Rheumatoid Arthritis (RA) is a persistent autoimmune disease affecting approximately 0.5-1 percent of the world population. RA prevalence is higher in woman aged between 35 to 50 years than in age matched men, though this difference is less evident among elderly patients. The profound immune specific effects of disrupted JAK3 (Janus kinase 3) signaling highlight the possibility of therapeutic targeting of JAK3 as a highly specific mode of immune system suppression. To address the above problem which is unendurable to patients and in the hope to cater some respite to such suffering we have targeted JAK3 protein and JAK/STAT signaling pathway with compounds downloaded from FDA database, and performed screening of all available compounds docked against JAK3 protein. The difference between the target protein and other proteins of the same family was studied using cross docking and the compounds having higher binding affinity to JAK3 protein also showed more selectivity towards the particular protein. Density functional theory and molecular dynamics simulation study was done to study the compounds at their atomic level to know more about their drug likeliness. At the end of the study and based on our analysis we have come up with three FDA approved drugs that can be proposed as a treatment option for Rheumatoid Arthritis.
Research Article
Computational drug repositioning for IL6 triggered JAK3 in Rheumatoid Arthritis using FDA database
https://doi.org/10.21203/rs.3.rs-3791539/v1
This work is licensed under a CC BY 4.0 License
Journal Publication
published 14 Aug, 2024
You are reading this latest preprint version
Rheumatoid Arthritis
JAK3
Molecular docking
DFT calculations
Molecular dynamics simulations
Autoimmune diseases comprise of more than 80 chronic ailments, type I diabetes and Rheumatoid Arthritis (RA) being the most common ones and their prevalence rate has been increasing ever since [1]. There are multiple causes that can trigger an autoimmune disease, starting from environmental factors to epigenetic elements. The annual incidence of the world population being affected by RA is approximately one percent that is one individual in every ten thousand is being affected by this incurable malady [2].
RA commonly triggered by proinflammatory cytokines causes muscular dystrophy, swelling of the proximal interphalangeal, metacarpophalangeal or wrist joints,accompanied with immeasurable pain [3]. The disease proliferates through three checkpoints, first is the recognition of modified proteins, second, T-cell metabolic reprogramming and third, is the recognition of modified proteins and emergence of autoantibodies [4]. The inflammation caused in RA is due to the clonal expansion of auto reactive T-cells, such as T helper (Th) 17 lymphocytes and B lymphocyte [5]. These B lymphocytes mature to their end stage of plasma cells producing auto antibodies, including anti citrullinated peptide antibodies and rheumatoid factor, which is why the diagnosis includes confirmation of presence of rheumatoid factor-IGM and CRP (cyclic citrullinated protein) in blood [6]. A multitude of research is being carried out on RA and the findings suggest that JAK/STAT pathway plays a crucial role towards the onset of the disease [7].
The JAK (Janus kinase) protein is a non-receptor tyrosine kinase beholding four members in its family namely JAK 1, JAK2, JAK3 and TYK2 [8]. Delving into the history of each of the proteins of this family, JAK3 has been selected as the target to address the yet immedicable disease RA for this particular study [9]. Cytokines that are dependent on these JAK proteins are crucial regulators of immunity and inflammation and also have been found to be essential in immunopathology and autoimmune diseases such as RA Previous studies also says that JAK3 is highly expressed in lymphocytes and it predominantly mediates and participates in the formation of cytokines [10] [11]. The deficiency or disorder of JAK3 leads to malfunction of lymphocytes and in due course causes loss of immune function [12]. One of the major reasons of selecting JAK3 is that the deficiency of this particular protein could avoid the adverse toxic effect damaging other tissues whereas deficiency of JAK1 and JAK2 could be lethal to human body [13].
Treatment of RA in current times include medicines divided into four distinct groups namely NSAIDs (Non-steroidal anti-inflammatory drugs), corticosteroids, conventional DMARDs (Disease Modifying Anti Rheumatic Drugs) and biological DMARDs [14]. NSAIDs are preferable in acute stages of the disease and corticosteroids act as adjunctive therapy for active disease that persists. Synthetic DMARDs are used to reduce inflammation and pain whereas biological DMARDs are drugs that act on the TNF-α inhibitors, co-stimulant inhibitors, Interleukin (IL) inhibitors and B-cell receptors. These biological DMARD’s comprises of a special case of medicines called JAK inhibitors.
