Skip to main content

Advertisement

SpringerLink
Log in

In silico quantitative prediction of peptides binding affinity to human MHC molecule: an intuitive quantitative structure–activity relationship approach

  • Original Article
  • Published:
Amino Acids Aims and scope Submit manuscript

Abstract

In this paper, we have handpicked 23 kinds of electronic properties, 37 kinds of steric properties, 54 kinds of hydrophobic properties and 5 kinds of hydrogen bond properties from thousands of amino acid structural and property parameters. Principal component analysis (PCA) was applied on these parameters and thus ten score vectors involving significant nonbonding properties of 20 coded amino acids were yielded, called the divided physicochemical property scores (DPPS) of amino acids. The DPPS descriptor was then used to characterize the structures of 152 HLA-A*0201-restricted CTL epitopes, and significant variables being responsible for the binding affinities were selected by genetic algorithm, and a quantitative structure–activity relationship (QSAR) model by partial least square was established to predict the peptide-HLA-A*0201 molecule interactions. Statistical analysis on the resulted DPPS-based QSAR models were consistent well with experimental exhibits and molecular graphics display. Diversified properties of the different residues in binding peptides may contribute remarkable effect to the interactions between the HLA-A*0201 molecule and its peptide ligands. Particularly, hydrophobicity and hydrogen bond of anchor residues of peptides may have a significant contribution to the interactions. The results showed that DPPS can well represent the structural characteristics of the antigenic peptides and is a promising approach to predict the affinities of peptide binding to HLA-A*0201 in a efficient and intuitive way. We expect that this physical-principle based method can be applied to other protein–peptide interactions as well.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Altfeld MA, Livingston B, Reshamwala N, Nguyen PT, Addo MM, Shea A, Newman M, Fikes J, Sidney J, Wentworth P, Chesnut R, Eldridge RL, Rosenberg ES, Robbins GK, Brander C, Sax PE, Boswell S, Flynn T, Buchbinder S, Goulder PJR, Walker BD, Sette A, Kalams SA (2001) Identification of novel HLA-A2-restricted human immunodeficiency virus type 1-specific cytotoxic T-lymphocyte epitopes predicted by the HLA-A2 supertype peptide-binding motif. J Virol 75:1301–1311

    Article  PubMed  CAS  Google Scholar 

  • Bigelow CC (1967) On the average hydrophobicity of proteins and the relation between it and protein structure. J Theor Biol 16:187–211

    Article  PubMed  CAS  Google Scholar 

  • Blythe MJ, Doytchinova IA, Flower DR (2002) JenPep: a database of quantitative functional peptide data for immunology. Bioinformatics 18:434–439

    Article  PubMed  CAS  Google Scholar 

  • Brusic V, Flower DR (2007) Bioinformatics tools for identifying T-cell epitopes. Drug Discov Today Biosilico 2:18–23

    Article  Google Scholar 

  • Brusic V, Rudy G, Honeyman G, Hammer J, Harrison L (1998) Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network. Bioinformatics 14:121–130

    Article  PubMed  CAS  Google Scholar 

  • Chang C, Ekins S, Bahadduri P, Swaan PW (2006) Pharmacophore-based discovery of ligands for drug transporters. Adv Drug Deliv Rev 58:1431–1450

    Article  PubMed  CAS  Google Scholar 

  • Chou KC (1996) Review: prediction of HIV protease cleavage sites in proteins. Anal Biochem 233:1–14

    Article  PubMed  CAS  Google Scholar 

  • Chou KC, Shen HB (2007) Review: recent progresses in protein subcellular location prediction. Anal Biochem 370:1–16

    Article  PubMed  CAS  Google Scholar 

  • Collantes ER, Dunn WJ (1995) Amino acid side chain descriptors for quantitative structure activity relationship studies of peptide analogues. J Med Chem 38:2705–2713

    Article  PubMed  CAS  Google Scholar 

  • Coyle AJ, Gutierrez-Ramos JC (2001) The expanding B7 superfamily: increasing complexity in costimulatory signals regulating T cell function. Nat Immunol 2:203–209

