TripHLApan: predicting HLA molecules binding peptides based on triple coding matrix and transfer learning.

Human leukocyte antigen (HLA) recognizes foreign threats and triggers immune responses by presenting peptides to T cells. Computationally modeling the binding patterns between peptide and HLA is very important for the development of tumor vaccines. However, it is still a big challenge to accurately predict HLA molecules binding peptides. In this paper, we develop a new model TripHLApan for predicting HLA molecules binding peptides by integrating triple coding matrix, BiGRU + Attention models, and transfer learning strategy. We have found the main interaction site regions between HLA molecules and peptides, as well as the correlation between HLA encoding and binding motifs. Based on the discovery, we make the preprocessing and coding closer to the natural biological process. Besides, due to the input being based on multiple types of features and the attention module focused on the BiGRU hidden layer, TripHLApan has learned more sequence level binding information. The application of transfer learning strategies ensures the accuracy of prediction results under special lengths (peptides in length 8) and model scalability with the data explosion. Compared with the current optimal models, TripHLApan exhibits strong predictive performance in various prediction environments with different positive and negative sample ratios. In addition, we validate the superiority and scalability of TripHLApan's predictive performance using additional latest data sets, ablation experiments and binding reconstitution ability in the samples of a melanoma patient. The results show that TripHLApan is a powerful tool for predicting the binding of HLA-I and HLA-II molecular peptides for the synthesis of tumor vaccines. TripHLApan is publicly available at https://github.com/CSUBioGroup/TripHLApan.git.

An integrated methodology for quality assessment of therapeutic antibodies with potential long circulation half-life in harvested cell culture fluid using FcRn immobilized hydrophilic magnetic graphene.

Designing modified therapeutic antibodies with enhanced FcRn-binding affinity holds promise in the extension of circulation half-lives and potential refinement of pharmacokinetics. During the development of these new-generation therapeutic antibodies, FcRn binding affinity of IgGs is emphasized and monitored as a critical quality attribute (CQA), alongside other critical assessments including titer and aggregation level. However, the traditional workflow for assessing the overall quality of expressed IgGs in harvested cell culture fluid (HCCF) is blamed to be cumbersome and time-consuming. This study presents an integrated methodology for the rapid quality assessment of IgGs in HCCF by selectively extracting IgGs with favorable high FcRn affinity for subsequent analysis using size exclusion chromatography (SEC). The approach utilizes innovative adsorbents known as FcRn immobilized hydrophilic magnetic graphene (MG@PDA@PAMAM-FcRn) in a magnetic solid-phase extraction (MSPE) process. To simulate the in vivo binding dynamics, MSPE binding and dissociation was performed at pH 6.0 and 7.4, respectively. The composite have demonstrated enhanced extraction efficiency and impurity removal ability in comparison to commercially available magnetic beads. The SEC monomer peak area value provides the output of this method, the ranking of which enabled the facile identification of superior HCCF samples with high overall quality of IgG. Optimization of MSPE parameters was performed, and the method was validated for specificity, precision, sensitivity, and accuracy. The proposed method exhibited an analytical time of 0.6 h, which is 7-22 times shortened in comparison to the conventional workflow.

MHCSeqNet2-improved peptide-class I MHC binding prediction for alleles with low data.

MOTIVATION: The binding of a peptide antigen to a Class I major histocompatibility complex (MHC) protein is part of a key process that lets the immune system recognize an infected cell or a cancer cell. This mechanism enabled the development of peptide-based vaccines that can activate the patient's immune response to treat cancers. Hence, the ability of accurately predict peptide-MHC binding is an essential component for prioritizing the best peptides for each patient. However, peptide-MHC binding experimental data for many MHC alleles are still lacking, which limited the accuracy of existing prediction models. RESULTS: In this study, we presented an improved version of MHCSeqNet that utilized sub-word-level peptide features, a 3D structure embedding for MHC alleles, and an expanded training dataset to achieve better generalizability on MHC alleles with small amounts of data. Visualization of MHC allele embeddings confirms that the model was able to group alleles with similar binding specificity, including those with no peptide ligand in the training dataset. Furthermore, an external evaluation suggests that MHCSeqNet2 can improve the prioritization of T cell epitopes for MHC alleles with small amount of training data. AVAILABILITY AND IMPLEMENTATION: The source code and installation instruction for MHCSeqNet2 are available at https://github.com/cmb-chula/MHCSeqNet2.

