Deep learning of antibody epitopes using positional permutation vectors.

Background: The accurate computational prediction of B cell epitopes can vastly reduce the cost and time required for identifying potential epitope candidates for the design of vaccines and immunodiagnostics. However, current computational tools for B cell epitope prediction perform poorly and are not fit-for-purpose, and there remains enormous room for improvement and the need for superior prediction strategies. Results: Here we propose a novel approach that improves B cell epitope prediction by encoding epitopes as binary positional permutation vectors that represent the position and structural properties of the amino acids within a protein antigen sequence that interact with an antibody. This approach supersedes the traditional method of defining epitopes as scores per amino acid on a protein sequence, where each score reflects each amino acids predicted probability of partaking in a B cell epitope antibody interaction. In addition to defining epitopes as binary positional permutation vectors, the approach also uses the 3D macrostructure features of the unbound protein structures, and in turn uses these features to train another deep learning model on the corresponding antibody-bound protein 3D structures. This enables the algorithm to learn the key structural and physiochemical features of the unbound protein and embedded epitope that initiate the antibody binding process helping to eliminate "induced fit" biases in the training data. We demonstrate that the strategy predicts B cell epitopes with improved accuracy compared to the existing tools. Additionally, we show that this approach reliably identifies the majority of experimentally verified epitopes on the spike protein of SARS-CoV-2 not seen by the model during training and generalizes in a very robust manner on dissimilar data not seen by the model during training. Conclusions: With the approach described herein, a primary protein sequence and a query positional permutation vector encoding a putative epitope is sufficient to predict B cell epitopes in a reliable manner, potentially advancing the use of computational prediction of B cell epitopes in biomedical research applications.

B-Cell Epitope Prediction for Antipeptide Paratopes with the HAPTIC2/HEPTAD User Toolkit (HUT).

B-cell epitope prediction is key to developing peptide-based vaccines and immunodiagnostics along with antibodies for prophylactic, therapeutic and/or diagnostic use. This entails estimating paratope binding affinity for variable-length peptidic sequences subject to constraints on both paratope accessibility and antigen conformational flexibility, as described herein for the HAPTIC2/HEPTAD User Toolkit (HUT). HUT comprises the Heuristic Affinity Prediction Tool for Immune Complexes 2 (HAPTIC2), the HAPTIC2-like Epitope Prediction Tool for Antigen with Disulfide (HEPTAD) and the HAPTIC2/HEPTAD Input Preprocessor (HIP). HIP enables tagging of residues (e.g., in hydrophobic blobs, ordered regions and glycosylation motifs) for exclusion from downstream analyses by HAPTIC2 and HEPTAD. HAPTIC2 estimates paratope binding affinity for disulfide-free disordered peptidic antigens (by analogy between flexible-ligand docking and protein folding), from terms attributed to compaction (in view of sequence length, charge and temperature-dependent polyproline-II helical propensity), collapse (disfavored by residue bulkiness) and contact (with glycine and proline regarded as polar residues that hydrogen bond with paratopes). HEPTAD analyzes antigen sequences that each contain two cysteine residues for which the impact of disulfide pairing is estimated as a correction to the free-energy penalty of compaction. All of HUT is freely accessible online ( https://freeshell.de/~badong/hut.htm ).

WUREN: Whole-modal union representation for epitope prediction.

B-cell epitope identification plays a vital role in the development of vaccines, therapies, and diagnostic tools. Currently, molecular docking tools in B-cell epitope prediction are heavily influenced by empirical parameters and require significant computational resources, rendering a great challenge to meet large-scale prediction demands. When predicting epitopes from antigen-antibody complex, current artificial intelligence algorithms cannot accurately implement the prediction due to insufficient protein feature representations, indicating novel algorithm is desperately needed for efficient protein information extraction. In this paper, we introduce a multimodal model called WUREN (Whole-modal Union Representation for Epitope predictioN), which effectively combines sequence, graph, and structural features. It achieved AUC-PR scores of 0.213 and 0.193 on the solved structures and AlphaFold-generated structures, respectively, for the independent test proteins selected from DiscoTope3 benchmark. Our findings indicate that WUREN is an efficient feature extraction model for protein complexes, with the generalizable application potential in the development of protein-based drugs. Moreover, the streamlined framework of WUREN could be readily extended to model similar biomolecules, such as nucleic acids, carbohydrates, and lipids.

