BioMed Research International
Volume 2016 (2016), Article ID 3832176, 10 pages
http://dx.doi.org/10.1155/2016/3832176

Research Article

A Novel Peptide Binding Prediction Approach for HLA-DR Molecule Based on Sequence and Structural Information

Zhao Li,¹ Yilei Zhao,¹ Gaofeng Pan,¹ Jijun Tang,1,2 and Fei Guo¹

¹School of Computer Science and Technology, Tianjin University, 92 Weijin Road, Nankai District, Tianjin 300072, China
²School of Computational Science and Engineering, University of South Carolina, Columbia, SC, USA

Received 10 March 2016; Accepted 4 May 2016

Academic Editor: Yungang Xu

Copyright © 2016 Zhao Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

MHC molecule plays a key role in immunology, and the molecule binding reaction with peptide is an important prerequisite for T cell immunity induced. MHC II molecules do not have conserved residues, so they appear as open grooves. As a consequence, this will increase the difficulty in predicting MHC II molecules binding peptides. In this paper, we aim to propose a novel prediction method for MHC II molecules binding peptides. First, we calculate sequence similarity and structural similarity between different MHC II molecules. Then, we reorder pseudosequences according to descending similarity values and use a weight calculation formula to calculate new pocket profiles. Finally, we use three scoring functions to predict binding cores and evaluate the accuracy of prediction to judge performance of each scoring function. In the experiment, we set a parameter in the weight formula. By changing value, we can observe different performances of each scoring function. We compare our method with the best function to some popular prediction methods and ultimately find that our method outperforms them in identifying binding cores of HLA-DR molecules.

1. Introduction

Histocompatibility refers to the degree of antigenic similarity between the tissues of different individuals, which determines the acceptance or rejection of allografts. Transplantation antigen or histocompatibility antigen is the cause of rejection of allografts [1, 2]. MHC (Major Histocompatibility Complex) is present on the chromosome encoding a major histocompatibility antigen, mutual recognition between control cells, and the regulation of immune response.

MHC molecule plays a key role in immunology, and the molecule binding reaction with peptide is an important prerequisite for T cell immunity induced [2, 3]. By detecting a wide variety of microbial pathogens, the immune system protects host against diseases. Because of this, the binding prediction of MHC molecules with peptides has always been a hot topic in bioinformatics. Many researches in this field not only help us to understand the process of immune but also develop the work of vaccine design assisted by computers.

MHC genes produce two different types of molecules, which are MHC I molecules and MHC II molecules [1, 2]. MHC I molecules contain two separate polypeptide chains: the MHC α chain encoded by MHC genes and the MHC β chain encoded by non-MHC genes [4, 5]. MHC I class molecules are expressed in almost all eukaryotic cell surfaces, recognized by CD8+ cells. MHC II class molecules consist of two non-covalently linked polypeptide chains, namely, α chain and β chain. MHC II class molecules are expressed on antigen-presenting cells in general. Foreign MHC II antigens only capture and present on the surface of antigen-presenting cells (APC) TH cell [6]. After that, APC secretes large amounts of cytoplasm, activating cell invasion defensed behavior. Only the binding of antigen peptides and MHC II class molecules can activate CD4+ TH cells (helper T cells) [7]. Then, the activated TH cells would differentiate into effector cells and activate the immune response.

The structures of MHC I molecules and MHC II molecules slightly differ in the binding grooves [5]. Close grooves form on the binding of MHC I molecules and antigenic peptides. On the other hand, MHC II molecules do not have conserved residues, so they appear as open grooves. As a consequence, this will increase the difficulty in predicting MHC II molecules binding peptides [7]. In this paper, we aim to solve more difficult problem of predicting MHC II binding peptides.

