Literature DB >> 15642409

Generation of predictive pharmacophore model for SARS-coronavirus main proteinase.

Xue Wu Zhang1, Yee Leng Yap, Ralf M Altmeyer.   

Abstract

Pharmacophore-based virtual screening is an effective, inexpensive and fast approach to discovering useful starting points for drug discovery. In this study, we developed a pharmacophore model for the main proteinase of severe acute respiratory syndrome coronavirus (SARS-CoV). Then we used this pharmacophore model to search NCI 3D database including 250, 251 compounds and identified 30 existing drugs containing the pharmacophore query. Among them are six compounds that already exhibited anti-SARS-CoV activity experimentally. This means that our pharmacophore model can lead to the discovery of potent anti-SARS-CoV inhibitors or promising lead compounds for further SARS-CoV main proteinase inhibitor development.

Entities:  

Mesh:

Substances:

Year:  2005        PMID: 15642409      PMCID: PMC7115589          DOI: 10.1016/j.ejmech.2004.09.013

Source DB:  PubMed          Journal:  Eur J Med Chem        ISSN: 0223-5234            Impact factor:   6.514


Introduction

The infection of the newly emerged severe acute respiratory syndrome coronavirus (SARS-CoV) is characterized by acute flu-like symptoms that progress to acute lung injury or acute respiratory distress syndrome with over 10% of mortality [1]. To date there are no universally recommended therapy for the disease. Many scientists are now making efforts to develop effective drugs against SARS. The combination therapy of corticosteroid with lopinavir, ribavirin and ritonavir can improve clinical response and reduce mortality rates apparently [2], [3]. Cinatl et al. [4] found that ribavirin, azauridine, pyrazofurin and glycyrrhizin are active against SARS-CoV. Barnard et al. [5] reported that calpain inhibitors and β-d-N4-hydroxycytidine exhibit inhibitory effects on SARS-CoV. Structure-based drug design focuses on two important approaches: one is receptor-based docking technique, another is pharmacophore-based virtual screening technique. A pharmacophore is the 3D arrangement of atoms or functional groups essential for the compound to bind to a specific receptor [6]. The power of a pharmacophore model is to discover new leads by using 3D database pharmacophore searching and guide chemists to synthesize new compounds [7]. Such a pharmacophore-based method has been successfully applied to many drug development programs [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18]. The main proteinase of SARS-CoV plays an important role in virus replication and is the primary target for drugs. The aim of this study is to develop 3D pharmacophore models for SARS-CoV main proteinase and expect to provide useful knowledge for anti-SARS drug design.

Material and methods

There are two methods to derive a reasonable pharmacophore model. One is from the crystal structure of protein–ligand complex, another is based on molecular modeling of enzyme with its potential inhibitors. Here we used the experimental structure of SARS-CoV main proteinase complexed with its peptide inhibitor CMK (PDB ID 1UK4) [19] and the predicted structures of SARS-CoV main proteinase with six drugs/compounds [20] for establishing pharmacophore models. The structures of CMK peptide and six compounds are shown in Fig. 1 .
Fig. 1

The peptide and compounds used for pharmacophore generation.

The peptide and compounds used for pharmacophore generation. The POCKET module in LigBuilder program [21] was employed to obtain the pharmacophore models of SARS-CoV main proteinase. This approach was successfully applied to the identification of novel inhibitors for alanine racemase [22]. The proposed pharmacophore model is a binding-site-derived pharmacophore model, which includes the following pharmacophore features of ligands binding to the enzyme’s active site: a positively charged nitrogen atom (ammonium cation) to represent a hydrogen bond donor (HBD), a negatively charged oxygen atom (as in a carboxyl group) to represent a hydrogen bond acceptor (HBA), and a carbon atom (methane) to represent a hydrophobic center (HPC). A pharmacophore model is generated for each protein–ligand complex.