The approved medicines under this category are tofaticinib, peficitinib and baricitinib. Tofacitinib works by blocking the action of janus kinase enzymes but its long-term use caused severe pulmonary embolism and the use of this medicines was not approved in the European countries[15]. Peficitinib has only been approved in the European countries and is still under clinical trial. Baricitinib is used as a mono or combination therapy, this drug is mostly used under an emergency condition and is more specific to interactions with JAK1 protein [16]. Going by the history of these drugs and their efficacy in RA patients there is need for a new drug that could overcome these drawbacks and be more specific to JAK3 protein as JAK1 and JAK2 has other functions essential to the human body unlike JAK3 which is restricted to expression in the immune system only[17].
Conventional drug discovery is a complex, multistep, tedious and time taking process. Lead compounds need to undergo preclinical studies, clinical trials, post marketing statistics and safety monitoring studies [18]. Whereas drug repurposing also called as drug re-profiling is an unprecedented approach which is cost effective, poses reduced risk of failure and also has lesser chances of adverse reactions in patients as its efficacy and safety studies have already been performed. In this study we have included this strategy to target JAK3 protein against FDA database for RA [19]. In this study molecular docking, Density functional theory calculations and molecular dynamic simulation has been carried out to substantiate the fact that existing drugs can be redirected as therapeutics for other diseases.
Selecting the protein structure and its preparation
Among the many available crystal structures of JAK3 in RCSB PDB database, there are 91 structures that belong to Homo sapiens. From these available structures, structure with pdb id: 5LWM was selected based on its lower resolution (1.55Å) [20]. This particular structure was downloaded in PDB format and processed further to make it amiable for docking studies. The co-crystal ligand and other compounds used to stabilize the protein during crystallization were removed using pymol (The PyMOL Molecular Graphics System, Version 1.2r3pre, Schrödinger, LLC.), a visualization tool. Water molecules were removed as they are not involved in the ligand and protein interaction and also to avoid any distortion in the binding site due to its polarity. A structure crystallized with an inhibitor is always recommended as it would help find the exact binding pocket in the chosen structure.
Ligand preparation
For this particular study the ligands are the FDA approved drugs available in the FDA data base. They were downloaded in MOL2 format and converted to pdbqt format using open bable software [21]. A total of 2000 compounds were downloaded and further energy minimization was carried out using pyrex software. The above steps are performed to know the atom types and charges for every atom in the ligand before it is subjected to molecular docking.
Identifying active site residues
The active site residues are those amino acid residues that can catalyze the reaction by forming temporary bonds with the substrate. The specificity of a protein to its ligand is determined by the presence of an active site which can be three or four amino acids in the sequence often comprising only ten percent of the atom. The information about the active site residue can be fetched from the uniprot database. The active site for the particular structure of JAK3 included in this study (5LWM) is arginine 949, and the binding site residue is glycine 855 which is also the ATP binding domain.
Molecular Docking
Molecular docking is a technique often used by bioinformaticians to study how two or more molecular structures fit together (Radwan et al., 2020). It is used to predict how a protein interacts with small molecules to form supramolecular structures (Tatu et al., 2018). Autodock 4.2.2 has been used to perform molecular docking in our study. The protein rid of water molecules and other macromolecules is first subjected to addition of polar hydrogen molecules,followed by addition of charges namely kolman charges and gasteiger charges and atom types are set to autodock type. The next important step in molecular docking is the grid generation; it is like a map for the ligand. This step will help generate the x, y and z coordinates for the ligand to go and bind in the particular pocket. The final step is docking where the genetic algorithm is selected to run autodock and the output file is in lamarkian format (Koichi et al., 2021). To analyze the results of molecular docking pymol is used to select the best conformation among the ten conformations generated from autodock.
Cross docking
Structure based virtual screenings are widely used for identification of new lead compounds for specific targets that their experimental 3D structure is available (Reza et al., 2020). In this case the compounds screened against JAK3 (5lwm) were cross docked with structures of JAK1 and JAK2 protein to draw conclusions on the binding affinity of the proteins with the selected compounds. This strategy would help us analyze the reactivity of the compounds towards JAK3 in our case. This method can also help us yield valuable information about the effects of induced fit upon binding.
