Open Access

Molecular models of NS3 protease variants of the Hepatitis C virus

  • Nelson JF da Silveira1,
  • Helen A Arcuri1,
  • Carlos E Bonalumi1,
  • Fátima P de Souza2,
  • Isabel MVGC Mello3,
  • Paula Rahal4,
  • João RR Pinho3 and
  • Walter F de AzevedoJr1, 5Email author
BMC Structural Biology20055:1

DOI: 10.1186/1472-6807-5-1

Received: 13 August 2004

Accepted: 21 January 2005

Published: 21 January 2005

Abstract

Background

Hepatitis C virus (HCV) currently infects approximately three percent of the world population. In view of the lack of vaccines against HCV, there is an urgent need for an efficient treatment of the disease by an effective antiviral drug. Rational drug design has not been the primary way for discovering major therapeutics. Nevertheless, there are reports of success in the development of inhibitor using a structure-based approach. One of the possible targets for drug development against HCV is the NS3 protease variants. Based on the three-dimensional structure of these variants we expect to identify new NS3 protease inhibitors. In order to speed up the modeling process all NS3 protease variant models were generated in a Beowulf cluster. The potential of the structural bioinformatics for development of new antiviral drugs is discussed.

Results

The atomic coordinates of crystallographic structure 1CU1 and 1DY9 were used as starting model for modeling of the NS3 protease variant structures. The NS3 protease variant structures are composed of six subdomains, which occur in sequence along the polypeptide chain. The protease domain exhibits the dual beta-barrel fold that is common among members of the chymotrypsin serine protease family. The helicase domain contains two structurally related beta-alpha-beta subdomains and a third subdomain of seven helices and three short beta strands. The latter domain is usually referred to as the helicase alpha-helical subdomain. The rmsd value of bond lengths and bond angles, the average G-factor and Verify 3D values are presented for NS3 protease variant structures.

Conclusions

This project increases the certainty that homology modeling is an useful tool in structural biology and that it can be very valuable in annotating genome sequence information and contributing to structural and functional genomics from virus. The structural models will be used to guide future efforts in the structure-based drug design of a new generation of NS3 protease variants inhibitors. All models in the database are publicly accessible via our interactive website, providing us with large amount of structural models for use in protein-ligand docking analysis.

Background

After the development of serological tests for hepatitis A and B viruses in the 1970s it became clear that an additional agent accounted for approximately 90% of transfusion-associated hepatitis (non-A non-B hepatitis, NANBH) [1].

The novel agent, hence termed hepatitis C virus (HCV), currently infects approximately 3% of the world's population and it was classified within the Flavivirideae family. Diagnostic tests for anti-HCV antibodies developed thereafter proved that HCV was indeed the predominant cause of NANBH [2]. In view of the lack of vaccines against HCV, there is an urgent need for a treatment of the disease by an effective antiviral drug. This necessity has boosted research on the structural biology of HCV with the primary focus being to identify possible targets for pharmaceutical intervention [3].

Rational drug design has not been the primary way for discovering major therapeutics. However, recent successes in the area give reason to expect that drug discovery projects will increasingly be structure based. One of the possible targets for drug development against HCV is the NS3 protease variants. HCV RNA is translated into a polyprotein that during maturation is cleaved into functional components. One component, nonstructural protein 3 (NS3), is a 631-residue bifunctional enzyme with protease and helicase activities.

The N-terminal portion of the NS3 protein was predicted to contain a serine protease domain as judged from conserved sequence patterns and by homology to Flavi- and Pestiviruses [46]. The NS3 serine protease processes the HCV polyprotein by both cis and trans mechanisms. The interative refinement and optimization of drug leads is an effective strategy for generating potent preclinical candidate [7, 8]. Ongoing genome sequencing efforts have led to the identification of hundreds of potential therapeutic targets, many of which represent possible sources of crossover pharmacology. Homology or comparative modeling is a key feature of an integrated drug discovery effort because it allows this genomics information to be utilized early in the development of target ligands or in the engineering of ligand specificity [9].