The pioneering and most popular pan-specific approach for MHC II binding prediction is the TEPITOPE method [8], and basic idea is the HLA-DR allele having identical pseudosequence. The same pocket will share the same quantitative profile. By using multiple instance learning, the MHCIIMulti method [9] can predict more than 500 HLA-DR molecules. Transforming each DRB allele into a pseudosequence with 21 amino acids and using the SMM-align method to identify binding cores, the NetMHCIIpan method [5] gets an accurate prediction by using an artificial neural network algorithm [10, 11]. Combining NN-align and NetMHCpan with NetMHCIIpan [9, 12], the MULTIPRED2 method [13–15] can get a perfect prediction for 1077 HLA-I and HLA-II alleles and 26 HLA supertypes.

In this paper, we propose a novel prediction method for predicting MHC II molecules binding peptides. First, we calculate sequence similarity and structural similarity between different MHC molecules [13, 16]. Then, we reorder pseudosequences according to descending similarity values and use a weight calculation formula to calculate new pocket profiles. Finally, we use three scoring functions to predict binding cores and evaluate the accuracy of prediction to judge performance of each scoring function [17, 18]. In the experiments, we set a parameter in the weight formula. By changing value, we can observe different performances of each of the scoring functions. We compare our method with the best function to some popular prediction methods and ultimately find that our method outperforms them in identifying binding cores of HLA-DR molecule [19]. The work would suggest a novel computational strategy for special protein identification instead of traditional machine learning based methods [20, 21].

2. Materials and Methods

2.1. Data Sets

We find 39 MHC molecules and peptides binding complexes from Protein Data Bank (http://www.rcsb.org/pdb/search/), which constitutes the data set used in this paper. In this data set, lengths are between 11 and 23, and we can find polypeptide-binding sites, namely, binding cores. Table 1 lists the details of these 39 MHC molecules and peptide binding complexes [14, 22, 23].

Table 1: Details of 39 MHC molecules and peptide binding complexes.

