Prediction of T cell epitopes - Part 1. Weight matrices

Overview

During this exercise you will use bioinformatics tools to predict peptide-MHC binding. The exercise has three parts:

1. Identification of MHC binding motif
2. Visualize the motif using sequence logos
3. Training of a weight matrix for MHC class I binding predictions

Background: Peptide MHC binding

The most selective step in identifying potential peptide immunogens is the binding of the peptide to the MHC complex. Only one in about 200 peptides will bind to a given MHC complex. A very large number of different MHC alleles exist each with a highly selective peptide binding specificity.

The binding motif for a given MHC class I complex is in most cases 9 amino acids long. The motif is characterized by a strong amino acid preference at specific positions in the motif. These position are called anchor positions. For many MHC complexes the anchor position are placed at P2 and P9 in the motif. However this is not always the case.

Large number of peptide data exist describing this MHC specificity variation. One important source of data is MHC Motif Atlas (http://mhcmotifatlas.org/home). This database contains information on MHC ligands and binding motifs.

Purpose of exercise, description of data

In this exercise you are going to

1. Search MHC Motif Atlas site to find information characterizing the binding motif for different MHC alleles.
2. Visualize the binding motif using sequence logos.
3. Use the Easypred web-interface to train bioinformatics predictor for MHC-peptide binding.

The exercise

Identification of MHC binding motifs

Go to MHC Motif Atlas. Select "Explore the Landscape of MHC Class I Specificities". Next, select a few different MHC alleles (say HLA-A*01:01, HLA-A02:01, HLA-A*03:01, and HLA-B*08:01)

• Q1: What are the characteristics of the peptides that bind to the different HLAs. That is which positions are anchor position and what amino acids are found at the anchor positions?

• Q2: Does the location of anchors differ, do the amino acid preferences differ, and does the peptide length distribution differ between the different HLA alleles?

Sequence logos

A powerful way to visualize the peptide characteristics of the binding motif of an MHC complex, is to plot a sequence logo. The files HLA-A01:01, HLA-A02:01, HLA-B27:05, in the exercise directory contain peptides known to bind to a particular MHC complex (HLA-A*0201 for example). The files are in the format used by a program that generates sequence logos. Go to the web-site Seq2Logo.

You shall use Seq2Logo to visualize sequence logos. This you by pasting in the three files one at the time into the Submission window and type Submit query. Do this for each of the three peptide files and examine the sequence logos.

• Q3: Do the anchor positions and amino acid preferences correspond to what you found using the MHC Motif atlas database?

Prediction of MHC-peptide binding

In this part of the exercise you shall use the EasyPred web-interface to train and evaluate a series of different MHC-peptide binding predictors. You shall use two data sets (eval.set, train.set) that contain peptides and binding affinity to the MHC alleles HLA-A*0201. The binding affinity is a number between 0 and 1, where a high value indicates strong binding (a value of 0.5 corresponds to a binding affinity of approximately 200 nM). The eval.set contains 66, and the train.set 1200 such peptides. Click on the filenames to view the content of the files.

You shall now use EasyPred web-server to train a series of methods to predict peptide-MHC binding. Go to the EasyPred web-server.

Weight Matrix construction

First you shall train a matrix predictor. On the EasyPred web-server press Clear fields. In the upload training examples window browse and select the train.set file from the Desktop, in the upload evaluation window browse and select the eval.set file from the Desktop. In the Matrix method parameters select Clustering at 62% identity, and set weight on prior (weight on pseudo counts) to 200. Press Submit query. This will calculate a weight-matrix using sequence weighting by clustering, and a weight on prior (pseudo counts) of 200.

• Q4: What is the predictive performance of the matrix method (Pearson coefficient and Aroc value)?
• Q5: How many of the 1200 peptides in the train set are included in the matrix construction?

Go back to the EasyPred server window (use the Back bottom). Set clustering method to No clustering and the weight on prior to zero and redo calculation.

• Q6: What is the predictive performance of the matrix method now?
• Q7: Does clustering and pseudo count (weight on prior) improve the prediction accuracy?

Now you are done!!