• Title/Summary/Keyword: ALBERT

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High Level Expression of a Protein Precursor for Functional Studies

  • Gathmann, Sven;Rupprecht, Eva;Schneider, Dirk
    • BMB Reports
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    • v.39 no.6
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    • pp.717-721
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    • 2006

  • In vitro analyses of type I signal peptidase activities require protein precursors as substrates. Usually, these pre-proteins are expressed in vitro and cleavage of the signal sequence is followed by SDS polyacrylamide gel electrophoresis coupled with autoradiography. Radioactive amino acids have to be incorporated in the expressed protein, since the amount of the in vitro expressed protein is usually very low and processing of the signal peptide cannot be followed by SDS polyacrylamide gel electrophoresis alone. Here we describe a rapid and simple method to express large amounts of a protein precursor in E. coli. We have analyzed the effect of ionophors as well as of azide on the accumulation of expressed protein precursors. Azide blocks the function of SecA and the ionophors dissipate the electrochemical gradient across the cytoplasmic membrane of E. coli. Addition of azide ions resulted in the formation of inclusion bodies, highly enriched with pre-apo-plastocyanine. Plastocyanine is a soluble copper protein, which can be found in the periplasmic space of cyanobacteria as well as in the thylakoid lumen of cyanobacteria and chloroplasts, and the pre-protein contains a cleavable signal sequence at its N-terminus. After purification of cyanobacterial pre-apo-plastocyanine, its signal sequence can be cleaved off by the E. coli signal peptidase, and protein processing was followed on Coomassie stained SDS polyacrylamide gels. We are optimistic that the presented method can be further developed and applied.

  • https://doi.org/10.5483/BMBRep.2006.39.6.717 인용 PDF

Open Domain Machine Reading Comprehension using InferSent (InferSent를 활용한 오픈 도메인 기계독해)

  • Jeong-Hoon, Kim;Jun-Yeong, Kim;Jun, Park;Sung-Wook, Park;Se-Hoon, Jung;Chun-Bo, Sim
    • Smart Media Journal
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    • v.11 no.10
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    • pp.89-96
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    • 2022

  • An open domain machine reading comprehension is a model that adds a function to search paragraphs as there are no paragraphs related to a given question. Document searches have an issue of lower performance with a lot of documents despite abundant research with word frequency based TF-IDF. Paragraph selections also have an issue of not extracting paragraph contexts, including sentence characteristics accurately despite a lot of research with word-based embedding. Document reading comprehension has an issue of slow learning due to the growing number of parameters despite a lot of research on BERT. Trying to solve these three issues, this study used BM25 which considered even sentence length and InferSent to get sentence contexts, and proposed an open domain machine reading comprehension with ALBERT to reduce the number of parameters. An experiment was conducted with SQuAD1.1 datasets. BM25 recorded a higher performance of document research than TF-IDF by 3.2%. InferSent showed a higher performance in paragraph selection than Transformer by 0.9%. Finally, as the number of paragraphs increased in document comprehension, ALBERT was 0.4% higher in EM and 0.2% higher in F1.

  • https://doi.org/10.30693/SMJ.2022.11.10.89 인용 PDF KSCI