PDB ID
DRB allele
Peptide sequence

1AQD
DRB10101
VGSDWRFLRGYHQYA

1PYW
DRB10101
XFVKQNAAALX

1KLG
DRB10101
GELIGILNAAKVPAD

1KLU
DRB10101
GELIGTLNAAKVPAD

2FSE
DRB10101
AGFKGEQGPKGEPG

1SJH
DRB10101
PEVIPMFSALSEG

1SJE
DRB10101
PEVIPMFSALSEGATP

1T5W
DRB10101
AAYSDQATPLLLSPR

1T5X
DRB10101
AAYSDQATPLLLSPR

2IAN
DRB10101
GELIGTLNAAKVPAD

2IAM
DRB10101
GELIGILNAAKVPAD

2IPK
DRB10101
XPKWVKQNTLKLAT

1FYT
DRB10101
PKYVKQNTLKLAT

1R5I
DRB10101
PKYVKQNTLKLAT

1HXY
DRB10101
PKYVKQNTLKLAT

1JWM
DRB10101
PKYVKQNTLKLAT

1JWS
DRB10101
PKYVKQNTLKLAT

1JWU
DRB10101
PKYVKQNTLKLAT

1LO5
DRB10101
PKYVKQNTLKLAT

2ICW
DRB10101
PKYVKQNTLKLAT

2OJE
DRB10101
PKYVKQNTLKLAT

2G9H
DRB10101
PKYVKQNTLKLAT

1A6A
DRB10301
PVSKMRMATPLLMQA

1J8H
DRB10401
PKYVKQNTLKLAT

2SEB
DRB10401
AYMRADAAAGGA

1BX2
DRB11501
ENPVVHFFKNIVTPR

1YMM
DRB11501
ENPVVHFFKNIVTPRGGSGGGGG

1FV1
DRB50101
NPVVHFFKNIVTPRTPPPSQ

1H15
DRB50101
GGVYHFVKKHVHES

1ZGL
DRB50101
VHFFKNIVTPRTPGG

4E41
DRB10101
GELIGILNAAKVPAD

1DLH
DRB10101
PKYVKQNTLKLAT

1KG0
DRB10101
PKYVKQNTLKLAT

3L6F
DRB10101
APPAYEKLSAEQSPP

3PDO
DRB10101
KPVSKMRMATPLLMQALPM

3PGD
DRB10101
KMRMATPLLMQALPM

3S4S
DRB10101
PKYVKQNTLKLAT

3S5L
DRB10101
PKYVKQNTLKLAT

1HQR
DRB50101
VHFFKNIVTPRTP

In Table 1, the first column is PDB ID of 39 complexes from PDB; the second column is the name of corresponding alleles from 39 complexes; the third column is the corresponding polypeptide sequences, in which the enlarged nine positions are the binding cores.

2.2. Methods

There are thousands of allele variants in nature [2, 4]. It is absolutely impossible to measure the binding specificity one by one. Motivated by this perspective, we propose a new computational method to predict the binding specificity of peptides without any biochemical experiment, which combines the sequence and structural information of these known specificity-binding MHC molecules, as showed in Figure 1. We evaluate the method on all general HLA-DRB data sets, and results indicate that our method is close to the state-of-the-art technology and our approach can predict all sequence-known MHC molecules and cost little time, extending the prediction space compared with other time-consuming approaches.

Figure 1: The architecture of our approach to MHC II and peptide binding problem.

2.3. Crucial Pockets relative to Binding Specificities of HLA-DR Molecules

We mainly use Position Specific Scoring Matrix (PSSM) [13, 24] in our approach, which is a popular technology in the problem of MHC binding. Roughly speaking, there are nine amino acids in MHC binding cores, and each position is a specific pocket as showed in Table 2. We use PSSM to quantify the binding affinity between twenty basic amino acids with these nine pockets.

Table 2: 30 HLA-complexes binding pockets.

PDB ID
Pocket  1
Pocket 2
Pocket 3
Pocket 4
Pocket 5
Pocket 6
Pocket 7
Pocket 8
Pocket 9