DiscoTope-3.0: improved B-cell epitope prediction using inverse folding latent representations.

Accurate computational identification of B-cell epitopes is crucial for the development of vaccines, therapies, and diagnostic tools. However, current structure-based prediction methods face limitations due to the dependency on experimentally solved structures. Here, we introduce DiscoTope-3.0, a markedly improved B-cell epitope prediction tool that innovatively employs inverse folding structure representations and a positive-unlabelled learning strategy, and is adapted for both solved and predicted structures. Our tool demonstrates a considerable improvement in performance over existing methods, accurately predicting linear and conformational epitopes across multiple independent datasets. Most notably, DiscoTope-3.0 maintains high predictive performance across solved, relaxed and predicted structures, alleviating the need for experimental structures and extending the general applicability of accurate B-cell epitope prediction by 3 orders of magnitude. DiscoTope-3.0 is made widely accessible on two web servers, processing over 100 structures per submission, and as a downloadable package. In addition, the servers interface with RCSB and AlphaFoldDB, facilitating large-scale prediction across over 200 million cataloged proteins. DiscoTope-3.0 is available at: https://services.healthtech.dtu.dk/service.php?DiscoTope-3.0.

Multi-perspectives and challenges in identifying B-cell epitopes.

The identification of B-cell epitopes (BCEs) in antigens is a crucial step in developing recombinant vaccines or immunotherapies for various diseases. Over the past four decades, numerous in silico methods have been developed for predicting BCEs. However, existing reviews have only covered specific aspects, such as the progress in predicting conformational or linear BCEs. Therefore, in this paper, we have undertaken a systematic approach to provide a comprehensive review covering all aspects associated with the identification of BCEs. First, we have covered the experimental techniques developed over the years for identifying linear and conformational epitopes, including the limitations and challenges associated with these techniques. Second, we have briefly described the historical perspectives and resources that maintain experimentally validated information on BCEs. Third, we have extensively reviewed the computational methods developed for predicting conformational BCEs from the structure of the antigen, as well as the methods for predicting conformational epitopes from the sequence. Fourth, we have systematically reviewed the in silico methods developed in the last four decades for predicting linear or continuous BCEs. Finally, we have discussed the overall challenge of identifying continuous or conformational BCEs. In this review, we only listed major computational resources; a complete list with the URL is available from the BCinfo website (https://webs.iiitd.edu.in/raghava/bcinfo/).

Construction, Expression, and Evaluation of the Naturally Acquired Humoral Immune Response against Plasmodium vivax RMC-1, a Multistage Chimeric Protein.

The PvCelTOS, PvCyRPA, and Pvs25 proteins play important roles during the three stages of the P. vivax lifecycle. In this study, we designed and expressed a P. vivax recombinant modular chimeric protein (PvRMC-1) composed of the main antigenic regions of these vaccine candidates. After structure modelling by prediction, the chimeric protein was expressed, and the antigenicity was assessed by IgM and IgG (total and subclass) ELISA in 301 naturally exposed individuals from the Brazilian Amazon. The recombinant protein was recognized by IgG (54%) and IgM (40%) antibodies in the studied individuals, confirming the natural immunogenicity of the epitopes that composed PvRMC-1 as its maintenance in the chimeric structure. Among responders, a predominant cytophilic response mediated by IgG1 (70%) and IgG3 (69%) was observed. IgM levels were inversely correlated with age and time of residence in endemic areas (p < 0.01). By contrast, the IgG and IgM reactivity indexes were positively correlated with each other, and both were inversely correlated with the time of the last malaria episode. Conclusions: The study demonstrates that PvRMC-1 was successfully expressed and targeted by natural antibodies, providing important insights into the construction of a multistage chimeric recombinant protein and the use of naturally acquired antibodies to validate the construction.

Design of Linear B Cell Epitopes and Evaluation of Their Antigenicity, Allergenicity, and Toxicity: An Immunoinformatics Approach.