Genome sequencing efforts are providing us with complete genetic blueprints for hundreds of organisms, including humans. We are now faced with assigning, understanding and modifying the functions of proteins encoded by these genomes. This task is generally facilitated by 3D structures [10], which are best determined by experimental methods such as X-ray crystallography and NMR spectroscopy. The theoretical approaches [11] can be divided into physical and empirical methods. The physical prediction methods are based on interactions between atoms and include molecular dynamics and energy minimization [12], whereas the empirical methods depend on the protein structures that have been already determined by experiment. They include combinatorial [13] and comparative modeling [14, 15].

Comparative modeling uses experimentally determined protein structures to predict conformation of other proteins with similar amino acid sequences. For modeling of proteins was used restrained-based modeling implemented in the program MODELLER [16]. The models consist of coordinates for all non-hydrogen atoms in the modeled part of a protein. Models are generated entirely automatically in a four-step procedure [17]: (i) fold assignment, (ii) sequence-structure alignment, (iii) model building, and (iv) model evaluation. This procedure was applied to variants of NS3 protease using Perl-CGI, C and MPI programming.

We modeled the structure of variants of NS3 protease variants available in the National Center for Biotchnology Information (Genbank), using structural bioinformatics tools. Knowledge of the three-dimensional structure variants will undoubtedly aid the design of useful inhibitors that may be used as a drug against hepatitis C virus. In order to speed up the modeling process all NS3 models were generated in a Beowulf cluster (BioComp, S.J. Rio Preto, Brazil). The potential of the structural bioinformatics for development of new antiviral drugs is discussed.

Results and discussion

Primary sequence comparasion

The identity between the sequences of a bifunctional protease structure (PDB access codes:1CU1, 1DY9) [31, 38] (templates) and NS3 protease variants (targets) is shown in Table 1. The secondary structural elements are indicated in the Figure 2 without inhibitor and in the Figure 3 with inhibitor. The sequence from crystallographic structure 1CU1 shows more than 79.1% identity with the sequences of NS3 protease variants, which provide high accuracy for the models (Table 1).
Figure 2

The strucuture of NS3 protease without inhibitor. The structure of NS3 protease variant, an engineered molecule that consists of 631 NS3 residues. It has six subdomains: two β barrels in the protease domain (down); two β-α-β subdomains (up on the left) and one α-helical subdomain in the helicase (up on the right). The figure was generated by Molmol [37].

Figure 3

The strucuture of NS3 protease complexed with 5,5-di-fluoro-2-keto-3-aminopentanoic acid. The NS3 protease-inhibitor complex shows the interaction between serine protease domain with 5,5-di-fluoro-2-keto-3-aminopentanoic acid. All models were generated using Parmodel [39].

Table 1

Analysis of the Ramachandran plot and identity between template and models of NS3 protease variants. The accuracy of comparative modeling is related to the percentage of sequence identity on which the model is based, correlating with the relationship between the structural and sequence similarities of two proteins. High accuracy comparative models are based on >50% sequence identity to their templates. They tend to have ~1 Å r.m.s. error for the main-chain atoms, which is comparable to the accuracy of a medium-resolution NMR structure or a low-relosution X-ray structure. All structure models in the database were generated using alignments with more than 79% sequence identity, which generating models with high accuracy.

Genbank access code

Residue Range

Identity (%)

Region of the Ramachandran plot (%)

   

