Prediction of MHC class I binding peptides, using SVMHC

As the genome projects proceed, we are presented with an exponentially increasing number of known protein sequences. Sequences from pathogens provide a huge amount of potential vaccine candidates, as the activation of cytotoxic T-cells requires recognition of specific peptides bound to Major Histocompatibility Complex (MHC) class I molecules (for humans the term Human Leukocyte Antigens, HLA, is often used instead of MHC). MHC-binding peptides (MHC-peptides) are also potential tools for diagnosis and treatment of cancer [1]. However, it is estimated that only one in 100 to 200 peptides actually binds to a particular MHC [2]. Therefore, a good computational prediction method could significantly reduce the number of peptides that have to be synthesized and tested.

Prediction of MHC-peptides can be divided into two groups: sequence based and structure based methods. Allele specific sequence motifs can be identified by studying the frequencies of amino acids in different positions of identified MHC-peptides. The peptides that bind to HLA-A*0201 are often 9 amino acids long (nonamers), and frequently have two anchor residues, a lysine in position 2 and a Valine in position 9 [3]. This type of sequence patterns has been used as a simple prediction method [4]. Besides the anchor residues, there are also weaker preferences for specific amino acids in other positions. One method to include this information is to use a profile, where a score is given for each type of amino acid in each position [5]. The scores can be calculated from observed amino-acid frequencies in each position or be set manually. The sum of the scores for a given peptide is then used to make predictions. One frequently used profile based prediction method is SYFPEITHI [6], which is freely available as a web service at http://www.syfpeithi.de/. The matrices in SYFPEITHI were adjusted manually, by assigning a high score (10) for frequently occurring anchor residues, a score of 8 to amino acids that occur in a significant amount and a score of 6 to rarely occurring residues. Preferred amino acids in other positions have scores that range from 1 to 6 and amino acids regarded as unfavorable have scores ranging from -3 to -1. SYFPEITHI prediction can be done for 13 different MHC class I types. Another profile based MHC-peptide predictor is HLA_BIND at http://bimas.dcrt.nih.gov/molbio/hla_bind/. This method estimates the half-time of dissociation of a given MHC-peptide complex [7]. HLA_BIND provides prediction for more than 40 different MHC class I types. It has been shown that profile based methods are correct in about 30% of the time, in the sense that one third of the predicted binders actually bind [8].

A profile based method does not take into account correlations between frequencies in different positions, neither they consider information from peptides that do not bind. This information can be used by machine learning methods [9]. Prediction of MHC-peptides has been made by using machine learning approaches such as artificial neural networks [10] and hidden Markov models [11]. Gulukota et al. (1997) [8] showed that one advantage of machine learning algorithms compared to profile methods seems to be that they have a higher specificity. This is possible due to the inclusion of non-binding data in the training. A machine learning approach extracts useful information from a large amount of data and creates a good probabilistic model [9]. In the case of MHC-peptide prediction, a data set of known binders and known (or supposed) non-binders is used. This set is then used to build a model that discriminates between binding peptides and non-binding peptides. This model can then be used to predict whether a novel peptide binds or not. Brusic reported a total accuracy of 88% on predictions for the mouse MHC H-2K ^d , using an artificial neural networks and hidden Markov models have been reported to perform 2–15% better than artificial neural networks [11, 12].

Structural approaches for prediction evaluate how well a peptide fit in the binding groove of a MHC molecule. A peptide is threaded through a structural template to obtain a rough estimate of the binding energy. The energy estimation is based on the interactions defined in the binding pocket of a particular MHC molecule [13]. To our knowledge no comparisons of the performance between structural and sequence based methods has been published. Obviously, a structural approach is limited to MHC types with a known structure. However, the advantage of a structural approach is that one known structure alone might be sufficient for creating a prediction model.

