Staged heterogeneity learning to identify conformational B-cell epitopes from antigen sequences

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.

Computational prediction of multiple antigen epitopes

MOTIVATION

Identifying antigen epitopes is essential in medical applications, such as immunodiagnostic reagent discovery, vaccine design, and drug development. Computational approaches can complement low-throughput, time-consuming, and costly experimental determination of epitopes. Currently available prediction methods, however, have moderate success predicting epitopes, which limits their applicability. Epitope prediction is further complicated by the fact that multiple epitopes may be located on the same antigen and complete experimental data is often unavailable.

RESULTS

Here, we introduce the antigen epitope prediction program ISPIPab that combines information from two feature-based methods and a docking-based method. We demonstrate that ISPIPab outperforms each of its individual classifiers as well as other state-of-the-art methods, including those designed specifically for epitope prediction. By combining the prediction algorithm with hierarchical clustering, we show that we can effectively capture epitopes that align with available experimental data while also revealing additional novel targets for future experimental investigations.

Computational Methods to Predict Conformational B-Cell Epitopes

Accurate computational prediction of B-cell epitopes can greatly enhance biomedical research and rapidly advance efforts to develop therapeutics, monoclonal antibodies, vaccines, and immunodiagnostic reagents. Previous research efforts have primarily focused on the development of computational methods to predict linear epitopes rather than conformational epitopes; however, the latter is much more biologically predominant. Several conformational B-cell epitope prediction methods have recently been published, but their predictive performances are weak. Here, we present a review of the latest computational methods and assess their performances on a diverse test set of 29 non-redundant unbound antigen structures. Our results demonstrate that ISPIPab performs better than most methods and compares favorably with other recent antigen-specific methods. Finally, we suggest new strategies and opportunities to improve computational predictions of conformational B-cell epitopes.

Expression of Recombinant Stonustoxin Alpha Subunit and Preparation of polyclonal antiserum for Stonustoxin Neutralization Studies

Stonustoxin (SNTX) is a lethal protein found in stonefish venom, responsible for many of the symptoms associated with stonefish envenomation. To counter stonefish venom challenges, antivenom is a well-established and effective solution. In this study, we aimed to produce the recombinant alpha subunit protein of Stonustoxin from Synanceia horrida and prepare antibodies against it The SNTXα gene sequence was optimized for E. coli BL21 (DE3) expression and cloned into the pET17b vector. Following purification, the recombinant protein was subcutaneously injected into rabbits, and antibodies were extracted from rabbit´s serum using a G protein column As a result of codon optimization, the codon adaptation index for the SNTXα cassette increased to 0.94. SDS-PAGE analysis validated the expression of SNTXα, with a band observed at 73.5 kDa with a yield of 60 mg/l. ELISA results demonstrated rabbits antibody titers were detectable up to a 1:256,000 dilution. The isolated antibody from rabbit´s serum exhibited a concentration of 1.5 mg/ml, and its sensitivity allowed the detection of a minimum protein concentration of 9.7 ng. In the neutralization assay the purified antibody against SNTXα protected mice challenged with 2 LD50. In conclusion, our study successfully expressed the alpha subunit of Stonustoxin in a prokaryotic host, enabling the production of antibodies for potential use in developing stonefish antivenom.

Machine Learning Techniques for the Prediction of B-Cell and T-Cell Epitopes as Potential Vaccine Targets with a Specific Focus on SARS-CoV-2 Pathogen: A Review

The only part of an antigen (a protein molecule found on the surface of a pathogen) that is composed of epitopes specific to T and B cells is recognized by the human immune system (HIS). Identification of epitopes is considered critical for designing an epitope-based peptide vaccine (EBPV). Although there are a number of vaccine types, EBPVs have received less attention thus far. It is important to mention that EBPVs have a great deal of untapped potential for boosting vaccination safety-they are less expensive and take a short time to produce. Thus, in order to quickly contain global pandemics such as the ongoing outbreak of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), as well as epidemics and endemics, EBPVs are considered promising vaccine types. The high mutation rate of SARS-CoV-2 has posed a great challenge to public health worldwide because either the composition of existing vaccines has to be changed or a new vaccine has to be developed to protect against its different variants. In such scenarios, time being the critical factor, EBPVs can be a promising alternative. To design an effective and viable EBPV against different strains of a pathogen, it is important to identify the putative T- and B-cell epitopes. Using the wet-lab experimental approach to identify these epitopes is time-consuming and costly because the experimental screening of a vast number of potential epitope candidates is required. Fortunately, various available machine learning (ML)-based prediction methods have reduced the burden related to the epitope mapping process by decreasing the potential epitope candidate list for experimental trials. Moreover, these methods are also cost-effective, scalable, and fast. This paper presents a systematic review of various state-of-the-art and relevant ML-based methods and tools for predicting T- and B-cell epitopes. Special emphasis is placed on highlighting and analyzing various models for predicting epitopes of SARS-CoV-2, the causative agent of COVID-19. Based on the various methods and tools discussed, future research directions for epitope prediction are presented.

Analysis of Xinjiang HPV16 L1 gene polymorphisms: a newly developed, low-cost enzyme-linked immunosorbent assay

BACKGROUND

Xinjiang, China shows the world's highest incidence and mortality rates of cervical cancer. Due to limited conditions available for medical examination, hybrid capture 2 (HC2) and other detection methods are used rarely, and early screening for human papillomavirus (HPV) cannot be carried out. Therefore, we established a double-antibody sandwich (DAS)-enzyme-linked immunosorbent assay (ELISA) based on a polymorphism of the Xinjiang HPV16 L1 strain (KU721788).

