Discovery of novel small molecule inhibitors targeting progranulin-sortilin: A virtual high throughput screening approach

doi:10.21203/rs.3.rs-2559741/v1

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Discovery of novel small molecule inhibitors targeting progranulin-sortilin: A virtual high throughput screening approach

https://doi.org/10.21203/rs.3.rs-2559741/v1

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Introduction:Reduction in progranulin (PGRN) have been associated with various neurodegenerative diseases. PGRN binds with high affinity to sortilin (SORT), a membrane transporter, resulting in its cellular uptake and eventual degradation in the lysosome. Inhibition of the SORT-PGRN interaction has the potential to increase PGRN levels up to 2.5-fold.

Methodology:A virtual screening of curated CNS library of >47K ligands was done with sortilin receptor (6X3L) through virtual screening workflow in Schrodinger suite. Co-crystallised ligand was used as a positive control. Docking was done through HTVS, then SP and finally XP model followed by binding free energy calculations (MMGBSA). Based on the result analysis of molecular docking, binding free energy and interactions, docked complexes were chosen for molecular dynamics (MD) studies. Drug likeliness and ADMET studies were also carried out.

Results: The virtual screening workflow yielded 139 ligands. Two test ligands and a control were selected and further evaluated through molecular dynamics studies. Both the test ligands (1625 & 127) had comparative docking score (-5.96 & -6.46 kcal/mol) as that of control ligand (-6.21 kcal/mol respectively) and but better binding free energy (-54.66, -53.12 & -43.21 kcal/mol respectively). MD simulations confirmed the docking results for all the three ligands where our test ligand 1625 reached equilibrium quickly as compared to the rest. Our test compounds also showed favourable characteristics of a CNS acting drug and favourable ADMET properties.

Conclusion: Our study results showed a promising CNS specific ligand as an inhibitor of PRGN-SORT interactions and has a potential to be developed as a drug through in-vitro and in-vivo studies.

Sortilin

Progranulin

Virtual screening

Molecular docking

Molecular dynamics

ADMET

PGRN (encoded by the GRN gene) is a conserved 593 amino acid, 88-kDa glycosylated, secreted protein which is produced in myeloid cells and a subset of neurons in the central nervous system (CNS), as well as in myeloid, epithelial, and activated fibroblasts, and in endothelial cells at the periphery[1]. PGRN serves as a neuronal survival and axonal growth factor [2] and appears to operate extracellularly via the tyrosine kinase ephrin type-A receptor 2 (EphA2) [3] and Notch signalling [4]. Through its interactions with prosaposin (PSAP) and its receptors, mannose 6-phosphate receptor (M6PR), and low-density lipoprotein receptor-related protein 1 (LRP1) [5], PGRN also makes its way into the lysosomes of cells [6]. PGRN is believed to function in the lysosome as a chaperone for lysosomal enzymes including cathepsin D (CTSD), which is involved in the breakdown of misfolded proteins [7].

PRGN also interacts with the sortilin clearance receptor, which transports it into the lysosomes for destruction [6]. Under healthy conditions, PRGN has roles in homeostasis, lysosomal function, neuroprotection and microglial regulation. Haploinsufficiency which occurs due to mutation in granulin (GRN) gene results in decreased progranulin levels and the most common form of inherited dementia, Frontotemporal dementia (FTD) [8, 9]. GRN knock-out (KO) mice exhibit age-dependent gliosis, decreased survival, and elevated levels of phosphorylated TDP-43 and other cellular ageing markers [10]. Additionally, GRN KO mice exhibit aberrant behaviour, such as a decline in social interaction and difficulties with learning and memory [11]. According to the evidence, progranulin levels that are reduced may set off mechanisms that are common to a number of neurodegenerative illnesses such as FTD, Huntington’s, Parkinson’s, and Alzheimer’s Disease and interventions that enhance progranulin levels may thus have a significant therapeutic advantage.