Accurate modeling of peptide-MHC structures with AlphaFold.

Major histocompatibility complex (MHC) proteins present peptides on the cell surface for T cell surveillance. Reliable in silico prediction of which peptides would be presented and which T cell receptors would recognize them is an important problem in structural immunology. Here, we introduce an AlphaFold-based pipeline for predicting the three-dimensional structures of peptide-MHC complexes for class I and class II MHC molecules. Our method demonstrates high accuracy, outperforming existing tools in class I modeling accuracy and class II peptide register prediction. We validate its performance and utility with new experimental data on a recently described cancer neoantigen/wild-type peptide pair and explore applications toward improving peptide-MHC binding prediction.

Functional studies of HLA and its role in SARS-CoV-2: Stimulating T cell response and vaccine development.

In the biological immune process, the major histocompatibility complex (MHC) plays an indispensable role in the expression of HLA molecules in the human body when viral infection activates the T-cell response to remove the virus. Since the first case of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in 2019, how to address and prevent SARS-CoV-2 has become a common problem facing all mankind. The T-cell immune response activated by MHC peptides is a way to construct a defense line and reduce the transmission and harm of the virus. Presentation of SARS-CoV-2 antigen is associated with different types of HLA phenotypes, and different HLA phenotypes induce different immune responses. The prediction of SARS-CoV-2 mutation information and the design of vaccines based on HLAs can effectively activate autoimmunity and cope with virus mutations, which can provide some references for the prevention and treatment of SARS-CoV-2.

Mucosal-associated invariant T cells for cancer immunotherapy.

Human mucosal-associated invariant T (MAIT) cells are characterized by their expression of an invariant TCR α chain Vα7.2-Jα33/Jα20/Jα12 paired with a restricted TCR ß chain. MAIT cells recognize microbial peptides presented by the highly conserved MHC class I-like molecule MR1 and bridge the innate and acquired immune systems to mediate augmented immune responses. Upon activation, MAIT cells rapidly proliferate, produce a variety of cytokines and cytotoxic molecules, and trigger efficient antitumor immunity. Administration of a representative MAIT cell ligand 5-OP-RU effectively activates MAIT cells and enhances their antitumor capacity. In this review, we introduce MAIT cell biology and their importance in antitumor immunity, summarize the current development of peripheral blood mononuclear cell-derived and stem cell-derived MAIT cell products for cancer treatment, and discuss the potential of genetic engineering of MAIT cells for off-the-shelf cancer immunotherapy.

An Fc variant with two mutations confers prolonged serum half-life and enhanced effector functions on IgG antibodies.

The pH-selective interaction between the immunoglobulin G (IgG) fragment crystallizable region (Fc region) and the neonatal Fc receptor (FcRn) is critical for prolonging the circulating half-lives of IgG molecules through intracellular trafficking and recycling. By using directed evolution, we successfully identified Fc mutations that improve the pH-dependent binding of human FcRn and prolong the serum persistence of a model IgG antibody and an Fc-fusion protein. Strikingly, trastuzumab-PFc29 and aflibercept-PFc29, a model therapeutic IgG antibody and an Fc-fusion protein, respectively, when combined with our engineered Fc (Q311R/M428L), both exhibited significantly higher serum half-lives in human FcRn transgenic mice than their counterparts with wild-type Fc. Moreover, in a cynomolgus monkey model, trastuzumab-PFc29 displayed a superior pharmacokinetic profile to that of both trastuzumab-YTE and trastuzumab-LS, which contain the well-validated serum half-life extension Fcs YTE (M252Y/S254T/T256E) and LS (M428L/N434S), respectively. Furthermore, the introduction of two identified mutations of PFc29 (Q311R/M428L) into the model antibodies enhanced both complement-dependent cytotoxicity and antibody-dependent cell-mediated cytotoxicity activity, which are triggered by the association between IgG Fc and Fc binding ligands and are critical for clearing cancer cells. In addition, the effector functions could be turned off by combining the two mutations of PFc29 with effector function-silencing mutations, but the antibodies maintained their excellent pH-dependent human FcRn binding profile. We expect our Fc variants to be an excellent tool for enhancing the pharmacokinetic profiles and potencies of various therapeutic antibodies and Fc-fusion proteins.

Fast peptide exchange on major histocompatibility complex class I molecules by acidic stabilization of a peptide-empty intermediate.

