.4 percent in United States adults network was analyzed based on these parameters. [11]. 2.4. Protein Modeling and Structure Validation Intellectual disability (ID) affects about 2–3% of the general population [12, 13]. Diagnostic criteria for identifying Obtained drug target from PPI network analysis for individuals with ID have undergone significant revisions, neurodevelopmental disorders: Autism, Intellectual Disability changes perhaps best chronicled through the versions of the (ID), and AHDH. For the identification of the target sequence, Diagnostic and Statistical Manual of Mental Disorders (DSM) the BLAST program was performed for the search of a [14]. structurally similar sequence with the protein data bank (PDB). Based on the structure similarity parameter, SNAP25 and Therefore for identification of potential drug targets for DRD4 were modeled using on-line modeling server SWISS- Autism, Intellectual Disability (ID), and Attention Deficit MODEL. The structure of target protein 4EJN (PDB-ID) for Hyperactivity Disorder (ADHD), first one should understand Autism was retrieved from PDB. Further, the modeled the underlying mechanism of disease pathogenesis. With the inception of network biology, it has become easier to structures were validated through the Ramachandran Plot and understand the whole disease mechanism at the system level by Errat using the SAVES server. constructing a network-based computational model for dise- 2.5. Ligand Identification ases. Exploring these networks will provide valuable information about the functional association of genes/proteins in the The structures of twenty-seven possible phytochemical network that in-turn helps in identifying the most potential compounds having neuroprotective activity were selected for drug target for disease among the vast array of genes/proteins study based on various literature reviews (Table 1). The 2D & in the network. The current in-silico study was attempted to 3D structures of compounds were retrieved from the PubChem study the underlying disease mechanism of Autism, Intellectual database. These compounds were downloaded in (sdf) file for- Disability (ID), and Attention Deficit Hyperactivity Disorder mat & converted into (PDB) format using Chimera software. Prediction of Molecular & bioactivity analysis of leads was * Address correspondence to the author at the AMITY Institute of Biotechnology, AMITY University Uttar Pradesh Lucknow Campus, UP, India; performed by using molinspiration chemoinformatics software Tel: 9453141916; E-mail: [email protected] that satisfied them as potential inhibitors. Network Biology Approaches The Open Bioinformatics Journal, 2020, Volume 13 17

Table 1. List of Phytochemical processing neuroprotective activity

S.No. Phytochemical Name Plant Name 1 Ginkgolide A Ginkgobiloba L 2 Ginkgolide B Ginkgobiloba L 3 Ginkgolide J Ginkgobiloba L 4 Ginkgolide M Ginkgobiloba L 5 Oroxylin A Scutellariabaicalensis 6 Salidroside Rhodiolarosea 7 Rosmarinic acid Melissa officinalis 8 Urosolic acid Melissa officinalis 9 Valepotriate Valerian 10 Valerenic acid Valerian 11 Acetoxyvalerenic Acid Valerian 12 Coriaria lactone Loranthusparasiticus 13 Huperzine A Huperzia serrate 14 Ajmalicine Catharanthusrosea 15 Lochnerine Catharanthusrosea 16 Ajmaline Rauwolfia serpentine 17 Serpentine Rauwolfia serpentine 18 Beta amyrin Canscoradiffusa 19 Hyoscyamine Daturametel 20 Scopolamine Daturametel 21 Sulphoraphane Broccoli 22 Ginkgolide C Ginkgo biloba L 23 Mangiferin Mangiferaindica 24 Reserpine Rauwolfia serpentine 25 Rosavin Rhodiolarosea 26 Vinblastine Madagascar periwinkle 27 Vincristine Madagascar periwinkle

