Prioritizing multiple therapeutic targets in parallel using automated DNA-encoded library screening

Documento escrito por un elevado número de autores/as, solo se referencia el/la que aparece en primer lugar y los/as autores/as pertenecientes a la UC3M.The identification and prioritization of chemically tractable therapeutic targets is a significant challenge in the discovery of new medicines. We have developed a novel method that rapidly screens multiple proteins in parallel using DNA-encoded library technology (ELT). Initial efforts were focused on the efficient discovery of antibacterial leads against 119 targets from Acinetobacter baumannii and Staphylococcus aureus. The success of this effort led to the hypothesis that the relative number of ELT binders alone could be used to assess the ligandability of large sets of proteins. This concept was further explored by screening 42 targets from Mycobacterium tuberculosis. Active chemical series for six targets from our initial effort as well as three chemotypes for DHFR from M. tuberculosis are reported. The findings demonstrate that parallel ELT selections can be used to assess ligandability and highlight opportunities for successful lead and tool discovery

Slow Onset Inhibition of KasA by Thiolactomycin: Mechanistic Insights and Lead Optimization for Anti-Bacterial Drug Discovery

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Prioritizing multiple therapeutic targets in parallel using automated DNA-encoded library screening

AbstractThe identification and prioritization of chemically tractable therapeutic targets is a significant challenge in the discovery of new medicines. We have developed a novel method that rapidly screens multiple proteins in parallel using DNA-encoded library technology (ELT). Initial efforts were focused on the efficient discovery of antibacterial leads against 119 targets from Acinetobacter baumannii and Staphylococcus aureus. The success of this effort led to the hypothesis that the relative number of ELT binders alone could be used to assess the ligandability of large sets of proteins. This concept was further explored by screening 42 targets from Mycobacterium tuberculosis. Active chemical series for six targets from our initial effort as well as three chemotypes for DHFR from M. tuberculosis are reported. The findings demonstrate that parallel ELT selections can be used to assess ligandability and highlight opportunities for successful lead and tool discovery.</jats:p

Assessing a Swedish Automatic Speech Recognition Model for Finland Swedish

Automatic speech recognition (ASR) technology advancements have revolutionized communication by converting spoken language into text. Despite these advancements, ASR systems often struggle with nonstandard dialects due to their training on standard language varieties. Speakers of minority dialects face the risk of marginalization by mainstream ASR technologies, potentially widening the digital divide for speakers of nonstandard dialects. This work investigates the challenges encountered in developing ASR models for pluricentric languages (language spoken in more than one country as a national or official language) and nonstandard dialects, focusing on Swedish, which is an official language in both Sweden and Finland. Specifically, it addresses how well a speech-to-text model trained solely on Sweden Swedish performs when transcribing Finland Swedish dialects. We have used a quantitative and experimental approach to evaluate transcriptions generated by VoxRex, a Wav2Vec 2.0 model developed by KBLab at the National Library of Sweden (KB), for the Aalto Finland Swedish Parliament ASR Corpus 2015-2020. The evaluation results show that the model achieves a mean word error rate (WER) of 15.96% for the original dataset and 15.11% for a cleaned dataset (after removing non-Swedish observations). The results show higher WER compared to prior evaluation results for the model, namely a WER of 2.5% for the datasets NST and Common Voice. The results imply that the model performs inferior when transcribing Finland Swedish than Sweden Swedish. The inferior performance may be due to dialectal differences between Sweden Swedish and Finland Swedish, the presence of Swedish spoken with a Finnish accent, or discrepancies between parliamentary references and the actual speech. Our findings indicate variability in WERs for different dialectal regions, with the highest scores for predominantly unilingual Finnish regions. The model also face challenges with some common Swedish words, parliamentary terminology, abbreviations, and compound words

Slow Onset Inhibition of KasA by Thiolactomycin: Mechanistic Insights and Lead Optimization for Anti-Bacterial Drug Discovery

Stony Brook University Libraries. SBU Graduate School in Chemistry. Lawrence Martin (Dean of Graduate School), Peter J. Tonge Ph.D. Advisor Professor, Department of Chemistry, Daniel P. Raleigh Ph.D. Chairman of Defense Professor, Department of Chemistry, Iwao Ojima Ph.D. Distinguished Professor, Department of Chemistry, Lizbeth Hedstrom Ph.D. Professor, Department of Biochemistry Brandeis University

Assessing a Swedish Automatic Speech Recognition Model for Finland Swedish

Automatic speech recognition (ASR) technology advancements have revolutionized communication by converting spoken language into text. Despite these advancements, ASR systems often struggle with nonstandard dialects due to their training on standard language varieties. Speakers of minority dialects face the risk of marginalization by mainstream ASR technologies, potentially widening the digital divide for speakers of nonstandard dialects. This work investigates the challenges encountered in developing ASR models for pluricentric languages (language spoken in more than one country as a national or official language) and nonstandard dialects, focusing on Swedish, which is an official language in both Sweden and Finland. Specifically, it addresses how well a speech-to-text model trained solely on Sweden Swedish performs when transcribing Finland Swedish dialects. We have used a quantitative and experimental approach to evaluate transcriptions generated by VoxRex, a Wav2Vec 2.0 model developed by KBLab at the National Library of Sweden (KB), for the Aalto Finland Swedish Parliament ASR Corpus 2015-2020. The evaluation results show that the model achieves a mean word error rate (WER) of 15.96% for the original dataset and 15.11% for a cleaned dataset (after removing non-Swedish observations). The results show higher WER compared to prior evaluation results for the model, namely a WER of 2.5% for the datasets NST and Common Voice. The results imply that the model performs inferior when transcribing Finland Swedish than Sweden Swedish. The inferior performance may be due to dialectal differences between Sweden Swedish and Finland Swedish, the presence of Swedish spoken with a Finnish accent, or discrepancies between parliamentary references and the actual speech. Our findings indicate variability in WERs for different dialectal regions, with the highest scores for predominantly unilingual Finnish regions. The model also face challenges with some common Swedish words, parliamentary terminology, abbreviations, and compound words

Crystal Structures of Mycobacterium Tuberculosis Kasa Show Mode of Action within Cell Wall Biosynthesis and its Inhibition by Thiolactomycin

Mycobacteria have a unique cell wall consisting of mycolic acids, very long chain lipids that provide protection and allow the bacteria to persist within human macrophages. Inhibition of cell wall biosynthesis is fatal for the organism and a starting point for the discovery and development of novel antibiotics. We determined the crystal structures of KasA, a key enzyme involved in the biosynthesis of long chain fatty acids, in its apo form and bound to the natural product inhibitor thiolactomycin. Detailed insights into the interaction of the inhibitor with KasA and the identification of a polyethylene glycol molecule that mimics a fatty acid substrate of approximately 40 carbon atoms length, represent the first atomic view of a mycobacterial enzyme involved in the synthesis of long chain fatty acids and provide a robust platform for the development of novel thiolactomycin analogs with high affinity for Kas