Correlated positions in protein evolution and engineering

Discovery of a Kojibiose Hydrolase by Analysis of Specificity-Determining Correlated Positions in Glycoside Hydrolase Family 65

The Glycoside Hydrolase Family 65 (GH65) is an enzyme family of inverting α-glucoside phosphorylases and hydrolases that currently contains 10 characterized enzyme specificities. However, its sequence diversity has never been studied in detail. Here, an in-silico analysis of correlated mutations was performed, revealing specificity-determining positions that facilitate annotation of the family’s phylogenetic tree. By searching these positions for amino acid motifs that do not match those found in previously characterized enzymes from GH65, several clades that may harbor new functions could be identified. Three enzymes from across these regions were expressed in E. coli and their substrate profile was mapped. One of those enzymes, originating from the bacterium Mucilaginibacter mallensis, was found to hydrolyze kojibiose and α-1,2-oligoglucans with high specificity. We propose kojibiose glucohydrolase as the systematic name and kojibiose hydrolase or kojibiase as the short name for this new enzyme. This work illustrates a convenient strategy for mapping the natural diversity of enzyme families and smartly mining the ever-growing number of available sequences in the quest for novel specificities.

Recent advances in user-friendly computational tools to engineer protein function

Progress in technology and algorithms throughout the past decade has transformed the field of protein design and engineering. Computational approaches have become well-engrained in the processes of tailoring proteins for various biotechnological applications. Many tools and methods are developed and upgraded each year to satisfy the increasing demands and challenges of protein engineering. To help protein engineers and bioinformaticians navigate this emerging wave of dedicated software, we have critically evaluated recent additions to the toolbox regarding their application for semi-rational and rational protein engineering. These newly developed tools identify and prioritize hotspots and analyze the effects of mutations for a variety of properties, comprising ligand binding, protein-protein and protein-nucleic acid interactions, and electrostatic potential. We also discuss notable progress to target elusive protein dynamics and associated properties like ligand-transport processes and allosteric communication. Finally, we discuss several challenges these tools face and provide our perspectives on the further development of readily applicable methods to guide protein engineering efforts.

Engineering of a Thermostable Biocatalyst for the Synthesis of 2-O-Glucosylglycerol

2‐O‐Glucosylglycerol is accumulated by various bacteria and plants in response to environmental stress. It is widely applied as a bioactive moisturising ingredient in skin care products, for which it is manufactured via enzymatic glucosylation of glycerol by the sucrose phosphorylase from Leuconostoc mesenteroides. This industrial process is operated at room temperature due to the mediocre stability of the biocatalyst, often leading to microbial contamination. The highly thermostable sucrose phosphorylase from Bifidobacterium adolescentis could be a better alternative in that regard, but this enzyme is not fit for production of 2‐O‐glucosylglycerol due to its low regioselectivity and poor affinity for glycerol. In this work, the thermostable phosphorylase was engineered to alleviate these problems. Several engineering approaches were explored, ranging from site‐directed mutagenesis to conventional, binary, iterative or combinatorial randomisation of the active site, resulting in the screening of ∼3,900 variants. Variant P134Q displayed a 21‐fold increase in catalytic efficiency for glycerol, as well as a threefold improvement in regioselectivity towards the 2‐position of the substrate, while retaining its activity for several days at elevated temperatures.

Structure-function relationships in NDP-sugar active SDR enzymes: Fingerprints for functional annotation and enzyme engineering

Short-chain Dehydrogenase/Reductase enzymes that are active on nucleotide sugars (abbreviated as NS-SDR) are of paramount importance in the biosynthesis of rare sugars and glycosides. Some family members have already been extensively characterized due to their direct implication in metabolic disorders or in the biosynthesis of virulence factors. In this review, we combine the knowledge gathered from studies that typically focused only on one NS-SDR activity with an in-depth analysis and overview of all of the different NS-SDR families (169,076 enzyme sequences). Through this structure-based multiple sequence alignment of NS-SDRs retrieved from public databases, we could identify clear patterns in conservation and correlation of crucial residues. Supported by this analysis, we suggest updating and extending the UDP-galactose 4-epimerase "hexagonal box model" to an "heptagonal box model" for all NS-SDR enzymes. This specificity model consists of seven conserved regions surrounding the NDP-sugar substrate that serve as fingerprint for each specificity. The specificity fingerprints highlighted in this review will be beneficial for functional annotation of the large group of NS-SDR enzymes and form a guide for future enzyme engineering efforts focused on the biosynthesis of rare and specialty carbohydrates.

