Gene3D: Extensive prediction of globular domains in proteins

Lewis, Tony E.; Sillitoe, Ian; Dawson, Natalie L.; S, Lam; Clarke, Tristan; Lee, David A.; Orengo, Christine A.; Lees, Jonathan G.

doi:10.1093/nar/gkx1069

Cited by 129 publications

(110 citation statements)

References 17 publications

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“…1A, and Fig. S1) [29]. The result showed that most of the plant species had the co-occurrence of the RNHLS domain with a DNA polymerase domain, a nuclease, and nucleotide triphosphate hydrolase domain ( Fig.…”

Section: Species Selected To Cover the Characters Of Rnhls Proteins Imentioning

confidence: 89%

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Comprehensive Classification of the RNase H-like domain-containing Proteins in Plants

Liu

Sun

2019

Preprint

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0This study showed that RNaseH-like domain containing proteins can form 27 subclusters and mainly 1 1 regulate nucleic acid metabolism in meristem tissue, revealing these proteins is critical in R-loop 1 2 regulation. Abstract 1 5 R-loop is a nucleic acid structure containing an RNA-DNA hybrid and a displaced single-stranded 1 6 DNA. Recently, accumulated evidences show that R-loops occur frequently in various organisms' 1 7 genomes and have crucial physiological functions, including replication, transcription and DNA repair.

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Section: Species Selected To Cover the Characters Of Rnhls Proteins Imentioning

confidence: 89%

“…The co-occurrence matrix of the RNHLS domain (3. 30.420.10) with other protein domains was downloaded from Gene3D [29]. The heatmap was processed by online software Morpheus (https://software.broadinstitute.org/morpheus/) using the default settings.…”

Section: Co-occurrence Of Rnhls Domain With Other Protein Domainsmentioning

confidence: 99%

Comprehensive Classification of the RNase H-like domain-containing Proteins in Plants

Liu

Sun

2019

Preprint

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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).…”

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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“…A given protein structure, corresponding to a single distance matrix, can be formed by many different homologous sequences, and those sequences all satisfy the constraints imposed by the distance matrix. Such solutions to this constraint satisfaction problem (CSP) are given to us by evolution and are available in sequence repositories such as Pfam (22) or Gene3D (23). While the rules of this CSP for a specific protein fold can be found by comparing sequences from one of such repositories and can be captured as Hidden Markov Models (HMM) or position weight matrices (PWMs), often represented as sequence logos, it has thus far not been possible to deduce general rules-those that would connect any given protein fold or distance matrix with a set of sequences.…”

Section: Introductionmentioning

confidence: 99%

Fast and flexible design of novel proteins using graph neural networks

Strokach¹,

Becerra

Corbi‐Verge

et al. 2019

Preprint

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Protein structure and function is determined by the arrangement of the linear sequence of amino acids in 3D space. Despite substantial advances, precisely designing sequences that fold into a predetermined shape (the "protein design" problem) remains difficult. We show that a deep graph neural network, ProteinSolver, can solve protein design by phrasing it as a constraint satisfaction problem (CSP). To sidestep the considerable issue of optimizing the network architecture, we first develop a network that is accurately able to solve the related and straightforward problem of Sudoku puzzles. Recognizing that each protein design CSP has many solutions, we train this network on millions of real protein sequences corresponding to thousands of protein structures. We show that our method rapidly designs novel protein sequences and perform a variety of in silico and in vitro validations suggesting that our designed proteins adopt the predetermined structures.One Sentence Summary: A neural network optimized using Sudoku puzzles designs protein sequences that adopt predetermined structures.

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Gene3D: Extensive prediction of globular domains in proteins

Cited by 129 publications

References 17 publications

Comprehensive Classification of the RNase H-like domain-containing Proteins in Plants

Comprehensive Classification of the RNase H-like domain-containing Proteins in Plants

Emerging concepts in pseudoenzyme classification, evolution, and signaling

Fast and flexible design of novel proteins using graph neural networks

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