The cell biology and biochemistry of peptide exchange on major histocompatibility complex class I (MHC-I) proteins are of great interest in the study of immunodominance, which requires iterative optimization of peptide affinity, and cross-presentation of pathogen and tumor antigens, in which endogenous peptides are exchanged for exogenous ones. Even though several methods exist to catalyze peptide exchange on recombinant MHC-I proteins, the cellular conditions and mechanisms allowing for peptide exchange in vivo remain unclear. Here, we demonstrate that low pH, as present in endosomes, indeed triggers peptide exchange, and we dissect the individual steps of the exchange reaction. We find that low pH stabilizes the peptide-empty forms of MHC-I that occur as intermediates of the exchange reaction, and that is synergizes with dipeptides and with disulfide-mediated stabilization of MHC-I.

Improving MHC class I antigen-processing predictions using representation learning and cleavage site-specific kernels.

In this work, we propose a new deep-learning model, MHCrank, to predict the probability that a peptide will be processed for presentation by MHC class I molecules. We find that the performance of our model is significantly higher than that of two previously published baseline methods: MHCflurry and netMHCpan. This improvement arises from utilizing both cleavage site-specific kernels and learned embeddings for amino acids. By visualizing site-specific amino acid enrichment patterns, we observe that MHCrank's top-ranked peptides exhibit enrichments at biologically relevant positions and are consistent with previous work. Furthermore, the cosine similarity matrix derived from MHCrank's learned embeddings for amino acids correlates highly with physiochemical properties that have been experimentally demonstrated to be instrumental in determining a peptide's favorability for processing. Altogether, the results reported in this work indicate that MHCrank demonstrates strong performance compared with existing methods and could have vast applicability in aiding drug and vaccine development.

Crystal structures of N-myristoylated lipopeptide-bound HLA class I complexes indicate reorganization of B-pocket architecture upon ligand binding.

Rhesus monkeys have evolved MHC-encoded class I allomorphs such as Mamu-B∗098 that are capable of binding N-myristoylated short lipopeptides rather than conventional long peptides; however, it remains unknown whether such antigen-binding molecules exist in other species, including humans. We herein demonstrate that human leukocyte antigen (HLA)-A∗24:02 and HLA-C∗14:02 proteins, which are known to bind conventional long peptides, also have the potential to bind N-myristoylated short lipopeptides. These HLA class I molecules shared a serine at position 9 (Ser9) with Mamu-B∗098, in contrast to most MHC class I molecules that harbor a larger amino acid residue, such as tyrosine, at this position. High resolution X-ray crystallographic analyses of lipopeptide-bound HLA-A∗24:02 and HLA-C∗14:02 complexes indicated that Ser9 was at the bottom of the B pocket with its small hydroxymethyl side chain directed away from the B-pocket cavity, thereby contributing to the formation of a deep hydrophobic cavity suitable for accommodating the long-chain fatty acid moiety of lipopeptide ligands. Upon peptide binding, however, we found the hydrogen-bond network involving Ser9 was reorganized, and the remodeled B pocket was able to capture the second amino acid residue (P2) of peptide ligands. Apart from the B pocket, virtually no marked alterations were observed for the A and F pockets upon peptide and lipopeptide binding. Thus, we concluded that the structural flexibility of the large B pocket of HLA-A∗2402 and HLA-C∗1402 primarily accounted for their previously unrecognized capacity to bind such chemically distinct ligands as conventional peptides and N-myristoylated lipopeptides.

TAPBPR employs a ligand-independent docking mechanism to chaperone MR1 molecules.

Chaperones tapasin and transporter associated with antigen processing (TAP)-binding protein related (TAPBPR) associate with the major histocompatibility complex (MHC)-related protein 1 (MR1) to promote trafficking and cell surface expression. However, the binding mechanism and ligand dependency of MR1/chaperone interactions remain incompletely characterized. Here in vitro, biochemical and computational studies reveal that, unlike MHC-I, TAPBPR recognizes MR1 in a ligand-independent manner owing to the absence of major structural changes in the MR1 α2-1 helix between empty and ligand-loaded molecules. Structural characterization using paramagnetic nuclear magnetic resonance experiments combined with restrained molecular dynamics simulations reveals that TAPBPR engages conserved surfaces on MR1 to induce similar adaptations to those seen in MHC-I/TAPBPR co-crystal structures. Finally, nuclear magnetic resonance relaxation dispersion experiments using 19F-labeled diclofenac show that TAPBPR can affect the exchange kinetics of noncovalent metabolites with the MR1 groove, serving as a catalyst. Our results support a role of chaperones in stabilizing nascent MR1 molecules to enable loading of endogenous or exogenous cargo.

