Skip to main content

Predicted antiviral drugs Darunavir, Amprenavir, Rimantadine and Saquinavir can potentially bind to neutralize SARS-CoV-2 conserved proteins

Abstract

Background

Novel Coronavirus disease 2019 or COVID-19 has become a threat to human society due to fast spreading and increasing mortality. It uses vertebrate hosts and presently deploys humans. Life cycle and pathogenicity of SARS-CoV-2 have already been deciphered and possible drug target trials are on the way.

Results

The present study was aimed to analyze Non-Structural Proteins that include conserved enzymes of SARS-CoV-2 like papain-like protease, main protease, Replicase, RNA-dependent RNA polymerase, methyltransferase, helicase, exoribonuclease and endoribonucleaseas targets to all known drugs. A bioinformatic based web server Drug ReposeER predicted several drug binding motifs in these analyzed proteins. Results revealed that anti-viral drugs Darunavir,Amprenavir, Rimantadine and Saquinavir were the most potent to have 3D-drug binding motifs that were closely associated with the active sites of the SARS-CoV-2 enzymes .

Conclusions

 Repurposing of the antiviral drugs Darunavir, Amprenavir, Rimantadine and Saquinavir to treat COVID-19 patients could be useful that can potentially prevent human mortality.

Graphic abstract

Background

SARS-CoV-2 has become a menace to the humanity and it imposed unprecedented epidemic condition. Great efforts were carried out by the scientists to develop potent vaccines like Astrazeneca/Oxford [1], Johnson & Johnson [2], Moderna [3], Pfizer/BionTech [4], Sinopharm, Sinovac [5], and COVISHIELD [6], having the potential to curb human mortality. The virus (a positive sense RNA virus with a genome of ~ 30 kb) has several types of vertebrate hosts including humans and transmission occurs through direct contact or aerosols [7, 8]. Like all animal viruses, their proteins hijack the cellular machineries to complete life cycle. These proteins are of great interest to the scientists to develop specific drug(s) or vaccine schemes against them. Search and trial of potential inhibitory drugs such as Remdesivir, Lopinavir-Ritonaviris were on the way but they were proven ineffective to prevent patient death [9,10,11]. The present work is based on the fact that most of the viral non-structural proteins (NSPs) which include enzymes remain structurally and chemically conserved as they have to interact with human proteins to carry out same biochemical processes within cell. SARS-CoV-2 genome encodes 16 non-structural proteins (NSPs), involved in genome replication and transcription [12, 13]. Nsp1 is a transcriptional, translational inhibitor and evades host immune system [14,15,16]. Nsp2 is involved in viral replication, disrupts host cell environment and, along with Nsp3, form proteases [12, 13]. Nsp4 interacts with Nsp3 to mediate viral replication [12, 13]. Main protease(Mpro) or NSP5 is essential for viral replication [7, 8, 12, 13]. Nsp6 generate autophagosomes that assemble replicase proteins [12, 13]. Nsp7, Nsp8 and Nsp12 form RNA polymerase complex [17, 18]. NSP9 replicase is dimeric and involved in viral RNA synthesis [7, 8, 12, 13, 19]. Nsp10 stimulate Nsp14 and Nsp16 which are methyl transferases [14, 20]. The function of Nsp11 is yet to be deciphered [12, 13]. Nsp13 together with Nsp12 exert helicase activity and is involved in capping of viral RNA [21]. Nsp14 has exoribonuclease and N7-methyltransferase activity [22]. Coronavirus endoribonuclease (NSP15/EndoU) is a hexameric protein that preferentially recognizes and cleaves RNA [7, 8, 12, 13, 23] and EndoU also evades host mediated viral double-stranded RNA recognition. Nsp16 has methyltransferase activity and complexes with Nsp10 [7, 8, 12, 13, 24].

In the present study, 11 PDB entries (7K3N, 6WEY, 6M03, 7JLT, 6W4B, 6ZCT, 6M71, 7NIO, 5C8S, 6VWW and 7BQ7) [25,26,27,28,29,30,31,32,33,34,35] representing twelve non-structural proteins and their complexes of SARS-CoV-2, i.e., NSP1, NSP3,NSP5, NSP7-8 complex, NSP9, NSP10, NSP7-8–12 complex, NSP13, NSP14, NSP15 and NSP16-10 complex respectively have been analyzed using Drug ReposeER web server program (http://27.126.156.175/drreposed/) [36] for their possible binding sites [37] to all drugs available in drug bank. Only the NSPs having 3D structures available in PDB, have been considered in the study as tertiary structures have utmost requirement to find 3D drug binding interfaces. The drug binding interfaces showed congruence with the known drug binding motifs (Additional file 1: S1, Additional file 2: S2, Additional file 3: S4, Additional file 4: S4, Additional file 5: S5, Additional file 6: S6, Additional file 7: S7, Additional file 8: S8, Additional file 9: S9, Additional file 10: S10 and Additional file 11: S11) .

