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Summary Biology is an analytical and informational science that is becoming increasingly dependent on chemical synthesis. One example is the high‐throughput and low‐cost synthesis of DNA, which is a foundation for the research field of synthetic biology (SB). The aim of SB is to provide biotechnological solutions to health, energy and environmental issues as well as unsustainable manufacturing processes in the frame of naturally existing chemical building blocks. Xenobiology (XB) goes a step further by implementing non‐natural building blocks in living cells. In this context, genetic code engineering respectively enables the re‐design of genes/genomes and proteins/proteomes with non‐canonical nucleic (XNAs) and amino (ncAAs) acids. Besides studying information flow and evolutionary innovation in living systems, XB allows the development of new‐to‐nature therapeutic proteins/peptides, new biocatalysts for potential applications in synthetic organic chemistry and biocontainment strategies for enhanced biosafety. In this perspective, we provide a brief history and evolution of the genetic code in the context of XB. We then discuss the latest efforts and challenges ahead for engineering the genetic code with focus on substitutions and additions of ncAAs as well as standard amino acid reductions. Finally, we present a roadmap for the directed evolution of artificial microbes for emancipating rare sense codons that could be used to introduce novel building blocks. The development of such xenomicroorganisms endowed with a ‘genetic firewall’ will also allow to study and understand the relation between code evolution and horizontal gene transfer. Virtually universal on earth, the genetic code is the set of rules for interpretation and realization of genetic information. It is the most fundamental and the earliest manifestation of life being shared among all living organisms, as it establishes the information flow in biology. Biology as an analytical and informational science reach point to start to find routes to change and expand the repertoire beyond the canonical 20 (+2) amino acids of the genetic code, embracing a new chemical makeup of life.

Multidimensional fitness landscapes provide insights into the molecular basis of laboratory and natural evolution. To date, such efforts usually focus on limited protein families and a single enzyme trait, with little concern about the relationship between protein epistasis and conformational dynamics. Here, we report a multiparametric fitness landscape for a cytochrome P450 monooxygenase that was engineered for the regio- and stereoselective hydroxylation of a steroid. We develop a computational program to automatically quantify non-additive effects among all possible mutational pathways, finding pervasive cooperative signs and magnitude epistasis on multiple catalytic traits. By using quantum mechanics and molecular dynamics simulations, we show that these effects are modulated by long-range interactions in loops, helices and β-strands that gate the substrate access channel allowing for optimal catalysis. Our work highlights the importance of conformational dynamics on epistasis in an enzyme involved in secondary metabolism and offers insights for engineering P450s. Connecting conformational dynamics and epistasis has so far been limited to a few proteins and a single fitness trait. Here, the authors provide evidence of positive epistasis on multiple catalytic traits in the evolution and dynamics of engineered cytochrome P450 monooxygenase, offering insights for in silico protein design.

Aliphatic α,ω‐dicarboxylic acids (DCAs) are a class of useful chemicals that are currently produced by energy-intensive, multistage chemical oxidations that are hazardous to the environment. Therefore, the development of environmentally friendly, safe, neutral routes to DCAs is important. We report an in vivo artificially designed biocatalytic cascade process for biotransformation of cycloalkanes to DCAs. To reduce protein expression burden and redox constraints caused by multi-enzyme expression in a single microbe, the biocatalytic pathway is divided into three basic Escherichia coli cell modules. The modules possess either redox-neutral or redox-regeneration systems and are combined to form E. coli consortia for use in biotransformations. The designed consortia of E. coli containing the modules efficiently convert cycloalkanes or cycloalkanols to DCAs without addition of exogenous coenzymes. Thus, this developed biocatalytic process provides a promising alternative to the current industrial process for manufacturing DCAs. Aliphatic α,ω-dicarboxylic acids (DCAs) are widely used chemicals that are synthesised by multistage chemical oxidations. Here, the authors report an artificially designed biocatalytic cascade for the oxidation of cycloalkanes or cycloalkanols to DCAs in the form of microbial consortia, composed of three Escherichia coli cell modules.