Most Favorable

Additional Allowed

Generously Allowed

Disallowed

AJ238800

1027 – 1657

95.0

94.9

5.1

0.0

0.0

AJ238799

1027 – 1657

95.7

95.1

4.6

0.2

0.2

AF139594

1028 – 1658

95.7

95.8

3.8

0.2

0.2

D17763

1033 – 1663

80.5

94.9

4.9

0.2

0.0

AF054247

1027 – 1657

94.0

95.0

4.8

0.2

0.0

AF054248

1027 – 1657

93.8

94.9

5.1

0.0

0.0

AF054249

1027 – 1657

93.8

95.2

4.8

0.0

0.0

D50409

1031 – 1661

80.3

95.1

4.9

0.0

0.0

D84262

1032 – 1662

84.2

95.0

5.0

0.0

0.0

D84263

1025 – 1665

83.1

95.2

4.8

0.0

0.0

D84264

1029 – 1659

83.9

95.2

4.8

0.0

0.0

D84265

1027 – 1657

82.5

94.9

5.1

0.0

0.0

D10749

1027 – 1657

90.5

94.9

5.1

0.0

0.0

D13558

1027 – 1657

94.1

94.8

5.2

0.0

0.0

D14853

1027 – 1657

88.8

95.3

4.7

0.0

0.0

D00944

1031 – 1661

79.8

95.1

4.9

0.0

0.0

D10988

1031 – 1661

79.7

95.1

4.9

0.0

0.0

AF046866

1033 – 1663

80.3

94.7

5.1

0.2

0.0

D49374

1035 – 1665

79.8

94.7

5.1

0.2

0.0

Y11604

1027 – 1657

84.7

95.4

4.6

0.0

0.0

Y13184

1028 – 1658

82.3

95.2

4.8

0.0

0.0

Y12083

1031 – 1661

81.9

94.6

5.2

0.0

0.2

U16362

1027 – 1657

92.6

95.2

4.6

0.2

0.0

U45476

1027 – 1657

94.7

94.9

5.1

0.0

0.0

AJ000009

1027 – 1657

95.2

95.1

4.9

0.0

0.0

M67463

1027 – 1657

89.6

94.9

4.9

0.2

0.0

D63822

1031 – 1661

79.2

95.2

4.8

0.0

0.0

D63821

1032 – 1662

79.1

94.7

5.1

0.2

0.0

D14484

1027 – 1657

94.3

95.8

4.2

0.0

0.0

D11168

1027 – 1657

94.9

95.0

5.0

0.0

0.0

D11355

1027 – 1657

94.6

95.0

5.0

0.0

0.0

D50480

1027 – 1657

93.5

95.0

5.0

0.0

0.0

D50481

1027 – 1657

94.6

95.4

4.6

0.0

0.0

D50482

1027 – 1657

94.7

95.5

4.3

0.2

0.0

D50483

1027 – 1657

93.6

95.2

4.8

0.0

0.0

D50485

1027 – 1657

94.3

95.6

4.4

0.0

0.0

D50484

1027 – 1657

94.1

95.3

4.7

0.0

0.0

D28917

1033 – 1663

80.5

95.2

4.6

0.2

0.0

D30613

1027 – 1657

93.8

95.4

4.6

0.0

0.0

D10934

1027 – 1657

95.2

95.3

4.7

0.0

0.0

AF207762

1027 – 1657

94.0

95.1

4.9

0.0

0.0

Quality of the models

The atomic coordinates of crystallographic structure 1CU1 solved to resolution of the 2.5 Å were used as starting model for modeling of the NS3 protease variant structures, and the structure of NS3 complexed with an inhibitor (PDB access code: 1DY9) was used to generate homology models for docking studies. Binding of an inhibitor to the active site of an enzyme is typically connected with local and possibly also global structural rearrangement of the enzyme (induced-fit mechanism). Therefore structure-based drug design preferentially relies on the crystal structures of enzyme-inhibitor complexes containing bound inhibitors of similar chemical structures to the compounds being designed. Such complexes offer more detailed and accurate picture of the inhibitor-enzyme interactions and structural complementarity between the inhibitor and the active site. The homology models of the variants of NS3 protease which used the NS3 complexed with an inhibitor are more adequate to docking simulations. The atomic coordinates of all water molecules were removed from the templates.

The analysis of the Ramachandran diagram φ-ψ plots of the 1CU1 structure (template) were used to compare the overall stereochemical quality of the NS3 protease variants structures against template solved by biocrystallography (Table 1). They present over 94.0% of the residues in the most favorable regions. The same analysis for crystallographic structure (1CU1) present 88.9% of residues in the most favorable, 10.5% additional allowed regions, 0.6% generously allowed regions, and 0.0% disallowed regions, which strongly indicates that the molecular models present good overall stereochemical quality.

Overall description

The NS3 protease variant structures are composed of six subdomains, which occur in sequence along the polypeptide chain (Figure 2 and 3). The protease domain exhibits the dual β-barrel fold that is common among members of the chymotrypsin serine protease family. The helicase domain contains two structurally related β-α-β subdomains and a third subdomain of seven helices and three short β strands. The latter domain is usually referred to as the helicase α-helical subdomain. The 13-residue protease activation domain of NS4A contributes one strand to the N-terminal protease β-barrel and is considered to be the sixth subdomain [31].