2.6. Molecular Docking Various literature finding suggests the active role of AKT1 gene as a key modulator of the AKT-mTOR signaling pathway Rigid molecular docking was performed between the target controlling the tempo of the process of newborn neurons protein and ligand using Autodock 1.5.6 tool to predict the integration during adult neurogenesis, including correct neuron binding affinity, ligand efficiency, and inhibitory constant. The positioning, dendritic development, and synapse formation. criteria of binding energy were taken into consideration (based Ile67Asn variation in SNAP-25 isoform B inhibits exocytosis on the lowest binding energy score) to select the best protein- of synaptic vesicles by altering the α-helical coiled structure of ligand complex. Binding energy was calculated using a semi- the SNARE complex. This mutation is a cause disorder for empirical free energy force field with charge-based desolvation Intellectual Disability(ID). SNAP25 is substantially involved in and grid-based docking. many major neurological disorders which makes it a primary target for diagnosis and treatment of threatening diseases [21]. 3. RESULT AND DISCUSSION Dopamine D4 receptor gene (DRD4) has an important role in the pathogenesis of ADHD and one of the most important 3.1. Network Analysis and Identification of Target genes and is held to be responsible for neuronal signaling in the The PPI networks were constructed inputting 494 genes for mesolimbic system of the brain area [22]. Autism, 399 genes for Intellectual Disability (ID), and 137 genes for Attention Deficit Hyperactivity Disorder (ADHD) 3.2. Protein Modeling and Structure Validation using the STRING database. Obtained networks were analyzed The total length of AKT1 protein is 480 amino acids, and visualized using Cytoscape 3.3.0 plug-in Network SNAP25 protein is 206 amino acids, and DRD4 protein is 467 Analyzer. Based on a topological parameter BC and node amino acids. The 3D structure of target protein 4EJN for degree: AKT serine/threonine kinase 1 (AKT1) for Autism, Autism was retrieved from PDB and the 3-D structures of Synaptosomal nerve-associated protein 25 (SNAP25) for SNAP25 and DRD4 were built using SWISS-MODEL. The Intellectual Disability (ID), and Dopamine receptor D4 model structure is further validated through online server (DRD4) for Attention Deficit Hyperactivity Disorder (ADHD) Rampage. The position of amino acid residues in allowed and are selected as a key drug target with highest BC value and disallowed region and overall Stereochemical property of the degree node Figs. (1, 2 & 3). protein was accessed by Ramachandran plot. The Ramachan- 18 The Open Bioinformatics Journal, 2020, Volume 13 Verma et al. dran plot indicates 91% for Autism, 89.8% for Intellectual Disorder (ADHD) in the favored region that reflects the good Disability (ID), and 93.9% for Attention Deficit Hyperactivity quality model as the expected value is above 85% in the favored region (Figs. 4, 5, & 6).

R17A

MET RPL10 CRMS RPS24 HCMER1 HYR2 FOXP3

SCTR SCT STX8P1

HGF APOE TGFB1 APP CACNA1C

FLT1 SNAP25 STX1A

KDR CACNO2 PTK2

NTF3 PXN THDS1

VEOFA GRN2B SRC NTF4 DLG1 DPP4 NTRK2 ADA

DOF PTGS2 BDNF

NLGN1

NRAS BCL2 PKC3CD ADPOQ TPS3 CONF

NRKN1 CASP3 RSI

LEP AKT1 RETN NLGN3 MTOR ARHGEF9 GPHN EF4E PTEN CTNND1

OXT GSTP1 SCN1A OXTR CTNNB1

RIF3AY T501 TSC2 APC

SCN3A CTNNB2 GSTT1

PRKAAI NLGN4Y WNT1 SCN2A

AKT1 as target protein GSTM1

TDLSY identified based on network

parameter for Autism

Fig. (1). PPI network of Autism constructed using the STRING 10.0 database. Network Biology Approaches The Open Bioinformatics Journal, 2020, Volume 13 19

SIN3A MTRR HOAC8 MTR AP4M1 MED25

AMT GAN GCSH MAP1B ARD18 AP4E1 AP401

MED23 TSC1 RNASEH2B RPS4KA3

SMARC4 MLD13L MTOR

RNASEH2C RNASEH2A TSC2 BDNF NTRK1

PCCA PCCB TAF1 SOS1 PTEN ARHGEP4 STK3 GPHN CPLX1 FKRP SNAP25 TAF2 SNAP25 as target HRAS TAF13 KRAS STXBP1 protein identified SCN1A RAF1

POMT2 TG based on network MAP2K1 TPO BRAF parameter for ID SCN1A NRAS

FKTN SCN2A

POMGNT1

Fig. (2). PPI network Of ID (Intellectual Disabilty) constructed using the STRING 10.0 database.

TPH2

TACR1 AVPR18 TPH1 HTR2C

DBH GRM5

OXT HTR2A GRK1 ORIX4 ADRA1B SLC6M MAOB DOCK3 UGT3A10 MADA SLC0A0

RASGRF2 UGT1A0 COMT HTR1A GNB5 DRD4 TNFRSF10 HTR1B HLA-DRB1 UGT1A8

OXER1 DRDS ADRA2A

DRD1 SNAP25 DRD3 DRD2 SYNC3 HTRD

MC4R CLOCK ARNTL NOS1 LMX10 SLC6A3 MCP2 BDNF

STS AR5B ASL OTC

P4CK1 MLCP2 DRD4 as target protein ANKK1 MBOAT1 BDNF

identified based on DGKH NCL HDAC1 TCDB1 FMR1 PARK2 network parameter for LPL

MTA3 ADHD

ADPOQ

CDH13

Fig. (3). PPI network of ADHD (Attention Deficit Hyperactivity Disorder) constructed using the STRING 10.0 database. 20 The Open Bioinformatics Journal, 2020, Volume 13 Verma et al.

