阅读说明：本技术 一种基于hla分型与结构的肿瘤新抗原的筛选方法 (Screening method of tumor neoantigen based on HLA typing and structure ) 是由 张崇骞 赵永浩 马赛 闫成海 张晓霞 J·彭 D·张 于 2019-01-16 设计创作，主要内容包括：本发明提供了一种基于HLA分型与结构的肿瘤新抗原的筛选方法,包括：A、获取肿瘤组织细胞的突变基因所对应编码的多肽序列,并将其作为潜在抗原的多肽集合；B、获取HLA分型在黄种人群中的频率超过指定阈值的HLA分型集合；并将多肽集合与所述HLA分型集合进行亲和力预测,选出亲和力超过指定阈值的多肽序列；C、将HLA分型集合中的HLA进行三维结构建模；以及将多肽序列进行三维结构建模；D、将HLA作为受体,将多肽序列作为配体进行分子对接；E、将打分超过指定阈值时对应的多肽序列作为肿瘤新抗原的候选多肽序列。由上,通过本申请的筛选方法,方便后续进一步的据此进行针对性的实验,可以大大的减少实验的次数,实现省时、省力且节约经费。(The invention provides a screening method of a tumor neoantigen based on HLA typing and structure, which comprises the following steps: A. acquiring a polypeptide sequence correspondingly coded by a mutant gene of a tumor tissue cell, and taking the polypeptide sequence as a polypeptide set of potential antigens; B. obtaining an HLA typing set of which the frequency of HLA typing in the yellow population exceeds a specified threshold; performing affinity prediction on the polypeptide set and the HLA typing set, and selecting a polypeptide sequence with the affinity exceeding a specified threshold; C. carrying out three-dimensional structure modeling on the HLA in the HLA typing set; and performing three-dimensional structural modeling on the polypeptide sequence; D. taking HLA as a receptor and taking a polypeptide sequence as a ligand to carry out molecular docking; E. and taking the corresponding polypeptide sequence when the score exceeds a specified threshold value as a candidate polypeptide sequence of the tumor neoantigen. Therefore, the screening method of the application facilitates subsequent further targeted experiments, can greatly reduce the times of the experiments, and realizes time saving, labor saving and cost saving.)

1. A screening method of tumor neoantigens based on HLA typing and structure is characterized by comprising the following steps:

A. obtaining each polypeptide sequence correspondingly coded by the mutant gene of the tumor tissue cell, and taking the polypeptide sequence as a polypeptide set of potential antigens;

B. acquiring the frequency of each HLA type in the yellow population and acquiring an HLA type set with the frequency exceeding a specified threshold value according to the frequency; respectively carrying out affinity prediction on the polypeptide sequences in the polypeptide set and HLA types in the HLA type setting, and screening out the polypeptide sequences with the affinity exceeding a specified threshold value;

C. respectively carrying out three-dimensional structure modeling on each HLA type in the HLA type set; and respectively carrying out three-dimensional structure modeling on the polypeptide sequences with the affinity exceeding a specified threshold;

D. taking the HLA typed three-dimensional structure model as a receptor and taking the three-dimensional structure model of the polypeptide sequence as a ligand to carry out molecular docking;

E. and (3) taking the corresponding polypeptide sequence when the score of the molecular docking exceeds a specified threshold as a candidate polypeptide sequence of the tumor neoantigen.

2. The method of claim 1, wherein step C further comprises:

and performing energy optimization on the HLA typed three-dimensional structure model after the three-dimensional structure modeling.

3. The method of claim 2, wherein the energy terms for energy optimization of the three-dimensional structure model for HLA typing include at least one of, but are not limited to:

the combined side chain interaction energy, the average free energy of the hydrophobic/hydrophilic interaction between the side chains, the combined side chain and the combined peptide group interaction energy, the combined peptide group electrostatic interaction energy, the virtual bond dihedral angle torsion energy, the virtual bond angle variation energy and the side chain rotation energy.