Differences in subdomain structure in the NS3 protease variant molecule and in the structures of the isolated protease and helicase domains were assessed in several ways. Inspection of the molecule revealed that the subdomain folds are similar. Overall preservation of structure is also apparent when the subdomains from the various structures are superposed [31].

The rmsd value of bond lengths and bond angles, the average G-factor and Verify 3D values are shown in Table 2 for NS3 protease variants structures. The same analysis for crystallographic structure (1CU1) present rmsd values of the 0.013Å bond lengths and 1.67°, the average G-fator values of the 0.14 torsion angles and 0.28 covalent geometry, and Verify 3D values of the 321.53 score total and 1.09S quality.
Table 2

Analysis of the rmsd from ideal geometry, 3D Profile, and G-factor.

Genbank access code

Residue Range

rmsd

3D Profile*

G-factor**

  

Bond Lengths (Å)

Bond Angles (°)

Total Score

Sideal Score

Torsion Angles

Covalent Geometry

AJ238800

1027 – 1657

0.020

2.171

299.14

1.03S

0.01

-0.17

AJ238799

1027 – 1657

0.021

2.425

166.68

0.58S

0.07

-0.21

AF139594

1028 – 1658

0.021

2.193

152.89

0.53S

0.09

-0.20

D17763

1033 – 1663

0.019

2.143

290.96

1.00S

0.03

-0.14

AF054247

1027 – 1657

0.020

2.162

288.28

1.00S

0.07

-0.16

AF054248

1027 – 1657

0.020

2.194

292.48

1.01S

0.01

-0.19

AF054249

1027 – 1657

0.020

2.207

290.64

1.00S

0.05

-0.18

D50409

1031 – 1661

0.020

2.188

287.09

0.99S

0.02

-0.17

D84262

1032 – 1662

0.020

2.181

276.59

0.95S

0.06

-0.17

D84263

1025 – 1665

0.020

2.173

283.17

0.98S

0.01

-0.16

D84264

1029 – 1659

0.020

2.174

296.59

1.02S

0.03

-0.15

D84265

1027 – 1657

0.020

2.155

277.67

0.96S

0.02

-0.15

D10749

1027 – 1657

0.020

2.589

287.57

0.99S

0.02

-0.16

D13558

1027 – 1657

0.020

2.157

280.60

0.97S

0.04

-0.15

D14853

1027 – 1657

0.020

2.168

274.13

0.95S

0.02

-0.16

D00944

1031 – 1661

0.020

2.149

287.90

0.99S

0.40

-0.15

D10988

1031 – 1661

0.020

2.171

287.55

0.99S

0.02

-0.16

AF046866

1033 – 1663

0.020

2.957

281.07

0.97S

0.03

-0.16

D49374

1035 – 1665

0.020

2.163

292.23

1.01S

0.02

-0.15

Y11604

1027 – 1657

0.020

2.382

281.93

0.97S

0.02

-0.16

Y13184

1028 – 1658

0.020

2.166

296.66

1.02S

0.02

-0.15

Y12083

1031 – 1661

0.020

2.271

290.58

1.00S

-0.01

-0.21

U16362

1027 – 1657

0.020

2.186

280.49

0.97S

0.01

-0.16

U45476

1027 – 1657

0.020

2.398

281.26

0.97S

0.03

-0.17

AJ000009

1027 – 1657

0.020

2.168

290.86

1.00S

0.03

-0.16

M67463

1027 – 1657

0.020

2.139

281.59

0.97S

0.03

-0.14

D63822

1031 – 1661

0.020

2.385

284.43

0.98S

0.03

-0.16

D63821

1032 – 1662

0.020

2.420

269.09

0.93S

0.03

-0.18

D14484

1027 – 1657

0.020

2.234

285.83

0.99S

0.00

-0.19

D11168

1027 – 1657

0.020

2.160

289.57

1.00S

0.04

-0.15

D11355

1027 – 1657

0.020

2.148

306.49

1.06

0.04

-0.14

D50480

1027 – 1657

0.020

2.123

291.79

1.01S

0.05

-0.13

D50481

1027 – 1657

0.020

2.151

277.60

0.96S

0.06

-0.14

D50482

1027 – 1657

0.020

2.172

284.69

0.98S

0.03

-0.16

D50483

1027 – 1657

0.020

2.149

291.04

1.00S

0.06

-0.15

D50485

1027 – 1657

0.020

2.138

293.16

1.01S

0.03

-0.14

D50484

1027 – 1657

0.020

2.204

289.47

1.00S

0.00

-0.17

D28917

1033 – 1663

0.020

2.221

295.32

1.02S

0.03

-0.19

D30613

1027 – 1657

0.020

2.248

282.94

0.98S

0.02

-0.20

D10934

1027 – 1657

0.020

2.434

289.87

1.00S

0.00

-0.18

AF207762

1027 – 1657

0.020

2.367

308.61

1.07S

0.04

-0.15

