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Repeat-length-independent broad-spectrum shuffling, a novel method of generating a random chimera library in vivo.

ABSTRACT: We describe a novel method of random chimeragenesis based on highly frequent deletion formation in the Escherichia coli ssb-3 strain and a deletion-directed chimera selection system that uses the rpsL(+) gene as a reporter. It enables the selection of chimeras without target gene expression and can therefore be applied to cytotoxic targets. When this system was applied to phospholipase D genes from Streptomyces septatus TH-2 and Streptomyces halstedii subsp. scabies K6 (examples of cytotoxic targets), chimeragenesis occurred between short identical sequences at the corresponding position of the parental genes with large variations. Chimeragenesis was >1,000 times more frequent in the ssb-3 background than in the ssb(+) background. We called this system repeat-length-independent broad-spectrum shuffling. It enables the convenient chimeragenesis and functional study of chimeric proteins. In fact, we found two amino acid residues related to the thermostability of phospholipase D (Phe426 and Thr433) by comparing thermostability among the chimeric enzymes obtained.

SUBMITTER: Mori K

PROVIDER: S-EPMC546693 | biostudies-literature | 2005 Feb

REPOSITORIES: biostudies-literature

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Repeat-length-independent broad-spectrum shuffling, a novel method of generating a random chimera library in vivo.

Mori Koichi K Mukaihara Takafumi T Uesugi Yoshiko Y Iwabuchi Masaki M Hatanaka Tadashi T

Applied and environmental microbiology 20050201 2

We describe a novel method of random chimeragenesis based on highly frequent deletion formation in the Escherichia coli ssb-3 strain and a deletion-directed chimera selection system that uses the rpsL(+) gene as a reporter. It enables the selection of chimeras without target gene expression and can therefore be applied to cytotoxic targets. When this system was applied to phospholipase D genes from Streptomyces septatus TH-2 and Streptomyces halstedii subsp. scabies K6 (examples of cytotoxic tar ...[more]

PMID: 15691927

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Project description:Pseudo-random number generators (PRNGs) are software algorithms generating a sequence of numbers approximating the properties of random numbers. They are critical components in many information systems that require unpredictable and nonarbitrary behaviors, such as parameter configuration in machine learning, gaming, cryptography, and simulation. A PRNG is commonly validated through a statistical test suite, such as NIST SP 800-22rev1a (NIST test suite), to evaluate its robustness and the randomness of the numbers. In this paper, we propose a Wasserstein distance-based generative adversarial network (WGAN) approach to generating PRNGs that fully satisfy the NIST test suite. In this approach, the existing Mersenne Twister (MT) PRNG is learned without implementing any mathematical programming code. We remove the dropout layers from the conventional WGAN network to learn random numbers distributed in the entire feature space because the nearly infinite amount of data can suppress the overfitting problems that occur without dropout layers. We conduct experimental studies to evaluate our learned pseudo-random number generator (LPRNG) by adopting cosine-function-based numbers with poor random number properties according to the NIST test suite as seed numbers. The experimental results show that our LPRNG successfully converted the sequence of seed numbers to random numbers that fully satisfy the NIST test suite. This study opens the way for the "democratization" of PRNGs through the end-to-end learning of conventional PRNGs, which means that PRNGs can be generated without deep mathematical know-how. Such tailor-made PRNGs will effectively enhance the unpredictability and nonarbitrariness of a wide range of information systems, even if the seed numbers can be revealed by reverse engineering. The experimental results also show that overfitting was observed after about 450,000 trials of learning, suggesting that there is an upper limit to the number of learning counts for a fixed-size neural network, even when learning with unlimited data.

Dataset Information

Repeat-length-independent broad-spectrum shuffling, a novel method of generating a random chimera library in vivo.

Publications

Repeat-length-independent broad-spectrum shuffling, a novel method of generating a random chimera library in vivo.

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