HLA–Peptide Binding Prediction Using Structural and ...

21 HLA–Peptide Binding Prediction Using Structural and Modeling Principles Pandjassarame Kangueane and Meena Kishore Sakharkar

Summary Short peptides binding to specific human leukocyte antigen (HLA) alleles elicit immune response. These candidate peptides have potential utility in peptide vaccine design and development. The binding of peptides to allele-specific HLA molecule is estimated using competitive binding assay and biochemical binding constants. Application of this method for proteomewide screening in parasites, viruses, and virulent bacterial strains is laborious and expensive. However, short listing of candidate peptides using prediction approaches have been realized lately. Prediction of peptide binding to HLA alleles using structural and modeling principles has gained momentum in recent years. Here, we discuss the current status of such prediction.

Key Words: HLA–peptide binding; modeling; dynamics simulation; threading; optimization; free energy; virtual matrix; virtual pockets; QSAR

1. Introduction The human leukocyte antigen (HLA)–peptide project (1,2) was explored with great momentum during the last decade by defining anchor residues in peptide-binding motifs (3) and by the subsequent utilization of those principles in computational tools (4–8). The difficulty in collecting immunologic or biochemical binding data, after rigorous cloning, in vitro expression and purification of every HLA allele to perform binding assay with a combinatorial library of peptides, sets the limitation to define anchor residues in peptide motifs for a wide array of HLA alleles. Simultaneous progress in computational structural biology (9) provided a structure-based methodology for HLA–peptide prediction (10,11). Assigning From: Methods in Molecular Biology, vol. 409: Immunoinformatics: Predicting Immunogenicity In Silico Edited by: D. R. Flower © Humana Press Inc., Totowa, NJ

293

294

Kangueane and Sakharkar

quantitative prediction score for the derived three-dimensional (3D) models and the subsequent grouping (12) or ranking (10,11,13) of the modeled complexes is of particular interest. Ranking of the modeled HLA–peptide complexes using pair-wise potentials has been proved to preferentially select hydrophobic– hydrophobic interaction (10). Recent attempts were made to exploit the available structural data on HLA–peptide binding to predict the binding affinities for HLA–peptide ligands (14). However, the analysis was restricted to very few alleles, and future cross-validation requires more biophysical information on diversified MHC–peptide complexes to recalibrate interaction functions. Addressing the fundamental question of what these complexes do and, most importantly, how they do it by taking into account the subtle polymorphic changes in the HLA binding groove is of interest. This will establish an understanding of the consequences of multiple interactions involved in the cascade of events during cell-mediated immune response. Both class I and class II HLA molecules possess a peptide-binding functional groove characterized by a similar structural fold (15). Subsequently, models were developed using the definition of virtual binding pockets, mapping of virtual pockets (VPs) to position-specific peptide residue anchors, and estimation of peptide residue— virtual binding pocket compatibility (16). VPs are defined using information gleaned from eight unique HLA alleles, and the mapping of VP to positionspecific residue anchors is done using the 29 HLA–peptide structures analyzed in this study. Here, we discuss the progress made in HLA–peptide-binding prediction using molecular modeling principles. 2. Prediction Methods 2.1. Molecular Dynamics Simulation Rognan et al. (17) showed the use of molecular dynamics simulation (MDS) to discriminate peptide binders from nonbinders to HLA molecules. The freeenergy change was calculated in AMBER force field for six peptides with HLA-B∗ 2705. Structural and dynamical properties of the solvated protein– peptide complexes (atomic fluctuations, solvent-accessible surface areas, and hydrogen-bonding pattern) were found to be in qualitative agreement with the available binding data in these cases. This method is not suitable for highthroughput prediction due to the high computing requirement simulation. 2.2. Self-Consistent Ensemble Optimization and Threading Kangueane and colleagues (12) modeled peptides in the HLA binding groove using self-consistent ensemble optimization (SCEO) discriminated binders from