*Total Score: is the sum of the 3D-1D scores (statistical preferences) of each residue present in protein. Ideal Score: Sideal = exp(-0.83+1.008xln(L)); where L is number of amino acids.

Sideal Score: is compatibility of the sequence with their 3D structure. It is obtained Total Score / Ideal Score. Sideal Score above 0.45Sideal.

**Ideally, scores should be above -0.5. Values below -1.0 may need investigation.

Database design, access, and interface

A MySQL database based on relational database management system (RDBMS) was developed to archive protein structure identified in infectious agents such as NS3 protease variants from hepatitis C virus. All supporting data related to the 3D structures modeling, such as protein codes, atomic coordinates in PDB format from modeled proteins, fasta sequence, links to others databases and various information about the protein were arranged in the MySQL [32] database under a master table. The aim this database is to provide access to a collection of annotated models generated by automated homology modelling of NS3 protease variants from hepatitis C virus. All models in the database are publicly accessible via our interactive website (Figure 1) [33]. The database user interface provides user friendly menus, so that all information can be printed in one step from any standard web browser. A small ribbon representation is included to obtain a first impression of the model structure (Figure 2 and 3). Atomic coordinates for the homology models can be downloaded in PDB format and their primary sequence in fasta format. The fields are defined with links to the target sequence, the template structure entries in PDB [34], structural information and analysis. There are two homology models for each sequence in the database, one obtained using 1CU1 as template and other using 1DY9 as template. The second model is adequade for docking simulation, since it was used as template a structure complexed with an inhibitor (PDB access code: 1DY9).
Figure 1

Database interface. The database user interface provides user friendly menus. In the menu of main page there are links to NS3 protein variant access codes of the Genbank, which lead to another page with the structural information and analysis about the generated model, such as a small ribbon representation, the atomic coordinates (model and template), and sequence in fasta format (model). The accuracy of the model can be viewed at link "NS3 protease variant table results" which shows results of values of the 3D Profile, rmsd, G-factor, and Ramachandran plot for each model.

Conclusions

Large scale protein homology modeling, in which whole sequence databases or whole genomes are used as input into automated modeling algorithms, have been reported by several groups [35]. By utilizing powerful computer systems with multiple processors, these efforts have allowed the creation of large databases of homology models of proteins. This project increases the certainty that homology modeling is an useful tool in structural biology and that it can be very valuable in annotating genome sequence information and contributing to structural and functional genomics from virus, bacteria and other organisms.

Inhibition studies have shown that NS3 is only modestly inactivated by classic serine protease inhibitors such as chloromethylketones or phenylmethyl sylfonylfluoride [36]. The structural models will be used to guide future efforts in the structure based design of a new generation of NS3 protease variants inhibitors. This database is freelly available for all users on the Web, providing us with large amount of structural models for use in protein-ligand docking analysis.

