Toward the quantitative prediction of T-cell epitopes: QSAR studies on peptides having affinity with the class I MHC molecular HLA-A*0201

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

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.

Identification of a common HLA-A*0201-restricted epitope among SSX family members by mimicking altered peptide ligands strategy

The synovial sarcoma X breakpoint (SSX) gene family contains nine members. The SSX proteins are CT (cancer/testis) antigens and can be expressed in many tumor types. T cell immune response against SSX protein can be detected in tumor patients and mice expressing any SSX. Screening predominant protective epitopes might improve the low immunogenicity against these "self" CT antigens. Herein, we predicted HLA-A*0201-restricted epitopes for all nine SSX family members, followed by validation with epitope molecular modeling, peptide/HLA-A*0201 affinity, and binding stability assays. We obtained four highly homologous candidate epitopes with the high immunogenicity scores designated P1, P4, P5 and P6, from the nine SSX members. Each of the four candidates could elicit strong epitope-specific CTL immune responses, but P4 could evoke more interferon gamma (IFN-gamma)-producing T cells and more potent CTLs that could lyse more target cells. Importantly, almost all of the four epitopes induced CTLs could cross-lyse the mutual targets both in vitro in human PBMCs and HLA-A2.1/K(b) transgenic mice, but P4 showed superiority to other epitopes in term of cross-cytolysis. All of these results demonstrate that P4 can induce anti-tumor immunity in a fashion superior to other candidates, and may be the "common" CTL epitope among all SSX-expressing tumors. Due to its documented responses herein, P4 has potential application in peptide-mediated immunotherapy.

Robust quantitative modeling of peptide binding affinities for MHC molecules using physical-chemical descriptors

Major histocompatibility complex (MHC) molecules bind short peptides resulting from intracellular processing of foreign and self proteins, and present them on the cell surface for recognition by T-cell receptors. We propose a new robust approach to quantitatively model the binding affinities of MHC molecules by quantitative structure-activity relationships (QSAR) that use the physical-chemical amino acid descriptors E1-E5. These QSAR models are robust, sequence-based, and can be used as a fast and reliable filter to predict the MHC binding affinity for large protein databases.

Modeling the adaptive immune system: predictions and simulations

MOTIVATION

Immunological bioinformatics methods are applicable to a broad range of scientific areas. The specifics of how and where they might be implemented have recently been reviewed in the literature. However, the background and concerns for selecting between the different available methods have so far not been adequately covered.

SUMMARY

Before using predictions systems, it is necessary to not only understand how the methods are constructed but also their strength and limitations. The prediction systems in humoral epitope discovery are still in their infancy, but have reached a reasonable level of predictive strength. In cellular immunology, MHC class I binding predictions are now very strong and cover most of the known HLA specificities. These systems work well for epitope discovery, and predictions of the MHC class I pathway have been further improved by integration with state-of-the-art prediction tools for proteasomal cleavage and TAP binding. By comparison, class II MHC binding predictions have not developed to a comparable accuracy level, but new tools have emerged that deliver significantly improved predictions not only in terms of accuracy, but also in MHC specificity coverage. Simulation systems and mathematical modeling are also now beginning to reach a level where these methods will be able to answer more complex immunological questions.

Amino acid biophysical properties in the statistical prediction of peptide-MHC class I binding

Background

A key step in the development of an adaptive immune response to pathogens or vaccines is the binding of short peptides to molecules of the Major Histocompatibility Complex (MHC) for presentation to T lymphocytes, which are thereby activated and differentiate into effector and memory cells. The rational design of vaccines consists in part in the identification of appropriate peptides to effect this process. There are several algorithms currently in use for making such predictions, but these are limited to a small number of MHC molecules and have good but imperfect prediction power.

Results

We have undertaken an exploration of the power gained by taking advantage of a natural representation of the amino acids in terms of their biophysical properties. We used several well-known statistical classifiers using either a naive encoding of amino acids by name or an encoding by biophysical properties. In all cases, the encoding by biophysical properties leads to substantially lower misclassification error.

Conclusion

Representation of amino acids using a few important bio-physio-chemical property provide a natural basis for representing peptides and greatly improves peptide-MHC class I binding prediction.

Three-dimensional holograph vector of atomic interaction field (3D-HoVAIF): a novel rotation-translation invariant 3D structure descriptor and its applications to peptides