Results and discussion

Using CMK peptide and six compounds in Fig. 1, we generated a set of seven eight-point pharmacophore models for SARS-CoV main proteinase, which is listed in Table 1 . These hypotheses exhibit different features due to the diversities of the compounds involved. Based on these models, we extracted a common four-point pharmacophore distance pattern shown in Fig. 2 , P1 is HBA, HBD and HPC, P2 is HBA and HPC, P3 is HBA and HBD, P4 is HBA and HBD. Such a pharmacophore distance pattern was subsequently used for the 3D database search.
Table 1

The eight-point pharmacophore models obtained by LigBuilder from seven peptide and compounds

PharmacophorePeptide/compoundsFeatures
1CMK peptideHBD HBD HPC HBD HPC HBD HPC HBA
2LopinavirHBA HBD HBA HBD HBA HBA HBA HBA
3RitonavirHPC HPC HBD HBD HBD HBA HBD HBA
4NiclosamideHBA HBD HBA HBA HBA HBD HPC HBA
5PromazineHBA HBD HBA HPC HBA HBA HBA HBA
6PNUHBA HPC HBA HBD HBD HBA HBA HBA
7UC2HPC HBD HBD HBA HBD HPC HPC HBA

HBA = hydrogen bond acceptor, HBD = hydrogen bond donor, HPC = hydrophobic center.

Fig. 2

Four-point pharmacophore distance pattern for SARS-CoV main proteinase. Here, P1 is HBA, HBD and HPC, P2 is HBA and HPC, P3 is HBA and HBD, P4 is HBA and HBD.

The eight-point pharmacophore models obtained by LigBuilder from seven peptide and compounds HBA = hydrogen bond acceptor, HBD = hydrogen bond donor, HPC = hydrophobic center. Four-point pharmacophore distance pattern for SARS-CoV main proteinase. Here, P1 is HBA, HBD and HPC, P2 is HBA and HPC, P3 is HBA and HBD, P4 is HBA and HBD. The pharmacophore searching in 3D database was conducted in the 3D NCI database, which has 250, 251 open structures ready for searching. Table 2 summarizes the results for similarity search of above-mentioned four-point pharmacophore model with constraints: (1) the distance ranges for P1P2, P1P3, P2P3, P1P4, P2P4 and P3P4 (Fig. 2) are 2–3, 2–3, 1.5–2.5, 5–6, 3–4 and 3–4 Å, respectively; (2) the compound is drug; (3) the antiviral probability is over 70%; (4) the compounds labeled as “No Name” is not included. After review of these hitlists, 30 drugs were selected for further analysis, their chemical structures and bioactivities (documented in anti-HIV and anti-opportunistic infection chemical compound database, which contains approximately 100,000 compounds, http://www.apps1.niaid.nih.gov/struct_search/an/an_search.htm) are shown in Fig. 3 and Table 3 , respectively. It is noted that almost all drugs exhibited anti-HIV activity, and some of them have activities against Mycobacterium tuberculosis (puromycin), influenza virus (5-bromo-2′-deoxycytidine), hepatitis virus (dideoxyguanosine, 2′,3′-dideoxycytidine and ribavirin), dengue virus (azauridine and ribavirin), rhinovirus and poliovirus (ribavirin). This should be very meaningful in consideration of the following facts: (1) there are some links between SARS-CoV and HIV and HBV [23], [24], [25]; (2) similar structure patterns exist in SARS-CoV main proteinase with rhinovirus 3c protease, poliovirus 3c proteinase, HAV 3c protease, HCV Ns3 protease and dengue virus Ns3 protease [26]; (3) SARS-CoV has clinically similar symptoms with influenza virus/M. tuberculosis, such as fever, cough, pains, pneumonia and death [27].
Table 2

Summary of NCI database search by four-point pharmacophores

Pharmacophore features in Fig. 2Hits
P1
P2
P3
P4
HBDHBAHBDHBD987
HBAHBAHBAHBA794
HPCHBAHBAHBD286
HPCHPCHBAHBD305
HPCHBAHBDHBD298
HBAHBAHBDHBD881
HBAHBAHBAHBD895

HBA = hydrogen bond acceptor, HBD = hydrogen bond donor, HPC = hydrophobic center.

Fig. 3

Chemical structures of 30 drugs obtained by four-point pharmacophore search in NCI 3D database.