Sortilin (SORT) is a type I membrane receptor that is ubiquitously expressed in both the central nervous system and periphery [12]. Targeted proteins are directed to certain destinations by SORT, which shuttles proteins between the cell surface and numerous intracellular compartments. These fates include cell surface exposure, signal transduction, controlled secretion, endocytic uptake, and anterograde/retrograde sorting. PGRN binds to SORT with a high affinity, causing the cell to take up and eventually degrade it in the lysosome [6]. According to the research, blocking the SORT-PGRN interaction may cause PGRN levels to rise by 2.5 times [10]. Thus, increased progranulin levels in the CNS may serve as a therapeutic target for preventing neurodegenerative disorders by disrupting the SORT-PGRN interaction. To increase the levels of PRGN, various therapeutic strategies targeting SORT1-PRGN axis have been tried which includes SORT 1 expression suppressors, SORT1 antagonists, small molecules which binds with PRGN to decrease SORT1 mediated endocytosis, gene therapy, protein replacement therapy and histone deacetylase inhibitors that increase the expression of PGRN [13, 14]. In this study, we attempted to explore the ligands which can inhibit the binding of PRGN with SORT, thereby increasing the levels/half-life of progranulin.

2.1. Preparation of ligands

For our study, we selected a commercial curated CNS library (Library code : CNS − 47) (https://enamine.net/compound-libraries/targeted-libraries/cns-library) of 47360 ligands along with a co-crystallised ligand as positive control for virtual screening high throughput screening. Ligand preparation was done in virtual screening workflow where input geometries were regularised, duplicates, if any, were removed. All possible states at target pH 7+/-2 were generated using the Epik tool (Epik, Schrödinger, LLC, New York, NY, 2021). High energy tautomeric states were also removed.

2.2. Protein structure retrieval and preparation

X ray crystallographic structure of sortilin with compound 2 was used in this study (PDB: 6X3L) (https://www.rcsb.org/structure/6x3l). This structure was selected based on X Ray resolution (< 3 A), absence of any mutated residue and presence of a known co-crystallized ligand with lowest IC50 value in inhibiting sortilin-progranulin interaction (2uM) [15]. The structure was first assessed for any missing residues. The protein Preparation Wizard tool of Schrodinger’s Glide module (Glide, Schrödinger, LLC, New York, NY, 2021) was used to prepare the protein structures before molecular docking study. In the pre-processing stage, bond orders were assigned, missing hydrogen atoms were added, disulphide bonds were created and using Epik tool (Epik, Schrödinger, LLC, New York, NY, 2021), all the states of protein were generated at 7+-2 pH. This is followed by all the removal of all the hetero atoms and finally geometry optimization, and energy minimization of protein were done with OPLS4 force field [16]. Water molecules were also removed from the active site.

2.3. Active site selection and receptor grid generation

Active site selection and receptor grid generation (Table 1) was done based on the interacting residues of the co-crystallised ligand present with the protein structure.

Table 1

Active site residues and receptor grid coordinates
Target	PDB ID	Active site residues	Receptor grid coordinates
Sortilin	6X3L	Y271, S272, F273, G274, F281, A282, S283, R292, I294, F317, Y318, I320, T365	X= -31.36, Y = 3.022, Z = 14.81

2.4. Virtual Screening, Molecular docking, and Binding Free Energy

Screening of ligand library and molecular docking was done through virtual screening workflow. For molecular docking studies, the LigPrep file (LigPrep, Schrödinger, LLC, New York, NY, 2021) and glide receptor grid file were used as input. In virtual screening docking was done through HTVS, then SP and finally XP mode. At every stage, 10% of the best compounds were selected for the next stage (Glide, Schrödinger, LLC, New York, NY, 2021). Finally, binding free energy calculations were done using prime MMGBSA (Prime, Schrödinger, LLC, New York, NY, 2021) (Fig. 1).

2.5. Molecular Dynamic (MD) simulations

MD simulation was done through the Desmond simulation package (Desmond Molecular Dynamics System, D. E. Shaw Research, New York, NY, 2021). Protein-ligand complexes were selected on the basis of binding pattern similar to co-crystallized ligand as well as binding free energy (MM-GBSA) for MD study. In system building, membrane model was first generated. POPC membrane was used [17], and membrane was set along beta strands. Transferable Intermolecular Interaction Potential 3 Points (TIP3P) [18] solvation model was chosen with an orthorhombic box of 10x10x10 Å. The system was made electrically neutral by adding counter ions (Na⁺ or Cl⁻). Salt (NaCl) at the concentration of 0.15 M was added to mimic physiological conditions. The model system was relaxed before the simulation. The OPLS4 force field [16] was used for MD simulation. NPyT ensemble with a temperature of 300K and a pressure of 1 atm was applied during all MD simulations. For Coulombic interactions, 9 Å was chosen as a cut-off radius. Langevin thermostat (relaxation time 1 ps) and barostat (relaxation time 2 ps) [19] were chosen to control temperature and pressure respectively. To calculate non-bonded forces, the RESPA integrator [20] was used, and recording was done at every 2 fs. The simulation was done for each protein-ligand complex for 100 ns. The trajectories were saved at every 100 ps and to evaluate the stability of the protein-ligand complexes, the root means square deviation (RMSD) of both the protein and the ligands was computed and inspected. To analyze the local fluctuations in the structures, we also calculated root mean square fluctuations (RMSF) of the apo form and the complexes. MD results were extracted and viewed using the simulation interaction diagram tool.