4. The method of claim 3, wherein the energy-optimized functional expression of the three-dimensional structure model for HLA typing is as follows:

wherein U represents a total virtual bond energy; i represents the ith alpha carbon atom, side chain or peptide group; j represents the jth alpha carbon atom, side chain or peptide group;

5. The method of claim 1, wherein step C further comprises:

performing model optimization on a three-dimensional structure model of the polypeptide sequence after the three-dimensional structure modeling;

wherein, the model optimization of the three-dimensional structure model of the polypeptide sequence comprises the following steps: hydrogenation, removal of water molecules, charge optimization and energy optimization.

6. The method of claim 1, wherein said tumor neoantigen of step E further comprises: the tumor tissue, the tumor tissue related protein, the mutant DNA sequence or the mutant RNA sequence of the tumor tissue cell.

7. The method of claim 1, wherein after step E, further comprising:

I. simulating by molecular dynamics the interaction and motor changes between the candidate polypeptide sequence and HLA typing; and analyzing the sequence composition of the binding part of the candidate polypeptide sequence and the HLA typing according to the sequence composition.

8. The method of claim 7, wherein after step I, further comprising:

J. when a mutant amino acid is judged to be present in the sequence composition of the binding site, and the mutant amino acid is judged to be tightly bound to HLA typing; and taking the candidate polypeptide sequence as the sequence of the screened tumor neoantigen.

9. The method of claim 1, wherein step a comprises:

a1, extracting DNA of tumor tissue cells, and carrying out DNA sequencing on the tumor tissue cells;

a2, comparing the sequenced DNA sequence with the DNA sequence of the normal wild tissue cell to obtain a mutant DNA sequence;

a3, obtaining the polypeptide sequence correspondingly coded by the mutated DNA sequence through biological software.

10. The method of claim 1, wherein the sequence of step a is: polypeptide sequences containing at least 8-30 amino acid residues or mRNA or DNA sequences encoding 8-30 amino acid residues.

Technical Field

The invention relates to the field of antigen screening, in particular to a screening method of a tumor neoantigen based on HLA typing and structure.

Background

Tumor vaccines (tumor vaccines) are one of the hot spots in recent years of research, whose principle takes tumor antigens in various forms such as: tumor cells, tumor-related proteins or polypeptides, genes for expressing tumor antigens, and the like are introduced into a patient body, so that the immunosuppression state caused by tumors is overcome, the immunogenicity is enhanced, the immune system of the patient is activated, and the cellular immunity and humoral immunity response of the organism are induced, thereby achieving the purpose of controlling or eliminating the tumors. In 4 months 2010, the Food and Drug Administration (FDA) approved Provenge/sipuleucel-T for treating advanced prostate cancer, making it the first autologous active immunotherapy drug and the first true therapeutic cancer vaccine, paving the way for the development of other similar products (1, 2).

In 2017, 2 technical teams have achieved favorable results in the personalized tumor vaccine clinical trial based on the NGS, and the clinical trial results of the U.S. team: of 6 melanoma patients vaccinated with the vaccine, 4 tumors completely disappeared and no recurrence within 32 months, and 2 tumors remained and completely disappeared after receiving adjuvant therapy; results of clinical trials on the german team: of the 13 vaccinated patients, 8 had completely disappeared tumors and no recurrence within 23 months, and the remaining 5 patients had 2 had developed tumor shrinkage due to the tumor spread at the time of vaccination, of which 1 had completely regressed 1,2 after receiving adjuvant therapy. The technology or the treatment method utilizes the individualized tumor neogenesis antigen to regulate or activate the immune system to kill the tumor, and is combined with other tumor treatment methods in principle to possibly change the tumor into the chronic disease, and the future market potential is huge if large-scale clinical verification is successful (3, 4).

However, the research and development of tumor vaccines are carried out one by one through experimental means, the process is time-consuming, labor-consuming and expensive, and suitable tumor vaccines (tumor antigens) are not easy to find, so that a method for screening tumor neoantigens is urgently needed at present, and suitable tumor vaccines are obtained through screening tumor neoantigens, so that the targeted experiments can be carried out conveniently in the future, the times of the experiments are greatly reduced, and time, labor and cost are saved.

Disclosure of Invention

In view of the above, the main objective of the present invention is to provide a screening method of tumor neoantigens based on HLA typing and structure, which facilitates further subsequent experiments with pertinence, greatly reduces the number of experiments, and realizes time saving, labor saving and cost saving.