Immunoinformatics is a modern branch of science formed as a result of the intersection between immunology and computer science. One of the important steps in the design of multi-epitope vaccines is the prediction of B cell epitopes. B cell epitopes are of two types, linear and discontinuous. Linear epitope residues lie next to each other in the primary structure of a protein. The amino acids that constitute discontinuous epitopes lie close to each other in the three-dimensional structure of the protein. Recognition of B cell epitopes by antibodies on an antigen constitutes an important event in the immune responses toward the antigenic challenge and also forms the basis for several immunological applications. Prediction of B cell epitopes in an antigen constitutes one of the important steps in the design of multi-epitope-based vaccines. This chapter explains the prediction of linear B cell epitopes in an antigen as well as their allergenicity, antigenicity, and toxicity by using online tools.

Production of recombinant proteins including the B-cell epitopes of autolysin A of Staphylococcus aureus isolated from clinical sheep mastitis and their potential for vaccine development.

Staphylococcus aureus is the most common clinical mastitis-associated pathogen in sheep which contributes to reduced welfare of affected animals and, therefore, compromises the quality and quantity of milk production. To prevent mastitis and its spread, it is essential to guarantee adequate breeding conditions and animal health, through the adoption of good farm management practices and the application of suitable biosecurity measures. Vaccination can play a strategic role in prevention, control, and eradication of diseases. The identification of secreted and cellular antigens of the predominant sheep-CC130/ST700/t1773 lineage would assist in the design of effective vaccine against mammary infections caused by S. aureus. In the current study, we carried out a 3D structural prediction analysis with the identification of the best B cell epitopes of the whole and secreted portion of S. aureus AtlA. Fragments of atlA, containing the main predicted epitopes, were amplified, cloned, and expressed in Escherichia coli for recombinant protein production. Two selected clones produced recombinant proteins (rAtl4 and rAtl8) showing strong reactivity with a hyperimmune serum against the native AtlA and with blood sera collected from sheep with clinical S. aureus mastitis. These may represent potential candidate protein-based vaccines able to elicit a protective immune response to be evaluated by vaccination and subsequent challenge of the vaccinated sheep.

Novel insights into the somatic proteome of Strongyloides stercoralis infective third-stage larvae.

BACKGROUND: Strongyloidiasis is a neglected tropical disease affecting an estimated 600 million people, particularly in resource-limited settings. The infection can persist lifelong due to unusual auto-infective cycle of Strongyloides stercoralis. The lack of a diagnostic gold standard and limited knowledge of the mechanisms underpinning this chronic infection are key issues in disease management. To date, only a few proteomics studies have been conducted to elucidate the molecular mechanisms associated with Strongyloides parasitism or to highlight novel immunological markers, with the result that our knowledge of S. stercoralis proteome remains limited. This study aims at expanding the characterization of S. stercoralis infective larvae (iL3) in order to further explore the mechanisms of parasitism and to highlight possible novel targets for serodiagnosis. METHODS: iL3 obtained from an infected subject were analysed by high-throughput tandem mass spectrometry. To achieve a more comprehensive characterization of the iL3 proteome we analysed the experimental dataset using an automatic search strategy combined with manual annotation, which included gene ontology (GO) analysis, InterPro annotation, assessment of the homology with Homo sapiens and other pathogens of clinical importance and B-cell epitope prediction. RESULTS: Our pipeline identified 430 S. stercoralis proteins, 187 (43%) of which were uncharacterized. Oxidoreductases and peptidases were amongst the most represented protein categories, as highlighted by molecular function GO analyses, while membrane and mitochondrial proteins were the most represented cellular component GO categories. A high proportion of proteins bearing the CAP, SCP or thioredoxin domain or belonging to cysteine-rich secretory, transthyretin-like or peptidase protein families were also identified. Additionally, we highlighted nine proteins displaying low homology with H. sapiens or other related pathogens and bearing amino acid sequences with immunogenic properties. CONCLUSIONS: Our comprehensive description and annotation of the S. stercoralis iL3 proteome contribute to expanding the 'omics characterization of this parasite and provide experimental evidence on the most represented proteins associated with S. stercoralis parasitism, as inferred from genomic and transcriptomic data. Moreover, novel candidate immunogenic proteins to be evaluated as novel serological diagnostic markers are highlighted.

Critical review of conformational B-cell epitope prediction methods.

