key: cord-0759356-b5lstgew
authors: Sonkar, Charu; Doharey, Pawan Kumar; Rathore, Anuranjan Singh; Singh, Vishal; Kashyap, Dharmendra; Sahoo, Amaresh Kumar; Mittal, Nitish; Sharma, Bechan; Jha, Hem Chandra
title: Repurposing of Gastric Cancer drugs against COVID-19
date: 2021-09-06
journal: Comput Biol Med
DOI: 10.1016/j.compbiomed.2021.104826
sha: 7022d36434c158883176232f2f3d8efa48b6686e
doc_id: 759356
cord_uid: b5lstgew

Corona Virus Disease 2019 (COVID-19) caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has become a global pandemic. Additionally, the SARS-CoV-2 infection in the patients of Gastric Cancer (GC; the third leading cause of death in the world) pose a great challenge for the health management of the patients. Since there have been uncertainties to develop a new drug against COVID-19, there is an urgent need for repurposing drugs that can target key proteins of both SARS-CoV-2 and GC. The SARS-CoV-2-RdRp protein contains the NiRAN domain, which is known to have kinase-like folds. A docking study of the FDA approved drugs against GC was performed using AutoDock 4.2 and Glide Schrodinger suite 2019 against SARS-CoV-2-RdRp protein. MMGBSA and MD simulation studies were performed to investigate the binding and stability of the inhibitors with the target protein. In this study, we have found 12 kinase inhibitors with high binding energies namely Baricitinib, Brepocitinib, Decernotinib, Fasudil, Filgotinib, GSK2606414, Peficitinib, Ruxolitinib, Tofacitinib, Upadacitinib, Pamapimod and Ibrutinib. These FDA approved drugs against GC can play a key role in the treatment of COVID-19 patients along with GC as comorbidity. We also hypothesize that JAK, ITK, Rho-associated kinases, FGFR2, FYN, PERK, TYK2, p38-MAPK and SYK kinases can be considered as key therapeutic targets in COVID-19 treatment. Taken altogether, we have proposed the SARS-CoV-2-RdRp as a potential therapeutic target through in-silico studies. However, further in-vitro and in-vivo studies are required for the validation of the proposed targets and drugs for the treatment of COVID-19 patients already suffering from GC.

are effective against these kinases [28, 31, [36] [37] [38] . Hence, we wanted to evaluate the possibility to target 133 COVID-19 with GC kinase inhibitors to prevent drug over-usage. Through this approach, we wanted 134 to observe the effect of already known GC kinase inhibitors against SARS-CoV-2-RdRp-NiRAN 135 domain. This study would further aid in determining COVID-19 comorbidity specific therapeutics and 136 reducing drug-induced complications in the patient. To the best of our knowledge, this is the first study 137 regarding the repurposing of FDA approved drugs against GC comorbidity specific to Moreover, this is also the first study that has attempted to target NIRAN-RdRp against drugs in 139 COVID-19 concerning GC. 140 In this study, with in-silico approaches, we proposed that N-terminal of RdRp of SARS-CoV- 141 2 possess the kinase-like folds and is involved in the phosphotransferase catalysis. We report the 142 docking of several FDA approved GC kinase inhibitors against nsp12 (NiRAN-RdRp). Further, we 143 performed the MMGBSA and MD simulation studies of four selected compounds, which gave the best 144 binding energies during our investigation. This study intends to find out the medicine/compounds that 145 could treat COVID-19 patients suffering from GC as comorbidity. We have also provided the 146 schematic representation of the workflow in Figure 1 . domain. This structure was further used for in-silico studies. First, it was docked with 12 compounds 153 using AutoDock 4.2, and the best four compounds were re-docked by using Glide Schrodinger suite. 154 Complex of these four compounds were further proceeded for MD simulations on Maestro Schrodinger 155 Suite. Finally, the compounds brepocitinib, decernocitinib, filgotinib, and ibrutinib were found . Table. 1) were downloaded from the PubChem data base in 3D sdf 168 format and later converted into pdb format. We did not use any large data set of the compounds. . Table. 2). Also, the output was visualized using discovery studio software. Van der Waals energy, Coulomb energy, lipophilic term, hydrogen bond term, metal-binding term, 218 rewards, and penalties.

Docking results were evaluated based on the scoring function given by Glide G-score, which 220 can be represented as: preparation. In our study, we used ProSA web server which is frequently used for the 3D model 281 validation. This server calculates overall quality scores for a specific input structure. For a good protein 282 model, this score should be within the range. If the score is out of range the structure may have error.

