RESUMO
Evolutionary changes in the hepatitis B virus (HBV) genome could reflect its adaptation to host-induced selective pressure. Leveraging paired human exome and ultra-deep HBV genome-sequencing data from 567 affected individuals with chronic hepatitis B, we comprehensively searched for the signatures of this evolutionary process by conducting "genome-to-genome" association tests between all human genetic variants and viral mutations. We identified significant associations between an East Asian-specific missense variant in the gene encoding the HBV entry receptor NTCP (rs2296651, NTCP S267F) and mutations within the receptor-binding region of HBV preS1. Through in silico modeling and in vitro preS1-NTCP binding assays, we observed that the associated HBV mutations are in proximity to the NTCP variant when bound and together partially increase binding affinity to NTCP S267F. Furthermore, we identified significant associations between HLA-A variation and viral mutations in HLA-A-restricted T cell epitopes. We used in silico binding prediction tools to evaluate the impact of the associated HBV mutations on HLA presentation and observed that mutations that result in weaker binding affinities to their cognate HLA alleles were enriched. Overall, our results suggest the emergence of HBV escape mutations that might alter the interaction between HBV PreS1 and its cellular receptor NTCP during viral entry into hepatocytes and confirm the role of HLA class I restriction in inducing HBV epitope variations.
Assuntos
Vírus da Hepatite B , Mutação , Transportadores de Ânions Orgânicos Dependentes de Sódio , Simportadores , Humanos , Vírus da Hepatite B/genética , Transportadores de Ânions Orgânicos Dependentes de Sódio/genética , Transportadores de Ânions Orgânicos Dependentes de Sódio/metabolismo , Simportadores/genética , Simportadores/metabolismo , Interações Hospedeiro-Patógeno/genética , Interações Hospedeiro-Patógeno/imunologia , Hepatite B Crônica/virologia , Hepatite B Crônica/genética , Genoma Viral , Antígenos de Superfície da Hepatite B/genética , Epitopos de Linfócito T/genética , Epitopos de Linfócito T/imunologia , Genômica/métodos , Antígenos de Histocompatibilidade Classe I/genética , Antígenos de Histocompatibilidade Classe I/metabolismoRESUMO
The rapid progress in the field of deep learning has had a significant impact on protein design. Deep learning methods have recently produced a breakthrough in protein structure prediction, leading to the availability of high-quality models for millions of proteins. Along with novel architectures for generative modeling and sequence analysis, they have revolutionized the protein design field in the past few years remarkably by improving the accuracy and ability to identify novel protein sequences and structures. Deep neural networks can now learn and extract the fundamental features of protein structures, predict how they interact with other biomolecules, and have the potential to create new effective drugs for treating disease. As their applicability in protein design is rapidly growing, we review the recent developments and technology in deep learning methods and provide examples of their performance to generate novel functional proteins.