Accurate in silico prediction of conformational B-cell epitopes would lead to major improvements in disease diagnostics, drug design and vaccine development. A variety of computational methods, mainly based on machine learning approaches, have been developed in the last decades to tackle this challenging problem. Here, we rigorously benchmarked nine state-of-the-art conformational B-cell epitope prediction webservers, including generic and antibody-specific methods, on a dataset of over 250 antibody-antigen structures. The results of our assessment and statistical analyses show that all the methods achieve very low performances, and some do not perform better than randomly generated patches of surface residues. In addition, we also found that commonly used consensus strategies that combine the results from multiple webservers are at best only marginally better than random. Finally, we applied all the predictors to the SARS-CoV-2 spike protein as an independent case study, and showed that they perform poorly in general, which largely recapitulates our benchmarking conclusions. We hope that these results will lead to greater caution when using these tools until the biases and issues that limit current methods have been addressed, promote the use of state-of-the-art evaluation methodologies in future publications and suggest new strategies to improve the performance of conformational B-cell epitope prediction methods.

B-Cell Epitope Predictions Using Computational Methods.

Identifying protein antigenic epitopes that are recognizable by antibodies is a key step in immunologic research. This type of research has broad medical applications, such as new immunodiagnostic reagent discovery, vaccine design, and antibody design. However, due to the countless possibilities of potential epitopes, the experimental search through trial and error would be too costly and time-consuming to be practical. To facilitate this process and improve its efficiency, computational methods were developed to predict both linear epitopes and discontinuous antigenic epitopes. For linear B-cell epitope prediction, many methods were developed, including PREDITOP, PEOPLE, BEPITOPE, BepiPred, COBEpro, ABCpred, AAP, BCPred, BayesB, BEOracle/BROracle, BEST, LBEEP, DRREP, iBCE-EL, SVMTriP, etc. For the more challenging yet important task of discontinuous epitope prediction, methods were also developed, including CEP, DiscoTope, PEPITO, ElliPro, SEPPA, EPITOPIA, PEASE, EpiPred, SEPIa, EPCES, EPSVR, etc. In this chapter, we will discuss computational methods for B-cell epitope predictions of both linear and discontinuous epitopes. SVMTriP and EPCES/EPCSVR, the most successful among the methods for each type of the predictions, will be used as model methods to detail the standard protocols. For linear epitope prediction, SVMTriP was reported to achieve a sensitivity of 80.1% and a precision of 55.2% with a fivefold cross-validation based on a large dataset, yielding an AUC of 0.702. For discontinuous or conformational B-cell epitope prediction, EPCES and EPCSVR were both benchmarked by a curated independent test dataset in which all antigens had no complex structures with the antibody. The identified epitopes by these methods were later independently validated by various biochemical experiments. For these three model methods, webservers and all datasets are publicly available at http://sysbio.unl.edu/SVMTriP , http://sysbio.unl.edu/EPCES/ , and http://sysbio.unl.edu/EPSVR/ .

BepiPred-3.0: Improved B-cell epitope prediction using protein language models.

B-cell epitope prediction tools are of great medical and commercial interest due to their practical applications in vaccine development and disease diagnostics. The introduction of protein language models (LMs), trained on unprecedented large datasets of protein sequences and structures, tap into a powerful numeric representation that can be exploited to accurately predict local and global protein structural features from amino acid sequences only. In this paper, we present BepiPred-3.0, a sequence-based epitope prediction tool that, by exploiting LM embeddings, greatly improves the prediction accuracy for both linear and conformational epitope prediction on several independent test sets. Furthermore, by carefully selecting additional input variables and epitope residue annotation strategy, performance was further improved, thus achieving unprecedented predictive power. Our tool can predict epitopes across hundreds of sequences in minutes. It is freely available as a web server and a standalone package at https://services.healthtech.dtu.dk/service.php?BepiPred-3.0 with a user-friendly interface to navigate the results.

Predicting Antigenic Peptides from Rocio Virus NS1 Protein for Immunodiagnostic Testing Using Immunoinformatics and Molecular Dynamics Simulation.

The mosquito-borne disease caused by the Rocio virus is a neglected threat, and new immune inputs for serological testing are urgently required for diagnosis in low-resource settings and epidemiological surveillance. We used in silico approaches to identify a specific antigenic peptide (p_ROCV2) in the NS1 protein of the Rocio virus that was theoretically predicted to be stable and exposed on its surface, where it demonstrated key properties allowing it to interact with antibodies. These findings related to the molecular dynamics of this peptide provide important insights for advancing diagnostic platforms and investigating therapeutic alternatives.