In our case, we got the Z-Score -7.16 which is within range and shows the model is of good quality it was also excluded from the study. Re-docking of these compounds was performed using Glide Asp221 make direct H-bond while Thr51, Arg116, Asp208, and Asp218 make H-bond through water 414 molecules. Lys73 and Arg116 make strong H-bond as compared to other residues (Fig. 4a) . 415 Protein-ligand contact (SARS-CoV-2-RdRp-NiRAN domain/Brepocitinib) analysis revealed 416 different types of bond formation during the simulation (Fig. 5a) . It was observed that Thr51, Lys73,

Arg116, Asp208, Asn209, Asp218, and Asp221 make strong interaction with the brepocitinib. Lys73 NiRAN domain (Fig. 4b) . The main interacting residues were Thr206, Asp208, 452 Asp209, Asp218, and Asp221. These residues showed interaction more than 20% of the simulation 453 time. Among these residues, only Asn209 donated an H-bond to the decernocitinib while the other 454 residues Thr206, Asp208, Asp218, and Asp221 received H-bond from the decernocitinib. represents different types of bond interaction fractions (Fig. 5b) . The main interacting residues 457 Val204, Thr206, Asp208, Asn209, Asp218, Asp221, Phe222, and Ile223. Thr206, Asn209, Asp218, Fig. 4a) . Fig. 4b) . 469 received the H-bonds from the filgotinib. And these were the only interactions that remained present 478 for more than twenty percent of simulation time (Fig. 4c) . 479 Histogram of protein-ligand contacts showed that the residues taking part in the interaction of 480 the SARS-CoV-2-RdRp-NiRAN domain with the filgotinib (Fig. 5c) . This also represents the bond 481 fractions. The main interacting residues which show considerable effect were Tyr38, Val204, Thr206, 482 Asp208, Asn209, Asp218 and Asp221. Asp218 and Asp221 showed strong H-bond interactions.

Asp218 and Asp221 also showed water bridges while Asp221 showed ionic interaction. Tyr38 and 484 Val204 showed hydrophobic and water bridge interactions. Asn209 showed H-bond, water bridge, and 485 ionic interactions (Fig. 5c) . Fig. 5a) . Supp. RMSD graph till 50 ns of MD simulation and after that an elevation in the graph was observed. In the 501 case of ligand RMSD up to 38 nanoseconds fluctuations were observed and after that stability in the 502 RMSD was observed till the end of the simulation (Fig. 6C.a) . The RMSD mean for backbone, 503 sidechain, and ligands were calculated to be 4.292±1.034Å, 5.275±1.008Å and 1.308±0.256Å 504 respectively. 2D interactions diagram after MD simulation showed stable residues for more than 20% of 507 simulation time. Fig. 4d shows the interacting residues of the SARS-CoV-2-RdRp-NiRAN domain 508 with the ibrutinib. The main interacting residues were Asp36, Thr296, Asp208, Asp218 and Asp221.

All the residues received the H-bond from the ibrutinib and the residues Asp36, Thr296 and Asp208 showed the main interacting residues and bond fractions (Fig. 5d) . The main residues of the SARS- Thr206, Asp208, Asn209, Asp218 and Asp221. Asp36, Asp208, Asp218 and Asp221 showed strong 515 H-bond interactions, as well as these residues also showed water bridges and ionic interactions. Thr206 516 and Asn209 also showed H-bond and water bridge interactions. Val204 also showed water bridge and 517 hydrophobic interaction (Fig. 5d) . 518 Total contact analysis of the SARS-CoV-2-RdRp-NiRAN domain showed maximum number 519 of contacts between 50 to 60 ns and rest of the time contacts remained almost similar (Supp. Fig. 6a) . 520 Timeline representation of the contacts showed that residues Phe35, Asp36, Thr38, Val204, Thr206, 521 Asn209, Asp218, and Asp221 interacted with ligands throughout the simulation. Out of these, Asp36, 522 Asp208, Asp218 and Asp221 showed strong interactions as compared to other residues (Supp. Fig.   523 6b). Figure 6D .a. 525 Protein RMSD became stable after 50 ns of simulation. Similarly, ligand RMSD also became stable 526 after 50 ns of simulation. The RMSD mean for backbone sidechain and ligand were calculated to be Asp36, Lys73, Asp218 and Asp221 (Fig. 3a) . Post MD simulation 2D interaction diagram showed 577 Thr51, Lys73, Arg116, Asp208, Asn209, Asp218, and Asp221 as main interacting residues that 578 remained stable for more than 20 percent of simulation time. (Fig. 4a) . When compared, residues 579 Lys73, Asp218 and Asp221 were found common in both docking and post MD simulation interactions.