Quantitative structure-activity relationship (QSAR) study, important in drug design, mainly involves two aspects, molecular structural characterization (MSC) and construction of a statistical model. MSC focuses on transforming molecular structural and property characteristics into a group of numerical codes, dedicated to minimizing information loss during this process. In this context, common atoms in organic compounds are classified according to their families in the periodic table, and hybridization states, and on the basis of these, three nonbonding interactions (i.e. electrostatic, van der Waals and hydrophobic) are calculated, ultimately resulting in a new rotation-translation invariant, 3D-MSC, as a three-dimensional holograph vector of atomic interaction field (3D-HoVAIF). By applying 3D-HoVAIF to QSAR studies on two classical peptides including 58 angiotensin-converting enzyme (ACE) inhibitors and 48 bitter-tasting dipeptides, we get two excellent genetic algorithm-partial least squares (GA-PLS) models, with statistics r(2), q(2), root mean square error (RMSEE), and root mean square error of cross-validation (RMSCV) of 0.857, 0.811, 0.376, and 0.432 for ACE inhibitors and 0.940, 0.892, 0.153 and 0.205 for bitter-tasting dipeptides, respectively. By equally dividing the two datasets into training and test sets by D-optimal, the 3D-HoVAIF approach undergoes rigorous statistical validation. Furthermore, the superior performance of 3D-HoVAIF is confirmed in comparison with two other peptide MSC approaches referring to z-scale and ISA-ECI. For 58 ACE inhibitors, the GA-PLS model yields two principal components, with the following statistics: r(2) = 0.893, q(2) = 0.824, RMSEE = 0.349, RMSCV = 0.425, q2(ext) = 0.739, r2(ext)= 0.784, r2(0.ext) = 0.781, rf2(0.ext) = 0.77, k = 0.962, k' = 1.019, and RMSEP = 0.460; for 48 bitter-tasting dipeptides, three principal components resulted, with the statistics as: r(2) = 0.950, q(2) = 0.893, RMSEE = 0.152, RMSCV = 0.222, q2(ext)= 0.875, r2(ext) = 0.919, r2(0.ext)= 0.919, rf2(0.ext)= 0.919, k = 1.018, k' = 0.974, and RMSEP = 0.198. In addition, the relationship of ACE-inhibiting activities with bitter-tasting thresholds has been investigated by applying the above-constructed models to predictions on 400 theoretically possible dipeptides. Through analysis, the ACE-inhibiting activities are found to be prominently related to bitter-tasting intensities. Thus, it is deemed to be difficult to find such dipeptides that simultaneously satisfy pharmacodynamic action (high ACE-inhibiting activities) and comfortable tastes, suggesting that active components of dipeptides that are served as functional food to lower blood pressure are not very ideal.

An altered peptide ligand for naïve cytotoxic T lymphocyte epitope of TRP-2(180-188) enhanced immunogenicity

Tyrosinase-related protein-2 (TRP-2) is a non-mutated melanocyte differentiation antigen. The TRP-2-recognizing CD8(+) T cells can evoke immune responses to melanoma in both humans and mice. Developing epitopes with amino acid replacements in their sequences might improve the low immunogenicity against this 'self' tumor antigen. We designed altered peptide ligands (APLs) of TRP-2((180-188)) (SVYDFFVWL) with preferred primary and auxiliary HLA-A*0201 molecule anchor residue replacement. These APLs were screened for MHC-affinity by affinity prediction plots and molecular dynamics simulation, and analyzed in vitro for stability and binding-affinity to molecular HLA-A*0201. We also investigated the CTLs activities induced by TRP-2 wild-type epitope and the APLs both in vitro in human PBMCs and HLA-A2.1/K(b) transgenic mice. The results indicate that TRP-2 2M analog simultaneously had stronger binding-affinity and a lower dissociation rate to HLA-A*0201, than wild-type peptide. In addition, the analog 2M was superior to other APLs and wild-type epitope in terms of immunological efficacy ex vivo as measured by the ELISPOT assays of IFN-gamma and granzyme B. These results demonstrate that TRP-2 2M is an agonist epitope that can induce anti-tumor immunity superior to its wild-type epitope, and has potential application in peptide-mediated immunotherapy.

Prediction of T-cell epitopes based on least squares support vector machines and amino acid properties

T-lymphocyte (T-cell) is a very important component in human immune system. It possesses a receptor (TCR) that is specific for the foreign epitopes which are in a form of short peptides bound to the major histocompatibility complex (MHC). When T-cell receives the message about the peptides bound to MHC, it makes the immune system active and results in the disposal of the immunogen. The antigenic determinants recognized and bound by the T-cell receptor is known as T-cell epitope. The accurate prediction of T-cell epitopes is crucial for vaccine development and clinical immunology. For the first time we developed new models using least squares support vector machine (LSSVM) and amino acid properties for T-cell epitopes prediction. A dataset including 203 short peptides (167 non-epitopes and 36 epitopes) was used as the input dataset and it was randomly divided into a training set and a test set. The models based on LSSVM and amino acid properties were evaluated using leave-one-out cross-validation method and the predictive ability of the test set, and obtained the results of 0.9875 and 0.9734 under the ROC curves, respectively. This result is more satisfactory than that were reported before. Especially, the accuracy of true positive gets a marked enhancement.

Prediction of supertype-specific HLA class I binding peptides using support vector machines

Experimental approaches for identifying T-cell epitopes are time-consuming, costly and not applicable to the large scale screening. Computer modeling methods can help to minimize the number of experiments required, enable a systematic scanning for candidate major histocompatibility complex (MHC) binding peptides and thus speed up vaccine development. We developed a prediction system based on a novel data representation of peptide/MHC interaction and support vector machines (SVM) for prediction of peptides that promiscuously bind to multiple Human Leukocyte Antigen (HLA, human MHC) alleles belonging to a HLA supertype. Ten-fold cross-validation results showed that the overall performance of SVM models is improved in comparison to our previously published methods based on hidden Markov models (HMM) and artificial neural networks (ANN), also confirmed by blind testing. At specificity 0.90, sensitivity values of SVM models were 0.90 and 0.92 for HLA-A2 and -A3 dataset respectively. Average area under the receiver operating curve (A(ROC)) of SVM models in blind testing are 0.89 and 0.92 for HLA-A2 and -A3 datasets. A(ROC) of HLA-A2 and -A3 SVM models were 0.94 and 0.95, validated using a full overlapping study of 9-mer peptides from human papillomavirus type 16 E6 and E7 proteins. In addition, a large-scale experimental dataset has been used to validate HLA-A2 and -A3 SVM models. The SVM prediction models were integrated into a web-based computational system MULTIPRED1, accessible at antigen.i2r.a-star.edu.sg/multipred1/.