The amount of known binding data for different MHC molecules varies significantly. For some MHC molecules only a few MHC-peptides are known, while for others, there are several hundred verified binders. Since all machine learning methods need a sufficient amount of data for training, we investigated the number of known binders needed for training, using three examples with a large set of known binders. A varying number of training examples was tested using the nonamers in MHCPEP binding to HLA-A*0201, HLA-A3 and HLA-B*2705. The ratio of positive/negative examples was kept constant at 1:2. The test sets for each of the three HLA types consisted of 20 binders and 40 non-binders, unrelated from the training sets. The Mc for the test set was calculated for each size of the training set. A significant improvement of Mc was observed when the size of the training set was increased up to about 20 MHC-peptides, see figure 1. Further, a smaller improvement was observed for up to 50 peptides. From the similar behavior of these three examples we concluded that it seemed necessary to include at least 20 known peptides for successful predictions. This resulted in that the current version of SVMHC can make predictions for 26 different MHC molecules, using MHCPEP data. If SYFPEITHI data was used prediction could only be done for 6 different MHC molecules.

Finally, we tested SVMHC by performing a prediction for four proteins with recently identified known MHC-peptides. All possible binding nonamers were run through the predictors and a ranked list of candidate binders was produced from the output (the SVMHC models used were trained on SYFPEITHI data). In table 2 it can be seen that for all four proteins SVMHC ranks the known binders higher than the other two methods. This also indicates that fewer non-binders are given high scores when using SVMHC.

This example further supports the suggestion that machine learning methods might improve the specificity over profile based methods. However, the increase over SYFPEITHI, seems quite marginal and the major advantage of SVMHC might be that it (a) contains more MHC-types, (b) the scores are comparable between different MHC types (c) a slightly higher specificity.

Here, we present a novel approach based on support vector machines to predict the binding of peptides to MHC class I molecules, SVMHC. This method seems to perform slightly better than profile based methods. Most importantly the scoring is more comparable between different MHC types and therefore provides a higher overall specificity. Moreover, the implementation of SVMHC was done in such way that so that it will be easy to update when new binding peptides are identified. Better methods for purification and sequencing of MHC-binding peptides are developed all the time, giving more accurate databases. Therefore, the use of more "high quality" data will increase the performance of SVMHC prediction in the future, and predictions of a larger number of MHC-I classes should also be available. SVMHC currently contains prediction models for 26 MHC class I types from the MHCPEP database and 6 MHC class I types from the SYFPEITHI database. The prediction models for these MHC types are implemented in a public web service available at http://www.sbc.su.se/svmhc/.

This paper presents a support vector machine based method (SVMHC) to predict peptides that bind MHC class I molecules. Support vector machines are a class of machine learning methods that recently has been applied for classification of microarray data, protein structure prediction and other biological problems [14–16]. In preliminary studies, it was indicated that support vector machines performed better than neural networks for MHC-peptide predictions. SVMHC is based on the support vector machine package SVM-LIGHT [17].

Support vector machines

A full coverage of the use of SVM for pattern recognition is given by Vapnik [18], but some basic concepts are introduced here. Lets assume that we have a series of examples (or input vectors) ∈ R ^d (i = 1, 2 ..., N) with corresponding labels y _i ∈ {+1, -1} (i = 1, 2, ..., N). In the case of MHC class I binding peptides, corresponds to the amino acid sequence of the peptide and y _i (+1 or -1) represents binder/non-binder. The amino acid sequence of a peptide is represented by sparse encoding [9].

This task is carried out by (i) mapping of the input vectors

into a high dimensional feature space and (ii) construction of an optimal separating hyperplane (OSH) in the new feature space. The OSH is the hyperplane with the maximum distance to the nearest data points of each class in the feature space H. One of the most central points in using SVM is the choice of mapping φ (·), which is defined by a kernel function K( , ). The decision function used by SVM can be written:

The coefficients α _i are given by the solution of the quadratic programming task: Maximize

subject to

c in equation (2) is a parameter controlling the trade off between the margin and and the training error. It is the kernel function that determines the dimension of the feature space, meaning that different kernels will represent the input vectors in different ways. The aim of the SVM is then to find an OSH without loosing the ability of generalization, often referred to as over-training. The kernels tested for MHC class I peptide predictions were linear, polynomial and radial basis function.

Equation (3) is an example of the radial basis function and equation (4) shows the polynomial kernel function.

The problem of choosing the most suitable kernel for a SVM is analogous to the problem of choosing the architecture for a neural network [15]. One main feature of SVM is that the quadratic programming task is a convex optimization problem, which ensure a global optimum. This can be compared to ANN that uses gradient based training functions with the risk of getting stuck in a local minimum.