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This article shows you how to use NetMHCpan to predict and analyze new tumor antigens. The content is concise and easy to understand. It will definitely brighten your eyes. I hope you can get something through the detailed introduction of this article.

The NetMHCpan software is used to predict the affinity between peptides and MHC type I molecules. The latest version is v4.0. Based on the artificial neural network algorithm, the model is constructed with more than 180000 quantitative binding data and the combination of MS-derived MHC eluting ligands as the training set. Binding affinity data come from MHC molecules of human, mouse, pig and other species, and ligand data of MS elution come from 55 human and mouse HLA alleles.

Just upload the protein sequence in fasta format directly, as shown below

The first step is to upload the amino acid sequence of the somatic mutation site. The uploaded amino acid sequence is the mutated sequence, not the wild type protein sequence.

The second step is to choose the way of cutting the peptide. The antigen binds to the MHC molecule through the antigen epitope, and the length of the antigen epitope that the MHC type I molecule can bind is 8 to 11 amino acids. Corresponding to the 8-11mer here, the protein sequence is divided into short peptides and then the MHC molecular affinity is predicted.

The third step is to select HLA allel, and then click the submit button. The output is shown below

There are many columns, in which Peptide is a peptide with a length of 8-11 amino acids extracted from the original input sequence. Pos corresponds to the starting position of the peptide on the original sequence, and the first position is counted from 0. Core corresponds to the sequence of peptides bound to MHC, similar to blast, allowing insertion and deletion.% Rank represents the possibility that the peptide is a natural peptide. The smaller the number, the better. The BindLevel in the last column represents the level of affinity, SB represents strong binding, and WB represents weak bingding. For a detailed explanation of each column, see the link below

Http://www.cbs.dtu.dk/services/NetMHCpan/output.php

The official filter results are based on the rank value. By default, a Rank value of less than 0.5 is defined as strong affinity, and a rank value between 0.5 and 2 is defined as weak affinity. Through this software, the peptide with strong affinity to MHC type I molecule can be predicted from the mutated amino acid sequence as a candidate new tumor antigen.

In order to further simplify the analysis, the relevant data analysis pipeline is developed, which only needs to provide the somatic mutation data of tumor patients and the results of HLA typing. The software automatically extracts the mutant amino acid sequence and carries out NetMHCpan analysis. There are many similar software, and NeoPredPipe software is one of them. The website of the software is as follows.

Https://github.com/MathOnco/NeoPredPipe

The basic usage is as follows

Python NeoPredPipe.py\

-I somatic.vcf\

-H hlatypes.txt\

-o. /\

-n TestRun\

-c 1 2-E 8 9 10

Two input files need to be provided,-I specifies the somatic mutation vcf file, and-H specifies the HLA typing result file. Please refer to the official documentation of the software for more details.

Through the above data analysis, we can quickly locate the candidate new antigen, but the false positive rate is still very high, which needs to be further screened and filtered in combination with in vitro experiments.

The above content is how to use NetMHCpan to predict and analyze new tumor antigens. Have you learned any knowledge or skills? If you want to learn more skills or enrich your knowledge reserve, you are welcome to follow the industry information channel.

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