1AQD
82N 85V 86G
77T 78Y 81H 82N
78Y
13F 74A 78Y
13F 71R
11L
47Y 61W 67L 70Q 71R
60Y 61W
9W 57D 61W

1PYW
82N 85V 86G 89F
77T 78Y 81H 82N
78Y
13F 70Q 71R 74A 78Y
13F 71R
11L
11L 28E 61W 71R
60Y 61W
57D 61W

1KLG
82N 85V
78Y 81H 82N
78Y
13F 71R 78Y
13F 71R
11L
61W
60Y 61W
57D 61W

2FSE
82N 85V 86G 89F
77T 78Y 82N

13F 28E 70Q 71R 74A 78Y
13F 71R
71R
28E 47Y 61W 67L
71R
61W
57D

1KLU
82N 85V
78Y 81H 82N

13F 71R 78Y
13F 71R
11L
61W
60Y 61W
57D 61W

1SJH
82N
78Y 81H 82N

13F 26L 70Q 71R 74A 78Y
71R
11L
61W
60Y 61W
57D 61W

1SJE
82N
78Y 81H 82N
78Y
13F 26L 70Q 71R 74A 78Y
71R
11L
61W
60Y 61W
57D 60Y 61W

1T5W
82N 86G 89F
78Y 81H 82N
78Y
13F 70Q 71R 74A 78Y
13F 71R
11L
61W 71R
60Y 61W
9W 57D 61W

1T5X
82N 86G 89F
78Y 81H 82N
78Y
13F 70Q 71R 74A 78Y
71R
11L
61W 71R
61W
57D 61W

2IAN
82N 85V
78Y 81H 82N
78Y
13F 70Q 74A 78Y
13F 70Q 71R
11L
61W 71R
61W
57D 61W

2IPK
82N 85V 86G 89F
77T 78Y 81H 82N

13F 70Q 71R 74A 78Y
71R
11L
47Y 61W 67L 71R
60Y 61W
9W 57D 61W

1FYT
82N 85V 86G 89F
78Y 81H 82N
78Y
13F 70Q 71R 74A 78Y
13F 71R
11L
28E 47Y 61W 67L 71R
60Y 61W
9W 57D 61W

1R5I
82N 85V 86G 89F
77T 78Y 81H 82N
78Y
13F 70Q 71R 74A 78Y
70Q 71R
11L
47Y 61W 67L 71R
61W
9W 57D 61W

1HXY
82N 85V 86G 89F
78Y 81H 82N

13F 70Q 71R 74A 78Y
71R
11L
28E 47Y 61W 67L 71R
60Y 61W
9W 57D 61W

1JWM
82N 85V 86G 89F
78Y 81H 82N
78Y
13F 70Q 71R 74A 78Y
71R
11L
28E 47Y 61W 67L 71R
61W
57D 61W

1JWS
82N 85V 86G 89F
78Y 81H 82N
78Y
13F 70Q 71R 74A 78Y
13F 71R
11L
47Y 61W 67L 71R
61W
9W 57D 61W

1JWU
82N 85V 86G 89F
78Y 81H 82N
78Y
13F 70Q 71R 74A 78Y
13F 71R
11L
28E 47Y 61W 67L 71R
61W
9W 57D 61W

1LO5
82N 85V 86G 89F
78Y 81H 82N
78Y
13F 70Q 78Y
13F 71R
11L
47Y 61W 67L 71R
61W
9W 57D 60Y 61W

2ICW
82N 85V 86G 89F
78Y 81H 82N
78Y
13F 70Q 71R 74A 78Y
13F 71R
11L
28E 47Y 61W 67L 71R
61W
9W 57D 61W

2OJE
82N 85V 86G
77T 78Y 81H 82N
78Y
13F 70Q 71R 74A 78Y
70Q 71R
11L
28E 47Y 61W 67L 71R
61W
9W 57D 61W

2G9H
82N 85V 86G 89F
77T 78Y 81H 82N
78Y
13F 70Q 71R 74A 78Y
71R
11L 13F
28E 47Y 61W 67L 71R
60Y 61W
9W 57D 61W

2IAM
82N
78Y 81H 82N
78Y
13F 70Q 71R 74A 78Y
70Q 71R
11L
61W 67L
71R
60Y 61W
57D 61W

1A6A
82N 85V 86V
77T 78Y 81H 82N
78Y
13S 26Y 74R 78Y
71K 74R
11S 30Y
30Y 47F 61W 67L 71K
61W
9E 30Y 57D 61W

1J8H
82N 85V 86G 89F
77T 78Y 81H 82N
78Y
13H
26F 28D 70Q 74A 78Y
13H 70Q 71K
11V 13H 30Y
30Y 47Y 61W 67L
60Y 61W
37Y 57D 61W

2SEB
82N
77T 78Y 81H 82N

13H
26F
71K
78Y
13H 71K
30Y
30Y 47Y 61W
60Y 61W
61W

1BX2
82N 85V
77T 78Y 81H 82N
78Y
13H
26F 28D 70Q 74A 78Y
70Q
13R

57D 60Y 61W

1YMM
82N
77T 78Y 81H 82N
78Y
13R
26F 28D 70Q 74A 78Y
70Q
13R
61W 67I
61W
57D 61W

1FV1
82N 85V 86G 89F
78Y 81H 82N
78Y
13Y 71R 78Y
71R
13Y
61W 67L
71K
61W
57D

1H15
82N 89F
77T 78Y 81H 82N
78Y
13Y 71R 78Y
71R
11D 13Y 30D
61W

57D 60Y

1ZGL
82N 85V 89F
77T 78Y 81H 82N

13Y 26F 71R 78Y
13Y
13Y 28H 61W 71R
61W

57D 60Y 61W

There are five anchor sites (1, 4, 6, 7, and 9) at the binding core for MHC II molecules, which determine the binding strength of peptides with MHC II molecules. Because site 1 of MHC II is consistent with different MHC II molecules and peptides, it is important to identify the precise quantification of its binding core in site 1, yet we use weights of four anchor sites (4, 6, 7, and 9) to define profiles. For other sites, the same approach, such as TEPITOPE, is to specify their quantitative profiles.