    Article  PubMed  CAS  Google Scholar 

  • del Guercio MF, Sidney J, Hermanson G, Perez C, Grey HM, Kubo RT, Sette A (1995) Binding of a peptide antigen to multiple HLA alleles allows definition of an A2-like supertype. J Immunol 154:685–693

    PubMed  CAS  Google Scholar 

  • DeLano WL (2002) The PyMOL molecular graphics system. DeLano Scientific, San Carlos

    Google Scholar 

  • Doytchinova IA, Flower DR (2001) 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 44:3572–3581

    Article  PubMed  CAS  Google Scholar 

  • Doytchinova IA, Flower DR (2002) Physicochemical explanation of peptide binding to HLA-A*0201 major histocompatibility complex: a three-dimensional quantitative structure–activity relationship-study. Proteins 48:505–518

    Article  PubMed  CAS  Google Scholar 

  • Doytchinova IA, Flower DR (2007) Predicting class I major histocompatibility complex (MHC) binders using multivariate statistics: comparison of discriminant analysis and multiple linear regression. J Chem Inf Model 47:234–238

    Article  PubMed  CAS  Google Scholar 

  • Doytchinova IA, Blythe MJ, Flower DR (2002) Additive method for the prediction of protein–peptide binding affinity: application to the MHC class I molecule HLA-A*0201. J Proteome Res 1:263–272

    Article  PubMed  CAS  Google Scholar 

  • Du QS, Wei YT, Pang ZW, Chou KC, Huang RB (2007) Predicting the affinity of epitope-peptides with class I MHC molecule HLA-A*0201: an application of amino acid-based peptide prediction. Protein Eng Des Sel 20:417–423

    Article  PubMed  CAS  Google Scholar 

  • Falk K, Rötzschke O, Stefanovic S, Jung G, Rammensee HG (1991) Allele specific motifs revealed by sequencing of self-peptides eluted from MHC molecules. Nature 351:290–296

    Article  PubMed  CAS  Google Scholar 

  • Germain RN (1994) MHC-dependent antigen processing and peptide presentation: providing ligands for T lymphocyte activation. Cell 76:287–299

    Article  PubMed  CAS  Google Scholar 

  • Golbraikh A, Tropsha A (2002) Beware of q2!. J Mol Graphics Mod 20:269–276

    Article  CAS  Google Scholar 

  • Guan P, Doytchinova IA, Walshe VA, Borrow P, Flower DR (2005) Analysis of peptide-protein binding using amino acid descriptors: prediction and experimental verification for HLA-A*0201. J Med Chem 48:7418–7425

    Article  PubMed  CAS  Google Scholar 

  • Gulukota K, Sidney J, Sette A, DeLisi C (1997) Two complementary methods for predicting peptides binding major histocompatibility complex molecules. J Mol Biol 267:1258–1267

    Article  PubMed  CAS  Google Scholar 

  • Hagmann M (2000) Computers aid vaccine design. Science 290:80–82

    Article  PubMed  CAS  Google Scholar 

  • Hattotuwagama CK, Toseland CP, Guan P, Taylor DJ, Hemsley SL, Doytchinova IA, Flower DR (2006) Toward prediction of class II mouse major histocompatibility complex peptide binding affinity: in silico bioinformatic evaluation using partial least squares, a robust multivariate statistical technique. J Chem Inf Model 46:1491–1502

    Article  PubMed  CAS  Google Scholar 

  • Hellberg S, Sjostrom M, Skagerberg B, Wold S (1987) Peptide quantitative structure–activity relationships, a multivariate approach. J Med Chem 30:1126–1135

    Article  PubMed  CAS  Google Scholar 

  • Hill AV, Elvin J, Willis AC, Aidoo M, Allsopp CE, Gotch FM, Gao XM, Takiguchi M, Greenwood BM, Townsend AR (1992) Molecular analysis of the association of HLA-B53 and resistance to severe malaria. Nature 360:434–439

    Article  PubMed  CAS  Google Scholar 

  • Honeyman MC, Brusic V, Stone NL, Harrison LC (1998) Neural network-based prediction of candidate T-cell epitopes. Nat Biotechnol 16:966–969