Results and discussion

DrReposER predicted numerous potential 3D-drug binding motifs of both left (L) and right (R) superpositions for 7K3N, 6WEY, 6M03, 7JLT, 6W4B, 6ZCT, 6M71, 7NIO, 5C8S, 6VWW and 7BQ7 (Additional file 1: S1, Additional file 2: S2, Additional file 3: S4, Additional file 4: S4, Additional file 5: S5, Additional file 6: S6, Additional file 7: S7, Additional file 8: S8, Additional file 9: S9, Additional file 10: S10 and Additional file 11: S11). Known drugs that bind these motifs bind either human, bacterial or viral proteins. Results after analyzing the 3D structures of the target molecules and complexes were further filtered for anti-viral drugs. From the hit results, 14 anti-viral drugs i.e., Amphetamine (Drug bank ID-DB00182), Amprenavir (Drug bank ID-DB00701), Atazanavir (Drug bank ID-DB01072), Darunavir (Drug bank ID-DB01264), Grazoprevir (Drug bank ID-DB11575), Indinavir (Drug bank ID-DB00224), Lopinavir (Drug bank ID-DB01601), Nelfinavir (Drug bank ID-DB00220), Nevirapine (Drug bank ID-DB00238), Ribavirin (Drug bank ID-DB00811), Rimantadine (Drug bank ID-DB00478), Ritonavir (Drug bank ID-DB00503), Saquinavir (Drug bank ID-DB01232), and Tipranavir (Drug bank ID-DB00932) were selected for having unique 3D-drug binding motifs (Tables 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 and 11). The findings showed that several anti-viral drugs had binding interfaces on a single protein or protein complexes and moreover, each anti-viral drug had one to several binding motifs (Tables 12 and 13).

Table 1 Possible binding sites of NSP1 against known anti-viral drugs
Table 2 Possible binding sites of NSP3 against known anti-viral drugs
Table 3 Possible binding sites of NSP5 against known anti-viral drugs.
Table 4 Possible binding sites of NSP7-NSP8 against known anti-viral drugs
Table 5 Possible binding sites of NSP9 against known anti-viral drugs
Table 6 Possible binding sites of NSP10 against known anti-viral drugs
Table 7 Possible binding sites of NSP7-NSP8-NSP12 complex against known anti-viral drugs
Table 8 Possible binding sites of NSP13 against known anti-viral drugs
Table 9 Possible binding sites of NSP14 against known anti-viral drugs
Table 10 Possible binding sites of NSP15 against known anti-viral drugs
Table 11 Possible binding sites of NSP16-NSP10 complex against known anti-viral drugs
Table 12 Comparison of drug binding motifs of analyzed NSPs for antiviral drugs
Table 13 Comparison of NSPs binding of the drugs analyzed

Amphetamine (DB00182) targeted only a single binding interface on Nsp5 (6M03) (Tables 3, 12, 13). Amprenavir (DB00701) targeted four binding motifs on Nsp3 (6WEY), three motifs onNsp1 (7K3N), Nsp7-8-12 complex (6M71), Nsp13 (7NIO) and Nsp14 (5C8S), and two binding motifs on Nsp7-8 complex (7JLT), Nsp15 (6VWW) and Nsp16-10 complex (7BQ7) (Tables 2, 1, 7, 8, 9, 4, 10, 11, 12, Figs. 1, 23, 4, 5, 6, 7, 8, 9, 10 and 11). Atazanavir (DB01072) targeted three motifs on Nsp16-10 complex (7BQ7), two motifs on Nsp10 (6ZCT) and single motif each on Nsp1, Nsp7-8-12, Nsp13, Nsp14 and Nsp15 (Tables 11, 6, 12). Darunavir (DB01264) is the most promising drug as it targeted the greatest number of binding motifs and targeted every molecule except Nsp9. It targeted ten motifs on Nsp1 (7K3N), seven motifs on Nsp14 (5C8S), six motifs on Nsp3 (6WEY), five motifs on Nsp15 (6VWW) and Nsp16-10 complex (7BQ7), four motifs on Nsp7-8-12 complex (6M71), three motifs on Nsp10 (6ZCT), two motifs each on Nsp5 (6M03) and Nsp13 (7NIO), respectively and a single motif on Nsp7-8 complex (Tables 1, 9, 2, 10, 11, 7, 6, 3, 8, 4, 12, Figs. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 and 11). Grazoprevir (DB11575) targeted two motifs, on Nsp10 (6ZCT) and two on Nsp16-10 complex (7BQ7) and single motif each on Nsp9 and Nsp14 (Tables 6, 11, 5, 9, 12). Indinavir (DB00224) significantly targeted three motifs, each on Nsp13 (7NIO) and Nsp15 (6VWW) (Tables 8, 10, 12). Lopinavir significantly targeted three motifs on Nsp15 and 2 motifs each on Nsp13 and Nsp14 (Tables 10, 8, 9). Nelfinavir targeted two interfaces on Nsp1 and Nsp7-8–12 complexes (Tables 1, 7). On the other hand, Nevirapine targeted only a single motif on Nsp5 (Table 3). Rimantadine (DB00478) significantly targeted five binding interfaces on Nsp14 (5C8S), three binding motifs each on Nsp5 (6M03) and Nsp9 (6W4B), and two motifs on Nsp3 (6WEY), Nsp13 (7NIO), Nsp16-10 (7BQ7) and a single motif on Nsp1, Nsp7-8 and Nsp7-8-12 complex (Tables 9, 3, 5, 2, 8, 11, 1, 4, 7, 12, Figs. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 and 11). Ritonavir targeted two motifs on Nsp16-10 complex (Table 11). Saquinavir (DB01232) targeted four motifs on Nsp16-10 complex (7BQ7), three interfaces each on Nsp7-8–12 (6M71) and Nsp15 (6VWW), two motifs on Nsp1 and Nsp14 (5C8S) and a single motif on Nsp3, Nsp7-8, Nsp10 and Nsp13 (Tables 11, 7, 10, 1, 9, 3, 4, 6, 8, Figs. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 and 11). Finally, Tipranavir (DB00932) targeted two binding motifs; each on Nsp3, Nsp7-8–12 complex and Nsp14 (Tables 3, 7, 9), whereas single binding interface each on Nsp1, Nsp5, Nsp9, Nsp13, Nsp15 and Nsp16-10 (Table 12).

