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These new methods continue to inform the design of chemical inhibitors and the identification of substrates of proteases.
In particular, computational modeling and virtual screening of small molecule libraries based on structural biology have accelerated the identification of substrates and drug candidates of transporters.
Such tools should find applications in fields ranging from high-throughput identification of substrates for tissue engineering and regenerative medicine to cell-based screening of drug candidates.
Eventually, various screening methodologies have been developed for determining the activity of P-glycoprotein, kinetics of drug transport and identification of substrates and inhibitors.
Where they have been used towards the study of ubiquitylation, protein microarrays have usually functioned as platforms for the identification of substrates for specific E3 ubiquitin ligases.
However, identification of substrates of ubiquitin ligases is generally a difficult task because there is no established method to systematically identify the substrates.
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The model is oriented towards optimization and control and enables identification of substrate film thickness uniformity sensitivities to process operating parameters, reactor system design and gas flow distribution.
From the first identification of substrate-derived peptide inhibitors to the complex, macrocyclic compounds, including paritaprevir and grazoprevir, that are currently available, the field has used structure-based design to confront the issues of potency, resistance and pharmacokinetics.
Although the method proved useful for identification of substrate peptides, the exact cut position cannot be determined directly with our assay, as also is the case with many competing methods for substrate identification.
Finally, our system is unique in that it allows for the identification of substrate peptides as well as directed evolution of proteases in such a straightforward way, which is due to the strong and simple genotype-phenotype link (the protease and substrate reporter are plasmid-encoded and coexpressed within the analyzed cells).
In this regard, the partition metric clearly impacts the identification of substrate cycles in modules.
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