CATH: expanding the horizons of structure-based functional annotations for genome sequences

Sillitoe, Ian; Dawson, Natalie L.; Lewis, Tony E.; Das, Sayoni; Lees, Jonathan G.; Ashford, Paul; Adeyelu, Tolulope; Scholes, Harry M.; Senatorov, Ilya S.; Bujan, Andra; Rodriguez-Conde, Fatima Ceballos; Dowling, Benjamin; Thornton, Janet M.; Orengo, Christine A.

doi:10.1093/nar/gky1097

Cited by 138 publications

(137 citation statements)

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“…Here, we provide a systematic overview of the current knowledge of enzymatic catalysis from the point of view of the catalytic residues. We use the Mechanism and Catalytic Site Atlas (M-CSA) 3 (15) database as a manually curated data set of catalytic residues and other mechanistic information. We start by considering the frequency of the 20 amino acid types as catalytic residues and whether they act through their side chain or backbone.…”

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confidence: 99%

A global analysis of function and conservation of catalytic residues in enzymes

Ribeiro

Tyzack

Borkakoti

et al. 2020

Journal of Biological Chemistry

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The catalytic residues of an enzyme comprise the amino acids located in the active center responsible for accelerating the enzyme-catalyzed reaction. These residues lower the activation energy of reactions by performing several catalytic functions. Decades of enzymology research has established general themes regarding the roles of specific residues in these catalytic reactions, but it has been more difficult to explore these roles in a more systematic way. Here, we review the data on the catalytic residues of 648 enzymes, as annotated in the Mechanism and Catalytic Site Atlas (M-CSA), and compare our results with those in previous studies. We structured this analysis around three key properties of the catalytic residues: amino acid type, catalytic function, and sequence conservation in homologous proteins. As expected, we observed that catalysis is mostly accomplished by a small set of residues performing a limited number of catalytic functions. Catalytic residues are typically highly conserved, but to a smaller degree in homologues that perform different reactions or are nonenzymes (pseudoenzymes). Cross-analysis yielded further insights revealing which residues perform particular functions and how often. We obtained more detailed specificity rules for certain functions by identifying the chemical group upon which the residue acts. Finally, we show the mutation tolerance of the catalytic residues based on their roles. The characterization of the catalytic residues, their functions, and conservation, as presented here, is key to understanding the impact of mutations in evolution, disease, and enzyme design. The tools developed for this analysis are available at the M-CSA website and allow for user specific analysis of the same data.

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confidence: 99%

A global analysis of function and conservation of catalytic residues in enzymes

Ribeiro

Tyzack

Borkakoti

et al. 2020

Journal of Biological Chemistry

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“…As an extension of the approach described above using enzyme terminology, we have also systematically investigated the distribution of pseudoenzymes in the protein universe, using protein families from the CATH (class, architecture, topology, homology)-Gene3D resource (57,58), which links protein domain sequences to structures and experimental functions. CATH-Gene3D classifies ~435,000 domain structures and ~95 million protein domain sequences into ~6100 evolutionary superfamilies (57). These can then be subclassified into functional families (FunFams) that share highly similar structures and functions based on sequence patterns, specificity-determining positions, and other conserved positions (Fig.…”