Fig. 1
figure1

3D-binding interfaces of NSP1with Amprenavir, Darunavir, Rimantadine &Saquinavir. ac Binding motifs of Amprenavir. d All the binding motifs of Amprenavir. en Binding interfaces of Darunavir. o All the binding motifs of Darunavir. p, q Rimantadine binding motif. r, s Saquinavir binding motifs and t All the motifs on NSP1. Numbers indicate the motif forming amino acids. Three letter codes of amino acids have been mentioned

Fig. 2
figure2

3D-binding interfaces of NSP3 with Amprenavir, Darunavir, Rimantadine &Saquinavir. ad Binding motifs of Amprenavir. e All the binding motifs of Amprenavir together. fk Binding interfaces of Darunavir. L Combined binding motifs of Darunavir. m, n Rimantadine binding motifs. o All motifs of RIM. p, q Saquinavir binding motif. Numbers indicate the motif forming amino acids. Three letter codes of amino acids have been mentioned

Fig. 3
figure3

3D-binding interfaces of NSP5 with Darunavir&Rimantadine. ac Binding motifs of Rimantadine. d All the binding motifs of RIM on NSP5. e, f Binding interfaces of Darunavir. g All the binding motifs of Darunavir. Numbers indicate the motif forming amino acids. Three letter codes of amino acids have been mentioned

Fig. 4
figure4

3D-binding interfaces of NSP7-8 complex with Amprenavir, Darunavir, Rimantadine &Saquinavir. a, b. Binding motifs of Amprenavir. c All the binding motifs of Amprenavir together. d, e Binding interfaces of Darunavir. f, g Rimantadine binding motifs. h, i Saquinavir binding motif. Numbers indicate the motif forming amino acids. Three letter codes of amino acids have been mentioned

Fig. 5
figure5

3D-binding interfaces of NSP9 with Rimantadine. ac Three binding motifs of Rimantadine. d All the binding motifs of RIM together. Numbers indicate the motif forming amino acids. Three letter codes of amino acids have been mentioned

Fig. 6
figure6

3D-binding interfaces of NSP10with Darunavir&Saquinavir. ac. Binding motifs of Darunavir. d Combined binding motifs of Darunavir. e, f Saquinavir binding motif. Numbers indicate the motif forming amino acids. Three letter codes of amino acids have been mentioned

Fig. 7
figure7

3D-binding interfaces of NSP7-8–12 complex with Amprenavir, Darunavir, Rimantadine &Saquinavir. ac Binding motifs of Amprenavir. d All the binding motifs of Amprenavir together. eh Binding interfaces of Darunavir. i Combined binding motifs of Darunavir. j, k Rimantadine binding motifs. ln Saquinavir binding motifs. o Combined motifs of ROC. Numbers indicate the motif forming amino acids. Three letter codes of amino acids have been mentioned

Fig. 8
figure8

3D-binding interfaces of NSP13 with Amprenavir, Darunavir, Rimantadine &Saquinavir. a–c Binding motifs of Amprenavir. d All the binding motifs of Amprenavir together. e, f. Binding interfaces of Darunavir. g Combined binding motifs of Darunavir. h, i. Rimantadine binding motifs. j All motifs of RIM. k, l Saquinavir binding motif. Numbers indicate the motif forming amino acids. Three letter codes of amino acids have been mentioned

Fig. 9
figure9

3D-binding interfaces of NSP14 with Amprenavir, Darunavir, Rimantadine &Saquinavir. ac Binding motifs of Amprenavir. d All the binding motifs of Amprenavir together. ek.Binding interfaces of Darunavir. l Combined binding motifs of Darunavir. mq Rimantadine binding motifs. r All motifs of RIM. s, t Saquinavir binding motifs. u All the Saquinavir motifs together. Numbers indicate the motif forming amino acids. Three letter codes of amino acids have been mentioned

Fig. 10
figure10

3D-binding interfaces of NSP15 with Amprenavir, Darunavir&Saquinavir. a, b Binding motifs of Amprenavir. c All the binding motifs of Amprenavir together. dh Binding interfaces of Darunavir. i Combined binding motifs of Darunavir. jl Saquinavir binding motifs. m All the ROC binding interfaces. Numbers indicate the motif forming amino acids. Three letter codes of amino acids have been mentioned

Fig. 11
figure11

3D-binding interfaces of NSP16-10 complex with Amprenavir, Darunavir, Rimantadine &Saquinavir. a, b Binding motifs of Amprenavir. c All the binding motifs of Amprenavir together. dh Binding interfaces of Darunavir. i Combined binding motifs of Darunavir. j, k Rimantadine binding motifs. l All motifs of RIM. mp Saquinavir binding motifs. q All the Saquinavir binding interfaces. Numbers indicate the motif forming amino acids. Three letter codes of amino acids have been mentioned

All the binding results were further compiled and analyzed. Results revealed that Darunavir (DB01264) had 45 unique binding sites and targeted 10 SARS-CoV-2 PDB entries or 10 NSPs (Tables 12, 13). The Lowest Root Mean Square Deviation (RMSD) value of Darunavir among all the target molecules was 0.54 Å for Nsp16-10 complex and maximum number of residues involved in interaction was 27 (Tables 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 and 11). Significant binding interfaces were again targeted by Amprenavir (DB00701) and Saquinavir (DB01232) with 22 and 18 (Tables 12, 13), respectively. The two drugs had eight and nine binding partners, respectively (Tables 12, 13). The lowest RMSDs for them were 0.54 Å and 0.52 Å and maximum residues involved in drug-target binding were 28 and 31, respectively (Tables 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 and 11). Additionally, Rimantadine (DB00478) had 20 drug binding motifs that targeted nine binding partners (Tables 12, 13) with the lowest RMSD value of 0.67 Å and maximum number of residues involved in binding were 10 (Tables 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 and 11). Again, Tipranavir (DB00932) and Indinavir (DB00224) both showed 12 binding motifs for nine and eight binding partners, respectively (Tables 12, 13). Lowest RMSD values for these two drugs were 0.53 Å and 0.72 Å and maximum number of residues involved in binding were 27 and 24, respectively (Tables 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 and 11).

