Janet M. Thornton

The rapidly growing universe of predicted protein structures offers opportunities for data driven exploration but requires computationally scalable and interpretable tools. We developed a method to detect catalytic features in protein structures, providing insights into enzyme function and mechanism. A library of 6780 3D coordinate sets describing enzyme catalytic sites, referred to as templates, has been collected from manually curated examples of 762 enzyme catalytic mechanisms described in the Mechanism and Catalytic Site Atlas. For template searching we optimized the geometric‐matching algorithm Jess. We implemented RMSD and residue orientation filters to differentiate catalytically informative matches from spurious ones. We validated this approach on a non‐redundant set of high quality experimental ( n = 3751, <40% amino acid identity) enzyme structures with well annotated catalytic sites as well as predicted structures of the human proteome. We show matching catalytic templates solely on structure is more sensitive than sequence‐ and 3D‐structure‐based approaches in identifying homology between distantly related enzymes. Since geometric matching does not depend on conserved sequence motifs or even common evolutionary history, we are able to identify examples of structural active site similarity in highly divergent and possibly convergent enzymes. Such examples make interesting case studies into the evolution of enzyme function. Though not intended for characterizing substrate‐specific binding pockets, the speed and knowledge‐driven interpretability of our method make it well suited for expanding enzyme active‐site annotation across large predicted proteomes. We provide the method and template library as a Python module, Enzyme Motif Miner, at https://github.com/rayhackett/enzymm and as a webserver at https://www.ebi.ac.uk/thornton-srv/m-csa/enzymm .

Oxidosqualene cyclases (OSCs) catalyse one of nature's most intricate enzyme reactions, converting the linear precursor 2,3-oxidosqualene into an array of cyclic triterpene scaffolds through sequential carbocation cascades. Predicting OSC function based on sequence is challenging beyond broad family-level classification. Here, we develop a structure-based computational framework to identify amino acid determinants of OSC product specificity. Using 169 functionally characterised OSCs, we deploy a multifaceted approach combining differential conservation along with structural information, physico-chemical properties of amino acids and binding pocket electrostatics in order to understand the determinants of product specificity. Using Arabidopsis thaliana cycloartenol synthase AtCAS as a model, we then validate our predictions through targeted mutagenesis, achieving stepwise reprogramming towards the protosteryl-type products cucurbitadienol and lanosterol, including complete product switches. Molecular dynamics simulations support a mechanism in which subtle pocket remodelling alters active-site volume, water access and proton-elimination chemistry. These findings provide a blueprint for OSC engineering.

The rapidly growing universe of predicted protein structures offers opportunities for data driven exploration but requires computationally scalable and interpretable tools (1–3) . We developed a method to detect catalytic features in protein structures, providing insights into enzyme function and mechanism. A library of 6780 3D coordinate sets describing enzyme catalytic sites, referred to as templates, has been collected from manually curated examples of 762 enzyme catalytic mechanisms described in the Mechanism and Catalytic Site Atlas (4–6) . For template searching we optimised the geometric-matching algorithm Jess (7) . We implemented RMSD and residue orientation filters to differentiate catalytically informative matches from spurious ones. We validated this approach on a non-redundant set of high quality experimental (n=3751, (8) . Such examples make interesting case studies into the evolution of enzyme function. Though not intended for characterizing substrate-specific binding pockets, the speed and knowledge-driven interpretability of our method make it well suited for expanding enzyme active-site annotation across large predicted proteomes. We provide the method and template library as a Python module, Enzyme Motif Miner, at https://github.com/rayhackett/enzymm .

Similarity measures for protein sequence, structure and enzyme reactions have been essential tools for translating an abundance of experimental data about enzymes into biological insights. Enzymes with similar sequence and structure, for example, can be organised into evolutionary families, and within families, reaction similarity can highlight examples of conservation or divergent evolution. When it comes to reaction mechanisms, despite their importance in explaining the catalytic power of enzymes and their evolution, no similarity measures have been developed until now. We addressed this gap by developing a method to calculate mechanism similarity based on the bond changes and charge transfers occurring at each catalytic step, where we have the ability to adjust the size of the chemical environment surrounding the atoms directly involved in these transformations. Using this newly developed method, we performed a pairwise comparison of all the mechanisms stored in the Mechanism and Catalytic Site Atlas (M‐CSA) database. This analysis illustrates how mechanism similarity can be a powerful tool to navigate the known catalytic space and to discover and characterise both convergent and divergent evolutionary relationships.