2.6. Drug likeliness prediction and ADMET studies

Drug likeliness prediction studies were carried out using datawarrior [21] and QikProp (QikProp, Schrödinger, LLC, New York, NY, 2021). One of the crucial factors in computer-aided drug discovery to screen the compounds is the prediction of ADMET characteristics. We used the pkCSM web server [22] to assess the ADMET profiles of our test compounds and the control ligand. ADMET properties were computed using the smiles of all the ligands.

3.1. Molecular docking analysis

First, to validate our protocol, we redock co-crystalized with 3 pose per ligand as output and again redocking was done to get 9 poses. RMSD was computed after each docking procedure until RMSD of the docked ligand matched (< 1 A) with the ligand in crystalized structure (Fig. 2).

The virtual screening workflow yielded 139 ligands with docking score. Based on the interaction pattern of the test ligands with positive control ligand, docking score and binding free energy, two test ligands (Fig. 3) and a control were selected and further evaluated through molecular dynamics studies (Fig. 4).

Both the test ligands and positive control had similar binding pattern (Table 2 and Supplementary S1). The screened compound-1 had three H-bonds with S283, R292 and Y318 residues of protein. The keto group present in the pyridine nucleus at 2nd position forms an H-bond with R292 and Y318 while the keto group present in ethyl oxo bridge forms an H-bond with S283. The screened compound-2 had three H-bonds with S283, R292 and Y318 residues of protein. Keto group present in benzoxazole nucleus at 2nd position forms R292 and Y318 and keto group present in ethyl oxo bridge forms an H-bond with S283. Cognate ligand also showed three H-bonds with S283, R292 and Y318 residues of protein. Keto group of carboxylic acid group forms two H-bond with S283 and R292 while OH group of carboxylic acid forms H-bond with Y318.

Title	Smiles	Docking score (kcal/mol)	MMGBSA ΔG Bind (kcal/mol)	H bond forming residues	Other interactions
6X3L_1625	c1cccc2c1n(c(= O)o2)CC(= O)N1CCC[C@@H]1Cn1cc(cn1)C	-5.96	-54.66	S283, R292, Y318, I320	Pi-Pi: Y271
					P: S272, S283, S319, T365
					HP: Y271, F273, L275, F281, A282, I294, F317, Y318, I320, M330
					PC: R292, K227
6X3L_cognate	OC(= O)c1n(nc(C(C)(C)C)c1)Cc1ccccc1	-6.21	-43.21	S283, R292, Y318	Pi-Pi: Y271
					SB: R292
					P: S272, S283, S319, T365
					HP: Y271, F273, F281, A282, I294, F317, Y318, I320, M330
6X3L_127	C1CC[C@H]2[C@@H]1CCN2C(= O)c1c(= O)[nH]c(C)cc1	-6.46	-53.12	S283, R292, Y318	Pi-Pi: Y271
					P: S272, S283, S319, T365
					HP: Y271, F273, F281, A282, I294, F317, Y318, I320, M330
					PC: R292

HP- Hydrophobic, PC-Positively Charged, P - Polar, SB - Salt Bridge

Table 2: Docking results of selected test ligand and positive control ligand

3.2. Drug likeliness prediction studies

Compared to other therapeutic drugs, CNS drugs have lower molecular weights. The marketed CNS drugs have average molecular weight around 319 compared to other oral drugs which have avg 330 molecular weight. Lipophilicity is the very crucial parameter for the molecules which act on CNS. As reported by Hansch and leo [23], CNS penetration is optimal when Log P values are in the range between 1.5 to 2.7. The log P value of screened compounds suggest that they may have good CNS penetration. Hydrogen bonding (O + N atom) also has an impact on CNS drugs. Marketed CNS drugs have an average 2.12 hydrogen bond acceptor and 1.5 hydrogen bond donors. The hetero atoms (O + N) should not be more than five. Overall ideal CNS active molecules should be less polar. The polar surface area of CNS active molecules is around 60–70 A. Drug likeliness prediction studies showed that our selected test ligands had almost all the properties of a CNS drug (Table 3).