ACES: A co-evolution simulator generates co-varying protein and nucleic acid sequences

Sequence-specific and consequential interactions within or between proteins and/or RNAs can be predicted by identifying co-evolution of residues in these molecules. Different algorithms have been used to detect co-evolution, often using biological data to benchmark a methods ability to discriminate against indirect co-evolution. Such a benchmark is problematic, because not all the interactions and evolutionary constraints underlying real data can be known a priori. Instead, sequences generated in silico to simulate co-evolution would be preferable, and can be obtained using aCES, the software tool presented here. Conservation and co-evolution constraints can be specified for any residue across a number of molecules, allowing the user to capture a complex, realistic set of interactions. Resulting alignments were used to benchmark several co-evolution detection tools for their ability to separate signal from background as well as discriminating direct from indirect signals. This approach can aid in refinement of these algorithms. In addition, systematic tuning of these constraints sheds new light on how they drive co-evolution between residues. Better understanding how to detect co-evolution and the residue interactions they predict can lead to a wide range of insights important for synthetic biologists interested in engineering new, orthogonal interactions between two macromolecules.

Determinants of the Nucleotide Specificity in the Carbohydrate Epimerase Family 1

In recent years, carbohydrate epimerases have attracted increasing attention as promising biocatalysts for the production of specialty sugars and derivatives. The vast majority of these enzymes are active on nucleotide-activated sugars, rather than on their free counterparts. Although such epimerases are known to have a clear preference for a particular nucleotide (UDP, GDP, CDP, or ADP), very little is known about the determinants of the respective specificities. In this work, sequence motifs are identified that correlate with the different nucleotide specificities in one of the main epimerase superfamilies, carbohydrate epimerase 1 (CEP1). To confirm their relevance, GDP- and CDP-specific residues are introduced into the UDP-glucose 4-epimerase from Thermus thermophilus, resulting in a 3-fold and 13-fold reduction in K_M for GDP-Glc and CDP-Glc, respectively. Moreover, several variants are severely crippled in UDP-Glc activity, which further underlines the crucial role of the identified positions. Hence, the analysis should prove to be valuable for the further exploration and application of epimerases involved in carbohydrate synthesis.

A machine learning approach for reliable prediction of amino acid interactions and its application in the directed evolution of enantioselective enzymes

Directed evolution is an important research activity in synthetic biology and biotechnology. Numerous reports describe the application of tedious mutation/screening cycles for the improvement of proteins. Recently, knowledge-based approaches have facilitated the prediction of protein properties and the identification of improved mutants. However, epistatic phenomena constitute an obstacle which can impair the predictions in protein engineering. We present an innovative sequence-activity relationship (innov'SAR) methodology based on digital signal processing combining wet-lab experimentation and computational protein design. In our machine learning approach, a predictive model is developed to find the resulting property of the protein when the n single point mutations are permuted (2n combinations). The originality of our approach is that only sequence information and the fitness of mutants measured in the wet-lab are needed to build models. We illustrate the application of the approach in the case of improving the enantioselectivity of an epoxide hydrolase from Aspergillus niger. n = 9 single point mutants of the enzyme were experimentally assessed for their enantioselectivity and used as a learning dataset to build a model. Based on combinations of the 9 single point mutations (29), the enantioselectivity of these 512 variants were predicted, and candidates were experimentally checked: better mutants with higher enantioselectivity were indeed found.

The visualCMAT: A web-server to select and interpret correlated mutations/co-evolving residues in protein families

The visualCMAT web-server was designed to assist experimental research in the fields of protein/enzyme biochemistry, protein engineering, and drug discovery by providing an intuitive and easy-to-use interface to the analysis of correlated mutations/co-evolving residues. Sequence and structural information describing homologous proteins are used to predict correlated substitutions by the Mutual information-based CMAT approach, classify them into spatially close co-evolving pairs, which either form a direct physical contact or interact with the same ligand (e.g. a substrate or a crystallographic water molecule), and long-range correlations, annotate and rank binding sites on the protein surface by the presence of statistically significant co-evolving positions. The results of the visualCMAT are organized for a convenient visual analysis and can be downloaded to a local computer as a content-rich all-in-one PyMol session file with multiple layers of annotation corresponding to bioinformatic, statistical and structural analyses of the predicted co-evolution, or further studied online using the built-in interactive analysis tools. The online interactivity is implemented in HTML5 and therefore neither plugins nor Java are required. The visualCMAT web-server is integrated with the Mustguseal web-server capable of constructing large structure-guided sequence alignments of protein families and superfamilies using all available information about their structures and sequences in public databases. The visualCMAT web-server can be used to understand the relationship between structure and function in proteins, implemented at selecting hotspots and compensatory mutations for rational design and directed evolution experiments to produce novel enzymes with improved properties, and employed at studying the mechanism of selective ligand's binding and allosteric communication between topologically independent sites in protein structures. The web-server is freely available at https://biokinet.belozersky.msu.ru/visualcmat and there are no login requirements.

Co-evolution techniques are reshaping the way we do structural bioinformatics

Co-evolution techniques were originally conceived to assist in protein structure prediction by inferring pairs of residues that share spatial proximity. However, the functional relationships that can be extrapolated from co-evolution have also proven to be useful in a wide array of structural bioinformatics applications. These techniques are a powerful way to extract structural and functional information in a sequence-rich world.