Assembling optimal microbial communities is key for various applications in biofuel production, agriculture, and human health. Finding the optimal community is challenging because the number of possible communities gr...
详细信息
We use computational design coupled with experimental characterization to systematically investigate the design principles for macrocycle membrane permeability and oral *** designed 1846-12 residue macrocycles with a ...
详细信息
We use computational design coupled with experimental characterization to systematically investigate the design principles for macrocycle membrane permeability and oral *** designed 1846-12 residue macrocycles with a wide range of predicted structures containing noncanonical backbone modifications and experimentally determined structures of 35;29 are very close to the computational *** such control,we show that membrane permeability can be systematically achieved by ensuring all amide(NH)groups are engaged in internal hydrogen bonding interactions.84 designs over the 6-12 residue size range cross membranes with an apparent permeability greater than 1×10^(-6)cm/s.
A critical assessment of computational hit-finding experiments (CACHE) challenge was conducted to predict ligands for the SARS-CoV-2 Nsp13 helicase RNA binding site, a highly conserved COVID-19 target. Twenty-three pa...
详细信息
A critical assessment of computational hit-finding experiments (CACHE) challenge was conducted to predict ligands for the SARS-CoV-2 Nsp13 helicase RNA binding site, a highly conserved COVID-19 target. Twenty-three participating teams comprised of computational chemists and data scientists used protein structure and data from fragment-screening paired with advanced computational and machine learning methods to each predict up to 100 inhibitory ligands. Across all teams, 1957 compounds were predicted and were subsequently procured from commercial catalogs for biophysical assays. Of these compounds, 0.7% were confirmed to bind to Nsp13 in a surface plasmon resonance assay. The six best-performing computational workflows used fragment growing, active learning, or conventional virtual screening with and without complementary deep-learning scoring functions. Follow-up functional assays resulted in identification of two compound scaffolds that bound Nsp13 with a below 10 μM and inhibited helicase activity. Overall, CACHE #2 participants were successful in identifying hit compound scaffolds targeting Nsp13, a central component of the coronavirus replication-transcription complex. Computational design strategies recurrently successful across the first two CACHE challenges include linking or growing docked or crystallized fragments and docking small and diverse libraries to train ultrafast machine-learning models. The CACHE #2 competition reveals how crowd-sourcing ligand prediction efforts using a distinct array of approaches followed with critical biophysical assays can result in novel lead compounds to advance drug discovery efforts.
作者:
Long GuiEric JurgensJamie L EbnerMatteo PorottoAnne MosconaKelly K LeeDepartment of Medicinal Chemistry
University of Washington Seattle Washington USA Biological Physics Structure and Design Graduate Program University of Washington Seattle Washington USA. Department of Pediatrics
Weill Medical College of Cornell University New York New York USA. Department of Medicinal Chemistry
University of Washington Seattle Washington USA. Department of Pediatrics
Weill Medical College of Cornell University New York New York USA kklee@uw.edu Anm2047@med.cornell.edu Map2028@med.cornell.edu. Department of Pediatrics
Weill Medical College of Cornell University New York New York USA Department of Microbiology and Immunology Weill Medical College of Cornell University New York New York USA kklee@uw.edu Anm2047@med.cornell.edu Map2028@med.cornell.edu. Department of Medicinal Chemistry
University of Washington Seattle Washington USA Biological Physics Structure and Design Graduate Program University of Washington Seattle Washington USA Department of Microbiology University of Washington Seattle Washington USA kklee@uw.edu Anm2047@med.cornell.edu Map2028@med.cornell.edu.
Background: Decades of steady improvements in life expectancy in Europe slowed down from around 2011, well before the COVID-19 pandemic, for reasons which remain disputed. We aimed to assess how changes in risk factor...
Background: Decades of steady improvements in life expectancy in Europe slowed down from around 2011, well before the COVID-19 pandemic, for reasons which remain disputed. We aimed to assess how changes in risk factors and cause-specific death rates in different European countries related to changes in life expectancy in those countries before and during the COVID-19 pandemic. Methods: We used data and methods from the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 to compare changes in life expectancy at birth, causes of death, and population exposure to risk factors in 16 European Economic Area countries (Austria, Belgium, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Luxembourg, the Netherlands, Norway, Portugal, Spain, and Sweden) and the four UK nations (England, Northern Ireland, Scotland, and Wales) for three time periods: 1990–2011, 2011–19, and 2019–21. Changes in life expectancy and causes of death were estimated with an established life expectancy cause-specific decomposition method, and compared with summary exposure values of risk factors for the major causes of death influencing life expectancy. Findings: All countries showed mean annual improvements in life expectancy in both 1990–2011 (overall mean 0·23 years [95% uncertainty interval [UI] 0·23 to 0·24]) and 2011–19 (overall mean 0·15 years [0·13 to 0·16]). The rate of improvement was lower in 2011–19 than in 1990–2011 in all countries except for Norway, where the mean annual increase in life expectancy rose from 0·21 years (95% UI 0·20 to 0·22) in 1990–2011 to 0·23 years (0·21 to 0·26) in 2011–19 (difference of 0·03 years). In other countries, the difference in mean annual improvement between these periods ranged from –0·01 years in Iceland (0·19 years [95% UI 0·16 to 0·21] vs 0·18 years [0·09 to 0·26]), to –0·18 years in England (0·25 years [0·24 to 0·25] vs 0·07 years [0·06 to 0·08]). In 2019–21, there was an overall decrease in mean annual life expectancy a
暂无评论