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Biology subjects

Paul, A. J.

Publications and source records attributed to Paul, A. J..

2 recordsLinked to original sources

Broad-Spectrum Coronavirus Inhibitors Discovered by Modeling Viral Fusion Dynamics

Broad-spectrum therapeutics capable of inhibiting SARS-CoV-2, its variants, and related coronaviruses hold promise in curbing the spread of COVID-19 and averting future pandemics. Here, we employed a multidisciplinary approach that included molecular dynamics simulation (MDS) and artificial intelligence (AI)-based docking predictions to identify potent inhibitors that target a conserved region within the SARS-CoV-2 spike protein that mediates membrane fusion by undergoing large-scale mechanical rearrangements. In silico binding screens honed in on this region, leading to the discovery of FDA-approved drugs and novel molecules predicted to disrupt spike protein conformational changes. These compounds significantly inhibited SARS-CoV-2 infection and blocked the entry of spike protein-bearing pseudotyped , {beta}, {gamma}, {delta} variants as well as SARS-CoV and MERS-CoV in cultured human ACE2-expressing cells. The optimized lead compound significantly inhibited SARS-CoV2 infection in mice when administered orally.

microbiology↗

Reducing structural non-identifiabilities in upstream bioprocess models using profile-likelihood

Process models are increasingly used to support upstream process development in the biopharmaceutical industry for process optimization, scale-up and to reduce experimental effort. Parametric unstructured models based biological mechanisms are highly promising, since they do not require large amounts of data. The critical part in the application is the certainty of the parameter estimates, since uncertainty of the parameter estimates propagates to model predictions and can increase the risk associated with those predictions. Currently Fisher-Information-Matrix based approximations or Monte-Carlo approaches are used to estimate parameter confidence intervals and regularization approaches to decrease parameter uncertainty. Here we apply profile likelihood to determine parameter identifiability of a recent upstream process model. We have investigated the effect of data amount on identifiability and found out that addition of data reduces non-identifiability. The likelihood profiles of non-identifiable parameters were then used to uncover structural model changes. These changes effectively alleviate the remaining non-identifiabilities except for a single parameter out of 21 total parameters. We present the first application of profile likelihood to a complete upstream process model. Profile likelihood is a highly suitable method to determine parameter confidence intervals in upstream process models and provides reliable estimates even with non-linear models and limited data.

bioengineering↗