HLA–Peptide Binding Prediction

295

nonbinders using van der Waals clashes. Altuvia, Schueler, and Margalit threaded (9,18,19) peptides into the HLA binding groove (10,11,20,21). This approach is dependent on the (1) availability of appropriate peptide structural template for threading and (2) the choice of a pair-wise potential table. The binding affinity is estimated, and peptides are ranked using a suitable pair-wise potential table (22–24). 2.3. Free Energy Scoring Function A number of free energy scoring functions (FRESNOs) have been developed for different purposes, and these functions are used for peptide binding to HLA molecules. Rognan and colleagues (14,25,26) developed FRESNO for predicting peptide binding to HLA molecules. However, this approach requires appropriate structural templates for model building. An extension to this work is EpiDock (26) applied to predict potential T-cell epitopes from viral proteomes. 2.4. Virtual Matrix Virtual matrices (VMs), like quantitative matrices, provide a detailed model in which binding of each peptide residue with HLA pockets is quantified using pocket profiles as shown by Hammer and colleagues (27). VMs are derived by assigning and combining pocket-specific binding properties using structural features or homology principles from known HLA structures and extrapolating to other alleles, whereas quantitative matrices are obtained using peptide data with known allele-specific binding data. The advantage over quantitative matrices is that the method is generic and can be applied to any given allele. One implementation of the algorithm is the software package TEPITOPE (27). The model is demonstrated for 11 HLA-DR alleles. Furthermore, they have been successfully applied to predict T-cell epitopes in oncology, allergy, and autoimmune diseases ( 17,28–31). 2.5. Virtual Pockets Kangueane and Sakharkar (32) and Zhao et al. (16) developed a method using VPs for each residue pockets gleaned from known structural data. In this method, nine VPs are defined, and the binding affinity between HLA and peptide is given by the sum of residue–residue compatibility between peptide residues and corresponding VPs. The quantification of the interaction between the HLA and peptide residue pair is calculated by the application of the Q matrix described by Mathura and Braun (33).

296

Kangueane and Sakharkar

2.6. Computational Combinatorial Ligand Design The computational combinatorial ligand design (CCLD) method uses the 3D information from the crystal structure of the molecule. Zeng et al. (34) applied CCLD for the prediction of peptides that bind HLA molecule with known structure. Using chemical fragments as models for amino acid residues, a set of peptides predicted to bind the HLA–peptide-binding groove were produced. The results showed that CCLD is sensitive to capture important features of peptide binding in sequence and structure. 2.7. Three-Dimensional Quantitative Structure Activity Relationship Three-dimensional quantitative structure activity relationship (3D QSAR) studies have been applied to explore the molecular interactions between HLA and peptides. They provide coefficient contour maps identifying areas of the peptides that require a particular physicochemical property to increase binding. Flower and Doytchinova (35,36) applied comparative molecular similarity indices analysis (CoMSIA) for HLA–peptide-binding prediction. CoMSIA uses the interaction potential around aligned sets of 3D peptide structures to describe the contributions to binding. Five types of similarity index (steric bulk, electrostatic potential, local hydrophobicity, and hydrogen-bond donor and hydrogenbond acceptor abilities) were calculated, using a common probe atom with 1 Å radius, charge +1, hydrophobicity +1, and hydrogen-bond donor and acceptor properties +1 in this method. CoMSIA can predict the binding affinity of a peptide with a residue not presented in the initial training set. However, it cannot assess the contribution of residues at each position and the interactions between them. CoMSIA also returns 3D representations for visual inspection. This approach is partly data-driven and is dependent on the quality of binding data. 3. Conclusion The use of MDS, threading, SCEO, VM, VP, FRESNO, CCLD, and CoMSIA is discussed for HLA–peptide-binding prediction. The complete mapping of HLA-specific short antigenic peptides in a given viral/bacterial genome will ultimately result in the rational identification of immunogenic epitopes. Although, peptide–HLA specificity plays an important role in the generation of the cell-mediated immune response, other coupled and undoubtedly important mechanisms such as antigen processing, peptide transport, loading of peptides to HLA molecules, and the phenomenon of T-cell receptor (TCR) repertoires need to be considered. A better understanding of HLA specificity and the multitude of variables associated with the cell-mediated immune response will

HLA–Peptide Binding Prediction

297

lead to the development of a methodology for the generation of a library of epitopes in silico that could be tested for HLA–peptide binding and immunogenicity. The successful sampling of short antigenic peptides from a pool of viral/bacterial genome sequence using computational tools will aid in faster and cost-effective means of identifying immunogenic peptides that could be further tested using in vivo models, for consideration as vaccines and therapeutics. The application of mathematical models using computational tools is rapidly advancing to uncover the hidden mystery behind cell-mediated immune response. However, caveats regarding model refinement and cross-validation have to be addressed in future.