Prediction of Variable-Length B-Cell Epitopes for Antipeptide Paratopes Using the Program HAPTIC.

BACKGROUND: B-cell epitope prediction for antipeptide antibody responses enables peptide-based vaccine design and related translational applications. This entails estimating epitopeparatope binding free-energy changes from antigen sequence; but attempts to do so assuming uniform epitope length (e.g., of hexapeptide sequences, each spanning a typical paratope diameter when fully extended) have neglected empirically established variation in epitope length. OBJECTIVE: This work aimed to develop a sequence-based physicochemical approach to variablelength B-cell epitope prediction for antipeptide paratopes recognizing flexibly disordered targets. METHODS: Said approach was developed by analogy between epitope-paratope binding and protein folding modeled as polymer collapse, treating paratope structure implicitly. Epitope-paratope binding was thus conceptually resolved into processes of epitope compaction, collapse and contact, with epitope collapse presenting the main entropic barrier limiting epitope length among nonpolyproline sequences. The resulting algorithm was implemented as a computer program, namely the Heuristic Affinity Prediction Tool for Immune Complexes (HAPTIC), which is freely accessible via an online interface (http://badong.freeshell.org/haptic.htm). This was used in conjunction with published data on representative known peptide immunogens. RESULTS: HAPTIC predicted immunodominant epitope sequences with lengths limited by penalties for both compaction and collapse, consistent with known paratope-bound structures of flexibly disordered epitopes. In most cases, the predicted association constant was greater than its experimentally determined counterpart but below the predicted upper bound for affinity maturation in vivo. CONCLUSION: HAPTIC provides a physicochemically plausible means for estimating the affinity of antipeptide paratopes for sterically accessible and flexibly disordered peptidic antigen sequences by explicitly considering candidate B-cell epitopes of variable length.

EPCES and EPSVR: Prediction of B-Cell Antigenic Epitopes on Protein Surfaces with Conformational Information.

Accurate prediction of discontinuous antigenic epitopes is important for immunologic research and medical applications, but it is not an easy problem. Currently, there are only a few prediction servers available, though discontinuous epitopes constitute the majority of all B-cell antigenic epitopes. In this chapter, we describe two online servers, EPCES and EPSVR, for discontinuous epitope prediction. All methods were benchmarked by a curated independent test set, in which all antigens had no complex structures with the antibody, and their epitopes were identified by various biochemical experiments. The servers and all datasets are available at http://sysbio.unl.edu/EPCES/ and http://sysbio.unl.edu/EPSVR/ .

SVMTriP: A Method to Predict B-Cell Linear Antigenic Epitopes.

Identifying protein antigenic epitopes recognizable by antibodies is the key step for new immuno-diagnostic reagent discovery and vaccine design. To facilitate this process and improve its efficiency, computational methods were developed to predict antigenic epitopes. For the linear B-cell epitope prediction, many methods were developed, including BepiPred, ABCPred, AAP, BCPred, BayesB, BEOracle/BROracle, BEST, and SVMTriP. Among these methods, SVMTriP, a frontrunner, utilized Support Vector Machine by combining the tri-peptide similarity and Propensity scores. Applied on non-redundant B-cell linear epitopes extracted from IEDB, SVMTriP achieved a sensitivity of 80.1% and a precision of 55.2% with a five-fold cross-validation. The AUC value was 0.702. The combination of similarity and propensity of tri-peptide subsequences can improve the prediction performance for linear B-cell epitopes. A webserver based on this method was constructed for public use. The server and all datasets used in the corresponding study are available at http://sysbio.unl.edu/SVMTriP . This chapter describes the webserver of SVMTriP.

Evaluation of the immune response against Trypanosoma cruzi cytosolic tryparedoxin peroxidase in human natural infection.