Results showed that Darunavir, Amprenavir, Rimantadine, Saquinavir, Tipranavir and Indinavir were more effective in targeting the twelve SARS-CoV-2 proteins and their complexes (Tables 12, 13). Darunavir is a nonpeptidic benzenesulfonamide inhibitor that targets active site of HIV-1 protease [38, 39]. Amprenavir is a hydroxyethylamine sulfonamide derivative that inhibits HIV-1 protease [40, 41]. Rimantadine is an alkylamine that specifically targets Influenza A virus M2 protein [42,43,44]. Saquinavir is a L-asparagine derivative that acts as HIV-1 protease inhibitor [45, 46]. Tipranavir is a sulfonamide that acts as HIV-1 protease inhibitor [47]. Moreover, Indinavir is a piperazinecarboxamide having HIV-1 protease inhibitory activity [48, 49]. The drug binding interfaces determined in the present study is very much significant as the analysis considered previously known potent binding information between specific drugs and target proteins that were again supported by very low RMSD values of the motifs such as 0.54 Å for both Darunavir and Amprenavir, 0.52 Å for Saquinavir and 0.67 Å for Rimantadine (Tables 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 and 11). RMSD values well below 1.0 was indicative of presence of similar drug binding structures or motifs as in active site of HIV-1 protease or M2 of Influenza A and these results emphasized that the selected drugs would effectively target those similar interfaces found on different NSPs of SARS-Cov2 to inhibit them. Furthermore, considering the produced results, it has been proposed that combination of Darunavir, Amprenavir and Rimantadine could effectively target and inhibit all the NSPs that were studied. Darunavir targeted all NSPs except Nsp9, whereas Amprenavir targeted all except Nsp5, Nsp9 and Nsp10 and interestingly Rimantadine complementarily and significantly targeted Nsp5 and Nsp9, which are two key enzymes (Tables 12, 13). However, it has been reported that Darunavir was unable to protect HIV patients from SARS-Cov2 infection who were under Darunavir treatment [50]. Though, the claim has to be experimentally proven. In such cases, if Darunavir fails to prevent infection, then another potent inhibitor Saquinavir, having similar target profiles, could be used in combination along with Amprenavir and Rimantadine, in replacement of Darunavir (Tables 12, 13, 14).

Table 14 Active site residues of the analyzed SARS-CoV2 enzymes and the inhibitory drug binding motifs

Among the twelve proteins studied, eight were key enzymes involved in viral replication, transcription and life cycle processes. Hence, the study was further extended to provide insight whether the binding motifs of the selected drugs were significant in inhibiting these enzymes possibly by intercepting active sites of those enzymes. Active sites of enzymes are surface regions that are highly conserved and involved in catalysis or substrate binding. In this study, active sites of SARS-CoV-2 enzymes were predicted by a web server, GASS-WEB (http://gass.unifei.edu.br/) that uses Genetic Active Site Search based on genetic algorithms [51]. Active site residues and the drug binding interfaces of the four drugs viz. Amprenavir (478), Darunavir (017), Rimantadine (RIM) and Saquinavir (ROC) were presented in surface topography presentations of each of the enzymes and were analyzed for their inhibitory association. Results revealed that active site residues of the papain- like protease NSP3 were in close association with drug binding motifs of Amprenavir (270D, 252G, 253 V, 335I, 300 V, 304 V, 287L), Darunavir (252G, 227I, 253 V, 335I, 286 V, 297L, 287L), Rimantadine (337G, 333A, 315S, 281 V) and Saquinavir (252G, 253 V, 335I) (Fig. 12, Table 14). Active sites of protease NSP5 were closely apposed to Darunavir (133 N, 194A, 195G, 200I, 109G, 293P) and Rimantadine (254S, 255A, 251G) binding residues (Fig. 13, Table 14). NSP9 active sites were exclusively targeted by Rimantadine (108 V, 109A, 111 V, 106S, 105G) (Fig. 14, Table 14). RNA polymerase NSP12 active sites were targeted by Amprenavir (166 V, 760D, 203G, 204 V, 201I), Darunavir (53 V, 106I, 119I, 203G, 204 V, 201I), Rimantadine (774G, 771A, 772S) and Saquinavir (623D, 817 T, 820 V, 203G, 204 V, 201I) (Fig. 15, Table 14). The helicase NSP13 active residues were targeted by Amprenavir (195I, 151I, 226 V, 258I), Darunavir (195I, 226 V, 258I), Rimantadine (1A, 3G, 523S, 527G) and Saquinavir (258I) (Fig. 16, Table 14). Exoribonuclease NSP14 active sites were closely apposed to Amprenavir (31I, 14I, 87I, 412P), Darunavir (389 V, 26A, 78R, 390D, 108 V, 152L, 118 V, 120 V), Rimantadine (32A, 34G, 35G, 33S) and Saquinavir (31I, 14I, 84R) binding residues (Fig. 17, Table 14). On the other hand, endonuclease NSP15 active sites were targeted by Amprenavir (276 V, 156 V), Darunavir (80I, 23 V, 212I, 156 V, 3L, 86I), and Saquinavir (119P, 80I, 156 V) (Fig. 18, Table 14). Finally, methyltransferase NSP16 active site residues were targeted by Amprenavir (71A, 70G), Darunavir (21 V, 22D, 26A, 71A, 290I, 121A, 200S), Rimantadine (32A, 33S, 34G, 199A, 197 V, 200S) and Saquinavir (71A, 70G) (Fig. 19, Table 14). Close association of drug binding motifs with the active sites indicated that these would interfere with catalytic activity and substrate binding of the enzymes.

