Ranking Enzyme Structures in the PDB by Bound Ligand Similarity to Biological Substrates

ChannelsDB 2.0: a comprehensive database of protein tunnels and pores in AlphaFold era

ChannelsDB 2.0 is an updated database providing structural information about the position, geometry and physicochemical properties of protein channels-tunnels and pores-within deposited biomacromolecular structures from PDB and AlphaFoldDB databases. The newly deposited information originated from several sources. Firstly, we included data calculated using a popular CAVER tool to complement the data obtained using original MOLE tool for detection and analysis of protein tunnels and pores. Secondly, we added tunnels starting from cofactors within the AlphaFill database to enlarge the scope of the database to protein models based on Uniprot. This has enlarged available channel annotations ∼4.6 times as of 1 September 2023. The database stores information about geometrical features, e.g. length and radius, and physico-chemical properties based on channel-lining amino acids. The stored data are interlinked with the available UniProt mutation annotation data. ChannelsDB 2.0 provides an excellent resource for deep analysis of the role of biomacromolecular tunnels and pores. The database is available free of charge: https://channelsdb2.biodata.ceitec.cz.

Design, synthesis, and pharmacological evaluation of indazole carboxamides of N-substituted pyrrole derivatives as soybean lipoxygenase inhibitors

In this paper, we attempted to develop a novel class of compounds against lipoxygenase, a key enzyme in the biosynthesis of leukotrienes implicated in a series of inflammatory diseases. Given the absence of appropriate human 5-lipoxygenase crystallographic data, solved soybean lipoxygenase-1 and -3 structures were used as a template to generate an accurate pharmacophore model which was further used for virtual screening purposes. Eight compounds (1-8) have been derived from the in-house library consisting of N-substituted pyrroles conjugated with 5- or 6-indazole moieties through a carboxamide linker. This study led to the discovery of hit molecule 8 bearing a naphthyl group with the IC50 value of 22 μM according to soybean lipoxygenase in vitro assay. Isosteric replacement of naphthyl ring with quinoline moieties and reduction of carbonyl carboxamide group resulted in compounds 9-12 and 13, respectively. Compound 12 demonstrated the most promising enzyme inhibition. In addition, compounds 8 and 12 were found to reduce the carrageenan-induced paw edema in vivo by 52.6 and 49.8%, respectively. In view of the encouraging outcomes concerning their notable in vitro and in vivo anti-inflammatory activities, compounds 8 and 12 could be further optimized for the discovery of novel 5-lipoxygenase inhibitors in future. A structure-based 3D pharmacophore model was used in the virtual screening of in-house library to discover novel potential 5-lipoxygenase inhibitors.

Coenzyme-binding pathway on glutamate dehydrogenase suggested from multiple-binding sites visualized by cryo-electron microscopy

The structure of hexameric glutamate dehydrogenase (GDH) in the presence of the coenzyme nicotinamide adenine dinucleotide phosphate (NADP) was visualized using cryogenic transmission electron microscopy to investigate the ligand-binding pathways to the active site of the enzyme. Each subunit of GDH comprises one hexamer-forming core domain and one nucleotide-binding domain (NAD domain), which spontaneously opens and closes the active-site cleft situated between the two domains. In the presence of NADP, the potential map of GDH hexamer, assuming D3 symmetry, was determined at a resolution of 2.4 Å, but the NAD domain was blurred due to the conformational variety. After focused classification with respect to the NAD domain, the potential maps interpreted as NADP molecules appeared at five different sites in the active-site cleft. The subunits associated with NADP molecules were close to one of the four metastable conformations in the unliganded state. Three of the five binding sites suggested a pathway of NADP molecules to approach the active-site cleft for initiating the enzymatic reaction. The other two binding modes may rarely appear in the presence of glutamate, as demonstrated by the reaction kinetics. Based on the visualized structures and the results from the enzymatic kinetics, we discussed the binding modes of NADP to GDH in the absence and presence of glutamate.

Enzyme function and evolution through the lens of bioinformatics

Enzymes have been shaped by evolution over billions of years to catalyse the chemical reactions that support life on earth. Dispersed in the literature, or organised in online databases, knowledge about enzymes can be structured in distinct dimensions, either related to their quality as biological macromolecules, such as their sequence and structure, or related to their chemical functions, such as the catalytic site, kinetics, mechanism, and overall reaction. The evolution of enzymes can only be understood when each of these dimensions is considered. In addition, many of the properties of enzymes only make sense in the light of evolution. We start this review by outlining the main paradigms of enzyme evolution, including gene duplication and divergence, convergent evolution, and evolution by recombination of domains. In the second part, we overview the current collective knowledge about enzymes, as organised by different types of data and collected in several databases. We also highlight some increasingly powerful computational tools that can be used to close gaps in understanding, in particular for types of data that require laborious experimental protocols. We believe that recent advances in protein structure prediction will be a powerful catalyst for the prediction of binding, mechanism, and ultimately, chemical reactions. A comprehensive mapping of enzyme function and evolution may be attainable in the near future.

