Manual classification strategies in the <scp>ECOD</scp> database

Cheng, Hua; Liao, Yuxing; Schaeffer, R. Dustin; Grishin, Nick V.

doi:10.1002/prot.24818

Cited by 68 publications

(67 citation statements)

References 102 publications

(158 reference statements)

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“…12 ECOD is a hierarchical classification based on evolutionary concepts that consists of five levels: architecture (A), possible homology (X), homology (H), topology (T), and family (F). 13 We identified and described protein families of bacterial, archeal, and eukaryotic viruses in the light of their classification in ECOD and defined their functional significance using enzymatic EC number assignments as well as domain-level family annotations. 81 protein families were defined as viral proteins containing a minimal Rossmann fold motif.…”

Section: Introductionmentioning

confidence: 99%

Functional and evolutionary analysis of viral proteins containing a Rossmann‐like fold

2018

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Viruses are the most abundant life form and infect practically all organisms. Consequently, these obligate parasites are a major cause of human suffering and economic loss. Rossmann-like fold is the most populated fold among α/β-folds in the Protein Data Bank and proteins containing Rossmann-like fold constitute 22% of all known proteins 3D structures. Thus, analysis of viral proteins containing Rossmann-like domains could provide an understanding of viral biology and evolution as well as could propose possible targets for antiviral therapy. We provide functional and evolutionary analysis of viral proteins containing a Rossmann-like fold found in the evolutionary classification of protein domains (ECOD) database developed in our lab. We identified 81 protein families of bacterial, archeal, and eukaryotic viruses in light of their evolution-based ECOD classification and Pfam taxonomy. We defined their functional significance using enzymatic EC number assignments as well as domain-level family annotations.

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Section: Introductionmentioning

confidence: 99%

Functional and evolutionary analysis of viral proteins containing a Rossmann‐like fold

2018

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“…All PDB IDs were matched against the ECOD [27] domain assignment from 2016-03-28. This set was obtained from the set used in the development of PconsC2 [17] and homology reduced such that no protein included in the test set shared an ECOD H-class with any of the proteins in the new training dataset.…”

Section: Methodsmentioning

confidence: 99%

Accurate contact predictions for thousands of protein families using PconsC3

Skwark

Michel

et al. 2016

Preprint

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Protein structure prediction was for decades one of the grand unsolved challenges in bioinformatics. A few years ago it was shown that by using a maximum entropy approach to describe couplings between columns in a multiple sequence alignment it was possible to significantly increase the accuracy of residue contact predictions. For very large protein families with more than 1000 effective sequences the accuracy is sufficient to produce accurate models of proteins as well as complexes. Today, for about half of all Pfam domain families no structure is known, but unfortunately most of these families have at most a few hundred members, i.e. are too small for existing contact prediction methods. To extend accurate contact predictions to the thousands of smaller protein families we present PconsC3, an improved method for protein contact predictions that can be used for families with as little as 100 effective sequence members. We estimate that PconsC3 provides accurate contact predictions for up to 4646 Pfam domain families. In addition, PconsC3 outperforms previous methods significantly independent on family size, secondary structure content, contact range, or the number of selected contacts. This improvement translates into improved de-novo prediction of three-dimensional structures. PconsC3 is available as a web server and downloadable version at http://c3.pcons.net. The downloadable version is free for all to use and licensed under the GNU General Public License, version 2.protein structure prediction | contact prediction | direct coupling analysis | protein family I n recent years great progress has been made in the area of residue contact prediction. The vast amount of available sequence data is utilized by direct coupling analysis (DCA) methods to predict contacts between residues with unprecedented quality [1,2]. This has enabled accurate blind predictions of the structure of soluble proteins [3][4][5], membrane proteins [6][7][8], and protein complexes [9,10]. However, the widespread use of such methods has been limited to protein families with more than 1000 members [11,12]. Unfortunately, the structure of at least one member of most large families is known (see Fig. S1). This limits the practical usefulness of DCA methods [13] and strongly suggests that methods that accurately predict residue contacts for smaller protein families would be of much greater utility.Before the advent of DCA methods there has been a longstanding effort in using machine learning techniques to predict residue contacts [14][15][16]. These methods utilize covariancebased evolutionary information (e.g. mutual information), as well as knowledge based constraints as inputs to a machine learning algoritm. The best non-DCA methods are less dependent on the size of the protein family and although their predictive quality is easily outperformed by DCA on large families, they perform significantly better on smaller families, Figure 1 (a). We have earlier used an iterative machine learning approach, built on the observation that contacts are ...

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“…S4 and Tables S3-8). The GR NTD sequence was also computationally analyzed for helical compatibility (28,35,55), represented by heat map coloring of the GR box in the center. "N/A" indicates a sequence around GR a.a. 300 that did not match any known structure and it is colored black.…”

Section: Figure 1 the Domains And Functions Of Gr And Tsg101mentioning

confidence: 99%

Tumor susceptibility gene-101 regulates glucocorticoid receptor through disorder-mediated allostery

White

Rives²,

Tharp

et al. 2020

Preprint

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Tumor Susceptibility Gene-101 (TSG101) is involved in endosomal maturation and has been implicated in the transcriptional regulation of several steroid hormone receptors (SHRs), although a detailed characterization of such regulation has yet to be conducted. Here we directly measure binding of TSG101 to one SHR, glucocorticoid receptor (GR). Using biophysical and cellular assays, we show that the coiled-coil domain of TSG101; 1) binds and folds the disordered N-terminal domain (NTD) of GR, 2) upon binding, improves DNA-binding of GR in vitro, and 3) enhances the transcriptional activity of GR in vivo. Our findings suggest that TSG101 is a bona fide transcriptional co-regulator of GR.

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Manual classification strategies in the ECOD database

Cited by 68 publications

References 102 publications

Functional and evolutionary analysis of viral proteins containing a Rossmann‐like fold

Functional and evolutionary analysis of viral proteins containing a Rossmann‐like fold

Accurate contact predictions for thousands of protein families using PconsC3

Tumor susceptibility gene-101 regulates glucocorticoid receptor through disorder-mediated allostery

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