Table 3

Thirty drugs obtained by four-point pharmacophore search in NCI 3D database

NameNSC numberFormulaBioactivity documented in HIV/OI therapeutics database (http://www.apps1.niaid.nih.gov/struct_search/an/an_search.htm)
PD-ADI218321C11H16N4O4HIV
Coformycin277817C11H16N4O5HIV
Zidovudine602670C10H13N5O4HIV, HSV, human cytomegalovirus, vaccinia virus, cowpox virus
Vira-A404241C10H13N5O4HIV, HSV, human cytomegalovirus, varicella-zoster virus, vaccinia virus, cowpox virus
Angustmycin C53104C11H15N5O5
ARA-AMP259272C10H14N5O7PHIV, HSV, vaccinia virus
Cordycepin63984C10H13N5O3HIV
Triciribine154020C13H16N6O4HIV, human cytomegalovirus, HSV
5-AZCR102816C8H12N4O5HIV
Puromycin3055C22H29N7O5HIV, M. tuberculosis
CHETOMIN289491C31H30N6O6S4HIV
Vengicide99843C12H13N5O4Human cytomegalovirus
Spongothymidin68929C10H14N2O6HIV, HSV, varicella-zoster virus
Arauridine68928C9H12N2O6
P-Ara-C135962C25H43N3O6HIV
Pyrazofurin143095C9H13N3O6HIV, vaccinia virus, West Nile virus
Thymidin21548C10H14N2O5HIV, varicella-zoster virus
Radibud38297C9H11BrN2O5
Alexan63878C9H13N3O5HIV, HSV, human cytomegalovirus, varicella-zoster virus, measles virus
Floxuridin27640C9H11FN2O5HIV, HSV, vaccinia virus
Gemcitabine613327C9H12ClF2N3O4HIV, cowpox virus, vaccinia virus
Dideoxyguanosine619072C10H13N5O3HIV, HBV
2′,3′-Dideoxycytidine606170C9H13N3O3HIV, HBV
5-Bromo-2′-deoxycytidine61765C9H12BrN3O4HSV
Ribavirin163039C8H12N4O5HIV, HSV, HCV, influenza virus, dengue virus, measles virus, respiratory syncytial virus, rhinovirus, polio virus, vaccinia virus, cowpox virus
Azauridine32074C8H11N3O6West Nile virus, cowpox virus, vaccinia virus, dengue virus, Japanese encephalitis virus, Yellow fever virus
Fialuridine678514C9H10FIN2O5Cowpox virus, vaccinia virus, HSV, varicella-zoster virus
Emanil39661C9H11IN2O5HIV, HSV, cowpox virus, vaccinia virus, varicella-zoster virus
Tubercidin56408C11H14N4O4HIV, vaccinia virus, human cytomegalovirus
Viroptic75520C10H11F3N2O5HSV, cowpox virus, vaccinia virus
Summary of NCI database search by four-point pharmacophores HBA = hydrogen bond acceptor, HBD = hydrogen bond donor, HPC = hydrophobic center. Chemical structures of 30 drugs obtained by four-point pharmacophore search in NCI 3D database. Thirty drugs obtained by four-point pharmacophore search in NCI 3D database Indeed, among the 30 drugs are six compounds that already exhibited anti-SARS-CoV activity experimentally: azauridine, pyrazofurin, ribavirin, 2′,3′-dideoxycytidine, dideoxyguanosine, and 5-bromo-2′-deoxycytidine [4], [5]. This shows that our pharmacophore model can lead to the discovery of potent anti-SARS-CoV inhibitors or at least provide some useful clues. Fig. 4 shows the mappings of the six compounds into the four-point pharmacophore model, which are mapped to 1–3 HBA, 1–2 HBD and 0–1 HPC. In addition, most of the remaining compounds have remarkable similarities with one of the above six compounds, for example, azauridine with 5-AZCR, alexan, arauridine and spongothymidin; 5-bromo-2′-deoxycytidine with emanil, fialuridine, floxuridin, radibud, thymidin and viroptic; dideoxyguanosine with angustmycin C, cordycepin, tubercidin, vengicide and vira-A. The superpositions for these compounds are shown in Fig. 5 . In summary, our results indicate that the existing 30 drugs identified by our pharmacophore model could be potential inhibitors against SARS-CoV, or at least good lead compounds for anti-SARS-CoV drug design.
Fig. 4

The mappings of six compounds that experimentally exhibited anti-SARS-CoV activity into the four-point pharmacophore model: (A) azauridine, (B) 5-bromo-2′-deoxycytidine, (C) dideoxycytidine, (D) dideoxyguanosine, (E) pyrazofurin, (F) ribavirin.

Fig. 5

Superpositions of anti-SARS-CoV compounds with other compounds. (A) azauridine with 5-AZCR, alexan, arauridine and spongothymidin; (B) 5-bromo-2′-deoxycytidine with email, fialuridine, floxuridin, radibud, thymidin and viroptic; (C) dideoxyguanosine with angustmycin C, cordycepin, tubercidin, vengicide and vira-A.