The application provides a screening method of a tumor neoantigen based on HLA typing and structure, which comprises the following steps:

A. obtaining each polypeptide sequence correspondingly coded by the mutant gene of the tumor tissue cell, and taking the polypeptide sequence as a polypeptide set of potential antigens;

D. acquiring the frequency of each HLA type in the yellow population and acquiring an HLA type set with the frequency exceeding a specified threshold value according to the frequency; respectively carrying out affinity prediction on the polypeptide sequences in the polypeptide set and HLA types in the HLA type setting, and screening out the polypeptide sequences with the affinity exceeding a specified threshold value;

E. respectively carrying out three-dimensional structure modeling on each HLA type in the HLA type set; and respectively carrying out three-dimensional structure modeling on the polypeptide sequences with the affinity exceeding a specified threshold;

D. taking an HLA typed three-dimensional structure model as a receptor and taking the three-dimensional structure model of the polypeptide sequence as a ligand to carry out molecular docking;

E. and (3) taking the corresponding polypeptide sequence when the score of the molecular docking exceeds a specified threshold as a candidate polypeptide sequence of the tumor neoantigen.

In view of the above, the screening method of the tumor neoantigen provided by the present application obtains the candidate polypeptide sequence as the tumor neoantigen by obtaining the polypeptide set of the potential antigen, obtaining the high-frequency HLA typing set according to the frequency of HLA typing in the yellow population, predicting the affinity of the two sets, and performing molecular docking scoring. The subsequent targeted experiment is convenient to be further carried out according to the candidate polypeptide sequence, the times of the experiment can be greatly reduced, and time, labor and cost are saved.

Preferably, the step C further includes:

and performing energy optimization on the HLA typed three-dimensional structure model after the three-dimensional structure modeling.

From the above, the three-dimensional structure model of HLA typing after the three-dimensional structure modeling is energy-optimized so that it is more advantageous to interface with the three-dimensional structure of a polypeptide sequence.

Preferably, the energy term for energy optimization of the three-dimensional structure model for HLA typing includes at least one of, but is not limited to:

the combined side chain interaction energy, the average free energy of the hydrophobic/hydrophilic interaction between the side chains, the combined side chain and the combined peptide group interaction energy, the combined peptide group electrostatic interaction energy, the virtual bond dihedral angle torsion energy, the virtual bond angle variation energy and the side chain rotation energy.

Therefore, by optimizing the energy items, the three-dimensional structure model for HLA typing of the application can be more beneficial to docking with the three-dimensional structure of the polypeptide sequence.

Preferably, the function expression of the energy optimization of the HLA typing three-dimensional structure model is as follows:

wherein U represents a total virtual bond energy; i represents the ith alpha carbon atom, side chain or peptide group; j represents the jth alpha carbon atom, side chain or peptide group;

represents the mean free energy of hydrophobic interactions between the side chains; sc (sc)_iRepresents the ith associated side chain; sc (sc)_jRepresents the jth linking side chain;represents the interaction potential between the side chain and the peptide group; p is a radical of_jRepresents the jth peptide group; omega_elRepresenting a weight of the electrostatic energy term;

represents the electrostatic interaction energy of peptide groups pi and pj; p is a radical of_iRepresents the ith peptide group; omega_torRepresenting the weight of the dihedral angle torsion energy of the virtual key;

represents the torsional energy of the ith dihedral angle; r is_iRepresents the ith dihedral angle; omega_locRepresenting a bending energy weight; u shape_b(θ_i) Represents the bending energy of the ith virtual key angle; theta_iRepresents the ith imaginary key angle;

represents the rotational isomeric energy of the ith side chain and the jth side chain;

represents the ith side chain angle α; beta is a_scjRepresents the jth side chain angle β; omega_corrRepresenting a relevance weight of each energy item; u shape_corrRepresenting the relevance of each energy term.

Preferably, the step C further includes:

performing model optimization on a three-dimensional structure model of the polypeptide sequence after the three-dimensional structure modeling;

wherein the model optimization comprises: hydrogenation, removal of water molecules, charge optimization and energy optimization.

