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This experimental result suggests that protein fold optimization by in vitro selection offers a viable approach to generating stable variants of many naturally occurring proteins whose structures and functions are otherwise difficult to study.
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Following purification, we observed that P4, P5, and P7 had a slightly lower soluble yield and slightly lower total fluorescence relative to GFPmut2 (Table 1), suggesting that these clones represent a novel solution to protein folding optimization as neither the function nor the soluble yield of the protein-of-interest was improved.
For example, according to Khanahmadi et al. (2015), CLEA-lipase activity was increased by 2.5-fold after optimization using response surface methodology and faced central composite design (FCCD) as compared to the activity obtained in One-Factor-at-a-Time method.
By converting Matlab M-files for differential equations into MEX-files (MEX stands for MATLAB Executable files, which are dynamically linked subroutines produced from C source code), as well as by opting for the stiff ode15s solver, up to 10-fold faster optimization can be achieved.
Optimization of melanin indicated the increase in the production of melanin from 3.4 mg/L to 6.6 mg/L resulting in a two fold increase after optimization.
We were able to improve the production of critical intermediates by 32-fold through genetic techniques and an additional 45-fold through culture optimization.
The expression of extracellular recombinant CGTase improved about 151-fold after the optimization was conducted.
The transcription level of the target gene was 1.4-fold higher following optimization of the signal peptide codons.
Using a small luciferase subunit (19 kDa) from the deep sea shrimp Oplophorus gracilirostris, we have improved luminescence expression in mammalian cells ∼2.5 million-fold by merging optimization of protein structure with development of a novel imidazopyrazinone substrate (furimazine).
The yield of α-L-arabinofuranosidase was enhanced by 2.34-fold after executing statistical optimization of various fermentative parameters.
Given the scoring scheme described in the previous section, the problem of simultaneous alignment and folding reduces to the optimization problem, (1) In principle, the solution to (1) follows immediately from the original dynamic programming algorithm for simultaneous alignment and folding presented by Sankoff (1985).
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