2.4. Computing Similarity between Different MHC Molecules

2.4.1. Sequence-Based Similarity

Sequence-based similarity can be calculated by alignment results. Here, pocket pseudosequences and associated profiles refer to raw pocket pseudosequences and raw pocket profiles, respectively. These raw pseudosequences are composed of several amino acids, whose associated residue indices are shown in Table 3. Eleven representative HLA-DR alleles are adopted to specify different profiles for anchor pockets 4, 6, 7, and 9. These eleven alleles are , , , , , , , , , , and . If two alleles have identical pseudosequences in the same pocket, they will have identical profiles. For a given pocket, we collect all the different raw pocket pseudosequences into one set , , and , where , , is the number of unique pseudosequences, and is the number of amino acids contained in a pseudosequence. Meanwhile, we collect all different raw profiles into one set , , and , where . There is a one-to-one correspondence between and . We use BLOSUM to calculate the sequence similarity between different MHC molecules, defined as . Then, we can get encoded pseudosequence, which is a 20-dimensional real vector . We use Radial Basis Function (RBF) to measure the similarity between encoded predicted pseudosequences and a raw encoded pseudosequence:

Table 3: Important positions at the binding core for MHC II molecules.

Pocket
Important positions

Pocket 1
82 85 86 89

Pocket 2
77 78 81 82

Pocket 3
78

Pocket 4
11 13 26 28 70 71 74 78

Pocket 5
11 13 28 70 71 74

Pocket 6
11 13 28 70 71 74

Pocket 7
11 28 30 47 61 67 70 71

Pocket 8
60 61

Pocket 9
9 30 37 57 60 61

2.4.2. Structure-Based Similarity

Using MHC II HLA-peptide complex structure from Protein Data Bank (PDB), we can get the residues 3D-coordinate of the pocket in each MHC molecule, . We define vector , where is the number of amino acids in the pseudocontained sequence; meanwhile, we collect a set , , is the number of different pseudosequences, and there is also one-to-one correspondence between and .

Next, we need to estimate the similarity of three-dimensional structures between a measured MHC molecule and five MHC molecules with known pseudosequence PSSM. Rigid transformation is to compare three-dimensional substructures of two proteins [25, 26].

Intuitively, we fix one of the structures, A, move (translation and rotation) the other structure, B, and find the best movement in three-dimensional space, with two atoms to the nearest structure. We calculate the Euclidean distance between two structures, defined as . We can get encoded pseudosequence and calculate the similarity between 3D structures of encoded predicted pseudosequences and a raw encoded pseudosequence:

2.4.3. Overall Similarity

After that, we have obtained sequence similarity and structural similarity. We calculate final similarity score functions according to the following three formulas:

2.5. Weights Calculation for New Pocket Profiles

We reorder all pseudosequences according to descending similarity values and use a weight calculation formula to calculate new pocket profiles. A new pocket profile is generated as a weighted average over raw pocket profiles in . Next, we use the gamma distribution to generate the weights. The gamma PDF distribution is defined as follows:

where and , , and denotes the gamma function.

The weight distribution is generated to discretize the gamma PDF as follows:

where is the dimension of the weights and and are the shape and scale parameters, respectively. The gamma distribution generates the weight vector to give a higher weight for more similarity pseudosequences.

