Gemma L. Holliday

Metal ions in biological catalysis: from enzyme databases to general principles

We analysed the roles and distribution of metal ions in enzymatic catalysis using available public databases and our new resource Metal-MACiE (http://www.ebi.ac.uk/thornton-srv/databases/Metal_MACiE/home.html). In Metal-MACiE, a database of metal-based reaction mechanisms, 116 entries covering 21% of the metal-dependent enzymes and 70% of the types of enzyme-catalysed chemical transformations are annotated according to metal function. We used Metal-MACiE to assess the functions performed by metals in biological catalysis and the relative frequencies of different metals in different roles, which can be related to their individual chemical properties and availability in the environment. The overall picture emerging from the overview of Metal-MACiE is that redox-inert metal ions are used in enzymes to stabilize negative charges and to activate substrates by virtue of their Lewis acid properties, whereas redox-active metal ions can be used both as Lewis acids and as redox centres. Magnesium and zinc are by far the most common ions of the first type, while calcium is relatively less used. Magnesium, however, is most often bound to phosphate groups of substrates and interacts with the enzyme only transiently, whereas the other metals are stably bound to the enzyme. The most common metal of the second type is iron, which is prevalent in the catalysis of redox reactions, followed by manganese, cobalt, molybdenum, copper and nickel. The control of the reactivity of redox-active metal ions may involve their association with organic cofactors to form stable units. This occurs sometimes for iron and nickel, and quite often for cobalt and molybdenum.

InterPro in 2017—beyond protein family and domain annotations

InterPro (http://www.ebi.ac.uk/interpro/) is a freely available database used to classify protein sequences into families and to predict the presence of important domains and sites. InterProScan is the underlying software that allows both protein and nucleic acid sequences to be searched against InterPros predictive models, which are provided by its member databases. Here, we report recent developments with InterPro and its associated software, including the addition of two new databases (SFLD and CDD), and the functionality to include residue-level annotation and prediction of intrinsic disorder. These developments enrich the annotations provided by InterPro, increase the overall number of residues annotated and allow more specific functional inferences.

Small Molecule Subgraph Detector (SMSD) toolkit

BackgroundFinding one small molecule (query) in a large target library is a challenging task in computational chemistry. Although several heuristic approaches are available using fragment-based chemical similarity searches, they fail to identify exact atom-bond equivalence between the query and target molecules and thus cannot be applied to complex chemical similarity searches, such as searching a complete or partial metabolic pathway.In this paper we present a new Maximum Common Subgraph (MCS) tool: SMSD (Small Molecule Subgraph Detector) to overcome the issues with current heuristic approaches to small molecule similarity searches. The MCS search implemented in SMSD incorporates chemical knowledge (atom type match with bond sensitive and insensitive information) while searching molecular similarity. We also propose a novel method by which solutions obtained by each MCS run can be ranked using chemical filters such as stereochemistry, bond energy, etc.ResultsIn order to benchmark and test the tool, we performed a 50,000 pair-wise comparison between KEGG ligands and PDB HET Group atoms. In both cases the SMSD was shown to be more efficient than the widely used MCS module implemented in the Chemistry Development Kit (CDK) in generating MCS solutions from our test cases.ConclusionPresently this tool can be applied to various areas of bioinformatics and chemo-informatics for finding exhaustive MCS matches. For example, it can be used to analyse metabolic networks by mapping the atoms between reactants and products involved in reactions. It can also be used to detect the MCS/substructure searches in small molecules reported by metabolome experiments, as well as in the screening of drug-like compounds with similar substructures.Thus, we present a robust tool that can be used for multiple applications, including the discovery of new drug molecules. This tool is freely available on http://www.ebi.ac.uk/thornton-srv/software/SMSD/

The Structure–Function Linkage Database

The Structure–Function Linkage Database (SFLD, http://sfld.rbvi.ucsf.edu/) is a manually curated classification resource describing structure–function relationships for functionally diverse enzyme superfamilies. Members of such superfamilies are diverse in their overall reactions yet share a common ancestor and some conserved active site features associated with conserved functional attributes such as a partial reaction. Thus, despite their different functions, members of these superfamilies ‘look alike’, making them easy to misannotate. To address this complexity and enable rational transfer of functional features to unknowns only for those members for which we have sufficient functional information, we subdivide superfamily members into subgroups using sequence information, and lastly into families, sets of enzymes known to catalyze the same reaction using the same mechanistic strategy. Browsing and searching options in the SFLD provide access to all of these levels. The SFLD offers manually curated as well as automatically classified superfamily sets, both accompanied by search and download options for all hierarchical levels. Additional information includes multiple sequence alignments, tab-separated files of functional and other attributes, and sequence similarity networks. The latter provide a new and intuitively powerful way to visualize functional trends mapped to the context of sequence similarity.

The Catalytic Site Atlas 2.0: cataloging catalytic sites and residues identified in enzymes

Understanding which are the catalytic residues in an enzyme and what function they perform is crucial to many biology studies, particularly those leading to new therapeutics and enzyme design. The original version of the Catalytic Site Atlas (CSA) (http://www.ebi.ac.uk/thornton-srv/databases/CSA) published in 2004, which catalogs the residues involved in enzyme catalysis in experimentally determined protein structures, had only 177 curated entries and employed a simplistic approach to expanding these annotations to homologous enzyme structures. Here we present a new version of the CSA (CSA 2.0), which greatly expands the number of both curated (968) and automatically annotated catalytic sites in enzyme structures, utilizing a new method for annotation transfer. The curated entries are used, along with the variation in residue type from the sequence comparison, to generate 3D templates of the catalytic sites, which in turn can be used to find catalytic sites in new structures. To ease the transfer of CSA annotations to other resources a new ontology has been developed: the Enzyme Mechanism Ontology, which has permitted the transfer of annotations to Mechanism, Annotation and Classification in Enzymes (MACiE) and UniProt Knowledge Base (UniProtKB) resources. The CSA database schema has been re-designed and both the CSA data and search capabilities are presented in a new modern web interface.