Density Functional Theory
Density functional theory is the proclaimed theory to calculate the electronic structures of the atoms, molecules and solids. It aims at the quantitative understanding of material properties from the fundamental laws of quantum mechanics. The foundations of the density- functional theory are Hohenberg-kohn and Kohn-sham theorems [22]. The underlying principle of DFT is that the total energy of the system is a unique functional of the electron density. Gauss view 09 software is included in the study to perform DFT calculations. The molecules are required to in SDF format. The Gaussian calculation setup is assigned, where the energy is in ground state, default spin and energy type is set to B3LYP and the basis is set to 6-31G. 2d and p orbitals has been chosen to gauge the electron density [23]. A chk file or checkpoint file is generated as the output file which is converted to formchk to analyze the images of the HOMO (highest occupied molecular orbital) and LUMO (lowest occupied molecular orbital) and further calculations are done based on the energies of the HOMO and LUMO values of the particular compound.
Molecular Dynamic Simulations
Molecular dynamics (MD) simulation is a program-based simulation method for anatomizing the random movements of atoms and molecules in a given environment. The atoms and molecules are allowed to interact for a given period of time, giving a view of the dynamic evolution of the system[24]. The trajectories of atoms and molecules are based on Newton’s equation of motion for a system of interacting particles where, forces between the particles and their potential energies are often calculated using inter-atomic potentials or molecular mechanics force field. Gromacs software is used to perform MD simulations in our study.The protein topology is generated by the above software and the ligand topology is determined using PRODGR2 tool available online [25]. The next steps of simulation include generating a solvation box and solvating the protein, adding ions and equilibrating temperature to 300 kelvin and pressure to 1 bar. After all the parameters are set MD simulations are run for required nano seconds. Further analysis is done using RMSD, RMSF and gyrate plots [26].
Molecular Docking
The compounds downloaded from FDA database were docked against 5LWM the selected structure of JAK3 protein using Autodock vina. A grid box was set having x, y and z coordinates using MGL tools Autodock at the binding pocket. Using python script, the results were obtained in the decreasing order of binding affinity. The top ten compounds namely ledipasvir, lomitamide, Ubrogepant, venetoclax, elbasvir, m_entrectinib, exatecan, irinotecan, paratiprevir, avapritinib showed binding affinity scores in the range of -12.6kcal/mol to -11.5kcal/mol. All the compounds manifested more than one hydrogen bonds and hydrophobic interaction with important active site residues such as Lys 830, Tyr 904, Cys 909,Arg 953 and Glu 988. From the above results we can infer that the compounds included in our study have some reciprocity with our protein. 2D structure of the compounds are represented in (Table 1) The results can be viewed in (Table 2) and the important residues and interactions of the ligands with the target protein have been portrayed in Fig. 1.
Cross docking
Cross docking is a kind of validation study that says about the specificity of the compounds towards a particular protein. It is done to check the interactions of the compounds with proteins of the same family to analyze the propensity of interaction of the compounds towards the protein of interest in comparison to the other proteins. In our study we have performed cross-docking of the top 10 compounds narrowed down by docking against structures of JAK1 and JAK2. The results showed that drugs in our study had more affinity towards JAK3 than JAK1 and JAK2. The results have been shown in (Table 3).
Density Functional theory
Density Functional Theory (DFT) based on Hohenberg and Kohn theorem is used to understand important chemical properties of the ligands and calculate an accurate electronic structure, HOMO (Highest occupied molecular orbital) and LUMO (Lowest unoccupied molecular orbital) energies, Mulliken charge of atoms, energetic orbital levels, global hardness, chemical potential and electrophilicity of systems Fig. 2. The HOMO and LUMO energies help us determine the total energy possessed by the ligand or compound. Based on the above value’s important parameters like electronegativity, electrophilicity index, energy gap etc. has been calculated. The more electronegative a compound is more is its tendency to attract bonding pair of electrons.In our study compounds namely Ledipasvir, Lomitapide, Ubrogepant, Irrinotecan and venetoclax have the highest electro negativity values i.e: -1.85,-2.21,-5.47,-4.14, -1.0 respectively. Absolute hardness is a companion parameter which further validates the compounds interactive likeliness towards the protein. The values of ledipasvir, lomitapide, Ubrogepant, venetoclax are 0.16, 0.49, 0.16, 1.89, 0.03 respectively. The final descriptors results have been tabulated for all the parameters and can be viewed in (Table 4).