    Article  PubMed  CAS  Google Scholar 

  • Horton R, Wilming L, Rand V, Lovering RC, Bruford EA, Khodiyar VK, Lush MJ, Povey S, Talbot CC Jr, Wright MW, Wain HM, Trowsdale J, Ziegler A, Beck S (2004) Gene map of the extended human MHC. Nat Rev Genet 5:889–899

    Article  PubMed  CAS  Google Scholar 

  • Kast WM, Brandt RM, Sidney J, Drijfhout JW, Kubo RT, Grey HM, Melief CJ, Sette A (1994) Role of HLA-A motifs in identification of potential CTL epitopes in human papillomavirus type 16 E6 and E7 proteins. J Immunol 152:3904–3912

    PubMed  CAS  Google Scholar 

  • Kitchen DB, Decornez H, Furr JR, Bajorath J (2004) Docking and scoring in virtual screening for drug discovery: Methods and applications. Nat Rev 3:935–949

    CAS  Google Scholar 

  • Kubo RT, Sette A, Grey HM, Appella E, Sakaguchi K, Zhu NZ, Arnott D, Sherman N, Shabanowitz J, Michel H (1994) Definition of specific peptide motifs for four major HLA-A alleles. J Immunol 152:3913–3925

    PubMed  CAS  Google Scholar 

  • Kawashima I, Ogata H, Kanehisa M (1999a) AAindex: amino acid index database. Nucleic Acids Res 27:368–369

    Article  PubMed  CAS  Google Scholar 

  • Kawashima I, Tsai V, Southwood S, Takesako K, Sette A, Celis E (1999b) Identification of HLA-A3-restricted cytotoxic T lymphocyte epitopes from carcinoembryonic antigen and HER–2/neu by primary in vitro immunization with peptide-pulsed dendritic cells. Cancer Res 59:431–435

    PubMed  CAS  Google Scholar 

  • Kidera A, Konishi Y, Oka M, Ooi T, Scheraga HA (1985) A statistical analysis of the physical properties of the 20 naturally occurring amino acids. J Protein Chem 4:23–55

    Article  CAS  Google Scholar 

  • Kirksey TJ, Pogue-Caley RR, Frelinger JA, Collins EJ (1999) The structural basis for the increased immunogenicity of two HIV-reverse transcriptase peptide variant/class I major histocompatibility complexes. J Biol Chem 274:37259–37264

    Article  PubMed  CAS  Google Scholar 

  • Khan AR, Baker BM, Ghosh P, Biddison WE, Wiley DC (2000) The structure and stability of an HLA-A*0201/octameric Tax peptide complex with an empty conserved peptide-N-terminal binding site. J Immunol 164:6398–6405

    PubMed  CAS  Google Scholar 

  • Kubinyi H (1997) QSAR and 3D-QSAR in drug design. Drug Discov Today 2:457–467

    Article  CAS  Google Scholar 

  • Lin Z, Wu Y, Zhu B, Ni B, Wang L (2004) Toward the quantitative prediction of T-Cell. epitopes: QSAR studies on peptides having affinity with the class I MHC molecular HLA-A*0201. J Comput Boil 11:683–694

    Article  CAS  Google Scholar 

  • Logean A, Sette A, Rognan D (2001) Customized versus universal scoring functions: application to class I MHC–peptide binding free energy predictions. Bioorg Med Chem Lett 11:675–679

    Article  PubMed  CAS  Google Scholar 

  • Lu Y, Bulka B, desJardins M, Freeland SJ (2007) Amino acid quantitative structure property relationship database: a web-based platform for quantitative investigations of amino acids. Protein Eng Des Sel 20:347–351

    Article  PubMed  CAS  Google Scholar 

  • Madden DR (1995) The three-dimensional structure of peptide–MHC complexes. Annu Rev Immunol 13:587–622

    Article  PubMed  CAS  Google Scholar 

  • Madden DR, Garboczi DN, Wiley DC (1993) The antigenic identity of peptide/MHC complexes, a comparison of the conformations of five viral peptides presented by HLA-A2. Cell 75:693–708