The mappings of six compounds that experimentally exhibited anti-SARS-CoV activity into the four-point pharmacophore model: (A) azauridine, (B) 5-bromo-2′-deoxycytidine, (C) dideoxycytidine, (D) dideoxyguanosine, (E) pyrazofurin, (F) ribavirin. Superpositions of anti-SARS-CoV compounds with other compounds. (A) azauridine with 5-AZCR, alexan, arauridine and spongothymidin; (B) 5-bromo-2′-deoxycytidine with email, fialuridine, floxuridin, radibud, thymidin and viroptic; (C) dideoxyguanosine with angustmycin C, cordycepin, tubercidin, vengicide and vira-A.
  25 in total

Review 1.  Predicting molecular interactions in silico: I. A guide to pharmacophore identification and its applications to drug design.

Authors:  Oranit Dror; Alexandra Shulman-Peleg; Ruth Nussinov; Haim J Wolfson
Journal:  Curr Med Chem       Date:  2004-01       Impact factor: 4.530

2.  Novel pharmacophore-based methods reveal gossypol as a reverse transcriptase inhibitor.

Authors:  Paul A Keller; Chris Birch; Scott P Leach; David Tyssen; Renate Griffith
Journal:  J Mol Graph Model       Date:  2003-03       Impact factor: 2.518

3.  HIV-1 integrase pharmacophore model derived from diverse classes of inhibitors.

Authors:  Gabriela Iurcu Mustata; Alessandro Brigo; James M Briggs
Journal:  Bioorg Med Chem Lett       Date:  2004-03-22       Impact factor: 2.823

4.  HIV-1 integrase pharmacophore: discovery of inhibitors through three-dimensional database searching.

Authors:  M C Nicklaus; N Neamati; H Hong; A Mazumder; S Sunder; J Chen; G W Milne; Y Pommier
Journal:  J Med Chem       Date:  1997-03-14       Impact factor: 7.446

5.  A predictive pharmacophore model of human melanocortin-4 receptor as derived from the solution structures of cyclic peptides.

Authors:  Hongmao Sun; David N Greeley; Xin-Jie Chu; Adrian Cheung; Waleed Danho; Joseph Swistok; Yao Wang; Chunlin Zhao; Li Chen; David C Fry
Journal:  Bioorg Med Chem       Date:  2004-05-15       Impact factor: 3.641

6.  Three dimensional pharmacophore modeling of human CYP17 inhibitors. Potential agents for prostate cancer therapy.

Authors:  Omoshile O Clement; Clive M Freeman; Rolf W Hartmann; Venkatesh D Handratta; Tadas S Vasaitis; Angela M H Brodie; Vincent C O Njar
Journal:  J Med Chem       Date:  2003-06-05       Impact factor: 7.446

7.  A structure-based design approach for the identification of novel inhibitors: application to an alanine racemase.

Authors:  Gabriela Iurcu Mustata; James M Briggs
Journal:  J Comput Aided Mol Des       Date:  2002-12       Impact factor: 3.686

8.  Role of lopinavir/ritonavir in the treatment of SARS: initial virological and clinical findings.

Authors:  C M Chu; V C C Cheng; I F N Hung; M M L Wong; K H Chan; K S Chan; R Y T Kao; L L M Poon; C L P Wong; Y Guan; J S M Peiris; K Y Yuen
Journal:  Thorax       Date:  2004-03       Impact factor: 9.139

9.  Old drugs as lead compounds for a new disease? Binding analysis of SARS coronavirus main proteinase with HIV, psychotic and parasite drugs.

Authors:  Xue Wu Zhang; Yee Leng Yap
Journal:  Bioorg Med Chem       Date:  2004-05-15       Impact factor: 3.641

10.  Fgl2: link between hepatitis B and SARS?

Authors:  Morag Robertson
Journal:  Drug Discov Today       Date:  2003-09-01       Impact factor: 7.851

View more
  2 in total

1.  Crystal structures of the main peptidase from the SARS coronavirus inhibited by a substrate-like aza-peptide epoxide.

Authors:  Ting-Wai Lee; Maia M Cherney; Carly Huitema; Jie Liu; Karen Ellis James; James C Powers; Lindsay D Eltis; Michael N G James
Journal:  J Mol Biol       Date:  2005-09-27       Impact factor: 5.469

2.  Molecular docking identifies the binding of 3-chloropyridine moieties specifically to the S1 pocket of SARS-CoV Mpro.

Authors:  Chunying Niu; Jiang Yin; Jianmin Zhang; John C Vederas; Michael N G James
Journal:  Bioorg Med Chem       Date:  2007-09-22       Impact factor: 3.641

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.