Mechanism and Catalytic Site Atlas (M-CSA): a database of enzyme reaction mechanisms and active sites

Ribeiro, António J. M.; Holliday, Gemma L.; Furnham, Nicholas; Tyzack, Jonathan D.; Ferris, Katherine; Thornton, Janet M.

doi:10.1093/nar/gkx1012

Cited by 187 publications

(177 citation statements)

References 40 publications

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“…To help take the pseudoenzyme field forward, we have designed and tested a simple computational pipeline to identify and annotate pseudoenzymes using sequence alignments, UniProt annotations assembled from the primary literature (53), and information from the Mechanism and Catalytic Site Atlas (M-CSA), a database that currently contains defined catalytic residues and mechanistic data for 964 enzymes (54). We have limited our current analysis to those enzymes for which we have a good or detailed knowledge of their catalytic mechanism (such as those in M-CSA) and therefore know the specific residues involved.…”

Section: A New Approach For the Identification Of Pseudoenzymes Usingmentioning

confidence: 99%

“…We examined 383 enzyme superfamilies in CATH-Gene3D v.4.2 that contain well-known (experimentally validated) catalytic domains (54,56) and identified the proportion of functional families that have enzyme annotations and compared them to those that lack any Estimated proportion of pseudoenzymes within known enzyme families. A family is defined here as the group of SwissProt sequences that are homologous to one enzyme/ entry in M-CSA.…”

Section: Identifying Pseudoenzymes In Proteomic Databases By Exploitimentioning

confidence: 99%

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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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Section: A New Approach For the Identification Of Pseudoenzymes Usingmentioning

confidence: 99%

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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“…ProMOL underwent many revisions with significant input from Herbert Bernstein and his students at Dowling College. Once ProMOL was mature, research students from RIT and Dowling College created a library of over 800 enzyme active sites in ProMOL based on annotated data from the Catalytic Site Atlas . As the students made predictions of protein function based on statistical and visual comparisons, they wanted to test their predictions in the wet lab.…”

Section: Introductionmentioning

confidence: 99%

Flexible Implementation of the BASIL CURE

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et al. 2019

Biochem Molecular Bio Educ

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Course-based Undergraduate Research Experiences (CUREs) can be a very effective means to introduce a large number of students to research. CUREs are often an extension of the instructor's research, which may make them difficult to replicate in other settings because of differences in expertise or facilities. The BASIL (Biochemistry Authentic Scientific Inquiry Lab) CURE has evolved over the past 4 years as faculty members with different backgrounds, facilities, and campus cultures have all contributed to a robust curriculum focusing on enzyme function prediction that is suitable for implementation in a wide variety of academic settings.

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“…On the overall, we correctly identify residues=23, 28, 29, 30, 32, 33, 34, 35, 46, 47, 49, 78, 85, 88, that is the 61 % of the residues known from experiments and predicted by I‐Tasser to be involved in either dimerisation, peptide binding or hydrophobic core . It is interesting to notice that we categorise as strictly structural only two of the residues=78,88, as mentioned earlier.…”

Section: Resultsmentioning

confidence: 99%

Identification of Protein Functional Regions

et al. 2020

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Protein sequence stores the information relative to both functionality and stability, thus making it difficult to disentangle the two contributions. However, the identification of critical residues for function and stability has important implications for the mapping of the proteome interactions, as well as for many pharmaceutical applications, e. g. the identification of ligand binding regions for targeted pharmaceutical protein design. In this work, we propose a computational method to identify critical residues for protein functionality and stability and to further categorise them in strictly functional, structural and intermediate. We evaluate single site conservation and use Direct Coupling Analysis (DCA) to identify co‐evolved residues both in natural and artificial evolution processes. We reproduce artificial evolution using protein design and base our approach on the hypothesis that artificial evolution in the absence of any functional constraint would exclusively lead to site conservation and co‐evolution events of the structural type. Conversely, natural evolution intrinsically embeds both functional and structural information. By comparing the lists of conserved and co‐evolved residues, outcomes of the analysis on natural and artificial evolution, we identify the functional residues without the need of any a priori knowledge of the biological role of the analysed protein.

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Mechanism and Catalytic Site Atlas (M-CSA): a database of enzyme reaction mechanisms and active sites

Cited by 187 publications

References 40 publications

Emerging concepts in pseudoenzyme classification, evolution, and signaling

Emerging concepts in pseudoenzyme classification, evolution, and signaling

Flexible Implementation of the BASIL CURE

Identification of Protein Functional Regions

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