Molecular Dynamics Simulation
Protein ligand complex with highest binding affinity was subjected to protein ligand simulation using GROMACS package (v.2016.3). The complex system was assigned SPC 216 water model applying GROMOS 96 53a6 force field. Periodic boundary conditions were set, energy was minimized abiding by the verlet list, temperature and pressure was brought to ideal conditions i.e., 300k and 1bar respectively. The complex was then simulated for 100ns obtaining trajectory files, root mean square deviation (rmsd) Fig. 3 (a), root mean square fluctuations (rmsf) Fig. 3 (b) and hydrogen bond graphs Fig. 3 (c). The rmsd values is the standard measure of the structural distance coordinates between a group of atoms, the deviations tell us how much the protein conformation has changed. The rmsf graph measures the average deviation of a particle from a reference point and helps analyze the portions of the structure that are fluctuating from the mean structure. The hydrogen bond graph shows the number of interactions between the drugs and protein at each nano seconds. In this study MD simulation was performed for four compounds namely ledipasvir, lomitapide, venetoclax, and elbasvir complexed with JAK3 for 100 ns. Images for the best complexes are shown in Fig. 4.
Drug repurposing is a far and nationally accepted strategy to suffice the need of the hour for various persistent diseases, one of them being Rheumatoid Arthritis. This strategy is economical as the clinical trials have already been completed and being in use the adverse effects if any have been addressed. In our study we have worked on the entire FDA database and virtual screening and molecular docking techniques have been used as the first line of filter. Based on the binding affinity, stable conformations, hydrogen bond interactions and hydrophobic interactions we have narrowed down our search to 10 drugs that could be more efficacious in pursuit of our study. As per the results of molecular docking, cross docking and density functional theory, top four drugs have been subjected to simulation study, among which lomitapide, ledipasvir and venetoclax have shown least deviations in rmsd plots and lesser peaks in rmsf plots, which makes these compounds a more potential candidate to be proposed for the treatment of Rheumatoid Arthritis. Currently the drug ledipasvir is used a part of combination therapy to treat chronic hepatitis c, lomitapide is used together with a low-fat diet and other treatments to decrease cholesterol in people with homozygous familial hypercholesterolemia and venetoclax is used to treat chronic lymphocytic lymphoma symptoms. Studying different aspects on the pathogenesis of Rheumatoid Arthritis we have also found that hepatitis C infections have also resulted in the development of different kinds of arthritis, so drugs being used for the treatment of HCV (hepatitis C virus) could possibly have the properties to curb the effects of Rheumatoid Arthritis. Referring several papers on drug repurposing it can be summarized that repurposed drugs may unveil new prospects of the drug usage and pathways of its action if further exploited, for example sildenafil was originally developed as an anti-hypersensitive drug and later is being used as a drug for erectile dysfunction, marketed by Pfizer.
An autoimmune disorder manifests in different ways in patients, though the major complaints remain similar, this may require a more discrete understanding of the JAK/STAT pathway and how the JAK3 protein is triggered that worsens the condition in patients. Drug repurposing may be an immediate rescue from the long-term suffering of the patients as it may act via a different mechanism that would benefit the individuals. Through our study we propose three drugs namely lomitapide, ledipasvir and venetoclax based on computational validation against JAK3 protein that plays a major role in the progression of the disease. Since these drugs are already approved by FDA and are in use, these would have the least adverse effects on patients and would be both cost effective and time saving. Drug repurposing is a propitious field in identifying existing drugs having potentials for cure of new diseases.
Acknowledgement
This study was carried out in the Computational biology lab of SRMIST and I also express my gratitude to SRMIST for allowing me to use the HPCC server.
Disclosure statement
The authors declared that there is no conflict of interest.
Compliance with ethical norms
This article does not contain description studies performed by the authors with humans and or animal’s participants as objects.
Funding
The author(s) reported there is no funding associated with the work featured in this article.
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Table 1. FDA Compounds having more affinity towards JAK3 and their 2-Dimensional structures.
Table 2. List of FDA compounds docked against JAK 3 protein and their observed hydrophobic and hydrogen bond interactions (binding affinity in kcal/mol).