Fig. 12
figure12

Active site residues & drug binding motifs of NSP3. a, b Two different surfaces showing drug binding motifs in close association with active site residues of the enzyme. Here Anprenavir, Darunavir and Saquinavir targeted active site residue VAL253 in a pocket. 478-Amprenavir; 017-Darunavir; RIM Rimantadine, ROC Saquinavir

Fig. 13
figure13

Active site residues & drug binding motifs of NSP5. a Position of active site residues within and near a pocket. b 017 & RIM targeted residues closely associated with that pocket which accounts for inhibition of active site functioning. 478-Amprenavir; 017-Darunavir; RIM Rimantadine, ROC Saquinavir

Fig. 14
figure14

Active site residues & drug binding motifs of NSP9. a Surface view showing position of active site residues. b Only Rimantadine showed numerous inhibitory binding. 105G and 106S active residues were targeted by RIM. RIM Rimantadine

Fig. 15
figure15

Active site residues & drug binding motifs of NSP12. a Position of active site residues. b, c Different surfaces showing 478, 017, RIM and ROC binding interfaces or residues. 478-Amprenavir; 017-Darunavir; RIM Rimantadine, ROC Saquinavir

Fig. 16
figure16

Active site residues & drug binding motifs of NSP13. a, b Position of active site residues and drug binding motifs in surface presentation. 478-Amprenavir; 017-Darunavir; RIM Rimantadine, ROC Saquinavir

Fig. 17
figure17

Active site residues & drug binding motifs of NSP14. a, b Position of active site residues and drug binding interfaces in surface presentation. 478-Amprenavir; 017-Darunavir; RIM Rimantadine, ROC Saquinavir

Fig. 18
figure18

Active site residues & drug binding motifs of NSP15. a–c Different surface projections of NSP15 showing positions of active residues and drug binding motifs. 478-Amprenavir; 017-Darunavir; RIM Rimantadine, ROC Saquinavir

Fig. 19
figure19

Active site residues & drug binding motifs of NSP16. ac Different surface projections showing inhibitory association of drug binding motifs with active site residues of the enzyme. 478-Amprenavir; 017-Darunavir; RIM Rimantadine, ROC Saquinavir

Previously, several drug repurposing analysis were performed by several groups to find potential drug inhibitors like sirolimus, dactinomycin, mercaptopurine, melatonin, toremifene, emodin, zotatifin, ternatin-4, hydroxychloroquine, clemastine, Atazanavir, remdesivir, efavirenz, Ritonavir, dolutegravir, carfilzomib, cyclosporine A, azithromycin, favipiravir, Ribavirin, galidesivir and many others against SARS-CoV-2 proteins but their efficacy is questionable in treating and curing COVID-19 patients [52,53,54,55,56,57].

Conclusion

The findings strongly suggested that among the fourteen anti-viral drugs predicted and analyzed, six drugs significantly targeted twelve SARS-Cov2 non structural proteins and specifically the key enzymes. Considering the binding parameters it can be concluded that combination of Darunavir (DB01264), Amprenavir(DB00701) and Rimantadine(DB00478) or Saquinavir (DB01232), Amprenavir (DB00701) and Rimantadine (DB00478) or all the four drugs together can potentially bind and inhibit the cellular activities of these proteins that are essential for viral replication and life cycle. Using anti-viral drug has great advantage in that these have specific target and less or no similar binding partners like Rimantadine had no other binding partners other than SARS-Cov-2 NSPs (Tables 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 and 11). Finally, these predicted drug combinations must be clinically tested to save thousands of lives in the vicinity of limited effectiveness of developed vaccines [58, 59].

Methods

Key resources table

Resource Source Identifier
Analyzed data
 SARS-CoV-2 NSP1 3D-structure [25] PDB ID: 7K3N
 SARS-CoV-2 NSP3 3D-structure [26] PDB ID: 6WEY
 SARS-CoV-2 NSP5 3D-structure [27] PDB ID: 6M03
 SARS-CoV-2 NSP7-8 complex 3D-structure [28] PDB ID: 7JLT
 SARS-CoV-2 NSP9 3D-structure [29] PDB ID: 6W4B
 SARS-CoV-2 NSP10 3D-structure [30] PDB ID: 6ZCT
 SARS-CoV-2 NSP7-8-12 complex 3D-structure [31] PDB ID: 6M71
 SARS-CoV-2 NSP13 3D-structure [32] PDB ID: 7NIO
 SARS-CoV-2 NSP14 3D-structure [33] PDB ID: 5C8S
 SARS-CoV-2 NSP15 3D-structure [34] PDB ID: 6VWW
 SARS-CoV-2 NSP16-10 complex 3D-structure [35] PDB ID: 7BQ7
Web server
 DrReposER [37] http://27.126.156.175/drreposed/
 GASS-WEB [51] http://gass.unifei.edu.br/

DrReposERhas been used to find binding interfaces or 3D-motifs of target proteins (PDB ID: 7K3N, 6WEY, 6M03, 7JLT, 6W4B, 6ZCT, 6M71, 7NIO, 5C8S, 6VWW and 7BQ7) for all possible drugs. The program uses SPRITE and ASSAM web servers to find amino acid side chains. Drug ReposER compares structurally similar side chain arrangements from PDB repository and assign hit results for different drug targets in the query PDB ID [37].

