RESUMO
Identifying the exact epitope positions for a monoclonal antibody (mAb) is of critical importance yet highly challenging to the Ab design of biomedical research. Based on previous versions of SEPPA 3.0, we present SEPPA-mAb for the above purpose with high accuracy and low false positive rate (FPR), suitable for both experimental and modelled structures. In practice, SEPPA-mAb appended a fingerprints-based patch model to SEPPA 3.0, considering the structural and physic-chemical complementarity between a possible epitope patch and the complementarity-determining region of mAb and trained on 860 representative antigen-antibody complexes. On independent testing of 193 antigen-antibody pairs, SEPPA-mAb achieved an accuracy of 0.873 with an FPR of 0.097 in classifying epitope and non-epitope residues under the default threshold, while docking-based methods gave the best AUC of 0.691, and the top epitope prediction tool gave AUC of 0.730 with balanced accuracy of 0.635. A study on 36 independent HIV glycoproteins displayed a high accuracy of 0.918 and a low FPR of 0.058. Further testing illustrated outstanding robustness on new antigens and modelled antibodies. Being the first online tool predicting mAb-specific epitopes, SEPPA-mAb may help to discover new epitopes and design better mAbs for therapeutic and diagnostic purposes. SEPPA-mAb can be accessed at http://www.badd-cao.net/seppa-mab/.
Assuntos
Anticorpos Monoclonais , Epitopos , Software , Complexo Antígeno-Anticorpo , Antígenos/química , Mapeamento de Epitopos , Epitopos/química , Glicoproteínas/metabolismoRESUMO
The current SARS-CoV-2 variants strikingly evade all authorized monoclonal antibodies and threaten the efficacy of serum-neutralizing activity elicited by vaccination or prior infection, urging the need to develop antivirals against SARS-CoV-2 and related sarbecoviruses. Here, we identified both potent and broadly neutralizing antibodies from a five-dose vaccinated donor who exhibited cross-reactive serum-neutralizing activity against diverse coronaviruses. Through single B-cell sorting and sequencing followed by a tailor-made computational pipeline, we successfully selected 86 antibodies with potential cross-neutralizing ability from 684 antibody sequences. Among them, PW5-570 potently neutralized all SARS-CoV-2 variants that arose prior to Omicron BA.5, and the other three could broadly neutralize all current SARS-CoV-2 variants of concern, SARS-CoV and their related sarbecoviruses (Pangolin-GD, RaTG13, WIV-1, and SHC014). Cryo-EM analysis demonstrates that these antibodies have diverse neutralization mechanisms, such as disassembling spike trimers, or binding to RBM or SD1 to affect ACE2 binding. In addition, prophylactic administration of these antibodies significantly protects nasal turbinate and lung infections against BA.1, XBB.1, and SARS-CoV viral challenge in golden Syrian hamsters, respectively. Importantly, post-exposure treatment with PW5-5 and PW5-535 also markedly protects against XBB.1 challenge in these models. This study reveals the potential utility of computational process to assist screening cross-reactive antibodies, as well as the potency of vaccine-induced broadly neutralizing antibodies against current SARS-CoV-2 variants and related sarbecoviruses, offering promising avenues for the development of broad therapeutic antibody drugs.
RESUMO
Emerging evidence has identified viral circular RNAs (circRNAs) in human cells infected by viruses, interfering with the immune system and inducing diseases including human cancer. However, the biogenesis and regulatory mechanisms of virus-encoded circRNAs in host cells remain unknown. In this study, we used the circRNA detection tool CIRI2 to systematically determine the virus-encoded circRNAs in virus-infected cancer cell lines and cancer patients, by analysing RNA-Seq datasets derived from RNase R-treated samples. Based on the thousands of viral circRNAs we identified, the biological characteristics and potential roles of viral circRNAs in regulating host cell function were determined. In addition, we developed a Viral-circRNA Database (http://www.hywanglab.cn/vcRNAdb/), which is open to all users to search, browse and download information on circRNAs encoded by viruses upon infection.