180 B ~b b

135 b ~b ~1 90 1

45 a A 0 ~a Psi (degrees) -45 ARG 243 (A)

-90 TRP 80 (A)

ASP 398 (A) -135 ~b ~p b P ~b

-180 -135 -90 -45 0 45 90 135 180 Phi (degrees)

(A) (B)

ERRAT Error Values 30

99% Error 15

95% Warning 10

0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99 101103 105 107109 111 113 115 117 119 121 124

(C)

Fig. 4. (A) The 3D structure 4EJN protein structure retrieved from PDB of Autism. (B) Ramachandran plot of 4EJN generated from procheck server (C) Overall quality factor of 4EJN generated by ERRAT server.

180 B ~b b ASN 309 (A)

135 b ~b

~1 90 1

45 L a A 0 ~a

Psi (degrees) -45

-90

~b ~p -135 PRO 246 (A) b p ~b

-180 -135 -90 -45 0 45 90 135 180 Phi (degrees)

(A) (B)

ERRAT Error Values 30

99% Error 15

95% Warning 10

1 6 5 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 10 15 20 25 30 35 40 45 50 55 60 65 72 101106 111 116121 126 131136141 146151 156161 166171176 181186 191 196201206 211 216221226 236236 241 246 251 256261266271276 281286 291296301

(C)

Fig. (5). (A) Predicted the 3D structure of Dopamine receptor (DRD4) in ADHD (B) Ramachandran plot of DRD4 generated from procheckserver (C) Overall quality factor of DRD4 generated by ERRAT server. Network Biology Approaches The Open Bioinformatics Journal, 2020, Volume 13 21

180 B ~b b

135 b ~b

~1 ILE 129 (A)

90 LYS 12 (A) GLU 25 (A) 1 PHE 133 (A)

45 L a ARG 136 (A) A 0 ~a

Psi (degrees) -45

CYS 84 (A) -90

-135 ~b ~p b p ~b

-180 -135 -90 -45 0 45 90 135 180 Phi (degrees)

(A) (B)

ERRAT Error Values 30

99% Error 15

95% Warning 10

0 1 3 5 7 9 11 3 5 7 9 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99 101103105107109 11111 11 11 11 121123125127 129131133135137139141143145147149 151153155157159161163165167169171173175 177 180

(C)

Fig. (6). (A) Predicted the 3D structure of SNAP25 protein of ID (B) Ramachandran plot of SNAP25 protein generated from Procheck server (C) Overall quality factor of SNAP25 protein generated by ERRAT server.

3.3. Molecular Docking Analysis pared by adding essential hydrogen atoms, Kollman charges, The molecular docking analysis is the probably most sui- and solvation parameters and converting it into PDBQT format table method to identify best-fit interaction between protein by using the Auto Dock Tool (ADT). The ligand file was and ligand. Protein-ligand interaction plays a key role in drug prepared by detecting root and torsion and converting it into discovery. Docking uses the computational simulation to pre- PDBQT format. The grid map was set of 60 x 60 x 60- dict the preferred orientation of ligand to a receptor when angstrom grid points that were generated using the Auto grid interacted with each other to form a higher stability complex. program. The docking was performed using a Lamarckian Molecular docking was performed for best 5 phytoche- Genetic Algorithm (LGA) parameters and run on the Cygwin micals having neuroprotective activity reported in various platform. Docking results were analysed based on the lowest literature searches, against AKT1, SNAP25, and DRD4 pro- binding free energy (BE) and optimal docking energy teins using Autodock 1.5.6 tool. The protein PDB file was pre- conformation (Tables 2, 3 and 4 & Figs. 7, 8 and 9).

Table 2. Docking Result of Autism in terms of binding energy. The compound highlighted in bold shows the lowest binding energy.

S.No. Ligand (Phytochemical) Affinity (kcal/mol) Plant source PubChem ID 1. Hyoscyamine -2.23 Daturametel 154417 2. Sulphoraphane -3.49 Broccoli 5350 3. Oroxylin A (5,7-dihydroxy-6-methoxyflavone) -4.18 Scutellariabaicalensis 5320315 4. Valerenic acid -4.50 Valerian 6440940 5. Huperzine A -6.70 Lycopodiumserratum 5912039 22 The Open Bioinformatics Journal, 2020, Volume 13 Verma et al.

Table 3. Docking result of Intellectual Disability (ID) in terms of binding energy. The compound highlighted in bold shows the lowest binding energy.

S.No. Ligand (Phytochemical) Affinity (kcal/mol) Plant source PubChem ID 1. Sulphoraphane -2.27 Broccoli 5350 2. Valerenic acid -2.46 Valerian 6440940 3. Oroxylin A (5,7-dihydroxy-6-methoxyflavone) -2.98 Scutellariabaicalensis 5320315 4. Hyoscyamine -3.51 Daturametel 154417 5. Huperzine A -4.32 Lycopodiumserratum 5912039

Table 4. Docking result of Attention Deficit Hyperactivity Disorder (ADHD) in terms of binding energy. The compound highlighted in bold shows the lowest binding energy.