Parameter	Range ^$	Test ligand 1625	Test ligand 127	Positive control
MW	170–350	340.38	246.31	258.32
ClogP	-0.5–3.0	1.27	1.12	2.25
Hb Donors	< 2	0	1	1
TPSA	70 A 2	86.3	67.3	59.6
Amide groups	≤ 1	1	0	0
Total H-bonding	< 8	4	3	3
Carboxylic acids	≤ 1	0	0	1
CNS MIPO	≥ 4	4	4	4
RotBonds	≤ 4	4	1	4
Aromatic rings	1–3	2	0	2
FSP 3	0.15–0.8	8	8	6
QPPCaco-2	> 500	598	1035	347
Basic N	≤ 2	0	0	0
QPlogBB	-1 .0-0.8	-3.06	-2.06	-3.90
^$ https://enamine.net/compound-libraries/targeted-libraries/cns-library

Table 3: CNS properties of the test and control ligands

3.3. ADMET Analysis

When designing novel medicines, the ADMET qualities are of utmost importance. One of the important methods that reduces the likelihood that drug compounds will fail in subsequent pre-clinical and clinical investigations is the ADMET analysis. We computed PkCSM server to predict the ADMET properties (Table 4).

An oral drug's capacity to cross the intestinal epithelial barrier, which controls the rate and degree of human absorption and ultimately influences its bioavailability, is one of the most significant issues addressing ADMET characteristics. HIA and Caco-2 were therefore chosen to assess the absorption properties. Our test compounds showed excellent intestinal absorption and hence can be considered suitable for oral formulations.

About 90% of oxidative metabolic processes are carried out by the CYP enzymes, especially isoforms 1A2, 2C9, 2C19, 2D6, and 3A4 [24]. A given small molecule is more likely to be implicated in DDI with many other medications if it inhibits more CYP isoforms than others [25]. Test ligand 1625 and control ligand were found to be substrate of CYP3A4, hence likely to show drug interactions with CYP3A4 inducers and inhibitors, while ligand 127 didn’t undergo metabolism with reported CYPs and hence less likely to show significant drug interactions at the level of metabolism. Ligand 1625 was also an inhibitor of CYP1A2, thus likely to be involved in interactions with its substrate. P-Glycoprotein (P-gp or ABCB1) is an ABC transporter protein involved in numerous crucial procedures including gastrointestinal absorption, medication metabolism, distribution, and excretion [26]. P-gp inhibition or substrate is utilised as a significant index to assess the medications from many aspects. P-gp in the intestines decreases the absorption of drugs in the blood while in the brain, P-gp decrease CNS bioavailability. None of our drugs was a substrate or inhibitor of P-gp.

Total Clearance is a crucial PK parameter because it affects a drug's half-life, bioavailability, and oral absorption, all of which have an impact on the dosage schedule (how frequently) and dose size (how much). Its forecast gives us a framework for the starting dose for the first human investigations and aids in assessing the viability of clinical dosing [27]. Total clearance of our test compounds showed while 1625 had better clearance and less likely to be accumulated in the body, 127 showed poor clearance compared with the positive control. Many cationic medications are first secreted by the kidney through the OCT2, and its inhibitors may modify how drugs accumulate in the kidney, causing nephrotoxicity [28]. Both our test compounds are less likely to be nephrotoxic as none of them was an inhibitor of OCT2.

One of the main causes of failure in late-stage drug development is toxicity, which is the extent to which a chemical might harm an organism or organs of the organism, such as cells and tissues. Thus, early toxicity detection would be extremely beneficial [29]. The human ether-a-go-go-related gene (hERG) encodes a potassium ion (K+) channel that is linked to prolonged QT intervals and prolonged ventricular repolarization, which can result in arrhythmia and more severe heart failure [30]. Drugs that are structurally and functionally unrelated to hERG channels have been demonstrated to block them, and some of these have been taken off the market. In the early stages of drug discovery, Ames’s mutagenicity is utilized to assess possible teratogenicity and genotoxicity. Additionally, the toxicological endpoints with the highest potential for harm to human health are acute oral toxicity. A chemical with a lower dose is more lethal than a compound with a higher LD50 when the LD50 doses are compared. Our test ligands have almost same LD50 values as compared to the control ligand. Here also in the toxicity profile, our test compounds showed comparative profile as that of the control and were found to be safer. Other toxicity profile is similar.