After normalizing, the weight vector is defined as follows:

Given a predicted DRB allele , let , where , , and α is a positive number and enhances the weight vector to protect the outstanding contribution of most similarity pseudosequences. Associated raw pocket profiles are . Elements of are sorted in descending order, and the reordered vector of is denoted as . The corresponding weight vector is denoted as . We denote pocket profiles associated with the reordered vector as , . We define the pocket profile for allele as follows:

where .

3. Result

First, we design an experiment to choose appropriate scoring function to combine sequence similarity and structural similarity. Then, we compare with other state-of-the-art technologies, which are TEPITOPE, MultiRTA, NetMHCIIpan-2.0, and NetMHCIIpan-1.0. The result indicates that our approach can obtain better prediction and effectively extend current prediction methods. Finally, we test on more data sets.

3.1. Evaluation of Different Scoring Functions

Here, we use 30 of 39 MHC molecules and peptide complexes as test set and get the appropriate scoring functions as showed above. The value of the parameter α is set to 1, 2, 3, 4, 5, 10, 15, and 20, followed by results shown in Figure 2. We find that no significant changes can be found by ; for and , when prediction error number is 10 and 9 and when prediction errors reduced to 8, we set the value of α to 3. Comparing these three functions, the least numbers of errors by three functions are 4, 8, and 8. Details are shown in Tables S1, S2, and S3, in the Supplementary Material available online at http://dx.doi.org/10.1155/2016/3832176.

Figure 2: Predicted results by different score functions. -axis represents different values, and the -axis refers to predicted results of different score functions.

3.2. Compared with Conventional Well-Known Methods

From the above experimental results, obtains the most accurate prediction, so we will select with α = 3 as our final approach. We compare our current prediction results with conventional well-known methods TEPITOPE [23], MultiRTA [13], NetMHCIIpan-2.0 [12], and NetMHCIIpan-1.0 [12], and these results are shown in Table 4.

Table 4: Comparison of our binding prediction with other approaches. The 5th column is the result of our method, and 6th to 8th columns are results of TEPITOPE, MultiRTA, and NetMHCIIpan. The bold cell means one error.

