| 1. |
- Norder, Helene, et al.
(författare)
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High Frequency of Either Altered Pre-Core StartCodon or Weakened Kozak Sequence in the CorePromoter Region in Hepatitis B Virus A1 Strainsfrom Rwanda.
- 2019
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Ingår i: Genes. - : MDPI AG. - 2073-4425. ; 10:3
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Tidskriftsartikel (refereegranskat)abstract
- Hepatitis B virus (HBV) is endemic in Rwanda and is a major etiologic agent for chronic liver disease in the country. In a previous analysis of HBV strains from Rwanda, the S genes of most strains segregated into one single clade of subgenotype, A1. More than half (55%) of the anti-HBe positive individuals were viremic. In this study, 23 complete HBV genomes and the core promoter region (CP) from 18 additional strains were sequenced. Phylogenetic analysis of complete genomes confirmed that most Rwandan strain formed a single unique clade, within subgenotype A1. Strains from 17 of 22 (77%) anti-HBe positive HBV carriers had either mutated the precore start codon (9 strains with either CUG, ACG, UUG, or AAG) or mutations in the Kozak sequence preceding the pre-core start codon (8 strains). These mutually exclusive mutations were also identified in subgenotypes A1 (70/266; 26%), A2 (12/255; 5%), and A3 (26/49; 53%) sequences from the GenBank. The results showed that previous, rarely described HBV variants, expressing little or no HBeAg, are selected in anti-HBe positive subgenotype Al carriers from Rwanda and that mutations reducing HBeAg synthesis might be unique for a particular HBV clade, not just for a specific genotype or subgenotype.
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| 2. |
- Yan, R. X., et al.
(författare)
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Prediction of zinc-binding sites using multiple sequence profiles and machine learning methods
- 2019
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Ingår i: Molecular Omics. - : Royal Society of Chemistry (RSC). - 2515-4184. ; 15:3, s. 205-215
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Tidskriftsartikel (refereegranskat)abstract
- The zinc (Zn2+) cofactor has been proven to be involved in numerous biological mechanisms and the zinc-binding site is recognized as one of the most important post-translation modifications in proteins. Therefore, accurate knowledge of zinc ions in protein structures can provide potential clues for elucidation of protein folding and functions. However, determining zinc-binding residues by experimental means is usually lab-intensive and associated with high cost in most cases. In this context, the development of computational tools for identifying zinc-binding sites is highly desired, especially in the current post-genomic era. In this work, we developed a novel zinc-binding site prediction method by combining several intensively-trained machine learning models. To establish an accurate and generative method, we downloaded all zinc-binding proteins from the Protein Data Bank and prepared a non-redundant dataset. Meanwhile, a well-prepared dataset by other groups was also used. Then, effective and complementary features were extracted from sequences and three-dimensional structures of these proteins. Moreover, several well-designed machine learning models were intensively trained to construct accurate models. To assess the performance, the obtained predictors were stringently benchmarked using the diverse zinc-binding sites. Furthermore, several state-of-the-art in silico methods developed specifically for zinc-binding sites were also evaluated and compared. The results confirmed that our method is very competitive in real world applications and could become a complementary tool to wet lab experiments. To facilitate research in the community, a web server and stand-alone program implementing our method were constructed and are publicly available at http:// bioinformatics. fzu. edu. cn/ znMachine. html. The downloadable program of our method can be easily used for the high-throughput screening of potential zinc-binding sites across proteomes.
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