HLAB: learning the BiLSTM features from the ProtBert-encoded proteins for the class I HLA-peptide binding prediction.

Human Leukocyte Antigen (HLA) is a type of molecule residing on the surfaces of most human cells and exerts an essential role in the immune system responding to the invasive items. The T cell antigen receptors may recognize the HLA-peptide complexes on the surfaces of cancer cells and destroy these cancer cells through toxic T lymphocytes. The computational determination of HLA-binding peptides will facilitate the rapid development of cancer immunotherapies. This study hypothesized that the natural language processing-encoded peptide features may be further enriched by another deep neural network. The hypothesis was tested with the Bi-directional Long Short-Term Memory-extracted features from the pretrained Protein Bidirectional Encoder Representations from Transformers-encoded features of the class I HLA (HLA-I)-binding peptides. The experimental data showed that our proposed HLAB feature engineering algorithm outperformed the existing ones in detecting the HLA-I-binding peptides. The extensive evaluation data show that the proposed HLAB algorithm outperforms all the seven existing studies on predicting the peptides binding to the HLA-A*01:01 allele in AUC and achieves the best average AUC values on the six out of the seven k-mers (k=8,9,...,14, respectively represent the prediction task of a polypeptide consisting of k amino acids) except for the 9-mer prediction tasks. The source code and the fine-tuned feature extraction models are available at http://www.healthinformaticslab.org/supp/resources.php.

Dynamics of peptide loading into major histocompatibility complex class I molecules chaperoned by TAPBPR.

Major histocompatibility complex class I (MHC-I) molecules display antigenic peptides on the cell surface for T cell receptor scanning, thereby activating the immune response. Peptide loading into MHC-I molecules is thus a critical step during the antigen presentation process. Chaperone TAP-binding protein related (TAPBPR) plays a critical role in promoting high-affinity peptide loading into MHC-I, by discriminating against the low-affinity ones. However, the complete peptide loading dynamics into TAPBPR-bound MHC-I is still elusive. Here, we constructed kinetic network models based on hundreds of short-time MD simulations with an aggregated simulation time of ∼21.7 µs, and revealed, at atomic level, four key intermediate states of one antigenic peptide derived from melanoma-associated MART-1/Melan-A protein during its loading process into TAPBPR-bound MHC-I. We find that the TAPBPR binding at the MHC-I pocket-F can substantially reshape the distant pocket-B via allosteric regulations, which in turn promotes the following peptide N-terminal loading. Intriguingly, the partially loaded peptide could profoundly weaken the TAPBPR-MHC stability, promoting the dissociation of the TAPBPR scoop-loop (SL) region from the pocket-F to a more solvent-exposed conformation. Structural inspections further indicate that the peptide loading could remotely affect the SL binding site through both allosteric perturbations and direct contacts. In addition, another structural motif of TAPBPR, the jack hairpin region, was also found to participate in mediating the peptide editing. Our study sheds light on the detailed molecular mechanisms underlying the peptide loading process into TAPBPR-bound MHC-I and pinpoints the key structural factors responsible for dictating the peptide-loading dynamics.

Structural Prediction of Peptide-MHC Binding Modes.

The immune system is constantly protecting its host from the invasion of pathogens and the development of cancer cells. The specific CD8+ T-cell immune response against virus-infected cells and tumor cells is based on the T-cell receptor recognition of antigenic peptides bound to class I major histocompatibility complexes (MHC) at the surface of antigen presenting cells. Consequently, the peptide binding specificities of the highly polymorphic MHC have important implications for the design of vaccines, for the treatment of autoimmune diseases, and for personalized cancer immunotherapy. Evidence-based machine-learning approaches have been successfully used for the prediction of peptide binders and are currently being developed for the prediction of peptide immunogenicity. However, understanding and modeling the structural details of peptide/MHC binding is crucial for a better understanding of the molecular mechanisms triggering the immunological processes, estimating peptide/MHC affinity using universal physics-based approaches, and driving the design of novel peptide ligands. Unfortunately, due to the large diversity of MHC allotypes and possible peptides, the growing number of 3D structures of peptide/MHC (pMHC) complexes in the Protein Data Bank only covers a small fraction of the possibilities. Consequently, there is a growing need for rapid and efficient approaches to predict 3D structures of pMHC complexes. Here, we review the key characteristics of the 3D structure of pMHC complexes before listing databases and other sources of information on pMHC structures and MHC specificities. Finally, we discuss some of the most prominent pMHC docking software.