Methods

Molecular modeling

Molecular modeling is usually the method of choice when there is a clear relationship of homology between the sequence of a target protein and at least one known structure. This computational technique is based on the assumption that tertiary structures of two proteins will be similar if their sequences are related, and it is the approach most likely to give accurate results [18]. There are two main approaches to homology modeling: (1) fragment-based comparative modeling [14, 19] and (2) restrained-based modeling [16]. For modeling of NS3 protease variants from hepatitis C virus we used the second approach. Model building of NS3 protease variants was carried out using the program MODELLER [16]. MODELLER is an implementation of an automated approach to comparative modeling by satisfaction of spatial restraints [2022]. The modeling procedure begins with an alingment of the sequence to be modeled (target) with related known three-dimensional structure (templates). This alignment is usually the input to the program. The output is a three-dimensional model for the target sequence containing all main-chain and sidechain non-hydrogen atoms.

Next, the spatial restraints and CHARMM energy terms enforcing proper stereochemistry [23] were combined into an objective function. Finally, the model is obtained by optimizing the objective function in Cartesian space. The optimization is carried out by the use of the variable target function method [24] employing methods of conjugate gradients and molecular dynamics with simulated anneling. Several slightly different models can be calculated by varying the initial structure. A total of 1000 models were generated for each enzyme and the final models were selected based on stereochemical quality. All optimization process was performed on a Beowulf cluster with 16 nodes (BioComp, AMD Athlon XP 2100+).

Analysis of the models

The overall stereochemical quality of the final models for each NS3 protease variants from hepatitis C virus was acessed by the program PROCHECK [25]. The root mean square deviations (rmsd) differences from ideal geometries for bond lengths and bond angles were calculated with X-PLOR [26, 27]. G-factor value is essentially just log-odds score based on the observed distributions of the stereochemical parameters. It was computed for the following properties: torsion angles (the analyses provided the observed distributions of φ-δ, χ12, χ-1, χ-3, χ-4 and ω values for each of the 20 amino acid types) and covalent geometry (for the main-chain, bond lengths and bond angles) these average values were calculated using PROCHECK [25]. The Verify-3D measures the compatibility of a protein model with its sequence, these values were calculated using 3D profile [2830].

Declarations

Acknowledgements

This work was supported by grants from FAPESP (SMOLBNet 01/07532-0, 02/04383-7, 04/00217-0), CNPq, CAPES and Instituto do Milênio (CNPq-MCT). WFA (CNPq, 300851/98-7) is researcher for the Brazilian Council for Scientific and Technological Development.

Authors’ Affiliations

(1)
Department of Physics, IBILCE/UNESP, São José do Rio Preto
(2)
Department of Microbiology, Institute of Biomedical Science, USP, São Paulo
(3)
Adolfo Lutz Institute, São Paulo
(4)
Department of Biology, IBILCE/UNESP, São José do Rio Preto
(5)
Center for Applied Toxicology, Butantan Institute, São Paulo