|
Compound Name |
Binding Affinity (kcal/mol) |
Hydrogen bond interaction |
Hydrophobic Interaction |
|
Ledipasvir |
-12.6 |
Asp 867, Arg 870, Lys 972 |
Leu 828, 956, 970, Val 836, Ala 966 |
|
Lomitapide |
-12.6 |
Phe 833 |
Leu 828, 857, 905, 956, 970, Val 836, Met 902, Ala 966 |
|
Ubrogepant |
-12.3 |
Lys 855, Asp 967
|
Leu828,857,905,956,970, Val 836, Ala 853, Met 902 |
|
Venetoclax |
-12.1 |
Cys 909, Arg 953 |
Leu 828, 857, 856, 970, Val 836, Ala853, Met 902 |
|
Elbasvir |
-11.9 |
Asn 832, Gly 831, Arg 984
|
Leu 828,956,967 Ala 966, Val 983 |
|
Entrectinib |
-11.9 |
Lys 830, Asp 967, Gln 864
|
Leu 828, 857, 870, Val 836, Ala 853, 966, Phe 868, Met 902 |
|
Exatecan |
-11.8 |
Lys 855 |
Leu 828, 956, Val 836, Ala 853 |
|
Irinotecan |
-11.8 |
Asn 832, Gly 969 |
Leu 828, 956, 970, Phe 833, Val 836, Ala 853 |
|
Paratiprevir |
11.8 |
Asp 912, Asp 967
|
Leu 828, 856, 970, Ala 853, 966, Met 902 |
|
Avapritinib |
-11.5 |
Leu 905 |
Leu 828, 905, 956, Val 836, Met 902, Ala 951, Trp 993 |
Table 3. Cross docking of FDA compounds with JAK 1, JAK 2 and JAK 3 (binding energy in kcal/mol) showing more selectivity of the compounds towards JAK 3 protein
|
Compounds |
Jak3 (5LWM) Binding affinity (kcal/mol) |
Jak1 (3EYH) Binding affinity (kcal/mol) |
Jak2 (4IVA) Binding affinity (kcal/mol) |
|
Ledipasvir |
-12.6 |
-9.0 |
-9.4 |
|
Lomitapide |
-12.6 |
-7.7 |
-9.9 |
|
Ubrogepant |
-12.3 |
-9.1 |
-9.0 |
|
Venetoclax |
-12.1 |
-9.3 |
-10 |
|
Elbasvir |
-11.9 |
-8.8 |
-8.6 |
|
Entrectinib |
-11.9 |
-8.2 |
-10.1 |
|
Exatecan |
-11.8 |
-7.8 |
-8.2 |
|
Irinotecan |
-11.8 |
-8.4 |
-9.3 |
|
Paritaprevir |
-11.8 |
-10.1 |
-10.8 |
|
Avapritinib |
-11.5 |
-8.3 |
-8.8 |
Table 4. Values of Global and local descriptors of DFT calculations that helps substantiate, compounds that have more interaction with JAK3. Selected compounds have been highlighted in bold fonts.
|
Compounds |
Total Energy (E γ) |
Molecular dipole moment (Debye) |
EHOMO |
ELUMO |
HOMO/ LUMO Gap |
Absolute Hardness |
Global Softness |
Electro-negativity |
Chemical potential |
Electrophilicity index
(ω) |
|
(in eV) |
(ΔE) |
(η) |
(σ) |
(χ) |
(μ) |
|||||
|
Ledipasvir |
-81337.27 |
10.39 |
-2.01 |
-1.68 |
0.33 |
0.16 |
2.98 |
-1.85 |
1.85 |
5.52 |
|
Lomitapide |
-65868.42 |
4.31 |
-2.70 |
-1.72 |
0.98 |
0.49 |
1.02 |
-2.21 |
2.21 |
2.25 |
|
Ubrogepant |
-51827.61 |
9.74 |
-5.63 |
-5.31 |
0.32 |
0.16 |
3.08 |
-5.47 |
5.47 |
16.90 |
|
Venetoclax |
-95529.10 |
6.23 |
-0.14 |
-0.07 |
0.07 |
0.03 |
12.86 |
-0.10 |
0.10 |
1.41 |
|
Elbasvir |
-79486.39 |
7.81 |
-0.05 |
-0.04 |
0.01 |
0.05 |
98.42 |
-0.05 |
0.05 |
4.95 |
|
Entectinib |
-51166.65 |
3.86 |
-0.20 |
-0.04 |
0.16 |
0.08 |
6.13 |
-0.12 |
0.12 |
0.78 |
|
Exatecan |
-40289.99 |
10.98 |
-0.20 |
-0.18 |
0.02 |
0.01 |
44.20 |
-0.19 |
0.19 |
8.52 |
|
Irinotecan |
-53083.15 |
8.59 |
-6.04 |
-2.25 |
3.79 |
1.89 |
0.26 |
-4.14 |
4.14 |
1.09 |
|
Paritaprevir |
-77754.35 |
13.57 |
-0.22 |
-0.09 |
0.13 |
0.06 |
7.56 |
-0.15 |
0.15 |
1.18 |
|
Avapritinib |
-45014.70 |
7.1 |
-0.21 |
-0.57 |
-0.36 |
-0.18 |
-2.76 |
-0.39 |
0.39 |
-1.10 |
No competing interests reported.