HLA3D: an integrated structure-based computational toolkit for immunotherapy.

MOTIVATION: The human major histocompatibility complex (MHC), also known as human leukocyte antigen (HLA), plays an important role in the adaptive immune system by presenting non-self-peptides to T cell receptors. The MHC region has been shown to be associated with a variety of diseases, including autoimmune diseases, organ transplantation and tumours. However, structural analytic tools of HLA are still sparse compared to the number of identified HLA alleles, which hinders the disclosure of its pathogenic mechanism. RESULT: To provide an integrative analysis of HLA, we first collected 1296 amino acid sequences, 256 protein data bank structures, 120 000 frequency data of HLA alleles in different populations, 73 000 publications and 39 000 disease-associated single nucleotide polymorphism sites, as well as 212 modelled HLA heterodimer structures. Then, we put forward two new strategies for building up a toolkit for transplantation and tumour immunotherapy, designing risk alignment pipeline and antigenic peptide prediction pipeline by integrating different resources and bioinformatic tools. By integrating 100 000 calculated HLA conformation difference and online tools, risk alignment pipeline provides users with the functions of structural alignment, sequence alignment, residue visualization and risk report generation of mismatched HLA molecules. For tumour antigen prediction, we first predicted 370 000 immunogenic peptides based on the affinity between peptides and MHC to generate the neoantigen catalogue for 11 common tumours. We then designed an antigenic peptide prediction pipeline to provide the functions of mutation prediction, peptide prediction, immunogenicity assessment and docking simulation. We also present a case study of hepatitis B virus mutations associated with liver cancer that demonstrates the high legitimacy of our antigenic peptide prediction process. HLA3D, including different HLA analytic tools and the prediction pipelines, is available at http://www.hla3d.cn/.

CRESSP: a comprehensive pipeline for prediction of immunopathogenic SARS-CoV-2 epitopes using structural properties of proteins.

The development of autoimmune diseases following SARS-CoV-2 infection, including multisystem inflammatory syndrome, has been reported, and several mechanisms have been suggested, including molecular mimicry. We developed a scalable, comparative immunoinformatics pipeline called cross-reactive-epitope-search-using-structural-properties-of-proteins (CRESSP) to identify cross-reactive epitopes between a collection of SARS-CoV-2 proteomes and the human proteome using the structural properties of the proteins. Overall, by searching 4 911 245 proteins from 196 352 SARS-CoV-2 genomes, we identified 133 and 648 human proteins harboring potential cross-reactive B-cell and CD8+ T-cell epitopes, respectively. To demonstrate the robustness of our pipeline, we predicted the cross-reactive epitopes of coronavirus spike proteins, which were recognized by known cross-neutralizing antibodies. Using single-cell expression data, we identified PARP14 as a potential target of intermolecular epitope spreading between the virus and human proteins. Finally, we developed a web application (https://ahs2202.github.io/3M/) to interactively visualize our results. We also made our pipeline available as an open-source CRESSP package (https://pypi.org/project/cressp/), which can analyze any two proteomes of interest to identify potentially cross-reactive epitopes between the proteomes. Overall, our immunoinformatic resources provide a foundation for the investigation of molecular mimicry in the pathogenesis of autoimmune and chronic inflammatory diseases following COVID-19.

Prothymosin Alpha: A Novel Contributor to Estradiol Receptor Alpha-Mediated CD8+ T-Cell Pathogenic Responses and Recognition of Type 1 Collagen in Rheumatic Heart Valve Disease.

