International Journal of Medicinal Chemistry

International Journal of Medicinal Chemistry / 2013 / Article

Research Article | Open Access

Volume 2013 |Article ID 690513 | 6 pages | https://doi.org/10.1155/2013/690513

HLA-Modeler: Automated Homology Modeling of Human Leukocyte Antigens

Academic Editor: Armando Rossello
Received10 Aug 2013
Revised08 Oct 2013
Accepted08 Oct 2013
Published27 Nov 2013

Abstract

The three-dimensional (3D) structures of human leukocyte antigen (HLA) molecules are indispensable for the studies on the functions at molecular level. We have developed a homology modeling system named HLA-modeler specialized in the HLA molecules. Segment matching algorithm is employed for modeling and the optimization of the model is carried out by use of the PFROSST force field considering the implicit solvent model. In order to efficiently construct the homology models, HLA-modeler uses a local database of the 3D structures of HLA molecules. The structure of the antigenic peptide-binding site is important for the function and the 3D structure is highly conserved between various alleles. HLA-modeler optimizes the use of this structural motif. The leave-one-out cross-validation using the crystal structures of class I and class II HLA molecules has demonstrated that the rmsds of nonhydrogen atoms of the sites between homology models and crystal structures are less than 1.0 Å in most cases. The results have indicated that the 3D structures of the antigenic peptide-binding sites can be reproduced by HLA-modeler at the level almost corresponding to the crystal structures.

1. Introduction

The cause of various diseases involves the human leukocyte antigen (HLA) system which is the human version of the major histocompatibility complex. The HLA genes involved in the immune response fall into two classes, I and II, which are structurally and functionally different. Typical diseases associated with HLA molecules are autoimmune diseases [1] and infectious diseases [2]. The association between specific HLA alleles and adverse drug reactions which frequently cause significant morbidity and mortality for patients is also widely known [3]. A reliable three-dimensional (3D) structure of the particular HLA allele responsible for the specific event is essential to understand the underlying molecular mechanism in order to develop effective therapeutic agents or/and countermeasures. Since the pioneering work by Wiley et al. [4], various crystal structures of the HLA molecules have been disclosed so far. The crystal structures have shown that the peptide-binding groove of an HLA molecule consists of two parts, a floor and two walls. Although this canonical topology is highly conserved among different alleles, certain structural differences exist depending on the alleles. Therefore, the 3D structure of a particular HLA molecule is required for HLA studies. The HLA genes are the most polymorphic in the human genome and there are a large number of allelic variations. In the case of alleles belonging to the isotype A of class I HLA, the number of independent alleles deposited in the IMGT/HLA database [5] is 1,372 as of 27 July 2013 (version 3.13.1). On the other hand, the number of the corresponding nonredundant alleles whose 3D structures are deposited in Protein Data Bank [6] as of 24 April 2013 is just nine. This shows that a huge gap exists between known allele sequences and available 3D structures. Therefore, it is reasonable to assume that experimental structures for all the possible HLA alleles will not be available in the near future.

In the absence of experimental structures, in silico homology modeling can provide a viable alternative to generate reasonably accurate models of the allele of interest. Homology modeling is a methodology to predict protein structure based on the general observation that proteins with similar sequences have similar structures. The accuracy of homology models compared to the actual experimental structure is generally judged by C atomic pair root-mean-square deviation (rmsd). Depending on the degree of sequence identity or similarity and the quality of the alignment, the rmsd can be up to ca. 1-2 Å [7].

Homology modeling package specialized in HLA molecules is not available until now as far as we know. The purpose of this study is to create an automated HLA modeling application suitable for HLA studies at the molecular level.

2. Methods

The application named HLA-modeler was coded by the use of scientific vector language (svl) implemented in MOE [8]. All of the crystal structures of the HLA molecules deposited in the PDB were retrieved and a local HLA structural database named HLA-3DDB was compiled on April 24th, 2013. Information items stored in HLA-3DDB are given in Table 1. The data for the PDB entry of 1AO7 are given as examples. The proper allele names were obtained from IMGT/HLA. The template structure which is most homologous to the query sequence is selected from HLA-3DDB and used for homology modeling. Therefore, the only required input data is a query sequence. Segment matching algorithm [9] implemented in MOE is used for homology modeling. The optimization of the models is carried out by use of the PFROSST force field [10, 11] considering the implicit solvent model [12]. Multiple intermediate structures are constructed. The best structure in terms of the free energy of hydration calculated based on generalized Born/volume integral implicit solvent model [12] is selected. In the final optimization of the structure, nonhydrogen atoms are tethered. The antigenic peptide-binding site is primarily used in HLA-modeler in order to best use the 3D characteristics of the essential site of the template structure. A flow chart of HLA-modeler is shown in Figure 1.