References

  1. Houghton M: Hepatitis C viruses. In "Fields Virology". 3rd edition. Edited by: Fields BN, Knipe DM, Howley PM. Philadelphia, New York; 1996:1035–1058.Google Scholar
  2. Kuo G, Choo QL, Alter HJ, Gitnick GL, Redeker AG, Purcell RH, Miyamura T, Dienstag JL, Alter MJ, Stevens CE, Tagtmeyer GE, Bonino F, Colombo M, Lee WS, Kuo C, Berger K, Shister JR, Overby LR, Brandley DW, Houghton M: An assay for circulating antibodies to a major etiologic virus of human non-A, non-B hepatitis. Science 1989, 244(4902):362–364.View ArticlePubMedGoogle Scholar
  3. Urbani A, de Francesco R, Steinkuhler C: Proteases of the Hepatitis C Virus. In Proteases of Infectious Agents. Edited by: Ben M. Dunn, Academic Press, San Diego; 1999:61–91.View ArticleGoogle Scholar
  4. Miller RH, Purcell RH: Hepatitis C Virus Shares Amino Acid Sequence Similarity with Pestiviruses and Flaviviruses as Well as Members of Two Plant Virus Supergroups. PNAS 1990, 87: 2057–2061.PubMed CentralView ArticlePubMedGoogle Scholar
  5. Francki RIB, Fauquet CM, Knudson DL, Brown F: Classification ans nomentclature of viruses: Fifth report of the International Committee on Taxonomy of Viruses. Arch Virol Suppl 1991, 2: 223–233.Google Scholar
  6. Rice CM: Flavivirideae: The viruses and their replication. In "Fields Virology". 3rd edition. Edited by: Fields BN, Knipe DM, Howley PM. Philadelphia, New York; 1996:931–959.Google Scholar
  7. de Azevedo WF, Mueller-Dieckmann HJ, Schulze-Gahmen U, Worland PJ, Sausville E, Kim SH: Structural basis for specificity and potency of a flavonoid inhibitor of human CDK2, a cell cycle kinase. Proc Natl Acad Sci USA 1996, 93: 2735–2740. 10.1073/pnas.93.7.2735PubMed CentralView ArticlePubMedGoogle Scholar
  8. de Azevedo WF, Leclerc S, Meijer L, Havlicek L, Strnad M, Kim SH: Inhibition of cyclin-dependent kinases by purine analogues: crystal structure of human cdk2 complexed with roscovitine. Eur J Biochem 1997, 243: 518–526. 10.1111/j.1432-1033.1997.0518a.xView ArticlePubMedGoogle Scholar
  9. Veerapandian P: Structure-based drug design. Edited by: Marcel Dekker. New York, INC; 1997.Google Scholar
  10. Brenner SE, Levitt M: Expectations from structural genomics. Proteins Sci 2000, 9: 197–200.View ArticleGoogle Scholar
  11. Fasman GD: Prediction of Protein Sturcture and the Principles of Protein Conformation. New York, Plenum Press; 1989.View ArticleGoogle Scholar
  12. Brooks CL III, Karplus M, Pettit BM: Proteins: A Theoretical Perspective of Dynamics, Structure and Thermodynamics. John Wiley & Sons, New York; 1988.Google Scholar
  13. Cohen FE, Kuntz ID: Tertiary structure prediction. In Prediction of Protein Structutre and the Principles of Protein Conformation. Edited by: Fasman GD. Plenum Press, New York; 1989:647–705.View ArticleGoogle Scholar
  14. Blundell TL, Sibanda BL, Sternberg MJE, Thornton JM: Kownledge-based prediction of protein structures and the design of novel molecules. Nature (London) 1987, 326: 347–352. 10.1038/326347a0View ArticleGoogle Scholar
  15. Sali A, Blundell TL: Definition of general topological equivalence in protein structures. A procedure involving comparison of properties and relationships through simulated anneling and dynamic programming. J Mol Biol 1990, 212: 403–428.View ArticlePubMedGoogle Scholar
  16. Sali A, Blundell TL: Comparative Protein Modelling by Satisfaction of Spatial Restraints. J Mol Biol 1993, 234: 779–815. 10.1006/jmbi.1993.1626View ArticlePubMedGoogle Scholar
  17. Sánchez R, Sali A: ModBase: A database of comparative protein structure models. Bioinformatics 1999, 15: 1060–1061. 10.1093/bioinformatics/15.12.1060View ArticlePubMedGoogle Scholar
  18. Kroemer RT, Doughty SWW, Robinson AJ, Richards WG: Prediction of the three-dimensional structure of human interleukin-7 by homology modeling. Protein Eng 1996, 9(6):493–498.View ArticlePubMedGoogle Scholar
  19. Blundell TL, Carney D, Gardner S, Hayes F, Howlin B, Hubbard T, Overington J, Singh DA, Sibanda BL, SutCliffe M: 18th Krebs, Hans Lecture knowledge-based protein modeling and design. Eur J Biochem 1988, 172(3):513–520.View ArticlePubMedGoogle Scholar