Table 4

ADMET properties of the selected ligands
Property	Model Name	Test ligand 1625	Test ligand 127	Positive control	Unit
Absorption	Water solubility	-3.475	-3.324	-3.728	Numeric (log mol/L)
Absorption	Caco2 permeability	0.948	1.241	1.369	Numeric (log Papp in 10 cm/s)
Absorption	Intestinal absorption (human)	96.757	95.717	92.278	Numeric (% Absorbed)
Absorption	Skin Permeability	-3.092	-3.212	-2.615	Numeric (log Kp)
Absorption	P-glycoprotein substrate	No	No	No	Categorical (Yes/No)
Absorption	P-glycoprotein I inhibitor	No	No	No	Categorical (Yes/No)
Absorption	P-glycoprotein II inhibitor	No	No	No	Categorical (Yes/No)
Distribution	VDss (human)	-0.454	0.224	-0.505	Numeric (log L/kg)
Distribution	Fraction unbound (human)	0.2	0.463	0.097	Numeric (Fu)
Distribution	BBB permeability	-0.568	0.266	0.25	Numeric (log BB)
Distribution	CNS permeability	-2.891	-2.959	-2.143	Numeric (log PS)
Metabolism	CYP2D6 substrate	No	No	No	Categorical (Yes/No)
Metabolism	CYP3A4 substrate	Yes	No	Yes	Categorical (Yes/No)
Metabolism	CYP1A2 inhibitior	Yes	No	No	Categorical (Yes/No)
Metabolism	CYP2C19 inhibitior	No	No	No	Categorical (Yes/No)
Metabolism	CYP2C9 inhibitior	No	No	No	Categorical (Yes/No)
Metabolism	CYP2D6 inhibitior	No	No	No	Categorical (Yes/No)
Metabolism	CYP3A4 inhibitior	No	No	No	Categorical (Yes/No)
Excretion	Total Clearance	0.81	0.353	0.693	Numeric (log ml/min/kg)
Excretion	Renal OCT2 substrate	No	No	No	Categorical (Yes/No)
Toxicity	AMES toxicity	No	No	No	Categorical (Yes/No)
Toxicity	Max. tolerated dose (human)	-0.455	0.429	0.645	Numeric (log mg/kg/day)
Toxicity	hERG I inhibitor	No	No	No	Categorical (Yes/No)
Toxicity	hERG II inhibitor	No	No	No	Categorical (Yes/No)
Toxicity	Oral Rat Acute Toxicity (LD50)	2.572	2.135	2.134	Numeric (mol/kg)
Toxicity	Oral Rat Chronic Toxicity (LOAEL)	1.347	2.465	1.964	Numeric (log mg/kg_bw/day)
Toxicity	Hepatotoxicity	Yes	Yes	Yes	Categorical (Yes/No)
Toxicity	Skin Sensitisation	No	No	No	Categorical (Yes/No)
Toxicity	T.Pyriformis toxicity	0.667	0.428	0.784	Numeric (log ug/L)
Toxicity	Minnow toxicity	1.348	1.739	0.708	Numeric (log mM)

3.4. Comparative analysis of MD simulation

To comprehend the stability of the protein-ligand complex and the binding interactions between them with respect to time in a hypothetical physiological condition, MD experiments are carried out. We ran 100 ns for apo form and each of our protein-ligand complexes. RMSD, percentage of contact of binding residues during 100ns simulation and protein-ligand contact mapping was computed.

Apo form of the protein underwent 2–3 conformational changes before reaching to equilibrium around 40-45ns. In complex with co-crystalized ligand, the protein went conformational changes till 20ns after that RMSD keeps on increasing till 50ns, around which it stabilized and showing minor fluctuations as in apo form. In presence of test ligand 1625, protein quickly attained equilibrium by around 30ns. RMSD kept on increasing till 40ns in protein complex with test ligand 127 after which the protein showed 2 conformational changes with a little higher fluctuation towards the end of simulation (Fig. 5). Mean RMSD was least for 6X3L-1625 complex indicating that the complex became more stabilized with test ligand 1625 (Table 5).