Therefore, the three-dimensional structure model of the HLA type after the three-dimensional structure modeling is optimized so as to be more beneficial to docking with the three-dimensional structure of the polypeptide sequence, the energy optimization of the polypeptide uses an empirical function, and the energy items mainly comprise bond length, bond angle, torsion and other related energy items, which are not described again.

Preferably, the tumor neoantigen of step E further comprises: the tumor tissue, the tumor tissue related protein, the mutant DNA sequence or the mutant RNA sequence of the tumor tissue cell.

Preferably, after the step E, the method further comprises:

I. simulating by molecular dynamics the interaction and motor changes between the candidate polypeptide sequence and HLA typing; and analyzing the sequence composition of the binding part of the candidate polypeptide sequence and the HLA typing according to the sequence composition.

From the above, the molecular dynamics simulation is to simulate the interaction and movement change of macromolecules and polypeptides according to the basic principle of Newton mechanics, so as to explore the rules behind the life phenomena which cannot be solved by experimental means. The interaction rule and the movement change between the HLA typing and the polypeptide are discussed by a molecular dynamics simulation means, the interaction and the affinity between the polypeptide and the HLA in a stable state can be visually shown, and whether the polypeptide can be stably combined with the HLA can be accurately predicted.

Preferably, after the step I, the method further comprises:

J. when a mutant amino acid is judged to be present in the sequence composition of the binding site, and the mutant amino acid is judged to be tightly bound to HLA typing; and taking the candidate polypeptide sequence as the sequence of the screened tumor neoantigen.

From the above, it was demonstrated that the polypeptide stably binds to MHCI, and it is the generation of this mutated amino acid that makes it possible to use the candidate polypeptide sequence as a sequence for a new tumor antigen to be screened.

Preferably, the step a includes:

A. extracting DNA of tumor tissue cells, and performing DNA sequencing on the tumor tissue cells;

B. comparing the sequenced DNA sequence with the normal DNA sequence of the tissue cell to obtain a mutant DNA sequence;

C. and acquiring a polypeptide sequence correspondingly coded by the mutant DNA sequence through biological software according to the mutant DNA sequence, and taking the polypeptide sequence as the target.

Thus, the polypeptide sequence encoded by the mutant gene of the tumor tissue cell can be obtained through the steps.

Preferably, in step a, the polypeptide sequence is: polypeptide sequences containing 8-30 amino acid residues.

From the above, a polypeptide sequence having a length of 8 to 30 amino acid residues is preferred in affinity, and too long affects the affinity of the polypeptide sequence, while too short affects the efficacy of the polypeptide.

In summary, the screening method of the tumor neoantigen provided by the application obtains the polypeptide set of the potential antigen, obtains the high-frequency HLA typing set according to the frequency of HLA typing in the yellow population, predicts the affinity of the HLA typing set and the high-frequency HLA typing set, performs molecular docking scoring, and discusses the law of action and motion change between HLA typing and the polypeptide by a molecular dynamics simulation means to obtain the candidate polypeptide sequence as the tumor neoantigen. The subsequent targeted experiment is convenient to be further carried out according to the candidate polypeptide sequence, the times of the experiment can be greatly reduced, and time, labor and cost are saved.

Drawings

FIG. 1 is a flow chart of a method for screening tumor neoantigens based on HLA typing and structure according to the present embodiment;

FIG. 2 is a representation of 64 polypeptides and a template polypeptide in molecular alignment according to the embodiments of the present application;

fig. 3 is a schematic representation of the HLA-a x 0201 receptor docking pocket of an embodiment of the present application;

FIG. 4 is a schematic view of the butt-joint scoring of TOP10 strips according to the embodiment of the present application;

fig. 5 is a schematic representation of the docking of HLA-a0201 receptors with polypeptides according to embodiments of the present application;

FIG. 6 is a schematic representation of the interaction of a polypeptide ligand with a receptor "sink" according to the examples of the present application.

FIG. 7 is a graph showing the results of activity evaluation of the novel antigenic peptide of the examples of the present application.

Detailed Description

The present application will be described below with reference to the drawings in the embodiments of the present application.