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Analog Accessibility Score (AAscore) for Rational Compound Selection
No full textVarious in silico scores have been proposed to objectively assess the characteristics and properties of a compound. However, there is still no score that represents the analog accessibility of a compound. Such a score would be valuable for selecting compounds proposed by virtual screening or for prioritizing hit compounds for the hit-to-lead phase. This study proposes an analog accessibility score (AAscore), where retrosynthesis prediction and forward product prediction models were utilized to generate virtual analogs. The AAscore is defined as the number of unique analogs and virtual synthetic routes. To evaluate the AAscore in terms of the number of actually synthesized analog compounds, analog compounds were prepared by using the compound-core relationship (CCR) method. It was found that the AAscore was little correlated with the number of CCR-based analogs. Furthermore, AAscores were found to be significantly influenced by the number of extracted candidate reactants from a reactant database. A case study targeting compounds active against carbonic anhydrase 2 showed that the AAscore could identify compounds that were synthesized into analogs.journal articl
YeeD is an essential partner for YeeE-mediated thiosulfate uptake in bacteria and regulates thiosulfate ion decomposition
No full textUptake of thiosulfate ions as an inorganic sulfur source from the environment is important for bacterial sulfur assimilation. Recently, a selective thiosulfate uptake pathway involving a membrane protein YeeE (TsuA) in Escherichia coli was characterized. YeeE-like proteins are conserved in some bacteria, archaea, and eukaryotes. However, the precise function of YeeE, along with its potential partner protein in the thiosulfate ion uptake pathway, remained unclear. Here, we assessed selective thiosulfate transport via Spirochaeta thermophila YeeE in vitro and characterized E. coli YeeD (TsuB) as an adjacent and essential protein for YeeE-mediated thiosulfate uptake in vivo. We further showed that S. thermophila YeeD possesses thiosulfate decomposition activity and that a conserved cysteine in YeeD was modified to several forms in the presence of thiosulfate. Finally, the crystal structures of S. thermophila YeeE-YeeD fusion proteins at 3.34-Å and 2.60-Å resolutions revealed their interactions. The association was evaluated by a binding assay using purified S. thermophila YeeE and YeeD. Based on these results, a model of the sophisticated uptake of thiosulfate ions by YeeE and YeeD is proposed.journal articl
Exportin-5 binding precedes 5′- and 3′-end processing of tRNA precursors in Drosophila
No full textExportin5 (Exp5) is the major miRNA nuclear export factor and recognizes structural features of pre-miRNA hairpins, while it also exports other minihelix-containing RNAs. In Drosophila, Exp5 is suggested to play a major role in tRNA export because the gene encoding the canonical tRNA export factor Exportin-t is missing in its genome. To understand molecular functions of fly Exp5, we studied the Exp5/RNA interactome in the cell line S2R + using the crosslinking and immunoprecipitation (CLIP) technology. The CLIP experiment captured known substrates such as tRNAs and miRNAs and detected candidates of novel Exp5 substrates including various mRNAs and long non-coding RNAs (lncRNAs). Some mRNAs and lncRNAs enriched PAR-CLIP tags compared to their expression levels, suggesting selective binding of Exp5 to them. Intronless mRNAs tended to enrich PAR-CLIP tags; therefore, we proposed that Exp5 might play a role in the export of specific classes of mRNAs/lncRNAs. This result suggested that Drosophila Exp5 might have a wider variety of substrates than initially thought. Surprisingly, Exp5 CLIP reads often contained sequences corresponding to the flanking 5′-leaders and 3′-trailers of tRNAs, which were thought to be removed prior to nuclear export. In fact, we found pre-tRNAs before end-processing were present in the cytoplasm, supporting the idea that tRNA end-processing is a cytoplasmic event. In summary, our results provide a genome-wide list of Exp5 substrate candidates and suggest that flies may lack a mechanism to distinguish pre-tRNAs with or without the flanking sequences.journal articl
Predictive Model for Extended-Spectrum β-Lactamase–Producing Bacterial Infections Using Natural Language Processing Technique and Open Data in Intensive Care Unit Environment: Retrospective Observational Study
No full textBackground: Machine learning has advanced medical event prediction, mostly using private data. The public MIMIC-3 (Medical Information Mart for Intensive Care III) data set, which contains detailed data on over 40,000 intensive care unit patients, stands out as it can help develop better models including structured and textual data. Objective: This study aimed to build and test a machine learning model using the MIMIC-3 data set to determine the effectiveness of information extracted from electronic medical record text using a named entity recognition, specifically QuickUMLS, for predicting important medical events. Using the prediction of extended-spectrum β-lactamase (ESBL)-producing bacterial infections as an example, this study shows how open data sources and simple technology can be useful for making clinically meaningful predictions. Methods: The MIMIC-3 data set, including demographics, vital signs, laboratory results, and textual data, such as discharge summaries, was used. This study specifically targeted patients diagnosed with Klebsiella pneumoniae or Escherichia coli infection. Predictions were based on ESBL-producing bacterial standards and the minimum inhibitory concentration criteria. Both the structured data and extracted patient histories were used as predictors. In total, 2 models, an L1-regularized logistic regression model and a LightGBM model, were evaluated using the receiver operating characteristic area under the curve (ROC-AUC) and the precision-recall curve area under the curve (PR-AUC). Results: Of 46,520 MIMIC-3 patients, 4046 were identified with bacterial cultures, indicating the presence of K pneumoniae or E coli. After excluding patients who lacked discharge summary text, 3614 patients remained. The L1-penalized model, with variables from only the structured data, displayed a ROC-AUC of 0.646 and a PR-AUC of 0.307. The LightGBM model, combining structured and textual data, achieved a ROC-AUC of 0.707 and a PR-AUC of 0.369. Key contributors to the LightGBM model included patient age, duration since hospital admission, and specific medical history such as diabetes. The structured data-based model showed improved performance compared to the reference models. Performance was further improved when textual medical history was included. Compared to other models predicting drug-resistant bacteria, the results of this study ranked in the middle. Some misidentifications, potentially due to the limitations of QuickUMLS, may have affected the accuracy of the model. Conclusions: This study successfully developed a predictive model for ESBL-producing bacterial infections using the MIMIC-3 data set, yielding results consistent with existing literature. This model stands out for its transparency and reliance on open data and open-named entity recognition technology. The performance of the model was enhanced using textual information. With advancements in natural language processing tools such as BERT and GPT, the extraction of medical data from text holds substantial potential for future model optimization.journal articl
Future morphology prediction : Using machine learning models to simulate the development of organoid
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Augmented Reality-Based Explainer for Reducing Autonomous Vehicle Stress
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A Study on Automatic Parallelization with OpenMP using Large Language Model
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Ultra-Efficient Universal Cryptographic Accelerators for Blockchain-based IoT Systems
Get PDF奈良先端科学技術大学院大学博士(工学)doctoral thesi