GASS-WEB has been used to predict active sites of SARS-CoV-2 enzymes (NSP3, NSP5, NSP9, NSP12, NSP13, NSP14, NSP15 and NSP16) considered in this study. It uses genetic algorithms to find active sites of enzymes that are meant for catalytic activity or substrate binding [51].

Availability of data and materials

Not applicable.

References

  1. 1.

    Knoll MD, Wonodi C. Oxford-AstraZeneca COVID-19 vaccine efficacy. Lancet. 2021;397:72–4.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  2. 2.

    Sadoff J, Le Gars M, Shukarev G, Heerwegh D, Truyers C, de Groot AM, et al. Interim Results of a Phase 1–2a Trial of Ad26.COV2.S Covid-19 Vaccine. N Engl J Med. 2021;384:1824–35.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  3. 3.

    Baden LR, El Sahly HM, Essink B, Kotloff K, Frey S, Novak R, et al. Efficacy and safety of the mRNA-1273 SARS-CoV-2 vaccine. N Engl J Med. 2021;384:403–16.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  4. 4.

    Polack FP, Thomas SJ, Kitchin N, Absalon J, Gurtman A, Lockhart S, et al. Safety and efficacy of the BNT162b2 mRNA Covid-19 vaccine. N Engl J Med. 2020;383:2603–15.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  5. 5.

    Dong Y, Dai T, Wei Y, Zhang L, Zheng M, Zhou F. A systematic review of SARS-CoV-2 vaccine candidates. Signal Transduct Target Ther. 2020;5:237.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  6. 6.

    Sah R, Shrestha S, Mehta R, Sah SK, Rabaan AA, Dhama K, et al. AZD1222 (Covishield) vaccination for COVID-19: experiences, challenges, and solutions in Nepal. Travel Med Infect Dis. 2021;40:101989.

    PubMed  PubMed Central  Article  Google Scholar 

  7. 7.

    Chen Y, Liu Q, Guo D. Emerging coronaviruses: Genome structure, replication, and pathogenesis. J Med Virol. 2020;92:418–23.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  8. 8.

    Fehr AR, Perlman S. Coronaviruses: an overview of their replication and pathogenesis. Methods Mol Biol. 2015;1282:1–23.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  9. 9.

    Dong L, Hu S, Gao J. Discovering drugs to treat coronavirus disease 2019 (COVID-19). Drug Discov Ther. 2020;14:58–60.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  10. 10.

    Wang M, Cao R, Zhang L, Yang X, Liu J, Xu M, et al. Remdesivir and chloroquine effectively inhibit the recently emerged novel coronavirus (2019-nCoV) in vitro. Cell Res. 2020;30:269–71.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  11. 11.

    Cao B, Wang Y, Wen D, Liu W, Wang J, Fan G, et al. A trial of lopinavir-ritonavir in adults hospitalized with severe Covid-19. N Engl J Med. 2020;382:1787–99.

    PubMed  PubMed Central  Article  Google Scholar 

  12. 12.

    Yoshimoto FK. The proteins of severe acute respiratory syndrome coronavirus-2 (SARS CoV-2 or n-COV19), the cause of COVID-19. Protein J. 2020;39:198–216.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  13. 13.

    Mariano G, Farthing RJ, Lale-Farjat SLM, Bergeron JRC. Structural characterization of SARS-CoV-2: where we are, and where we need to be. Front Mol Biosci. 2020;7:605236.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. 14.

    Zhang K, Miorin L, Makio T, Dehghan I, Gao S, Xie Y, et al. Nsp1 protein of SARS-CoV-2 disrupts the mRNA export machinery to inhibit host gene expression. Sci Adv. 2021. https://doi.org/10.1126/sciadv.abe7386.

    Article  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Schubert K, Karousis ED, Jomaa A, Scaiola A, Echeverria B, Gurzeler LA, et al. SARS-CoV-2 Nsp1 binds the ribosomal mRNA channel to inhibit translation. Nat Struct Mol Biol. 2020;27:959–66.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  16. 16.

    Min Y-Q, Mo Q, Wang J, Deng F, Wang H, Ning Y-J. SARS-CoV-2 nsp1: bioinformatics, potential structural and functional features, and implications for drug/vaccine designs. Front Microbiol. 2020;11:587317.

    PubMed  PubMed Central  Article  Google Scholar 

  17. 17.

    Hillen HS, Kokic G, Farnung L, Dienemann C, Tegunov D, Cramer P. Structure of replicating SARS-CoV-2 polymerase. Nature. 2020;584:154–6.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  18. 18.

    Kirchdoerfer RN, Ward AB. Structure of the SARS-CoV nsp12 polymerase bound to nsp7 and nsp8 co-factors. Nat Commun. 2019;10:2342.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  19. 19.

    Zhang C, Chen Y, Li L. Structural basis for the multimerization of nonstructural protein nsp9 from SARS-CoV-2. Mol Biomed. 2020;1:5.

    Article  Google Scholar 

  20. 20.

    Krafcikova P, Silhan J, Nencka R, Boura E. Structural analysis of the SARS-CoV-2 methyltransferase complex involved in RNA cap creation bound to sinefungin. Nat Commun. 2020;11:3717.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  21. 21.

    Jang KJ, Jeong S, Kang DY, Sp N, Yang YM, Kim DE. A high ATP concentration enhances the cooperative translocation of the SARS coronavirus helicase nsP13 in the unwinding of duplex RNA. Sci Rep. 2020;10:4481.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  22. 22.