ItemsExample data (1AO7)

Allele nameA*02:01
ComplexHLA/peptide/TCR
PDB ID1AO7
PDB headerComplex between human T-cell receptor, viral peptide (Tax), and HLA-A 0201
Atomic parameters(a specific format is used in HLA-modeler, but it can be converted into the PDB format)
Date of deposition 1997/7/21
Date of the last modification2009/2/24
Experiment typeX-ray
Resolution (Å)2.60
Free value0.32
Mean value42.4
Other chemical componentsEthyl mercury ion

Specific binding of antigenic peptides to a particular HLA molecule is a central problem for most of the HLA studies. HLA-modeler can construct the homology model of HLA-peptide complex based on the supplied peptide sequence. The svl code of HLA-modeler is available from Ryoka Systems Inc. on request.

3. Results and Discussion

3.1. Validation of Homology Models Constructed by HLA-Modeler

It is of interest to validate the reproducibility of homology models routinely built by HLA-modeler. For this purpose, the leave-one-out cross-validation was undertaken. In the current HLA-3DDB, there are 41 and 27 crystal structures of nonredundant HLA molecules belonging to classes I and II, respectively. For each amino acid sequence in HLA-3DDB, homology models were constructed using all structures belonging to the same class except the identical structure as template structures. The results for classes I and II are illustrated in Figures 2(a) and 2(b), respectively. The rmsds of nonhydrogen atoms in the peptide-binding site are shown. The rmsds do not depend on the sequence identity and are generally lesser than 1.1 Å. The only exception is the structure of HLA-DR1 (PDB ID: 4GBX) modeled based on the template structure of HLA-DR52c (3C5J). Since the structure of 4GBX is the heterodimer between HLA-DR1 and HLA-DM, the structure of the HLA-DR1 molecule is deformed due to the interaction with HLA-DM. This is the major reason of the large rmsd of ca. 1.8 Å. If all domains are used for homology modeling, rmsds are up to 3.0 and 2.7 Å in classes I and II, respectively. It indicates that the homology modeling strategy adopted in HLA-modeler is suitable to construct the structure of antigenic peptide-binding site.

3.2. Two Examples of Homology Modeling of Class I HLA Molecules

In the first case, the identity between the query and the template sequences is 78.4%. This sequence identity is significantly low among HLA molecules belonging to the same class. A homology model was built using the sequence of the HLA-C*04:01 molecule (PDB ID: 1QQD) as a query sequence, and the structure of the HLA-B*44:02 molecule (PDB ID: 1M60) is selected as a template structure. The nonhydrogen atoms of the and domains of the homology model and the crystal structure are superimposed in Figure 3. The rmsd is 0.7 Å. The amino acid residues whose positions differ significantly between two structures are depicted. It is considered that such degree of discrepancy as shown in Figure 3 may be small enough for most qualitative analysis such as estimation of amino acid residues which should possibly bind to antigenic peptides. Even in the cases where sequence identity is low, reasonably accurate models can be constructed as illustrated in Figure 2. However, if it is necessary to predict the conformations of amino acid residues at the peptide-binding site as accurate as possible, it is better to use the template structure with higher sequence identity. In the second case of homology modeling of the HLA-A*02:01 molecule (PDB ID: 1LP9) based on the template structure of a mutant molecule of HLA-A*02:01 (2UWE), the sequence identity is very high (99.2%). Only one residue at the antigenic peptide-binding site is different, that is, Ala and Thr in 2UWE and 1LP9, respectively. The rmsd of nonhydrogen atoms of the peptide-binding sites is 0.23 Å. The superimposed structures are shown in Figure 4. The position of the relevant Thr residue in the homology model is almost identical to that in the crystal structure.

In summary, we can construct reasonably reliable 3D structures of class I HLA molecules by HLA-modeler.

3.3. An Example of Homology Modeling of Class II HLA Molecule

Japanese cedar pollinosis is a type I allergic disease caused by Japanese cedar pollen. Hori et al.found that the disease is significantly associated with HLA-DP5 and identified an immunodominant peptide [13]. The minimum antigenic sequence of KVTVAFNQF was suggested. Understanding the interactions between the peptide and the HLA molecule at the molecular level will greatly help to find therapeutic strategies against this disease. However, the 3D structure has not been disclosed so far. By use of HLA-Modeler, we have built the homology model of the HLA-DP5 molecule with the minimum immunodominant peptide.

The main chains of the antigenic peptides bound to the HLA molecules take highly conserved extended structures. In particular, the main chain structure of eight residues shown in Figure 5 is conserved. If the main chain atoms of the corresponding eight residues in 28 independent peptides bound to the class II HLA molecules in the crystal structures are superimposed, the median rmsd for each residue ranges from 0.26 to 0.50Å. It indicates that the main chain conformations of the peptides bound to class II HLA are conserved in this particular region. The eight residues involving P1, P4, and P6 anchoring residues should play significant role in binding to the class II HLA molecules.

By taking the structural conservation of the bound peptides into account, the most plausible binding site of the immunodominant nonapeptide was searched. The nonapeptide was shifted along the template peptide with the sequence of RKFHYLPFLPSTGGS. The structures of the complexes between the HLA DP5 molecule and the nonapeptide with seven different alignments were optimized. The binding affinity of each complex was judged by a scoring function of GBVI/WSA_dG which is considered to express protein-ligand binding free energy [14]. The complex structure with the minimum GBVI/WSA_dG value is shown in Figure 6. The two terminal K and F residues of the peptide protrude from the antigenic peptide-binding groove and point toward the T-cell receptor. The experimental data have demonstrated that these residues are essential for the interactions with T-cell receptor. Therefore, the credibility of this homology model is considered to be high.

