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An automated sequence selection algorithm, based on the dead-end elimination theorem, was used to find the optimal amino acid sequence fitting the target structure.
These encouraging results show that de novo design of an idealized protein structure of more than 200 amino acid residues is now possible, from construction of a particular backbone conformation to determination of an amino acid sequence with an automated sequence selection algorithm.
Developing a sequence selection algorithm that simultaneously takes a complete reaction trajectory into account is perhaps the biggest remaining challenge in computational enzyme design.
To obtain more difficult test cases having lower rates of identity, we constrained the reference sequence selection algorithm to exclude sequences above a cut-off value (see section parameter tuning).
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The link throughput improvements for these three methods will be described, when considering the receiver design, power control, and signature sequence selection algorithms.
We chose potential siRNA sequences with selection algorithms based on empirical criteria, as described in the materials and methods.
Probe sequences were selected using a score-based selection algorithm, as described.
In this study, we proposed two feature selection approaches based on hexamer sequences: the Frequency Distribution Analyzed Feature Selection Algorithm (FDAFSA) and the Random Triplet Pair Feature Selecting Genetic Algorithm (RTPFSGA).
Figure: Basic feature selection algorithm for selecting the best features.
We discuss the probe design algorithms, including virulence gene sequence selection, and our protocols for sample preparation, amplification, labeling, hybridization, and data analysis.
For randomisation of sequences >= 32,000 bp, a roulette wheel selection algorithm was used where a nucleotide's chance of selection was based on its frequency in the original sequence.
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