    Ogando NS, Zevenhoven-Dobbe JC, van der Meer Y, Bredenbeek PJ, Posthuma CC, Snijder EJ. The enzymatic activity of the nsp14 exoribonuclease is critical for replication of MERS-CoV and SARS-CoV-2. J Virol. 2020;94:e01246-e1320.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  23. 23.

    Pillon MC, Frazier MN, Dillard LB, Williams JG, Kocaman S, Krahn JM, et al. Cryo-EM structures of the SARS-CoV-2 endoribonuclease Nsp15 reveal insight into nuclease specificity and dynamics. Nat Commun. 2021;12:636.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  24. 24.

    Lin S, Chen H, Ye F, Chen Z, Yang F, Zheng Y, et al. Crystal structure of SARS-CoV-2 nsp10/nsp16 2′-O-methylase and its implication on antiviral drug design. Sig Transduct Target Ther. 2020;5:131.

    CAS  Article  Google Scholar 

  25. 25.

    Semper C, Watanabe N, Savchenko A. Structural characterization of nonstructural protein 1 from SARS-CoV-2. IScience. 2021;24:101903.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  26. 26.

    Frick DN, Virdi RS, Vuksanovic N, Dahal N, Silvaggi NR. Molecular Basis for ADP-Ribose Binding to the Mac1 Domain of SARS-CoV-2 nsp3. Biochemistry. 2020;59:2608–15.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  27. 27.

    Zhang B, Zhao Y, Jin Z, Liu X, Yang H, Rao Z. The crystal structure of COVID-19 main protease in apo form. PDB Data. 2020.

  28. 28.

    Biswal M, Diggs S, Xu D, Khudaverdyan N, Lu J, Fang J, et al. Two conserved oligomer interfaces of NSP7 and NSP8 underpin the dynamic assembly of SARS-CoV-2 RdRP. Nucleic Acids Res. 2021;49:5956–66.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  29. 29.

    Tan K, Kim Y, Jedrzejczak R, Maltseva N, Endres M, Michalska K, et al. The crystal structure of Nsp9 RNA binding protein of SARS CoV-2. PDB Data. 2020.

  30. 30.

    Rogstam A, Nyblom M, Christensen S, Sele C, Talibov VO, Lindvall T, et al. Crystal structure of non-structural protein 10 from severe acute respiratory syndrome coronavirus-2. Int J Mol Sci. 2020;21:7375.

    CAS  PubMed Central  Article  Google Scholar 

  31. 31.

    Gao Y, Yan L, Huang Y, Liu F, Zhao Y, Cao L, et al. Structure of the RNA-dependent RNA polymerase from COVID-19 virus. Science. 2020;368:779–82.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  32. 32.

    Newman JA, YosaatmadjaY, DouangamathA, Bountra C, Gileadi O. Crystal structure of the SARS-CoV-2 helicase APO form. PDB Data. 2021.

  33. 33.

    Ma YY, Wu LJ, Shaw N, Gao Y, Wang J, Sun YN, et al. Structural basis and functional analysis of the SARS coronavirus nsp14-nsp10 complex. Proc Natl Acad Sci U S A. 2015;112:9436–41.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  34. 34.

    Kim Y, Jedrzejczak R, Maltseva NI, Wilamowski M, Endres M, Godzik A, et al. Crystal structure of Nsp15 endoribonuclease NendoU from SARS-CoV-2. Protein Sci. 2020;29:1596–605.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  35. 35.

    Yan LM, Huang YC, Lou ZY, Rao ZH. Crystal structure of 2019-nCoV nsp16-nsp10 complex. PDB Data. 2020.

  36. 36.

    Drug ReposeER web server program. http://27.126.156.175/drreposed/. Accessed 25 Jun 2021.

  37. 37.

    Ab Ghani NS, Ramlan EI, Firdaus-Raih M. Drug ReposER: a web server for predicting similar amino acid arrangements to known drug binding interfaces for potential drug repositioning. Nucleic Acids Res. 2019;47:W350–6.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  38. 38.

    Mallolas J. Darunavir stands up as preferred HIV protease inhibitor. AIDS Rev. 2017;19:105–12.

    PubMed  PubMed Central  Google Scholar 

  39. 39.

    Humpolíčková J, Weber J, Starková J, Mašínová E, Günterová J, Flaisigová I, et al. Inhibition of the precursor and mature forms of HIV-1 protease as a tool for drug evaluation. Sci Rep. 2018;8:10438.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  40. 40.

    Yu Y, Wang J, Shao Q, Shi J, Zhu W. Effects of drug-resistant mutations on the dynamic properties of HIV-1 protease and inhibition by Amprenavir and Darunavir. Sci Rep. 2015;5:10517.

    PubMed  PubMed Central  Article  Google Scholar 

  41. 41.

    Borrajo A, Ranazzi A, Pollicita M, Bruno R, Modesti A, Alteri C, et al. Effects of amprenavir on HIV-1 maturation, production and infectivity following drug withdrawal in chronically-infected monocytes/macrophages. Viruses. 2017;9:277.

    PubMed Central  Article  CAS  Google Scholar 

  42. 42.

    Jefferson T, Demicheli V, Di Pietrantonj C, Rivetti D. Amantadine and rimantadine for influenza A in adults. Cochrane Database Syst Rev. 2006. https://doi.org/10.1002/14651858.CD001169.pub3.

    Article  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Govorkova EA, Fang HB, Tan M, Webster RG. Neuraminidase inhibitor-rimantadine combinations exert additive and synergistic anti-influenza virus effects in MDCK cells. Antimicrob Agents Chemother. 2004;48:4855–63.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. 44.

    Watkins LC, DeGrado WF, Voth GA. Influenza A M2 inhibitor binding understood through mechanisms of excess proton stabilization and channel dynamics. J Am Chem Soc. 2020;142:17425–33.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  45. 45.