PDB ID
Allele
Peptide
Core
Ours
TEPITOPE
MultiRTA
NetMHCIIpan-2.0

1AQD
DRB10101
VGSDWRFLRGYHQYA
WRFLRGYHQ
WRFLRGYHQ
WRFLRGYHQ
WRFLRGYHQ
WRFLRGYHQ

1PYW
DRB10101
XFVKQNAAALX
FVKQNAAAL
FVKQNAAAL
FVKQNAAAL
FVKQNAAAL
FVKQNAAAL

1KLG
DRB10101
GELIGILNAAKVPAD
IGILNAAKV
IGILNAAKV
IGILNAAKV
IGILNAAKV
LIGILNAAK

2FSE
DRB10101
GELIGTLNAAKVPAD
IGTLNAAKV
IGTLNAAKV
IGTLNAAKV
IGTLNAAKV
IGTLNAAKV

1KLU
DRB10101
AGFKGEQGPKGEPG
FKGEQGPKG
FKGEQGPKG
FKGEQGPKG
FKGEQGPKG
FKGEQGPKG

1SJH
DRB10101
PEVIPMFSALSEG
VIPMFSALS
VIPMFSALS
VIPMFSALS
VIPMFSALS
VIPMFSALS

1SJE
DRB10101
PEVIPMFSALSEGATP
VIPMFSALS
VIPMFSALS
VIPMFSALS
VIPMFSALS
VIPMFSALS

1T5W
DRB10101
AAYSDQATPLLLSPR
YSDQATPLL
SDQATPLLL
YSDQATPLL
SDQATPLLL
YSDQATPLL

1T5X
DRB10101
AAYSDQATPLLLSPR
YSDQATPLL
SDQATPLLL
YSDQATPLL
SDQATPLLL
YSDQATPLL

2IAN
DRB10101
GELIGTLNAAKVPAD
IGTLNAAKV
IGTLNAAKV
IGTLNAAKV
IGTLNAAKV
IGTLNAAKV

2IPK
DRB10101
GELIGILNAAKVPAD
IGILNAAKV
IGILNAAKV
IGILNAAKV
IGILNAAKV
LIGILNAAK

1FYT
DRB10101
XPKWVKQNTLKLAT
WVKQNTLKL
WVKQNTLKL
WVKQNTLKL
WVKQNTLKL
WVKQNTLKL

1R5I
DRB10101
PKYVKQNTLKLAT
YVKQNTLKL
YVKQNTLKL
YVKQNTLKL
YVKQNTLKL
YVKQNTLKL

1HXY
DRB10101
PKYVKQNTLKLAT
YVKQNTLKL
YVKQNTLKL
YVKQNTLKL
YVKQNTLKL
YVKQNTLKL

1JWM
DRB10101
PKYVKQNTLKLAT
YVKQNTLKL
YVKQNTLKL
YVKQNTLKL
YVKQNTLKL
YVKQNTLKL

1JWS
DRB10101
PKYVKQNTLKLAT
YVKQNTLKL
YVKQNTLKL
YVKQNTLKL
YVKQNTLKL
YVKQNTLKL

1JWU
DRB10101
PKYVKQNTLKLAT
YVKQNTLKL
YVKQNTLKL
YVKQNTLKL
YVKQNTLKL
YVKQNTLKL

1LO5
DRB10101
PKYVKQNTLKLAT
YVKQNTLKL
YVKQNTLKL
YVKQNTLKL
YVKQNTLKL
YVKQNTLKL

2ICW
DRB10101
PKYVKQNTLKLAT
YVKQNTLKL
YVKQNTLKL
YVKQNTLKL
YVKQNTLKL
YVKQNTLKL

2OJE
DRB10101
PKYVKQNTLKLAT
YVKQNTLKL
YVKQNTLKL
YVKQNTLKL
YVKQNTLKL
YVKQNTLKL

2G9H
DRB10101
PKYVKQNTLKLAT
YVKQNTLKL
YVKQNTLKL
YVKQNTLKL
YVKQNTLKL
YVKQNTLKL

2IAM
DRB10101
PKYVKQNTLKLAT
YVKQNTLKL
YVKQNTLKL
YVKQNTLKL
YVKQNTLKL
YVKQNTLKL

1A6A
DRB10301
PVSKMRMATPLLMQA
MRMATPLLM
MRMATPLLM
MRMATPLLM
MRMATPLLM
MRMATPLLM

1J8H
DRB10401
PKYVKQNTLKLAT
YVKQNTLKL
YVKQNTLKL
YVKQNTLKL
YVKQNTLKL
YVKQNTLKL

2SEB
DRB10401
AYMRADAAAGGA
MRADAAAGG
MRADAAAGG
MRADAAAGG
MRADAAAGG
YMRADAAAG

1BX2
DRB11501
ENPVVHFFKNIVTPR
VHFFKNIVT
VHFFKNIVT
VHFFKNIVT
VHFFKNIVT
VVHFFKNIV

1YMM
DRB11501
ENPVVHFFKNIVTPRGGSGGGGG
VHFFKNIVT
VHFFKNIVT
VHFFKNIVT
VHFFKNIVT
VHFFKNIVT

1FV1
DRB50101
NPVVHFFKNIVTPRTPPPSQ
FKNIVTPRT
KNIVTPRTP
FKNIVTPRT
VHFFKNIVT
FFKNIVTPR

1H15
DRB50101
GGVYHFVKKHVHES
YHFVKKHVH
YHFVKKHVH
YHFVKKHVH
YHFVKKHVH
YHFVKKHVH

1ZGL
DRB50101
VHFFKNIVTPRTPGG
FKNIVTPRT
KNIVTPRTP
FKNIVTPRT
VHFFKNIVT
FFKNIVTPR

Results

4 errors
0 errors
4 errors
6 errors

TEPITOPE is a relatively early method and is one of the most popular methods for predicting MHC II binding molecules. The basic idea is that if two HLA-DR alleles have the same pseudorandom sequence in the same pocket, they share the same number of profiles. Through multiple instances, MHCIIMulti has predicted over 500 HLA-DR molecules. NetMHCIIpan firstly converts each of the DRB alleles into a pseudorandom sequence of 21 amino acids, then uses the SMM-align method to identify binding residues in the peptide chain and the core side, and finally uses artificial neural network to train the model. MultiRTA makes prediction on HLA-DR and HLA-DP molecules. By thermodynamic