The 3D Modules of Enzyme Catalysis: Deconstructing Active Sites into Distinct Functional Entities

Enzyme catalysis is governed by a limited toolkit of residues and organic or inorganic co-factors. Therefore, it is expected that recurring residue arrangements will be found across the enzyme space, which perform a defined catalytic function, are structurally similar and occur in unrelated enzymes. Leveraging the integrated information in the Mechanism and Catalytic Site Atlas (M-CSA) (enzyme structure, sequence, catalytic residue annotations, catalysed reaction, detailed mechanism description), 3D templates were derived to represent compact groups of catalytic residues. A fuzzy template-template search, allowed us to identify those recurring motifs, which are conserved or convergent, that we define as the "modules of enzyme catalysis". We show that a large fraction of these modules facilitate binding of metal ions, co-factors and substrates, and are frequently the result of convergent evolution. A smaller number of convergent modules perform a well-defined catalytic role, such as the variants of the catalytic triad (i.e. Ser-His-Asp/Cys-His-Asp) and the saccharide-cleaving Asp/Glu triad. It is also shown that enzymes whose functions have diverged during evolution preserve regions of their active site unaltered, as shown by modules performing similar or identical steps of the catalytic mechanism. We have compiled a comprehensive library of catalytic modules, that characterise a broad spectrum of enzymes. These modules can be used as templates in enzyme design and for better understanding catalysis in 3D.

EzMechanism: an automated tool to propose catalytic mechanisms of enzyme reactions

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.

Annotation of biologically relevant ligands in UniProtKB using ChEBI

MOTIVATION

To provide high quality, computationally tractable annotation of binding sites for biologically relevant (cognate) ligands in UniProtKB using the chemical ontology ChEBI (Chemical Entities of Biological Interest), to better support efforts to study and predict functionally relevant interactions between protein sequences and structures and small molecule ligands.

RESULTS

We structured the data model for cognate ligand binding site annotations in UniProtKB and performed a complete reannotation of all cognate ligand binding sites using stable unique identifiers from ChEBI, which we now use as the reference vocabulary for all such annotations. We developed improved search and query facilities for cognate ligands in the UniProt website, REST API and SPARQL endpoint that leverage the chemical structure data, nomenclature and classification that ChEBI provides.

AVAILABILITY AND IMPLEMENTATION

Binding site annotations for cognate ligands described using ChEBI are available for UniProtKB protein sequence records in several formats (text, XML and RDF) and are freely available to query and download through the UniProt website (www.uniprot.org), REST API (www.uniprot.org/help/api), SPARQL endpoint (sparql.uniprot.org/) and FTP site (https://ftp.uniprot.org/pub/databases/uniprot/).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Conformational variation in enzyme catalysis: A structural study on catalytic residues

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We introduce a pipeline to compare and contrast active sites from homologous enzymes in 3D.

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Comprehensive structural study covering enzymes from a large functional space.

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High heterogeneity in magnitude of active site flexibililty between enzyme families.

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Diffferent catalytic residue types and functions relate to different degrees of flexibility.

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Four paradigms classify enzymes according to the structural behaviour during catalysis.

Conformational variation in catalytic residues can be captured as alternative snapshots in enzyme crystal structures. Addressing the question of whether active site flexibility is an intrinsic and essential property of enzymes for catalysis, we present a comprehensive study on the 3D variation of active sites of 925 enzyme families, using explicit catalytic residue annotations from the Mechanism and Catalytic Site Atlas and structural data from the Protein Data Bank. Through weighted pairwise superposition of the functional atoms of active sites, we captured structural variability at single-residue level and examined the geometrical changes as ligands bind or as mutations occur. We demonstrate that catalytic centres of enzymes can be inherently rigid or flexible to various degrees according to the function they perform, and structural variability most often involves a subset of the catalytic residues, usually those not directly involved in the formation or cleavage of bonds. Moreover, data suggest that 2/3 of active sites are flexible, and in half of those, flexibility is only observed in the side chain. The goal of this work is to characterise our current knowledge of the extent of flexibility at the heart of catalysis and ultimately place our findings in the context of the evolution of catalysis as enzymes evolve new functions and bind different substrates.

CATH functional families predict functional sites in proteins

MOTIVATION

Identification of functional sites in proteins is essential for functional characterization, variant interpretation and drug design. Several methods are available for predicting either a generic functional site, or specific types of functional site. Here, we present FunSite, a machine learning predictor that identifies catalytic, ligand-binding and protein-protein interaction functional sites using features derived from protein sequence and structure, and evolutionary data from CATH functional families (FunFams).