    Saen-oon S, Aruksakunwong O, Wittayanarakul K, Sompornpisut P, Hannongbua S. Insight into analysis of interactions of saquinavir with HIV-1 protease in comparison between the wild-type and G48V and G48V/L90M mutants based on QM and QM/MM calculations. J Mol Graph Model. 2007;26:720–7.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  46. 46.

    Wittayanarakul K, Aruksakunwong O, Sompornpisut P, Sanghiran-Lee V, Parasuk V, Pinitglang S, et al. Structure, dynamics and solvation of HIV-1 protease/saquinavir complex in aqueous solution and their contributions to drug resistance: molecular dynamic simulations. J Chem Inf Model. 2005;45:300–8.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  47. 47.

    Pietrucci F, Vargiu A, Kranjc A. HIV-1 protease dimerization dynamics reveals a transient druggable binding pocket at the interface. Sci Rep. 2016;5:18555.

    Article  CAS  Google Scholar 

  48. 48.

    Weber IT, Agniswamy J. HIV-1 protease: structural perspectives on drug resistance. Viruses. 2009;1:1110–36.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  49. 49.

    Shityakov S, Dandekar T. Lead expansion and virtual screening of Indinavir derivate HIV-1 protease inhibitors using pharmacophoric - shape similarity scoring function. Bioinformation. 2010;4:295–9.

    PubMed  PubMed Central  Article  Google Scholar 

  50. 50.

    Riva A, Conti F, Bernacchia D, Pezzati L, Sollima S, Merli S, et al. Darunavir does not prevent SARS-CoV-2 infection in HIV patients. Pharmacol Res. 2020;157:104826.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  51. 51.

    Moraes JPA, Pappa GL, Pires DEV, Izidoro SC. GASS-WEB: a web server for identifying enzyme active sites based on genetic algorithms. Nucleic Acids Res. 2017;45:W315–9.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  52. 52.

    Zhou Y, Hou Y, Shen J, Huang Y, Martin W, Cheng F. Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2. Cell Discov. 2020;6:14.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  53. 53.

    Gordon DE, Jang GM, Bouhaddou M, Xu J, Obernier K, O’Meara MJ, et al. A SARS-CoV-2-human protein-protein interaction map reveals drug targets and potential drug-repurposing. bioRxiv. 2020. https://doi.org/10.1038/s41586-020-2286-9.

    Article  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Beck BR, Shin B, Choi Y, Park S, Kang K. Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model. Comput Struct Biotechnol J. 2020;18:784–90.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  55. 55.

    Kouznetsova V, Huang D, Tsigelny IF. Potential COVID-19 protease inhibitors: repurposing FDA approved drugs. ChemRxiv. 2020. https://doi.org/10.26434/chemrxiv.12093900.v1.

    Article  Google Scholar 

  56. 56.

    Wu C, Liu Y, Yang Y, Zhang P, Zhong W, Wang Y, et al. Analysis of therapeutic targets for SARS-CoV-2 and discovery of potential drugs by computational methods. Acta Pharm Sin B. 2020;10:766–88.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  57. 57.

    Li G, Clercq ED. Therapeutic options for the 2019 novel coronavirus (2019-nCoV). Nat Rev Drug Discov. 2020;19:149–50.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  58. 58.

    Olliaro P, Torreele E, Vaillant M. COVID-19 vaccine efficacy and effectiveness-the elephant (not) in the room. Lancet Microbe. 2021. https://doi.org/10.1016/S2666-5247(21)00069-0.

    Article  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Calzetta L, Ritondo BL, Coppola A, Matera MG, Di Daniele N, Rogliani P. Factors influencing the efficacy of COVID-19 vaccines: a quantitative synthesis of phase III trials. Vaccines (Basel). 2021;9(4):341.

    Article  Google Scholar 

Download references

Acknowledgements

The author thanks The PyMOL Molecular Graphics System, Version 2.0 Schrödinger, LLC to prepare graphic content.

Funding

Not applicable.

Author information

Affiliations

Authors

Contributions

UCH has designed, performed all analysis, written the paper, and prepared the images and Tables. The author read and approved the final manuscript.

Corresponding author

Correspondence to Umesh C. Halder.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The author has no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: S1.

List of drug binding hits for 7K3N –NSP1.

Additional file 1: S2.

List of drug binding hits for 6WEY-NSP3.

Additional file 1: S3.

List of drug binding hits for 6M03 –NSP5.

Additional file 1: S4.

List of drug binding hits for 7JLT-NSP7-8.

Additional file 1: S5.

List of drug binding hits for 6W4B-NSP9.

Additional file 1: S6.

List of drug binding hits for 6ZCT-NSP10.

Additional file 1: S7.

List of drug binding hits for 6M71-NSP7-8-12.

Additional file 1: S8.

List of drug binding hits for 7NIO-NSP13.

Additional file 1: S9.

List of drug binding hits for 5C8S-NSP14.

Additional file 1: S10.

List of drug binding hits for 6VWW-NSP15.

Additional file 1: S11.

List of drug binding hits for 7BQ7-NSP16-10.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Halder, U.C. Predicted antiviral drugs Darunavir, Amprenavir, Rimantadine and Saquinavir can potentially bind to neutralize SARS-CoV-2 conserved proteins. J of Biol Res-Thessaloniki 28, 18 (2021). https://doi.org/10.1186/s40709-021-00149-2

Download citation

Keywords

  • SARS-CoV-2
  • COVID-19
  • Antiviral drugs
  • Darunavir
  • Amprenavir
  • Rimantadine
  • Saquinavir
  • Non-structural proteins
  • Enzymes