4. Conclusions

Since there are a large number of allelic variations, it is expected that the 3D structures of all the possible HLA alleles will not be experimentally determined in the near future. Under these circumstances, it is indispensable to use in silico methodology to predict the missing structures to promote HLA studies. The present study has demonstrated that if we properly use a strategy to build up homology models based on the structurally conserved antigenic peptide-binding site, we can build the reliable 3D structure of the site which is essential for the functions. The 3D structures of particular HLA molecules are useful to deepen our understanding of the molecular interactions between the HLA molecules and specific antigenic peptides. The 3D models of HLA molecules are also essential to disclose the molecular mechanisms of adverse drugs reactions closely related to specific HLA molecules. Moreover, the 3D models of the HLA molecules associated with certain autoimmune diseases will contribute to the discovery of drugs which could suppress the autoimmune reactions. The automatic modeling system such as HLA-modeler will be indispensable for extensive studies on these topics.

Conflict of Interests

The authors declare that they have no conflict of interests.

Acknowledgments

The authors thank Naoyuki Asakawa for his valuable comments and technical assistance. This work was partly supported by Grant-in-Aid for Scientific Research on Innovative Areas (22133012) from Ministry of Education, Culture, Sports, Science and Technology (MEXT) for Noriaki Hirayama.

References

  1. S. C. L. Gough and M. J. Simmonds, “The HLA region and autoimmune disease: associations and mechanisms of action,” Current Genomics, vol. 8, no. 7, pp. 453–465, 2007. View at: Google Scholar
  2. J. M. Blackwell, S. E. Jamieson, and D. Burgner, “HLA and infectious diseases,” Clinical Microbiology Reviews, vol. 22, no. 2, pp. 370–385, 2009. View at: Publisher Site | Google Scholar
  3. R. Pavlos, S. Mallal, and E. Phillips, “HLA and pharmacogenetics of drug hypersensitivity,” Pharmacogenomics, vol. 13, no. 11, pp. 1285–1306, 2012. View at: Google Scholar
  4. P. J. Bjorkman, M. A. Saper, B. Samraoui et al., “Structure of the human class I histocompatibility antigen, HLA-A2,” Nature, vol. 329, no. 6139, pp. 506–512, 1987. View at: Google Scholar
  5. J. Robinson, M. J. Waller, P. Parham et al., “IMGT/HLA and IMGT/MHC: sequence databases for the study of the major histocompatibility complex,” Nucleic Acids Research, vol. 31, no. 1, pp. 311–314, 2003. View at: Publisher Site | Google Scholar
  6. F. C. Bernstein, T. F. Koetzle, and G. J. B. Williams, “The protein data bank: a computer based archival file for macromolecular structures,” Journal of Molecular Biology, vol. 112, no. 3, pp. 535–542, 1977. View at: Google Scholar
  7. S. K. Burley, A. Joachimiak, G. T. Montelione, and I. A. Wilson, “Contributions to the NIH-NIGMS protein structure initiative from the PSI production centers,” Structure, vol. 16, no. 1, pp. 5–11, 2008. View at: Publisher Site | Google Scholar
  8. MOE (Molecular Operating Environment), 01, Chemical Computing Group, Montreal, Canada, 2011.
  9. M. Levitt, “Accurate modeling of protein conformation by automatic segment matching,” Journal of Molecular Biology, vol. 226, no. 2, pp. 507–533, 1992. View at: Publisher Site | Google Scholar
  10. D. A. Case, T. E. Cheatham III, T. Darden et al., “The Amber biomolecular simulation programs,” Journal of Computational Chemistry, vol. 26, no. 16, pp. 1668–1688, 2005. View at: Publisher Site | Google Scholar
  11. V. Hornak, R. Abel, A. Okur, B. Strockbine, A. Roitberg, and C. Simmerling, “Comparison of multiple amber force fields and development of improved protein backbone parameters,” Proteins, vol. 65, no. 3, pp. 712–725, 2006. View at: Publisher Site | Google Scholar
  12. P. Labute, “The generalized born/volume integral implicit solvent model: estimation of the free energy of hydration using London dispersion instead of atomic surface area,” Journal of Computational Chemistry, vol. 29, no. 10, pp. 1693–1698, 2008. View at: Publisher Site | Google Scholar
  13. T. Hori, N. Kamikawaji, A. Kimura et al., “Japanese cedar pollinosis and HLA-DP5,” Tissue Antigens, vol. 47, no. 6, pp. 485–491, 1996. View at: Google Scholar
  14. C. R. Corbeil, C. I. Williams, and P. Labute, “Variability in docking success rates due to dataset preparation,” Journal of Computer-Aided Molecular Design, pp. 1–12, 2012. View at: Publisher Site | Google Scholar

Copyright © 2013 Shinji Amari et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1589 Views | 714 Downloads | 5 Citations
 PDF  Download Citation  Citation
 Download other formatsMore
 Order printed copiesOrder