BACKGROUND: Rheumatic heart valve disease (RHVD) is a leading cause of cardiovascular death in low- and middle-income countries and affects predominantly women. The underlying mechanisms of chronic valvular damage remain unexplored and regulators of sex predisposition are unknown. METHODS: Proteomics analysis of human heart valves (nondiseased aortic valves, nondiseased mitral valves [NDMVs], valves from patients with rheumatic aortic valve disease, and valves from patients with rheumatic mitral valve disease; n=30) followed by system biology analysis identified ProTα (prothymosin alpha) as a protein associated with RHVD. Histology, multiparameter flow cytometry, and enzyme-linked immunosorbent assay confirmed the expression of ProTα. In vitro experiments using peripheral mononuclear cells and valvular interstitial cells were performed using multiparameter flow cytometry and quantitative polymerase chain reaction. In silico analysis of the RHVD and Streptococcuspyogenes proteomes were used to identify mimic epitopes. RESULTS: A comparison of NDMV and nondiseased aortic valve proteomes established the baseline differences between nondiseased aortic and mitral valves. Thirteen unique proteins were enriched in NDMVs. Comparison of NDMVs versus valves from patients with rheumatic mitral valve disease and nondiseased aortic valves versus valves from patients with rheumatic aortic valve disease identified 213 proteins enriched in rheumatic valves. The expression of the 13 NDMV-enriched proteins was evaluated across the 213 proteins enriched in diseased valves, resulting in the discovery of ProTα common to valves from patients with rheumatic mitral valve disease and valves from patients with rheumatic aortic valve disease. ProTα plasma levels were significantly higher in patients with RHVD than in healthy individuals. Immunoreactive ProTα colocalized with CD8+ T cells in RHVD. Expression of ProTα and estrogen receptor alpha correlated strongly in circulating CD8+ T cells from patients with RHVD. Recombinant ProTα induced expression of the lytic proteins perforin and granzyme B by CD8+ T cells as well as higher estrogen receptor alpha expression. In addition, recombinant ProTα increased human leukocyte antigen class I levels in valvular interstitial cells. Treatment of CD8+ T cells with specific estrogen receptor alpha antagonist reduced the cytotoxic potential promoted by ProTα. In silico analysis of RHVD and Spyogenes proteomes revealed molecular mimicry between human type 1 collagen epitope and bacterial collagen-like protein, which induced CD8+ T-cell activation in vitro. CONCLUSIONS: ProTα-dependent CD8+ T-cell cytotoxicity was associated with estrogen receptor alpha activity, implicating ProTα as a potential regulator of sex predisposition in RHVD. ProTα facilitated recognition of type 1 collagen mimic epitopes by CD8+ T cells, suggesting mechanisms provoking autoimmunity.

ERAP2 Inhibition Induces Cell-Surface Presentation by MOLT-4 Leukemia Cancer Cells of Many Novel and Potentially Antigenic Peptides.

Recent studies have linked the activity of ER aminopeptidase 2 (ERAP2) to increased efficacy of immune-checkpoint inhibitor cancer immunotherapy, suggesting that pharmacological inhibition of ERAP2 could have important therapeutic implications. To explore the effects of ERAP2 inhibition on the immunopeptidome of cancer cells, we treated MOLT-4 T lymphoblast leukemia cells with a recently developed selective ERAP2 inhibitor, isolated Major Histocompatibility class I molecules (MHCI), and sequenced bound peptides by liquid chromatography tandem mass spectrometry. Inhibitor treatment induced significant shifts on the immunopeptidome so that more than 20% of detected peptides were either novel or significantly upregulated. Most of the inhibitor-induced peptides were 9mers and had sequence motifs and predicted affinity consistent with being optimal ligands for at least one of the MHCI alleles carried by MOLT-4 cells. Such inhibitor-induced peptides could serve as triggers for novel cytotoxic responses against cancer cells and synergize with the therapeutic effect of immune-checkpoint inhibitors.

Human papillomavirus 68 prevalence and genetic variability based on E6/E7 genes in Sichuan.

HPV68 is a common HR-HPV, its persistent infection is closely related with the occurrence of cervical cancer. In this study, 2939 (27.60%, 2939/10650) positive samples were detected, and 174 (5.92%, 174/2939) were HPV68. 150 HPV68 E6-E7 were successful sequenced, 4 non-synonymous mutations were detected in E6, and E7 were 12. N133S non-synonymous mutations of HPV 68 E6 and C67G, T68 A/M of HPV68 E7 are E6, E7 positive selection sites, they all located in the key domains and major motifs of E6/E7 protein, the above amino-acid substitutions changed the protein structure, disturbed the interaction with other protein or cellular factors and make a difference in epitopes affinity, may affect the pathogenicity and adaptability of HPV68 to the environment. The enrichment of HPV68 data is of great significance for understanding the inherent geographical and biological differences of HPV68 in China.