method, it calculates a peptide chain and all other residues to predict the average binding affinity of binding strength and the introduction of standardization constraints to avoid overfitting. MULTIPRED2 can predict 1077 HLA-I and HLA-II genes and 26 HLA supertypes. Details are as shown in Figure 3. Our method obtains 4 errors; however, TEPITOPE, MultiRTA, NetMHCIIpan-2.0, and NetMHCIIpan-1.0 get the numbers of errors as 0, 4, 6, and 3, respectively. Because now we only find five MHC II molecules with three-dimensional structural information, we use the scoring matrix with only 5 MHC II molecules. If the three-dimensional structural information of MHC II molecules can be extended to all of the 11 MHC II molecules, our predictions will be more accurate. From the current view, our approach has reached a higher level of prediction.

Figure 3: Comparison of different methods by sequence logos of peptides on .

3.3. Other Prediction Results

When compared with other methods on the above experiments, we only use 30 of 39 MHC molecules and peptide complexes as test set. In this section, we test on the remaining nine MHC molecules. In this experiment, we choose and set the parameter α = 3. As seen in Table 5, eight of nine predictions are accurate. Therefore, our approach produces a considerably great performance.

Table 5: Other prediction results of nine MHC molecules. This table shows the prediction result of our method on 9 MHC molecules. The 5th column is the result. There is only one error result, which is shown using bold font.

PDB ID
Allele
Peptide
Core
Ours

4E41
DRB10101
GELIGILNAAKVPAD
IGILNAAKV
IGILNAAKV

1DLH
DRB10101
PKYVKQNTLKLAT
YVKQNTLKL
YVKQNTLKL

1KG0
DRB10101
PKYVKQNTLKLAT
YVKQNTLKL
YVKQNTLKL

3L6F
DRB10101
APPAYEKLSAEQSPP
YEKLSAEQS
YEKLSAEQS

3PDO
DRB10101
KPVSKMRMATPLLMQALPM
MRMATPLLM
KMRMATPLL

3PGD
DRB10101
KMRMATPLLMQALPM
MRMATPLLM
MRMATPLLM

3S4S
DRB10101
PKYVKQNTLKLAT
YVKQNTLKL
YVKQNTLKL

3S5L
DRB10101
PKYVKQNTLKLAT
YVKQNTLKL
YVKQNTLKL

1HQR
DRB50101
VHFFKNIVTPRTP
FKNIVTPRT
FKNIVTPRT

Results

1 error

4. Conclusion

In this paper, we try to solve the problem of predicting MHC II binding peptides with a novel metric and strategy. Sequence similarity and structural similarity between different MHC molecules are calculated to reorder pseudosequences according to descending similarity, and then a weight calculation formula is used to calculate new pocket profiles. Finally, we use three scoring functions to predict binding cores and evaluate the accuracy of prediction to judge performance of each scoring function. In the experiment, we set a parameter in the weight formula. By changing value, we can observe different performances of each scoring function. Then, we compare our method with the best function to some popular prediction methods and ultimately find that our method outperforms them in identifying binding cores of HLA-DR molecules.

Competing Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgments

This work is supported by a grant from the National Science Foundation of China (NSFC 61402326) and Peiyang Scholar Program of Tianjin University (no. 2016XRG-0009).

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