Summary: Specialized metabolites are molecules involved in plants' interaction with their environment. Elucidating their biosynthetic pathways is a challenging but rewarding task, leading to societal applications and ecological insights. Furanocoumarins emerged multiple times in Angiosperms, raising the question of how different enzymes evolved into catalyzing identical reactions. To identify enzymes producing lineage‐specific metabolites, an evolutionary‐based approach was developed and applied to furanocoumarin biosynthesis in Ficus carica (Moraceae). This led to the characterization of CYP71B129–131a, three P450 enzymes whose evolution of the function was investigated using phylogenetics, structural comparisons and site‐directed mutagenesis. CYP71B129 and CYP71B130,131a were found to hydroxylate umbelliferone (coumarin) and xanthotoxin (furanocoumarin), respectively. Results suggest that CYP71Bs xanthotoxin hydroxylase activity results from duplications and functional divergence of umbelliferone hydroxylase genes. Structural comparisons highlighted an amino acid affecting CYP71Bs substrate specificity, which may play a key role in allowing xanthotoxin hydroxylation in several P450 subfamilies. CYP71B130‐131a characterization validates the proposed enzyme‐discovery approach, which can be applied to different pathways and help to avoid the classic bottlenecks of specialized metabolism elucidation. The CYP71Bs also exemplify how furanocoumarin‐biosynthetic enzymes can stem from coumarin‐biosynthetic ones and provides insights into the molecular mechanisms underlying the multiple emergences of xanthotoxin hydroxylation in distant P450 subfamilies.

There are many occurrences of enzymes catalysing the same reaction but having significantly different structures. Leveraging the comprehensive information on enzymes stored in the Mechanism and Catalytic Site Atlas (M‐CSA), we present a collection of 34 cases for which there is sufficient evidence of functional convergence without an evolutionary link. For each case, we compare enzymes which have identical Enzyme Commission numbers (i.e. catalyse the same reaction), but different identifiers in the CATH data resource (i.e. different folds). We focus on similarities between their sequences, structures, active site geometries, cofactors and catalytic mechanisms. These features are then assessed to evaluate whether all the evidence for these structurally diverse proteins supports their independent evolution to catalyse the same chemical reaction. Our approach combines published literature information with knowledge‐based computational resources from, amongst others, M‐CSA, PDBe and PDBsum, supported by tailor‐made software to explore active site structures and assess similarities in mechanism. We find that there are multiple types of convergent functional evolution observed to date, and it is necessary to investigate sequence, structure, active site geometry and enzyme mechanisms to describe such convergence accurately.

Tunnels in enzymes with buried active sites are key structural features allowing the entry of substrates and the release of products, thus contributing to the catalytic efficiency. Targeting the bottlenecks of protein tunnels is also a powerful protein engineering strategy. However, the identification of functional tunnels in multiple protein structures is a non-trivial task that can only be addressed computationally. We present a pipeline integrating automated structural analysis with an in-house machine-learning predictor for the annotation of protein pockets, followed by the calculation of the energetics of ligand transport via biochemically relevant tunnels. A thorough validation using eight distinct molecular systems revealed that CaverDock analysis of ligand un/binding is on par with time-consuming molecular dynamics simulations, but much faster. The optimized and validated pipeline was applied to annotate more than 17,000 cognate enzyme–ligand complexes. Analysis of ligand un/binding energetics indicates that the top priority tunnel has the most favourable energies in 75% of cases. Moreover, energy profiles of cognate ligands revealed that a simple geometry analysis can correctly identify tunnel bottlenecks only in 50% of cases. Our study provides essential information for the interpretation of results from tunnel calculation and energy profiling in mechanistic enzymology and protein engineering. We formulated several simple rules allowing identification of biochemically relevant tunnels based on the binding pockets, tunnel geometry, and ligand transport energy profiles.Scientific contributionsThe pipeline introduced in this work allows for the detailed analysis of a large set of protein–ligand complexes, focusing on transport pathways. We are introducing a novel predictor for determining the relevance of binding pockets for tunnel calculation. For the first time in the field, we present a high-throughput energetic analysis of ligand binding and unbinding, showing that approximate methods for these simulations can identify additional mutagenesis hotspots in enzymes compared to purely geometrical methods. The predictor is included in the supplementary material and can also be accessed at https://github.com/Faranehhad/Large-Scale-Pocket-Tunnel-Annotation.git. The tunnel data calculated in this study has been made publicly available as part of the ChannelsDB 2.0 database, accessible at https://channelsdb2.biodata.ceitec.cz/.