    Article  PubMed  CAS  Google Scholar 

  • Pamer EG, Harty JT, Bevan MJ (1991) Precise prediction of a dominant class I MHC-restricted epitope of Listeria monocytogenes. Nature 353:852–855

    Article  PubMed  CAS  Google Scholar 

  • Parker KC, Bednarek MA, Coligan JE (1994) Scheme for ranking potential HLA-A2 binding peptides based on independent binding of individual peptide side-chain. J Immunol 152:163–175

    PubMed  CAS  Google Scholar 

  • Peoples GE, Goedegebuure PS, Smith R, Linehan DC, Yoshino I, Eberlein TY (1995) Breast and ovarian cancer-specific cytotoxic T lymphocytes recognize the same HER2/neu-derived peptide. Proc Natl Acad Sci USA 92:432–436

    Article  PubMed  CAS  Google Scholar 

  • Rammensee HG (1995) Chemistry of peptides associated with MHC class 1 and class II molecules. Curr Opin Immunol 7:85–96

    Article  PubMed  CAS  Google Scholar 

  • Rammensee HG (2003) Immunoinformatics: bioinformatic strategies for better understanding of immune function. Novartis Found Symp 254:1–2

    Article  PubMed  Google Scholar 

  • Rogers D, Hopfinger AJ (1994) Application of genetic function approximation to quantitative-structure activity relationships and quantitative structure–property relationships. J Chem Inf Comput Sci 34:854–866

    CAS  Google Scholar 

  • Ruppert J, Sidney J, Celis E, Kubo RT, Grey HM, Sette A (1993) Prominent role of secondary anchor residues in peptide binding to HLA-A*0201 molecules. Cell 74:929–937

    Article  PubMed  CAS  Google Scholar 

  • Sandberg M, Eriksson L, Jonsson J, Wold S (1998) New chemical descriptors for the design of biologically active peptides. A multivariate charaterrization of 87 amino acids. J Med Chem 41:2481–2491

    Article  PubMed  CAS  Google Scholar 

  • Sapper MA, Bjorkman PJ (1991) Refined structure of the human histocompatibility antigen HLA-A2 at 2.6Å resolution. J Mol Biol 219:277–319

    Article  Google Scholar 

  • Sarobe P, Pendleton CD, Akatsuka TD, Engelhard VH, Feinstone SM, Berzofsky JA (1998) Enhanced in vitro potency and in vivo immunogenicity of a CTL epitope from hepatitis C virus core protein following amino acid replacement at secondary HLA-A 2.1 binding positions. J Clin Invest 102:1239–1248

    Article  PubMed  CAS  Google Scholar 

  • Schefzick S, Bradley M (2004) Comparison of commercially available genetic algorithms: GAs as variable selection tool. J Comput Aided Mol Des 18:511–521

    Article  PubMed  CAS  Google Scholar 

  • Schueler-Furman O (2000) Structure-based prediction of binding peptides to MHC class I molecules: application to a broad range of MHC alleles. Protein Sci 9:1838–1846

    Article  PubMed  CAS  Google Scholar 

  • Sette A (2000) Tools of the trade in vaccine design. Science 290:2074–2075

    Article  PubMed  CAS  Google Scholar 

  • Sette A, Buus S, Appella E, Smith JA, Chesnut R, Miles C, Colon SM, Grey HM (1989) Prediction of major histocompatibility complex binding regions of protein antigens by sequence pattern analysis. Proc Natl Acad Sci USA 86:3296–3300

    Article  PubMed  CAS  Google Scholar 

  • Sette A, Sidney J (1998) HLA supertypes and supermotifs: a functional perspective on HLA polymorphism. Curr Opin Immunol 10:478–482

    Article  PubMed  CAS  Google Scholar 

  • Shen HB, Chou KC (2007a) EzyPred: a top–down approach for predicting enzyme functional classes and subclasses. Biochem Biophys Res Comm 364:53–59

    Article  PubMed  CAS  Google Scholar 

  • Shen HB, Chou KC (2007b) Signal–3L: a 3-layer approach for predicting signal peptide. Biochem Biophys Res Comm 363:297–303