S No. Ligand (Phytochemical) Affinity Plant source PubChem ID 1. Huperzine A -2.27 Lycopodiumserratum 5912039 2. Sulphoraphane -2.30 Broccoli 5350 3. Hyoscyamine -2.64 Daturametel 154417 4. Oroxylin A (5,7-dihydroxy-6-methoxyflavone) -2.78 Scutellariabaicalensis 5320315 5. Valerenic acid -3.00 Valerian 6440940

THR81

TRP80 ASP292 THR82 THR291

ILE290

LEU264

TYR272 ASP274

Fig. (7). The 3D & 2D molecular interaction between Huperzine A (ligand) and SNAP25A protein of Autism. Here, in the figures, the green dotted line shows hydrogen bond interaction.

GLN53

ILE178 LEU58

GLN174

ASN175

GLU170 ILE17

Fig. (8). The 3D & 2D molecular interaction between Huperzine A (ligand) and SNAP25A protein of ID. Here, in the figures, the green dotted line shows hydrogen bond interaction. Network Biology Approaches The Open Bioinformatics Journal, 2020, Volume 13 23

SER71

PRG65 147 GLN152 APG151 GLN146

PHE72 THR69

TYR143 LEU155

ASP132

Fig. (9). The 3D & 2D molecular interaction between Valerenic acid (ligand) and DRD4 protein of ADHD. Here, in the figures, the green dotted line shows hydrogen bond interaction.

CONCLUSION HUMAN AND ANIMAL RIGHTS The candidate genes responsible for the progression of No animals/humans were used for studies that are the basis Autism is AKT1 gene, SNAP25 gene for Intellectual Disability of this research. (ID), and DRD4 gene for Attention Deficit Hyperactivity Disorder (ADHD) and were identified as a potential target. CONSENT FOR PUBLICATION Information on these genes was retrieved from NCBI (National Not applicable. Center for Biotechnology Information. STRING and Cytoscape database aims to simplify access to this information by AVAILABILITY OF DATA AND MATERIALS providing a comprehensive, yet a quality-controlled collection of protein-protein associations for a large number of The data supporting the findings of the article is available organisms. The 3D structures of protein AKT1 were retrieved in the [National Center for Biotechnology Information ] at from PDB and SNAP25 and DRD4 were modeled and [https://www.ncbi.nlm.nih.gov/gene/]. validated. Various phytochemicals were curated from the FUNDING literature study that showed a therapeutic effect against various diseases. Natural products especially derived from plants have None. long been used in the treatment of human ailments. “Biological friendliness”, “drug-likeness”, low toxicity, and easy avail- CONFLICT OF INTEREST ability take natural products far ahead to be developed as drugs The authors declare no conflict of interest, financial or than synthetic compounds. otherwise. Docking is a valuable and skilled tool for in silico screening. The docking studies were performed between the ACKNOWLEDGEMENTS target proteins 4EJN for Autism, SNAP25 for Intellectual We gratefully acknowledge the support provided by Disability (ID), and DRD4 for Attention Deficit Hyperactivity Bioinformatics tools, software, & databases in the work. Disorder (ADHD) with known phytochemicals. Based on the lowest binding affinity: Huperzine A (extract of REFERENCES Lycopodiumserratum) against Autism and Intellectual [1] D’Souza H, Karmiloff-Smith A. Neurodevelopmental disorders. Wiley Disability and Valerenic acid (extract of Valerian) against Interdiscip Rev Cogn Sci 2017; 8(1-2)e1398 Attention Deficit Hyperactivity Disorder (ADHD) could be [http://dx.doi.org/10.1002/wcs.1398] [PMID: 27906503] possible potential therapeutic phytochemical for treatment. [2] Tran NQ, Miyake K. Neurodevelopmental disorders and environmental toxicants: Epigenetics as an underlying mechanism. Further, the obtained molecule if experimentally validated International journal of genomics 2017; 2017 would play a promising role in the treatment of NDDs in [3] American Psychiatric Association. Diagnostic and Statistical Manual humans. of Mental Disorders. 5th ed. Arlington, VA, USA: American Psychiatric Publishing 2013. [4] Ramtekkar UP. DSM-5 Changes in attention deficit hyperactivity ETHICS APPROVAL AND CONSENT TO PARTI- disorder and autism spectrum disorder: Implications for comorbid CIPATE sleep issues. Children (Basel) 2017; 4(8): 62. [http://dx.doi.org/10.3390/children4080062] [PMID: 28749421] Not applicable. [5] Diaz-Beltran L, Cano C, Wall DP, Esteban FJ. Systems biology as a 24 The Open Bioinformatics Journal, 2020, Volume 13 Verma et al.

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