Summary: Specialized metabolites are molecules involved in plants interaction with their environment. Elucidating their biosynthetic pathways is a challenging but rewarding task, leading to societal applications and ecological insights. Furanocoumarins emerged multiple times in Angiosperms, raising the question of how different enzymes evolved into catalyzing identical reactions. To identify enzymes producing lineage-specific metabolites, an evolutionary-based approach was developed and applied to furanocoumarin biosynthesis in Ficus carica (Moraceae). This led to the characterization of CYP71B129-131a, three P450 enzymes whose evolution of the function was investigated using phylogenetics, structural comparisons and site-directed mutagenesis. CYP71B129 and CYP71B130,131a were found to hydroxylate umbelliferone (coumarin) and xanthotoxin (furanocoumarin), respectively. Results suggest that CYP71Bs xanthotoxin hydroxylase activity results from duplications and functional divergence of umbelliferone hydroxylase genes. Structural comparisons highlighted an amino acid affecting CYP71Bs substrate specificity, which may play a key role in allowing xanthotoxin hydroxylation in several P450 subfamilies. CYP71B130-131a characterization validates the proposed enzyme-discovery approach, which can be applied to different pathways and help to avoid the classic bottlenecks of specialized metabolism elucidation. The CYP71Bs also exemplify how furanocoumarin-biosynthetic enzymes can stem from coumarin-biosynthetic ones and provides insights into the molecular mechanisms underlying the multiple emergences of xanthotoxin hydroxylation in distant P450 subfamilies.

There are many occurrences of enzymes catalysing the same reaction but having significantly different structures. Leveraging the comprehensive information on enzymes stored in the Mechanism and Catalytic Site Atlas (M-CSA), we present a collection of 38 cases for which there is sufficient evidence of functional convergence without an evolutionary link. For each case, we compare enzymes which have identical Enzyme Commission numbers (i.e. catalyse the same reaction), but different identifiers in the CATH data resource (i.e. different folds). We focus on similarities between their sequence, structure, active site geometry, cofactors and catalytic mechanism. These features are then assessed to evaluate whether all the evidence on these structurally diverse proteins supports their independent evolution to catalyse the same chemical reaction. Our approach combines literature information with knowledge-based computational resources from, amongst others, M-CSA, PDBe and PDBsum, supported by tailor made software to explore active site structure and assess mechanism similarity. We find that there are multiple varieties of convergent functional evolution observed to date and it is necessary to investigate sequence, structure, active site geometry and enzyme mechanisms to describe such convergence accurately.

Over the years, hundreds of enzyme reaction mechanisms have been studied using experimental and simulation methods. This rich literature on biological catalysis is now ripe for use as the foundation of new knowledge-based approaches to investigate enzyme mechanisms. Here, we present a tool able to automatically infer mechanistic paths for a given three-dimensional active site and enzyme reaction, based on a set of catalytic rules compiled from the Mechanism and Catalytic Site Atlas, a database of enzyme mechanisms. EzMechanism (pronounced as ‘Easy’ Mechanism) is available to everyone through a web user interface. When studying a mechanism, EzMechanism facilitates and improves the generation of hypotheses, by making sure that relevant information is considered, as derived from the literature on both related and unrelated enzymes. We validated EzMechanism on a set of 62 enzymes and have identified paths for further improvement, including the need for additional and more generic catalytic rules.