Engineering Ribosomal Machinery for Noncanonical Amino Acid Incorporation.

The introduction of noncanonical amino acids into proteins has enabled researchers to modify fundamental physicochemical and functional properties of proteins. While the alteration of the genetic code, via the introduction of orthogonal aminoacyl-tRNA synthetase:tRNA pairs, has driven many of these efforts, the various components involved in the process of translation are important for the development of new genetic codes. In this review, we will focus on recent advances in engineering ribosomal machinery for noncanonical amino acid incorporation and genetic code modification. The engineering of the ribosome itself will be considered, as well as the many factors that interact closely with the ribosome, including both tRNAs and accessory factors, such as the all-important EF-Tu. Given the success of genome re-engineering efforts, future paths for radical alterations of the genetic code will require more expansive alterations in the translation machinery.

Changes in Coding and Efficiency through Modular Modifications to a One Pot PURE System for In Vitro Transcription and Translation.

The incorporation of unnatural amino acids is an attractive method for improving or bringing new and novel functions in peptides and proteins. Cell-free protein synthesis using the Protein Synthesis Using Recombinant Elements (PURE) system is an attractive platform for efficient unnatural amino acid incorporation. In this work, we further adapted and modified the One Pot PURE to obtain a robust and modular system for enzymatic single-site-specific incorporation of an unnatural amino acid. We demonstrated the flexibility of this system through the introduction of two different orthogonal aminoacyl tRNA synthetase:tRNA pairs that suppressed two distinctive stop codons in separate reaction mixtures.

Improved Bst DNA Polymerase Variants Derived via a Machine Learning Approach.

The DNA polymerase I from Geobacillus stearothermophilus (also known as Bst DNAP) is widely used in isothermal amplification reactions, where its strand displacement ability is prized. More robust versions of this enzyme should be enabled for diagnostic applications, especially for carrying out higher temperature reactions that might proceed more quickly. To this end, we appended a short fusion domain from the actin-binding protein villin that improved both stability and purification of the enzyme. In parallel, we have developed a machine learning algorithm that assesses the relative fit of individual amino acids to their chemical microenvironments at any position in a protein and applied this algorithm to predict sequence substitutions in Bst DNAP. The top predicted variants had greatly improved thermotolerance (heating prior to assay), and upon combination, the mutations showed additive thermostability, with denaturation temperatures up to 2.5 °C higher than the parental enzyme. The increased thermostability of the enzyme allowed faster loop-mediated isothermal amplification assays to be carried out at 73 °C, where both Bst DNAP and its improved commercial counterpart Bst 2.0 are inactivated. Overall, this is one of the first examples of the application of machine learning approaches to the thermostabilization of an enzyme.