Section: Identifying Pseudoenzymes In Proteomic Databases By Exploitimentioning

confidence: 99%

Emerging concepts in pseudoenzyme classification, evolution, and signaling

et al. 2019

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The 21st century is witnessing an explosive surge in our understanding of pseudoenzyme-driven regulatory mechanisms in biology. Pseudoenzymes are proteins that have sequence homology with enzyme families but that are proven or predicted to lack enzyme activity due to mutations in otherwise conserved catalytic amino acids. The best-studied pseudoenzymes are pseudokinases, although examples from other families are emerging at a rapid rate as experimental approaches catch up with an avalanche of freely available informatics data. Kingdom-wide analysis in prokaryotes, archaea and eukaryotes reveals that between 5 and 10% of proteins that make up enzyme families are pseudoenzymes, with notable expansions and contractions seemingly associated with specific signaling niches. Pseudoenzymes can allosterically activate canonical enzymes, act as scaffolds to control assembly of signaling complexes and their localization, serve as molecular switches, or regulate signaling networks through substrate or enzyme sequestration. Molecular analysis of pseudoenzymes is rapidly advancing knowledge of how they perform noncatalytic functions and is enabling the discovery of unexpected, and previously unappreciated, functions of their intensively studied enzyme counterparts. Notably, upon further examination, some pseudoenzymes have previously unknown enzymatic activities that could not have been predicted a priori. Pseudoenzymes can be targeted and manipulated by small molecules and therefore represent new therapeutic targets (or anti-targets, where intervention should be avoided) in various diseases. In this review, which brings together broad bioinformatics and cell signaling approaches in the field, we highlight a selection of findings relevant to a contemporary understanding of pseudoenzyme-based biology.

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“…The pipeline was built using the Ruffus (Python) (Goodstadt, 2010) architecture that performs multiple tasks to generate models with substantial quality and reliability. In the modelling process, templates were selected from TOCCATA (Ochoa Montaño B, Blundell TL, unpublished) a database of consensus profiles built from CATH v4.2.0 and SCOPe v2.07 (62,63) based classification of protein structures (PDB files). PDBs within each profile are clustered based on sequence similarity using CD-HIT (64,65)) and structures are aligned using BATON, which was developed as a streamlined version of the in-house program COMPARER (66).…”

Section: Structure Modelling Of Selected Drug Targetsmentioning

confidence: 99%

Analysis of metabolic pathways in mycobacteria to aid drug-target identification

Bannerman

Vedithi

Júlvez

et al. 2019

Preprint

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Full Title: Comparative analyses of metabolic pathways in mycobacteria: Insights into the identification and characterisation of new drug targets in M. tuberculosis, M. leprae and M. abscessus Abstract Three related mycobacteria are the cause of widespread infections in man and are the focus of intense research and drug-discovery efforts in the face of growing antimicrobial resistance.Mycobacterium tuberculosis, the causative agent of tuberculosis, is currently one of the top ten causes of death in the world according to WHO; M. abscessus, a group of non-tuberculous mycobacteria causes lung infections and other opportunistic infections in humans; and M. leprae, the causative agent of leprosy, remains endemic in tropical countries. There is an urgent need to design alternatives to conventional treatment strategies, due to the increase in resistance to standard antibacterials. In this study, we present a comparative analysis of chokepoint and essentiality datasets that will provide insight into the development of new treatment regimes. We illustrate the key metabolic pathways shared between these three organisms and identify drug targets with a wide metabolic impact that are common to the three species. We demonstrate that 72% of the chokepoint enzymes are proteins essential to Mycobacterium tuberculosis. We show also that 78% of the drug targets, prioritized based on their presence in multiple paths on the metabolic network, are present in pathways shared by M. tuberculosis, M. leprae and M. abscessus, including biosynthesis of amino acids, carbohydrates, cell structures, fatty acid and lipid biosynthesis. A further 17% is found in the prioritised pathways shared between M. tuberculosis and M. abscessus. We have performed comparative structure modelling of potential drug targets identified using our analysis in order to assess druggability and demonstrate the importance of chokepoint analysis in terms of drug target identification. AUTHOR SUMMARYComputer simulation studies to design new drugs against mycobacteria

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CATH: expanding the horizons of structure-based functional annotations for genome sequences

Cited by 138 publications

References 23 publications

A global analysis of function and conservation of catalytic residues in enzymes

A global analysis of function and conservation of catalytic residues in enzymes

Emerging concepts in pseudoenzyme classification, evolution, and signaling

Analysis of metabolic pathways in mycobacteria to aid drug-target identification

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