Making Sense of “Nonsense” and More: Challenges and Opportunities in the Genetic Code Expansion, in the World of tRNA Modifications

The evolutional development of the RNA translation process that leads to protein synthesis based on naturally occurring amino acids has its continuation via synthetic biology, the so-called rational bioengineering. Genetic code expansion (GCE) explores beyond the natural translational processes to further enhance the structural properties and augment the functionality of a wide range of proteins. Prokaryotic and eukaryotic ribosomal machinery have been proven to accept engineered tRNAs from orthogonal organisms to efficiently incorporate noncanonical amino acids (ncAAs) with rationally designed side chains. These side chains can be reactive or functional groups, which can be extensively utilized in biochemical, biophysical, and cellular studies. Genetic code extension offers the contingency of introducing more than one ncAA into protein through frameshift suppression, multi-site-specific incorporation of ncAAs, thereby increasing the vast number of possible applications. However, different mediating factors reduce the yield and efficiency of ncAA incorporation into synthetic proteins. In this review, we comment on the recent advancements in genetic code expansion to signify the relevance of systems biology in improving ncAA incorporation efficiency. We discuss the emerging impact of tRNA modifications and metabolism in protein design. We also provide examples of the latest successful accomplishments in synthetic protein therapeutics and show how codon expansion has been employed in various scientific and biotechnological applications.

Orthogonal Activation of Metabotropic Glutamate Receptor Using Coordination Chemogenetics

Cell-surface receptors play a pivotal role as transducers of extracellular input. Although different cell types express the same receptor, the physiological roles of the receptor are highly dependent on cell type. To understand each role, tactics for cell-specific activation of the target receptor are in high demand. Herein, we developed an orthogonal activation method targeting metabotropic glutamate receptor 1 (mGlu1), a G-protein coupled receptor. In this method, direct activation via coordination-based chemogenetics (dA-CBC) was adopted, where activation of mGlu1 was artificially induced by a protein conformational change in response to the coordination of a metal ion or metal-ion complex. Our structure-based protein design and screening approach identified mGlu1 mutants that were directly activated by the coordination of Cu2+ or Zn2+, in addition to our previous Pd-complex-sensitive mGlu1 mutant. Notably, the activation of the mutants was mutually orthogonal, resulting in cell-type selective activation in a model system using HEK293 cells.

Intake of Animal Protein and Dietary Sources in the Colombian Population: Results of the National Nutrition Survey (ENSIN-2015)

Background. There is a lack of knowledge in Colombia about dietary intake and sources of animal protein. Design. Cross-sectional, nationally representative surveys. Setting. Colombia. Participants. n = 32,457 participants aged from 1 to 64 years. The sample analyzed included 21,036 boys and nonpregnant girls, 10,099 adults, and 1,322 pregnant women, 118 of whom were below 18 years of age. Results. Protein intake was 32.9 g/d (95% CI: 32.4, 33.4) per 1,000 kilocalories. The relative contribution (%) of total protein to the total energy intake/day (acceptable macronutrient distribution) was 13.2% (95% CI: 13.0, 13.3). The acceptable macronutrient distribution (AMDR) for animal protein for those aged 1 to 64 years was 7.8% (95% CI: 7.6, 8.0), with a minimum of 7.1% (95% CI: 6.7, 7.5), which was for children aged from 13 to 17 years, and a maximum of 8.3% (95% CI: 8.1, 8.5), for children aged from 1 to 4 years ( P = 0.018 ). For all groups, animal protein made up the majority of total proteins, with 62.6% (95% CI: 61.7, 63.6) for preschoolers, 55.8% (95% CI: 53.2, 58.4) for school-aged children, 54.6% (95% CI: 53.0, 56.1) for adolescents, 58.1% (95% CI: 57.5, 58.7) for adults, and 57.5% (95% CI: 55.2, 59.7) for pregnant women ( P = 0.027 ). The three main dietary sources of animal protein were red meat (17.8%), chicken (16.3%), and eggs (10.5%). The sources of vegetal protein were bread-arepa-pasta (20.0%), cereals (19.8%), and legumes (8.2%). Conclusions. Protein intake is excessive according to the Recommended Dietary Allowance (RDA), while it is not excessive from the perspective of the AMDR.

Generation of bright monomeric red fluorescent proteins via computational design of enhanced chromophore packing

Red fluorescent proteins (RFPs) have found widespread application in chemical and biological research due to their longer emission wavelengths. Here, we use computational protein design to increase the quantum yield...

Function-guided protein design by deep manifold sampling

Protein design is challenging because it requires searching through a vast combinatorial space that is only sparsely functional. Self-supervised learning approaches offer the potential to navigate through this space more effectively and thereby accelerate protein engineering. We introduce a sequence denoising autoencoder (DAE) that learns the manifold of protein sequences from a large amount of potentially unlabelled proteins. This DAE is combined with a function predictor that guides sampling towards sequences with higher levels of desired functions. We train the sequence DAE on more than 20M unlabeled protein sequences spanning many evolutionarily diverse protein families and train the function predictor on approximately 0.5M sequences with known function labels. At test time, we sample from the model by iteratively denoising a sequence while exploiting the gradients from the function predictor. We present a few preliminary case studies of protein design that demonstrate the effectiveness of this proposed approach, which we refer to as "deep manifold sampling", including metal binding site addition, function-preserving diversification, and global fold change.

Dissecting the stability determinants of a challenging de novo protein fold using massively parallel design and experimentation

Designing entirely new protein structures remains challenging because we do not fully understand the biophysical determinants of folding stability. Yet some protein folds are easier to design than others. Previous work identified the 43-residue αββ&#945 fold as especially challenging: the best designs had only a 2% success rate, compared to 39-87% success for other simple folds (1). This suggested the αββ&#945 fold would be a useful model system for gaining a deeper understanding of folding stability determinants and for testing new protein design methods. Here, we designed over ten thousand new αββ&#945 proteins and found over three thousand of them to fold into stable structures using a high-throughput protease-based assay. Nuclear magnetic resonance, hydrogen-deuterium exchange, circular dichroism, deep mutational scanning, and scrambled sequence control experiments indicated that our stable designs fold into their designed αββ&#945 structures with exceptional stability for their small size. Our large dataset enabled us to quantify the influence of universal stability determinants including nonpolar burial, helix capping, and buried unsatisfied polar atoms, as well as stability determinants unique to the αββ&#945 topology. Our work demonstrates how large-scale design and test cycles can solve challenging design problems while illuminating the biophysical determinants of folding.

Heterogeneity of the GFP fitness landscape and data-driven protein design

Studies of protein fitness landscapes reveal biophysical constraints guiding protein evolution and empower prediction of functional proteins. However, generalisation of these findings is limited due to scarceness of systematic data on fitness landscapes of proteins with a defined evolutionary relationship. We characterized the fitness peaks of four orthologous fluorescent proteins with a broad range of sequence divergence. While two of the four studied fitness peaks were sharp, the other two were considerably flatter, being almost entirely free of epistatic interactions. Counterintuitively, mutationally robust proteins, characterized by a flat fitness peak, were not optimal templates for machine-learning-driven protein design – instead, predictions were more accurate for fragile proteins with epistatic landscapes. Our work paves insights for practical application of fitness landscape heterogeneity in protein engineering.