    Article  PubMed  CAS  Google Scholar 

  • Šoškić M (1996) Link between orthogonal and standard multiple linear regression models. J Chem Inf Comput Sci 36:829–832

    Google Scholar 

  • Sturniolo T, Bono E, Ding J, Raddrizzani L, Tuereci O, Sahin U, Braxenthaler M, Gallazzi F, Protti MP, Sinigaglia F, Hammer J (1999) Generation of tissue-specific and promiscuous HLA ligand databases using DNA microarrays and virtual HLA class II matrices. Nat Biotechnol 17:555–561

    Article  PubMed  CAS  Google Scholar 

  • Sutter JM, Dixon SL, Jurs PC (1995) Automated descriptor selection for quantitative structure–activity relationships using generalized simulated annealing. J Chem Inf Comput Sci 35:77–84

    CAS  Google Scholar 

  • Tropsha A, Gramatica P, Gombar VK (2003) The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. QSAR Comb Sci 22:69–77

    Article  CAS  Google Scholar 

  • Trowsdale J, Ragoussis J, Campbell RD (1991) Map of the human MHC. Immunol Today 12:443–446

    Article  PubMed  CAS  Google Scholar 

  • Udaka K, Mamitsuka H, Nakaseko Y, Abe N (2002) Empirical evaluation of a dynamic experiment design method for prediction of MHC class I-binding peptides. J Immunol 169:5744–5753

    PubMed  CAS  Google Scholar 

  • Wallace AC, Laskowski RA, Thornton JM (1995) LIGPLOT: a program to generate schematic diagrams of protein–ligand interactions. Protein Engin 8:127–134

    Article  CAS  Google Scholar 

  • Xiao X, Shao S, Ding Y, Huang Z, Chen X, Chou KC (2005) An application of gene comparative image for predicting the effect on replication ratio by HBV virus gene missense mutation. J Theor Biol 235:555–565

    Article  PubMed  CAS  Google Scholar 

  • Zhou P, Tian F, Zhang M, Li Z (2006) Applying generalized hydrophobicity scale of amino acids to quantitative prediction of human leukocyte antigen-A*0201-restricted cytotoxic T lymphocyte epitope. Chin Sci Bull 51:1439–1443

    Article  CAS  Google Scholar 

  • Zhou P, Tian F, Li Z (2007) A structure-based, quantitative structure–activity relationship approach for predicting HLA-A*0201-restricted cytotoxic T lymphocyte epitopes. Chem Biol Drug Des 69:56–67

    Article  PubMed  CAS  Google Scholar 

  • Zhou P, Tian F, Wu Y, Li Z, Shang Z. (2008) Quantitative sequence-activity model (QSAM): Applying QSAR strategy to model and predict bioactivity and function of peptides, proteins and nucleic acids. Curr Comput Aided Drug Des (in press)

Download references

Acknowledgments

This work was supported by National Project 863 Fund (grant number 2006AA02Z312) and the National Natural Science Fund (grant number 30371339 and 30571748). We thank Prof. Zhiliang Li for commenting on this manuscript.

Author information

Authors and Affiliations

  1. Research Institute of Surgery, Daping Hospital, Third Military Medical University, Chongqing, China

    F. Tian, F. Lv & Q. Yang

  2. College of Bioengineering, Chongqing University, Chongqing, China

    F. Tian & L. Yang

  3. Department of Chemistry, Zhejiang University, Hangzhou, China

    P. Zhou

Authors
  1. F. Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. L. Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. F. Lv
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Q. Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. P. Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to F. Lv.

Appendix

Appendix

Table 6 The names of 119 physicochemical properties for amino acids
Full size table
Table 7 The values of 119 physicochemical properties for amino acids
Full size table

Rights and permissions

Reprints and permissions

About this article

Cite this article

Tian, F., Yang, L., Lv, F. et al. In silico quantitative prediction of peptides binding affinity to human MHC molecule: an intuitive quantitative structure–activity relationship approach. Amino Acids 36, 535–554 (2009). https://doi.org/10.1007/s00726-008-0116-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00726-008-0116-8

Keywords

Search

Navigation