References 1. Buus S: Description and prediction of peptide-MHC binding: the “human MHC project.” Curr Opin Immunol 11:209, 1999. 2. Pinilla C, Martin R, Gran B, Appel JR, Boggiano C, Wilson DB, Houghten RA: Exploring immunological specificity using synthetic peptide combinatorial libraries. Curr Opin Immunol 11:193, 1999. 3. Rammensee HG, Friede T, Stevanoviic S: MHC ligands and peptide motifs: first listing. Immunogenetics 41:178, 1995. 4. Milik M, Sauer D, Brunmark AP, Yuan L, Vitiello A, Jackson MR, Peterson PA, Skolnick J, Glass CA: Application of an artificial neural network to predict specific class I MHC binding peptide sequences. Nat Biotechnol 16:753, 1998. 5. Honeyman MC, Brusic V, Stone NL, Harrison LC: Neural network-based prediction of candidate T-cell epitopes. Nat Biotechnol 16:966, 1998. 6. Mamitsuka H: Predicting peptides that bind to MHC molecules using supervised learning of hidden Markov models. Proteins 33:460, 1998. 7. Parker KC, Shields M, DiBrino M, Brooks A, Coligan JE: Peptide binding to MHC class I molecules: implications for antigenic peptide prediction. Immunol Res 14:34, 1995. 8. Schafer JR, Jesdale BM, George JA, Kouttab NM, De Groot AS: Prediction of well-conserved HIV-1 ligands using a matrix-based algorithm, EpiMatrix. Vaccine 16:1880, 1998. 9. Jones DT, Thornton JM: Potential energy functions for threading. Curr Opin Struct Biol 6:210, 1996. 10. Altuvia Y, Sette A, Sidney J, Southwood S, Margalit H: A structurebased algorithm to predict potential binding peptides to MHC molecules with hydrophobic binding pockets. Hum Immunol 58:1, 1997. 11. Schueler-Furman O, Elber R, Margalit H: Knowledge-based structure prediction of MHC class I bound peptides: a study of 23 complexes. Fold Des 3:549, 1998.