  20. Sali A, Overington JP: Derivation of rules for comparative protein modeling from a database of protein structure alignments. Proteins Sci 1994, 3: 1582–1596.View ArticleGoogle Scholar
  21. Sali A, Potterton L, Yuan F, van Vlijmen H, Karplus M: Evaluation of comparative protein modeling by MODELLER. Proteins 1995, 23(3):318–326.View ArticlePubMedGoogle Scholar
  22. Sali A: Modeling mutations and homologous proteins. Curr Opin Biotechnol 1995, 6(4):437–451. 10.1016/0958-1669(95)80074-3View ArticlePubMedGoogle Scholar
  23. Brooks BR, Bruccoleri RE, Olafson BD, States DJ, Swaminathan S, Karplus M: CHARMM: A program for macromolecular energy minimization and dynamics calculations. J Comp Chem 1983, 4: 187–217. 10.1002/jcc.540040211View ArticleGoogle Scholar
  24. Braun W, Go N: Calculation of protein conformations by proton-proton distance constraints. A new efficient algorithm. J Mol Biol 1985, 186(3):611–626.View ArticlePubMedGoogle Scholar
  25. Laskowski RA, MacArthurm MW, Smith DK, Jones DT, Hutchinson EG, Morris AL, Naylor D, Moss DS, Thornton JM: PROCHECK v.3.0 – Program to check the stereochemistry quality of protein structures – Operating instructions. 1994.Google Scholar
  26. Schwieters CD, Kuszewski JJ, Tjandra N, Clore GM: The Xplor-NIH NMR Molecular Structure Determination Package. J Magn Res 2003, 160: 66–74. 10.1016/S1090-7807(02)00014-9View ArticleGoogle Scholar
  27. Brünger AT: X-PLOR, A System for Crystallography and NMR,. Yale Univ Press, New Haven, CT, Version 3.1; 1992.Google Scholar
  28. Bowie JU, Luthy R, Eisenberg D: A Method to Identify Protein Sequences That Fold into a Known Three-Dimensional Structure. Science 1991, 253: 164–170.View ArticlePubMedGoogle Scholar
  29. Luthy R, Bowie JU, Eisenberg K: Assessment of protein models with three-dimensional profiles. Nature 1992, 356: 83–85. 10.1038/356083a0View ArticlePubMedGoogle Scholar
  30. Kabsch W, Sander C: Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 1983, 22(12):2577–2637. 10.1002/bip.360221211View ArticlePubMedGoogle Scholar
  31. Yao N, Reichert P, Taremi SS, Prosise WW, Weber PC: Molecular Views of Viral Polyprotein Processing Revealed by the Crystal Structure of the Hepatitis C Virus Bifunctional Protease-Helicase. Structure (London) 1999, 7: 1353–1363.View ArticleGoogle Scholar
  32. DuBois: MySQL,. New Riders, Indianapolis, IN, USA; 2000.Google Scholar
  33. Database of NS3 Protease from Hepatitis C Virus[http://www.biocristalografia.df.ibilce.unesp.br/virus]
  34. Westbrook J, Feng Z, Chen L, Yang H, Berman HM: The Protein Data Bank and structural genomics. Nucleic Acids Res 2003, 31: 489–491. 10.1093/nar/gkg068PubMed CentralView ArticlePubMedGoogle Scholar
  35. Foster MJ: Molecular modeling in structural biology. Micron 2002, 33: 365–384. 10.1016/S0968-4328(01)00035-XView ArticleGoogle Scholar
  36. Bouffard P, Bartenschalager R, Ahlborn-Laake L, Mous J, Roberts N, Jacobsen H: An in vitro assay for hepatitis C virus NS3 serine proteinase. Virology 1995, 209: 52–59. 10.1006/viro.1995.1229View ArticlePubMedGoogle Scholar
  37. Koradi R, Billeter M, Wuthrich K: MOLMOL: a program for display and analysis of macromolecular structures. J Mol Graphics 1996, 14: 51–55. 10.1016/0263-7855(96)00009-4View ArticleGoogle Scholar
  38. Di Marco S, Rizzi M, Volpari C, Walsh M, Narjes F, Colarusso S, De Francesco R, Matassa VG, Sollazzo M: Inhibition of the Hepatitis C Virus Ns3/4A Protease the Crystal Structures of Two Protease-Inhibitor Complexes. J Biol Chem 2000, 275: 7152. 10.1074/jbc.275.10.7152View ArticlePubMedGoogle Scholar
  39. Uchôa HB, Jorge GE, da Silveira NJF, Camera JC Jr, Canduri F, de Azevedo WF Jr: Parmodel: a web server for automated comparative modeling of proteins. Biochem Biophys Res Commun 2004, 325: 1481–1486. 10.1016/j.bbrc.2004.10.192View ArticlePubMedGoogle Scholar

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