Trypanosoma cruzi, the aetiological agent of Chagas disease, has a highly efficient detoxification system to deal with the oxidative burst imposed by its host. One of the antioxidant enzymes involved is the cytosolic tryparedoxin peroxidase (c-TXNPx), which catalyses the reduction to hydrogen peroxide, small-chain organic hydroperoxides and peroxynitrite. This enzyme is present in all parasite stages, and its overexpression renders parasites more resistant to the oxidative defences of macrophages, favouring parasite survival. This work addressed the study of the specific humoral and cellular immune response triggered by c-TXNPx in human natural infection. Thus, sera and peripheral blood mononuclear cells (PBMC) were collected from chronically infected asymptomatic and cardiac patients, and non-infected individuals. Results showed that levels of IgG antibodies against c-TXNPx were low in sera from individuals across all groups. B-cell epitope prediction limited immunogenicity to a few, small regions on the c-TXNPx sequence. At a cellular level, PBMC from asymptomatic and cardiac patients proliferated and secreted interferon-γ after c-TXNPx stimulation, compared with mock control. However, only proliferation was higher in asymptomatic patients compared with cardiac and non-infected individuals. Furthermore, asymptomatic patients showed an enhanced frequency of CD19+ CD69+ cells upon exposure to c-TXNPx. Overall, our results show that c-TXNPx fails to induce a strong immune response in natural infection, being measurable only in those patients without any clinical symptoms. The low impact of c-TXNPx in the human immune response could be strategic for parasite survival, as it keeps this crucial antioxidant enzyme activity safe from the mechanisms of adaptive immune response.

Biochemical analysis and identification of linear B-cell epitopes from recombinant Sm21.7 antigen from Schistosoma mansoni.

Schistosoma mansoni tegument is a dynamic host-interactive layer that is an essential source of parasite antigens and a relevant field for schistosome vaccine research. Sm21.7 is a cytoskeleton antigen found in S. mansoni tegument that engenders protection in experimental challenge infection. Because of its crucial role in the parasite tegument and its promising protective capability, Sm21.7 is an exciting target for the development of therapeutic strategies. The present study describes Sm21.7 structural and biophysical features using circular dichroism spectroscopy and identifies linear B-cell epitopes of Sm21.7 using in-silico methods and immunoassay. The Sm21.7 gene was cloned into the pETDEST42 vector, and the recombinant protein was overexpressed in Escherichia coli DE3. The soluble protein was purified by affinity chromatography followed by ion-exchange chromatography. Purified recombinant Sm21.7 was analyzed by circular dichroism spectroscopy which demonstrated that the rSm21.7 structure was comprised of approximately 38% α-helices and its conformation remains stable at temperatures of up to 60 °C. Prediction of rSm21.7 B-cell epitopes was based on amino acid physicochemical properties. Sixteen peptides corresponding to predicted epitopes were synthesized and immunoreactivity assessed by spot peptide array using pooled rSm21.7-immunized mice sera or patients' sera with different clinical forms of S. mansoni infection. Immunoassays revealed that sera from rSm21.7-immunized mice reacted predominantly with peptides located in the dynein-light chain domain (DLC) at the C-terminal region of rSm21.7. Comparative analysis of the antibody response of acute, intestinal and hepatosplenic patients' sera to the Sm21.7 peptides showed that a differential recognition pattern of Sm21.7-derived peptides by intestinal patients' sera might contribute to down-regulate the immune response in chronic intestinal patients. Together, the results may help the development of S. mansoni vaccine strategies based on the rSm21.7 antigen.

Production of a chimeric protein and its potential application in sero-diagnosis of Mycoplasma hominis infection.

INTRODUCTION: Mycoplasma hominis is an opportunistic pathogen of the human genital tract. Detection of antibodies against this organism in human serum or plasma is theoretically unreliable because of high variation in bacterial surface antigens. In this study, we applied the bioinformatics tools to design a chimeric protein constructed of specific, conserved and predicted immuno-dominant epitopes from two different membrane proteins, P120 and P80. MATERIAL AND METHODS: Linear B-cell epitopes of P120 and P80 were predicted and evaluated by bioinformatics tools and the designed chimeric protein was expressed in Escherichia coli. The chimeric protein, Mh128, was further analyzed in terms of immuno-reactivity by western blotting and enzyme immuno-sorbent assay (ELISA). RESULTS: We found eight specific, conserved and immuno-dominant epitopes within P120 and P80 based on the bioinformatic studies. The constructed chimeric protein showed immuno-reaction in both western-blotting and ELISA tests. DISCUSSION: Because of extensive variation of genomic and antigenic structure, diagnosis of M. hominis infection is difficult. Mh128 as a predicted specific and conserved recombinant protein can be potentially used for sero-diagnosis of M. hominis infection. We plan to develop an immuno-assay based on Mh128 and further evaluate the clinical specificity and sensitivity of the method.