298

Kangueane and Sakharkar

12. Kangueane P, Sakharkar MK, Lim KS, Hao H, Lin K, Chee RE, Kolatkar PR: Knowledge-based grouping of modeled HLA peptide complexes. Hum Immunol 6:460, 2000. 13. Sette A, Sidney J, del Guercio MF, Southwood S, Ruppert J, Dahlberg C, Grey HM, Kubo RT: Peptide binding to the most frequent HLA-A class I alleles measured by quantitative molecular binding assays. Mol Immunol 31:813, 1994. 14. Rognan D, Lauemoller SL, Holm A, Buus S, Tschinke V: Predicting binding affinities of protein ligands from three-dimensional models: application to peptide binding to class I major histocompatibility proteins. J Med Chem 42:4650, 1999. 15. Batalia MA, Collins EJ: Peptide binding by class I and class II MHC molecules. Biopolymers 43:281, 1997. 16. Zhao B, Mathura VS, Rajaseger G, Moochhala S, Sakharkar MK, Kangueane P: A novel MHCp binding prediction model. Hum Immunol 64(12):1123–1143, 2003. 17. Rognan D, Scapozza L, Folkers G, Daser A: Molecular dynamics simulation of MHC-peptide complexes as a tool for predicting potential T cell epitopes. Biochemistry 33:11476, 1994. 18. Jernigan RL, Bahar I: Structure-derived potentials and protein simulations. Curr Opin Struct Biol 6:195, 1996. 19. Skolnick J, Jaroszewski L, Kolinski A, Godzik A: Derivation and testing of pair potentials for protein folding. When is the quasi-chemical approximation correct? Protein Sci 6:676, 1997. 20. Altuvia Y, Schueler O, Margalit H: Ranking potential binding peptides to MHC molecules by a computational threading approach. J Mol Biol 249:244, 1995. 21. Schueler-Furman O, Altuvia Y, Sette A, and Margalit H: Structure-based prediction of binding peptides to MHC class I molecules. Application to a broad range of MHC alleles. Protein Sci 9:1838, 2000. 22. Miyazawa S, Jernigan RL: Estimation of effective inter-residue contact energies from protein crystal structure, quasi-chemical approximation. Macromolecules 18:534, 1985. 23. Miyazawa S, Jernigan RL: Residue-residue potentials with a favorable contact pair term and an unfavorable high packing density term, for simulation and threading. J Mol Biol 256:623, 1996. 24. Betancourt MR, Thirumalai D: Pair potentials for protein folding: choice of reference states and sensitivity of predicted native states to variations in the interaction schemes. Protein Sci 8:361, 1999. 25. Logean A, Sette A, Rognan D: Customized versus universal scoring functions: application to class I MHC-peptide binding free energy predictions. Bioorg Med Chem Lett 11:675, 2001. 26. Logean A, Rognan D: Recovery of known T-cell epitopes by computational scanning of a viral genome. J Comput Aided Mol Des 16:229, 2002. 27. Sturniolo T, Bono E, Ding J, Raddrizzani L, Tuereci O, Sahin U, Braxenthaler M, Gallazzi F, Protti MP, Sinigaglia F, Hammer J: Generation of tissue-specific and

HLA–Peptide Binding Prediction

28.

29.

30.

31.

32. 33.

34.

35.

36.

299

promiscuous HLA ligand databases using DNA microarrays and virtual HLA class II matrices. Nat Biotechnol 17:555, 1999. Hammer J, Gallazzi F, Bono E, Karr RW, Guenot J, Valsasnini P, Nagy ZA, Sinigaglia F: Peptide binding specificity of HLA-DR4 molecules: correlation with rheumatoid arthritis association. J Exp Med 181:1847, 1995. Gross DM, Forsthuber T, Tary-Lehmann M, Etling C, Ito K, Nagy ZA, Field JA, Steere AC, Huber BT: Identification of LFA-1 as a candidate autoantigen in treatment-resistant Lyme arthritis. Science 281:703, 1998. Cochlovius B, Stassar M, Christ O, Raddrizzani L, Hammer J, Mytilineos I, Zoller M: In vitro and in vivo induction of a Th cell response toward peptides of the melanoma-associated glycoprotein 100 protein selected by the TEPITOPE program. J Immunol 165:4731, 2000. Stassar MJ, Raddrizzani L, Hammer J, Zoller M: T-helper cell-response to MHC class II-binding peptides of the renal cell carcinoma-associated antigen RAGE-1. Immunobiology 203:743, 2001. Kangueane, P Sakharkar MK: T-Epitope designer: A HLA-peptide binding prediction server. Bioinformation 1(1):21–24, 2005. Venkatarajan MS, Braun W: New quantitative descriptors of amino acids based on multidimensional scaling of a large number of physical–chemical properties. J Mol Model 7:445, 2001. Zeng J, Treutlein HR, Rudy GB: Predicting sequences and structures of MHCbinding peptides: a computational combinatorial approach. J Comput Aided Mol Des 15:573, 2001. Doytchinova IA, Flower DR: Toward the quantitative prediction of T-cell epitopes: coMFA and coMSIA studies of peptides with affinity for the class I MHC molecule HLA-A∗ 0201. J Med Chem 44:3572, 2001. Doytchinova IA, Flower DR: A comparative molecular similarity index analysis (CoMSIA) study identifies an HLA-A2 binding supermotif. J Comput Aided Mol Des 16:535, 2002.