RESULTS

FunSite's prediction performance was rigorously benchmarked using cross-validation and a holdout dataset. FunSite outperformed other publicly available functional site prediction methods. We show that conserved residues in FunFams are enriched in functional sites. We found FunSite's performance depends greatly on the quality of functional site annotations and the information content of FunFams in the training data. Finally, we analyze which structural and evolutionary features are most predictive for functional sites.

AVAILABILITYAND IMPLEMENTATION

https://github.com/UCL/cath-funsite-predictor.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Clustering FunFams using sequence embeddings improves EC purity

Motivation

Classifying proteins into functional families can improve our understanding of protein function and can allow transferring annotations within one family. For this, functional families need to be ‘pure’, i.e., contain only proteins with identical function. Functional Families (FunFams) cluster proteins within CATH superfamilies into such groups of proteins sharing function. 11% of all FunFams (22 830 of 203 639) contain EC annotations and of those, 7% (1526 of 22 830) have inconsistent functional annotations.

Results

We propose an approach to further cluster FunFams into functionally more consistent sub-families by encoding their sequences through embeddings. These embeddings originate from language models transferring knowledge gained from predicting missing amino acids in a sequence (ProtBERT) and have been further optimized to distinguish between proteins belonging to the same or a different CATH superfamily (PB-Tucker). Using distances between embeddings and DBSCAN to cluster FunFams and identify outliers, doubled the number of pure clusters per FunFam compared to random clustering. Our approach was not limited to FunFams but also succeeded on families created using sequence similarity alone. Complementing EC annotations, we observed similar results for binding annotations. Thus, we expect an increased purity also for other aspects of function. Our results can help generating FunFams; the resulting clusters with improved functional consistency allow more reliable inference of annotations. We expect this approach to succeed equally for any other grouping of proteins by their phenotypes.

Availability and implementation

Code and embeddings are available via GitHub: https://github.com/Rostlab/FunFamsClustering.

Supplementary information

Supplementary data are available at Bioinformatics online.

Exploring Chemical Biosynthetic Design Space with Transform-MinER

Transform-MinER (Transforming Molecules in Enzyme Reactions) is a web application facilitating the exploration of chemical biosynthetic space, guiding the user toward promising start points for enzyme design projects or directed evolution experiments. Two types of search are possible: Molecule Search allows a user to submit a source substrate enabling Transform-MinER to search for enzyme reactions acting on similar substrates, whereas Path Search additionally allows a user to submit a target molecule enabling Transform-MinER to search for a path of enzyme reactions acting on similar substrates to link source and target. Transform-MinER searches for potential reaction centers in the source substrate and uses chemoinformatic fingerprints to identify those that are situated in molecular environments similar to native counterparts, prioritizing steps that move closer to the target using reactions most similar to native in its exploration of search space. The ligand-based methodology behind Transform-MinER is presented, and its performance is validated yielding 90% success rates: first, on a data set of native pathways from the KEGG database, and second, on a data set of de novo enzyme reactions.

Finding enzyme cofactors in Protein Data Bank

MOTIVATION

Cofactors are essential for many enzyme reactions. The Protein Data Bank (PDB) contains >67 000 entries containing enzyme structures, many with bound cofactor or cofactor-like molecules. This work aims to identify and categorize these small molecules in the PDB and make it easier to find them.

RESULTS

The Protein Data Bank in Europe (PDBe; pdbe.org) has implemented a pipeline to identify enzyme cofactor and cofactor-like molecules, which are now part of the PDBe weekly release process.

AVAILABILITY AND IMPLEMENTATION

Information is made available on the individual PDBe entry pages at pdbe.org and programmatically through the PDBe REST API (pdbe.org/api).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Choosing the Best Enzyme Complex Structure Made Easy

In this issue of Structure, Tyzack et al. (2018) present a study of enzyme-ligand complexes in the PDB and show that the molecular similarity of bound and cognate ligands can be used to choose the most biologically appropriate complex structure for analysis when multiple structures are available.

Selection of protein conformations for structure-based polypharmacology studies

Several drugs exert their therapeutic effect through the modulation of multiple targets. Structure-based approaches hold great promise for identifying compounds with the desired polypharmacological profiles. These methods use knowledge of the protein binding sites to identify stereoelectronically complementary ligands. The selection of the most suitable protein conformations to be used in the design process is vital, especially for multitarget drug design in which the same ligand has to be accommodated in multiple binding pockets. Herein, we focus on currently available techniques for the selection of the most suitable protein conformations for multitarget drug design, compare the potential advantages and limitations of each method, and comment on how their combination could help in polypharmacology drug design.