Table 5

RMSD results after 100 ns Molecular Dynamics
Target	Mean (Å) ± SD
Apo	2.83 ± 0.42
6X3L_cognate	2.51 ± 0.32
6X3L_1625	2.31 ± 0.23
6X3L_127	2.42 ± 0.27

Interaction pattern during the 100 ns simulation showed that both the test ligands and positive control ligand confirmed the H bonding pattern of the docking results. While the positive control showing H bonds with S272, S283, R292 and Y318, it also formed water bridges with Y271, S272, P273, Y318 and I320. Hydrophobic interactions were mainly shown by Y271, P281, R292, I294, P317, I320 and M330. Only H bond remained consistent throughout the study duration. Test ligand 1625 formed H bonds with S272, S283, R292, Y318 and I320 and hydrophobic interactions with L248, Y271, P281, I294, P317 and I320. Here also H bonds remained stable throughout the simulation. Ligand 127 was involved in making H bonds and water bridges for appreciable duration of the simulation. Its key residues in forming H bonds were S283, R292 and Y318, while for forming water bridges, Y271, S283, R292 took part (Fig. 6).

PGRN is crucial for the survival and function of neurons, the autophagy-lysosomal pathway, astrogliosis, and neuroinflammation inside the CNS. PRGN deficiency is relatively common among various neurodegenerative diseases and therefore the search for therapeutic options that can increase PRGN levels to restore or normalize neuronal health is needed. In this regard, we attempted to explore computationally SORT1 antagonists to decrease the breakdown of PRGN. To the best of our knowledge, this is the first study which has conducted virtual high throughput screening of targeted CNS specific small molecule library to explore the SORT1 antagonists. Through molecular docking and dynamics studies we have shown that our selected test ligands have more stable interactions with SORT1 receptor and favourable pharmacokinetic profile as compared with the known inhibitor (compound 2). Lee et al. [13] conducted a high throughput screening of 4800 compounds to inhibit PGRN-SORT1 interactions and reported that the compound named BVFP prevented SORT1 mediated endocytosis and increases PGRN levels. As of now, no small molecule inhibitor of this interaction is in any stage of clinical trials. A human monoclonal antibody, AL001 is currently in phase 3 clinical trial (https://clinicaltrials.gov/ct2/show/NCT04374136), blocks the interaction of SORT1-PRGN interactions, thereby preventing the degradation of PRGN. Since recombinant PGRN is too big to pass the blood-brain barrier (BBB) for protein replacement and there is presently no practical means to pharmacologically alter PGRN levels in the brain, the development of bio-available, BBB permeable SORT1 antagonists will be an important tool to ascertain whether therapeutically regulating or raising PGRN levels will lessen the pathogenesis in various neurodegenerative diseases especially FTD-GRN, which occurs due to partial loss of PGRN as a result of mutation in GRN gene. Although ways to increase PGRN levels have evolved, minimizing off-target effects remains a difficulty.

Our study results showed a promising CNS specific ligand as an inhibitor of PRGN-SORT1 interactions and has a potential to be developed as a drug through in-vitro and in-vivo studies. The medical community has to unite behind this potential target immediately given the difficulties involved in the development of treatments and clinical trials for individuals with neurodegenerative illnesses.

Funding: The authors didn’t receive any funds, grants or other financial support for this study.

Conflict of interests/Competing interests: The authors didn’t have any conflict or competing interests.

Availability of data and material: Supplementary information is available with this article.

Author’s contributions: Conceptualization: BM; Experiment: AA, MJ; Manuscript writing: AA, MJ, AS; Review and editing: MJ, AA; Graphical abstract: AS; Overall supervision: BM

Acknowledgements: The authors acknowledge the Schrodinger Inc. India with special thanks to Dr. Prajwal Nandekar to provide academic licence for 15 days to complete this manuscript.

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No competing interests reported.

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Discovery of novel small molecule inhibitors targeting progranulin-sortilin: A virtual high throughput screening approach

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Abstract

Figures

1. Introduction

2. Methodology

2.1. Preparation of ligands

2.2. Protein structure retrieval and preparation

2.3. Active site selection and receptor grid generation

2.4. Virtual Screening, Molecular docking, and Binding Free Energy

2.5. Molecular Dynamic (MD) simulations

2.6. Drug likeliness prediction and ADMET studies

3. Results

3.1. Molecular docking analysis

3.2. Drug likeliness prediction studies

3.3. ADMET Analysis

3.4. Comparative analysis of MD simulation

4. Discussion

5. Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1