Unspecific peroxygenases (UPOs) are fungal enzymes that attract significant attention for their ability to perform versatile oxyfunctionalization reactions using H2O2. Unlike other oxygenases, UPOs do not require additional reductive equivalents or electron transfer chains that complicate basic and applied research. Nevertheless, UPOs generally exhibit low to no heterologous production levels and only four UPO structures have been determined to date by crystallography limiting their usefulness and obstructing research. To overcome this bottleneck, we implemented a workflow that applies PROSS stability design to AlphaFold2 model structures of 10 unique and diverse UPOs followed by a signal peptide shuffling to enable heterologous production. Nine UPOs were functionally produced in Pichia pastoris, including the recalcitrant CciUPO and three UPOs derived from oomycetes the first nonfungal UPOs to be experimentally characterized. We conclude that the high accuracy and reliability of new modeling and design workflows dramatically expand the pool of enzymes for basic and applied research.

Unspecific peroxygenases (UPOs) perform oxy-functionalizations for a wide range of substrates utilizing H2O2 without the need for further reductive equivalents or electron transfer chains. Tailoring these promising enzymes toward industrial application was intensely pursued in the last decade with engineering campaigns addressing the heterologous expression, activity, stability, and improvements in chemo- and regioselectivity. One hitherto missing integral part was the targeted engineering of enantioselectivity for specific substrates with poor starting enantioselectivity. In this work, we present the engineering of the short-type MthUPO toward the enantiodivergent hydroxylation of the terpene model substrate, β-ionone. Guided by computational modeling, we designed a small smart library and screened it with a GC−MS setup. After two rounds of iterative protein evolution, the activity increased up to 17-fold and reached a regioselectivity of up to 99.6% for the 4-hydroxy-β-ionone. Enantiodivergent variants were identified with enantiomeric ratios of 96.6:3.4 (R) and 0.3:99.7 (S), respectively.

In recent years, the engineering of flexible loops to improve enzyme properties has gained attention in biocatalysis. Herein, we report a loop engineering strategy to improve the stability of the substrate access tunnels, which reveals the molecular mechanism between loops and tunnels. Based on the dynamic tunnel analysis of CYP116B3, five positions (A86, T91, M108, A109, T111) in loops B-B′ and B′-C potentially affecting tunnel frequent occurrence were selected and subjected to simultaneous saturation mutagenesis. The best variant 8G8 (A86T/T91L/M108N/A109M/T111A) for the dealkylation of 7-ethoxycoumarin and the hydroxylation of naphthalene was identified with considerably increased activity (134-fold and 9-fold) through screening. Molecular dynamics simulations showed that the reduced flexibility of loops B-B′ and B′-C was responsible for increasing the stability of the studied tunnel. The redesign of loops B-B′ and B′-C surrounding the tunnel entrance provides loop engineering with a powerful and likely general method to kick on/off the substrate/product transportation.

Engineering proteins and enzymes with the desired functionality has broad applications in molecular biology, biotechnology, biomedical sciences, health, and medicine. The vastness of protein sequence space and all the possible proteins it represents can pose a considerable barrier for enzyme engineering campaigns through directed evolution and rational design. The nonlinear effects of coevolution between amino acids in protein sequences complicate this further. Data-driven models increasingly provide scientists with the computational tools to navigate through the largely undiscovered forest of protein variants and catch a glimpse of the rules and effects underlying the topology of sequence space. In this review, we outline a complete theoretical journey through the processes of protein engineering methods such as directed evolution and rational design and reflect on these strategies and data-driven hybrid strategies in the context of sequence space. We discuss crucial phenomena of residue coevolution, such as epistasis, and review the history of models created over the past decade, aiming to infer rules of protein evolution from data and use this knowledge to improve the prediction of the structure− function relationship of proteins. Data-driven models based on deep learning algorithms are among the most promising methods that can account for the nonlinear phenomena of sequence space to some degree. We also critically discuss the available models to predict evolutionary coupling and epistatic effects (classical and deep learning) in terms of their capabilities and limitations. Finally, we present our perspective on possible future directions for developing data-driven approaches and provide key orientation points and necessities for the future of the fast-evolving field of enzyme engineering.

Enzymatic hydroxylation of activated and nonactivated sp3-carbons attracts keen interest from the chemistry community as it is one of the most challenging tasks in organic synthesis. Nature provides a vast number of enzymes with an enormous catalytic versatility to fulfill this task. Given that those very different enzymes have a distinct specificity in substrate scope, selectivity, activity, stability, and catalytic cycle, it is interesting to outline similarities and differences. In this Review, we intend to delineate which enzymes possess considerable advantages within specific issues. Heterologous production, crystal structure availability, enzyme engineering potential, and substrate promiscuity are essential factors for the applicability of these biocatalysts.

Unspecific peroxygenases (UPOs) enable oxyfunctionalizations of a broad substrate range with unparalleled activities. Tailoring these enzymes for chemo- and regioselective transformations represents a grand challenge due to the difficulties in their heterologous productions. Herein, we performed protein engineering in Saccharomyces cerevisiae using the MthUPO from Myceliophthora thermophila. More than 5300 transformants were screened. This protein engineering led to a significant reshaping of the active site as elucidated by computational modelling. The reshaping was responsible for the increased oxyfunctionalization activity, with improved kcat/Km values of up to 16.5-fold for the model substrate 5-nitro-1,3-benzodioxole. Moreover, variants were identified with high chemo- and regioselectivities in the oxyfunctionalization of aromatic and benzylic carbons, respectively. The benzylic hydroxylation was demonstrated to perform with enantioselectivities of up to 95% ee. The proposed evolutionary protocol and rationalization of the enhanced activities and selectivities acquired by MthUPO variants represent a step forward toward the use and implementation of UPOs in biocatalytic synthetic pathways of industrial interest.