This suggests that the common bonds were quite strong and remained stable during the MD simulation. (Fig. 5a) . RMSD results also suggested that during the simulation the protein and ligand 584 remained stable because of the minimal fluctuations observed in the RMSD values. (Fig. 6A.a) . simulation results showed common residues Asn209, Asp218 and Asp221 that made strong 598 interactions during the simulation process (Fig. 4b) . Histogram diagram of protein-ligand (SARS- (Fig. 5b) . Minimum fluctuations in the protein RMSD 601 also suggested the stability of the protein-ligand complex during the simulation (Fig. 6B.a) . 602 Filgotinib is an oral selective JAK1 inhibitor, which was proved effective and safe in the 603 TORTUGA trial for patients suffering from ankylosing spondylitis, rheumatoid arthritis, Crohn's 604 disease, and psoriatic arthritis. Several successful phase-III trials have been completed for Filgotinib 605 against these diseases [79] . Filgotinib inhibits JAK1 for longer duration, this is evident from its 606 pharmacodynamic and pharmacokinetic studies. These studies indicate that filgotinib and its active 607 metabolite contribute to its pharmacodynamic properties [80] . Filgotinib showed approximately 30-608 fold higher efficacies towards JAK1 than JAK2 in human whole blood assay. Moreover, its metabolite 609 also target JAK2 with lower potency [81].

Our docking results showed that the filgotinib interacts with the Asp40, Asp208 and Asp221 611 of the SARS-CoV-2-RdRp-NiRAN domain (Fig. 3c) . Post MD simulation 2D interaction results 612 showed its main interactions with residues Asp218 and Asp221. This suggested that the Asp221 613 residue of SARS-CoV-2-RdRp-NiRAN domain showed strongest interaction with nilotinib. (Fig. 4c) . 614 Histogram diagram of protein-ligand (SARS-CoV-2-RdRp-NiRAN domain-filgotinib) contact also 615 showed the same results, and it represented the different bond fractions like H-bond, water bridges, 616 and hydrophobic interactions (Fig. 5c) . RMSD results suggested that the protein RMSD remained 617 stable from 5 to 55 ns and the ligand RMSD were remained stable from 30 till the end of 100 ns of 618 simulation (Fig. 6C.a) . RMSD results also suggested that the fluctuations in the RMSD values were 619 minimal, which suggested the stability of the protein-ligand complex during the simulation. NiRAN domain and interacted with the residues Asp208, Asn209, Asp218 and Asp221through H-642 bonds from ibrutinib (Fig. 3d) . Post MD simulation 2D interaction diagram showed the residues that 643 remained stable for more than 20 percent time of simulation (Fig. 4d) . Residues Asp36, Thr206, 644 Asp208, Asp218 and Asp221 showed H-bond interaction during the 100 ns of MD simulation. The 645 common residues obtained before and after simulation suggested that these residues have strong interactions during the simulation were H-bond, water bridges, and ionic interactions (Fig. 5d) . RMSD (Fig. 6D.a) . In both the cases, fluctuations in the RMSD values were minimal suggesting that 652 during the simulation protein and ligand both remained stable.

The docking scores showed strong affinity of brepocitinib, decernocitinib, filgotinib, and 654 ibrutinib with the SARS-CoV-2-RdRp-NiRAN domain ( Table 1) . The MMGBSA study also 655 supported docking interactions. The docking scores for the brepocitinib, decernocitinib, filgotinib, and 656 ibrutinib were calculated to be -5.543, -6.694, -4.917, and -6.137 respectively ( Mg 2+ binds to the NiRAN domain with the residues K73, E83, R116, D208, N209, G214,   670 D218, F219, and F222.

In our docking and simulation studies, it was observed that brepocitinib binds to residues 672 Asp36, Lys73, Asp218 and Asp221 at the active site before simulation and during simulation 673 brepocitinib interacted with the residues Arg55, Arg116, Asp218 and Asp221. Further, decernocitinib 674 docking studies showed that the main interacting residues were Asn209, Asp218, and Asp221, while 675 during the simulation the main interacting residues were Asp208, Asn209, Asp218, and Asp221. In 676 the case of filgotinib, the main docking interacting residues were Asp40, Asp208 and Asp221, while 677 during simulation the main interacting residues were Asn209, Asp218, Asp221. Similarly, in case of 678 ibrutinib, the main interacting residues in docking were Asp208, Asn209, Asp218, and Asp221, while 679 during the simulation the main interacting residues were Asp36, Asp208, Asp218, and Asp221. These J o u r n a l P r e -p r o o f

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