METHODS

According to the conserved sequence and specific epitope of Xinjiang strain HPV16 L1, we prepared two anti-HPV16 L1 monoclonal antibodies and combined them to construct a DAS-ELISA. Detection conditions for the DAS-ELISA were optimized, and HC2 was used as the control to verify the specificity, repeatability and coincidence detection of the DAS-ELISA.

RESULTS

The optimized conditions for the DAS-ELISA were: dilution of the capture antibody was 1:100; the enzyme-labelled antibody was 1:10; the sample reaction time was 45 min; the enzyme-labelled antibody was applied for 40 min, and the substrate color development time was 15 min. The quality of the DAS-ELISA for the detection of HPV 16 was very high, and there was no significant difference when compared with HC2.

CONCLUSION

The DAS-ELISA developed on the basis of the Xinjiang strain (KU721788) polymorphism possesses the advantages of a detection rate similar to that for the HC2 assay currently used clinically, but it is more convenient operationally and at lower cost. DAS-ELISA is thus easier to implement for cervical cancer screening in economically depressed areas.

Salvia miltiorrhiza-derived miRNAs suppress vascular remodeling through regulating OTUD7B/KLF4/NMHC IIA axis

Objective: Abnormal proliferation and migration of vascular smooth muscle cells (VSMCs) are essential for vascular remodeling. Natural compounds with diterpene chinone or phenolic acid structure from Salvia miltiorrhiza, an eminent medicinal herb widely used to treat cardiovascular diseases in China, can effectively attenuate vascular remodeling induced by vascular injury. However, it remains unknown whether Salvia miltiorrhiza-derived miRNAs can protect VSMCs from injury by environmental stimuli. Here, we explored the role and underlying mechanisms of Salvia miltiorrhiza-derived Sal-miR-1 and 3 in the regulation of VSMC migration and monocyte adhesion to VSMCs induced by thrombin. Methods: A mouse model for intimal hyperplasia was established by the ligation of carotid artery and the injured carotid arteries were in situ-transfected with Sal-miR-1 and 3 using F-127 pluronic gel. The vascular protective effects of Sal-miR-1 and 3 were assessed via analysis of intimal hyperplasia with pathological morphology. VSMC migration and adhesion were analyzed by the wound healing, transwell membrane assays, and time-lapse imaging experiment. Using loss- and gain-of-function approaches, Sal-miR-1 and 3 regulation of OTUD7B/KLF4/NMHC IIA axis was investigated by using luciferase assay, co-immunoprecipitation, chromatin immunoprecipitation, western blotting, etc. Results:Salvia miltiorrhiza-derived Sal-miR-1 and 3 can enter the mouse body after intragastric administration, and significantly suppress intimal hyperplasia induced by carotid artery ligation. In cultured VSMCs, these two miRNAs inhibit thrombin-induced the migration of VSMCs and monocyte adhesion to VSMCs. Mechanistically, Sal-miR-1 and 3 abrogate OTUD7B upregulation by thrombin via binding to the different sites of the OTUD7B 3'UTR. Most importantly, OTUD7B downregulation by Sal-miR-1 and 3 attenuates KLF4 protein levels via decreasing its deubiquitylation, whereas decreased KLF4 relieves its repression of transcription of NMHC IIA gene and thus increases NMHC IIA expression levels. Further, increased NMHC IIA represses VSMC migration and monocyte adhesion to VSMCs via maintaining the contractile phenotype of VSMCs. Conclusions: Our studies not only found the novel bioactive components from Salvia miltiorrhiza but also clarified the molecular mechanism underlying Sal-miR-1 and 3 inhibition of VSMC migration and monocyte adhesion to VSMCs. These results add important knowledge to the pharmacological actions and bioactive components of Salvia miltiorrhiza. Sal-miR-1 and 3-regulated OTUD7B/KLF4/NMHC IIA axis may represent a therapeutic target for vascular remodeling.

Identification of a protective B-cell epitope of the Staphylococcus aureus GapC protein by screening a phage-displayed random peptide library

The impact of epidemic Staphylococcus aureus (S. aureus) on public health is increasing. Because of the abuse of antibiotics, the antibiotic resistance of S. aureus is increasing. Thus, there is an urgent need to develop new immunotherapies and immunoprophylaxes. Previous studies showed that the GapC protein of S. aureus, which is a surface protein with high glyceraldehyde 3-phosphate dehydrogenase activity, transferrin binding activity, and other biological activities, is highly conserved. GapC induces an effective humoral immune response in vivo. However, the B-cell epitopes of S. aureus GapC have not been well identified. Here we used the bioinformatics tools to analyze the sequence of GapC, and we generated protective anti-GapC monoclonal antibodies (mAbs). A protective mAb (1F4) showed strong specificity to GapC and the ability to induce macrophages to phagocytose S. aureus. We screened the motif ²⁷²GYTEDEIVSSD²⁸², which was recognized by mAb 1F4, using a phage display system. Then, we used site-directed mutagenesis to identify key amino acids in the motif. Residues G272 D276 E277 I278 and V279 formed the core of the ²⁷²GYTEDEIVSSD²⁸² motif. In addition, we showed that this epitope peptide induced a protective humoral immune response against S. aureus infection in immunized mice. Our results will be useful for the further study of epitope-based vaccines against S. aureus infection.