BackgroundMolecule identification is a crucial step in metabolomics and environmental sciences. Besides in silico fragmentation, as performed by MetFrag, also machine learning and statistical methods evolved, showing an improvement in molecule annotation based on MS/MS data. In this work we present a new statistical scoring method where annotations of m/z fragment peaks to fragment-structures are learned in a training step. Based on a Bayesian model, two additional scoring terms are integrated into the new MetFrag2.4.5 and evaluated on the test data set of the CASMI 2016 contest.ResultsThe results on the 87 MS/MS spectra from positive and negative mode show a substantial improvement of the results compared to submissions made by the former MetFrag approach. Top1 rankings increased from 5 to 21 and Top10 rankings from 39 to 55 both showing higher values than for CSI:IOKR, the winner of the CASMI 2016 contest. For the negative mode spectra, MetFrag’s statistical scoring outperforms all other participants which submitted results for this type of spectra.ConclusionsThis study shows how statistical learning can improve molecular structure identification based on MS/MS data compared on the same method using combinatorial in silico fragmentation only. MetFrag2.4.5 shows especially in negative mode a better performance compared to the other participating approaches.

BackgroundTranscriptional gene regulation is a fundamental process in nature, and the experimental and computational investigation of DNA binding motifs and their binding sites is a prerequisite for elucidating this process. Approaches for de-novo motif discovery can be subdivided in phylogenetic footprinting that takes into account phylogenetic dependencies in aligned sequences of more than one species and non-phylogenetic approaches based on sequences from only one species that typically take into account intra-motif dependencies. It has been shown that modeling (i) phylogenetic dependencies as well as (ii) intra-motif dependencies separately improves de-novo motif discovery, but there is no approach capable of modeling both (i) and (ii) simultaneously.ResultsHere, we present an approach for de-novo motif discovery that combines phylogenetic footprinting with motif models capable of taking into account intra-motif dependencies. We study the degree of intra-motif dependencies inferred by this approach from ChIP-seq data of 35 transcription factors. We find that significant intra-motif dependencies of orders 1 and 2 are present in all 35 datasets and that intra-motif dependencies of order 2 are typically stronger than those of order 1. We also find that the presented approach improves the classification performance of phylogenetic footprinting in all 35 datasets and that incorporating intra-motif dependencies of order 2 yields a higher classification performance than incorporating such dependencies of only order 1.ConclusionCombining phylogenetic footprinting with motif models incorporating intra-motif dependencies leads to an improved performance in the classification of transcription factor binding sites. This may advance our understanding of transcriptional gene regulation and its evolution.

BackgroundFor three decades, sequence logos are the de facto standard for the visualization of sequence motifs in biology and bioinformatics. Reasons for this success story are their simplicity and clarity. The number of inferred and published motifs grows with the number of data sets and motif extraction algorithms. Hence, it becomes more and more important to perceive differences between motifs. However, motif differences are hard to detect from individual sequence logos in case of multiple motifs for one transcription factor, highly similar binding motifs of different transcription factors, or multiple motifs for one protein domain.ResultsHere, we present DiffLogo, a freely available, extensible, and user-friendly R package for visualizing motif differences. DiffLogo is capable of showing differences between DNA motifs as well as protein motifs in a pair-wise manner resulting in publication-ready figures. In case of more than two motifs, DiffLogo is capable of visualizing pair-wise differences in a tabular form. Here, the motifs are ordered by similarity, and the difference logos are colored for clarity. We demonstrate the benefit of DiffLogo on CTCF motifs from different human cell lines, on E-box motifs of three basic helix-loop-helix transcription factors as examples for comparison of DNA motifs, and on F-box domains from three different families as example for comparison of protein motifs.ConclusionsDiffLogo provides an intuitive visualization of motif differences. It enables the illustration and investigation of differences between highly similar motifs such as binding patterns of transcription factors for different cell types, treatments, and algorithmic approaches.

BackgroundOntology-based enrichment analysis aids in the interpretation and understanding of large-scale biological data. Ontologies are hierarchies of biologically relevant groupings. Using ontology annotations, which link ontology classes to biological entities, enrichment analysis methods assess whether there is a significant over or under representation of entities for ontology classes. While many tools exist that run enrichment analysis for protein sets annotated with the Gene Ontology, there are only a few that can be used for small molecules enrichment analysis.ResultsWe describe BiNChE, an enrichment analysis tool for small molecules based on the ChEBI Ontology. BiNChE displays an interactive graph that can be exported as a high-resolution image or in network formats. The tool provides plain, weighted and fragment analysis based on either the ChEBI Role Ontology or the ChEBI Structural Ontology.ConclusionsBiNChE aids in the exploration of large sets of small molecules produced within Metabolomics or other Systems Biology research contexts. The open-source tool provides easy and highly interactive web access to enrichment analysis with the ChEBI ontology tool and is additionally available as a standalone library.