Understanding the functional roles of amino acid residues in enzyme catalysis

The MACiE database contains 223 distinct step-wise enzyme reaction mechanisms and holds representatives from each EC sub-subclass where there is a crystal structure and sufficient evidence in the literature to support a mechanism. Each catalytic step of every reaction sequence in MACiE is fully annotated so that it includes the function of the catalytic residues involved in the reaction and the mechanism by which substrates are transformed into products. Using MACiE as a knowledge base, we have seen that the top 10 most catalytic residues are histidine, aspartate, glutamate, lysine, cysteine, arginine, serine, threonine, tyrosine and tryptophan. Of these only seven (cysteine, histidine, aspartate, lysine, serine, threonine and tyrosine) dominate catalysis and provide essentially five functional roles that are essential. Stabilisation is the most common and essential role for all classes of enzyme, followed by general acid/base (proton acceptor and proton donor) functionality, with nucleophilic addition following closely behind (nucleophile and nucleofuge). We investigated the occurrence of these residues in MACiE and the Catalytic Site Atlas and found that, as expected, certain residue types are associated with each functional role, with some residue types able to perform diverse roles. In addition, it was seen that different EC classes of enzyme have a tendency to employ different residues for catalysis. Further, we show that whilst the differences between EC classes in catalytic residue composition are not immediately obvious from the general classes of Ingold mechanisms, there is some weak correlation between the mechanisms involved in a given EC class and the functions that the catalytic amino acid residues are performing. The analysis presented here provides a valuable insight into the functional roles of catalytic amino acid residues, which may have applications in many aspects of enzymology, from the design of novel enzymes to the prediction and validation of enzyme reaction mechanisms.

MACiE (Mechanism, Annotation and Classification in Enzymes): novel tools for searching catalytic mechanisms

MACiE (Mechanism, Annotation and Classification in Enzymes) is a database of enzyme reaction mechanisms, and is publicly available as a web-based data resource. This paper presents the first release of a web-based search tool to explore enzyme reaction mechanisms in MACiE. We also present Version 2 of MACiE, which doubles the dataset available (from Version 1). MACiE can be accessed from

Exploring the Evolution of Novel Enzyme Functions within Structurally Defined Protein Superfamilies

In order to understand the evolution of enzyme reactions and to gain an overview of biological catalysis we have combined sequence and structural data to generate phylogenetic trees in an analysis of 276 structurally defined enzyme superfamilies, and used these to study how enzyme functions have evolved. We describe in detail the analysis of two superfamilies to illustrate different paradigms of enzyme evolution. Gathering together data from all the superfamilies supports and develops the observation that they have all evolved to act on a diverse set of substrates, whilst the evolution of new chemistry is much less common. Despite that, by bringing together so much data, we can provide a comprehensive overview of the most common and rare types of changes in function. Our analysis demonstrates on a larger scale than previously studied, that modifications in overall chemistry still occur, with all possible changes at the primary level of the Enzyme Commission (E.C.) classification observed to a greater or lesser extent. The phylogenetic trees map out the evolutionary route taken within a superfamily, as well as all the possible changes within a superfamily. This has been used to generate a matrix of observed exchanges from one enzyme function to another, revealing the scale and nature of enzyme evolution and that some types of exchanges between and within E.C. classes are more prevalent than others. Surprisingly a large proportion (71%) of all known enzyme functions are performed by this relatively small set of 276 superfamilies. This reinforces the hypothesis that relatively few ancient enzymatic domain superfamilies were progenitors for most of the chemistry required for life.

MACiE: exploring the diversity of biochemical reactions

MACiE (which stands for Mechanism, Annotation and Classification in Enzymes) is a database of enzyme reaction mechanisms, and can be accessed from http://www.ebi.ac.uk/thornton-srv/databases/MACiE/. This article presents the release of Version 3 of MACiE, which not only extends the dataset to 335 entries, covering 182 of the EC sub-subclasses with a crystal structure available (∼90%), but also incorporates greater chemical and structural detail. This version of MACiE represents a shift in emphasis for new entries, from non-homologous representatives covering EC reaction space to enzymes with mechanisms of interest to our users and collaborators with a view to exploring the chemical diversity of life. We present new tools for exploring the data in MACiE and comparing entries as well as new analyses of the data and new searches, many of which can now be accessed via dedicated Perl scripts.

EC-BLAST: a tool to automatically search and compare enzyme reactions

We present EC-BLAST (http://www.ebi.ac.uk/thornton-srv/software/rbl/), an algorithm and Web tool for quantitative similarity searches between enzyme reactions at three levels: bond change, reaction center and reaction structure similarity. It uses bond changes and reaction patterns for all known biochemical reactions derived from atom-atom mapping across each reaction. EC-BLAST has the potential to improve enzyme classification, identify previously uncharacterized or new biochemical transformations, improve the assignment of enzyme function to sequences, and assist in enzyme engineering.