Machine learning has revolutionized drug discovery by enabling the exploration of vast, uncharted chemical spaces essential for discovering novel patentable drugs. Despite the critical role of human G protein-coupled receptors in FDA-approved drugs, exhaustive in-distribution drug-target interaction testing across all pairs of human G protein-coupled receptors and known drugs is rare due to significant economic and technical challenges. This often leaves off-target effects unexplored, which poses a considerable risk to drug safety. In contrast to the traditional focus on out-of-distribution exploration (drug discovery), we introduce a neighborhood-to-prediction model termed Chemical Space Neural Networks that leverages network homophily and training-free graph neural networks with labels as features. We show that Chemical Space Neural Networks’ ability to make accurate predictions strongly correlates with network homophily. Thus, labels as features strongly increase a machine learning model’s capacity to enhance in-distribution prediction accuracy, which we show by integrating labeled data during inference. We validate these advancements in a high-throughput yeast biosensing system (3773 drug-target interactions, 539 compounds, 7 human G protein-coupled receptors) to discover novel drug-target interactions for FDA-approved drugs and to expand the general understanding of how to build reliable predictors to guide experimental verification. This study presents CSNN, a tool leveraging network homophily and training-free graph neural networks with labels as features to predict drug-target-interactions (DTIs). The model is then experimentally validated on a new dataset of 3773 DTIs from a yeast-based screen on 7 human GPCRs.

Steroids are the most widely marketed products by the pharmaceutical industry after antibiotics. Steroid hydroxylation is one of the most important functionalizations because their derivatives enable a higher biological activity compared to their less polar non-hydroxylated analogs. Bacterial cytochrome P450s constitute promising biocatalysts for steroid hydroxylation due to their high expression level in common workhorses like Escherichia coli. However, they often suffer from wrong or insufficient regio- and/or stereoselectivity, low activity, narrow substrate range as well as insufficient thermostability, which hampers their industrial application. Fortunately, these problems can be generally solved by protein engineering based on directed evolution and rational design. In this work, an overview of recent developments on the engineering of bacterial cytochrome P450s for steroid hydroxylation is presented.

Saturation mutagenesis is a powerful technique for engineering proteins, metabolic pathways and genomes. In spite of its numerous applications, creating high-quality saturation mutagenesis libraries remains a challenge, as various experimental parameters influence in a complex manner the resulting diversity. We explore from the economical perspective various aspects of saturation mutagenesis library preparation: We introduce a cheaper and faster control for assessing library quality based on liquid media; analyze the role of primer purity and supplier in libraries with and without redundancy; compare library quality, yield, randomization efficiency and annealing bias using traditional and emergent randomization schemes based on mixtures of mutagenic primers; and establish a methodology for choosing the most cost-effective randomization scheme given the screening costs and other experimental parameters. We show that by carefully considering these parameters, laboratory expenses can be significantly reduced.

Site-saturation mutagenesis (SSM) has been used in directed evolution of proteins for a long time. As a special form of saturation mutagenesis, it involves individual randomization at a given residue with formation of all 19 amino acids. To date, the most efficient embodiment of SSM is a one-step PCR-based approach using NNK codon degeneracy. However, in the case of difficult-to-randomize genes, SSM may not deliver all of the expected 19 mutants, which compels the user to invest further efforts by applying site-directed mutagenesis for the construction of the missing mutants. To solve this problem, we developed a two-step PCR-based technique in which a mutagenic primer and a non-mutagenic (silent) primer are used to generate a short DNA fragment, which is recovered and then employed as a megaprimer to amplify the whole plasmid. The present two-step and older one-step (partially overlapped primer approach) procedures were compared by utilizing cytochrome P450-BM3, which is a “difficult-to-randomize” gene. The results document the distinct superiority of the new method by checking the library quality on DNA level based on massive sequence data, but also at amino acid level. Various future applications in biotechnology can be expected, including the utilization when constructing mutability landscapes, which provide semi-rational information for identifying hot spots for protein engineering and directed evolution.

The second EMBO conference on ‘Catalytic Mechanisms by Biological Systems’ took place in Groningen, the Netherlands, in October 2012. Structural, molecular and computational biologists, as well as chemists, biophysicists and engineers discussed technologies to improve our mechanistic understanding of enzymes, as well as the design of robust biocatalysts. The second EMBO conference on ‘Catalytic Mechanisms by Biological Systems’ took place in October 2012 and covered technologies to improve our mechanistic understanding of enzymes, as well as the design of robust biocatalysts.