1,344 research outputs found
Directed evolution converts subtilisin E into a functional equivalent of thermitase
We used directed evolution to convert Bacillus subtilis subtilisin E into an enzyme functionally equivalent to its thermophilic homolog thermitase from Thermoactinomyces vulgaris. Five generations of random mutagenesis, recombination and screening created subtilisin E 5-3H5, whose half-life at 83°C (3.5 min) and temperature optimum for activity (Topt, 76°C) are identical with those of thermitase. The Topt of the evolved enzyme is 17°C higher and its half-life at 65°C is >200 times that of wild-type subtilisin E. In addition, 5-3H5 is more active towards the hydrolysis of succinyl-Ala-Ala-Pro-Phe-p-nitroanilide than wild-type at all temperatures from 10 to 90°C. Thermitase differs from subtilisin E at 157 amino acid positions. However, only eight amino acid substitutions were sufficient to convert subtilisin E into an enzyme equally thermostable. The eight substitutions, which include known stabilizing mutations (N218S, N76D) and also several not previously reported, are distributed over the surface of the enzyme. Only two (N218S, N181D) are found in thermitase. Directed evolution provides a powerful tool to unveil mechanisms of thermal adaptation and is an effective and efficient approach to increasing thermostability without compromising enzyme activity
Enzyme engineering for synthetic biology
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Programmable DNA-guided artificial restriction enzymes: Discovery, engineering, and applications
Restriction enzymes are essential tools for recombinant DNA technology that have revolutionized modern biological research. However, they have limited sequence specificity and availability. To address these limitations, we recently developed a Pyrococcus furiosus Argonaute (PfAgo) based platform for generating artificial restriction enzymes (AREs) capable of recognizing and cleaving DNA sequences at virtually any arbitrary site and generating defined sticky ends of varying length. We demonstrated the utility of AREs in DNA profiling and PCR-based gene cloning. In addition, we are developing a new versatile and flexible strategy based on AREs for assembly of large DNA molecules. This strategy can be readily implemented in our biofoundry for automated DNA assembly of pathways, plasmids, and genetic circuits, which will accelerate the development of new synthetic biology applications
A deep learning tool for protein engineering
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Cognitive computation of compressed sensing for watermark signal measurement
As an important tool for protecting multimedia contents, scrambling and randomizing of original messages is used in generating digital watermark for satisfying security requirements. Based on the neural perception of high-dimensional data, compressed sensing (CS) is proposed as a new technique in watermarking for improved security and reduced computational complexity. In our proposed methodology, watermark signal is extracted from the CS of the Hadamard measurement matrix. Through construction of the scrambled block Hadamard matrix utilizing a cryptographic key, encrypting the watermark signal in CS domain is achieved without any additional computation required. The extensive experiments have shown that the neural inspired CS mechanism can generate watermark signal of higher security, yet it still maintains a better trade-off between transparency and robustness
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