The value of protein structure classification information—Surveying the scientific literature

The Structural Classification of Proteins (SCOP) and Class, Architecture, Topology, Homology (CATH) databases have been valuable resources for protein structure classification for over 20 years. Development of SCOP (version 1) concluded in June 2009 with SCOP 1.75. The SCOPe (SCOP–extended) database offers continued development of the classic SCOP hierarchy, adding over 33,000 structures. We have attempted to assess the impact of these two decade old resources and guide future development. To this end, we surveyed recent articles to learn how structure classification data are used. Of 571 articles published in 2012–2013 that cite SCOP, 439 actually use data from the resource. We found that the type of use was fairly evenly distributed among four top categories: A) study protein structure or evolution (27% of articles), B) train and/or benchmark algorithms (28% of articles), C) augment non‐SCOP datasets with SCOP classification (21% of articles), and D) examine the classification of one protein/a small set of proteins (22% of articles). Most articles described computational research, although 11% described purely experimental research, and a further 9% included both. We examined how CATH and SCOP were used in 158 articles that cited both databases: while some studies used only one dataset, the majority used data from both resources. Protein structure classification remains highly relevant for a diverse range of problems and settings. Proteins 2015; 83:2025–2038. © 2015 The Authors. Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.     

Structural similarity between the pleckstrin homology domain and verotoxin: the problem of measuring and evaluating structural similarity.

An unexpected structural similarity is described between the pleckstrin homology (PH) domain and verotoxin. This similarity has escaped detection primarily due to the differences in topology that exist between the two proteins. By comparing this result with two previously reported similarities for the PH domain, one with the lipocalins and another with the FK506 binding protein, we discuss the problems of measuring and assessing structural similarities.     

Database on diagnosis of arthropod diseases in the Internet of the Julius Kühn‐Institute

A database on diagnosis of diseases of insects and other arthropods is available in a German and English version at http://arthropodenkrankheiten.jki.bund.de . It is based on 55 years of diagnostic investigations of living, diseased, or dead arthropods of about 450 species at the ‘Institute for Biological Control’ in Darmstadt, Germany. Results of 1951 accessions with several thousands of diagnosed specimens from 1953 to 2008 are presented. The diagnosed pathogens belong to six groups: viruses, bacteria including rickettsiae, fungi, microsporidia (recently assigned to fungi), protists, and nematodes. The database will be updated regularly.     

A network pharmacology approach to explore the potential role of Panax ginseng on exercise performance

[Purpose]As Panax ginseng C. A. Meyer (ginseng) exhibits various physiological activities and is associated with exercise, we investigated the potential active components of ginseng and related target genes through network pharmacological analysis. Additionally, we analyzed the association between ginseng-related genes, such as the G-protein-coupled receptors (GPCRs), and improved exercise capacity.[Methods]Active compounds in ginseng and the related target genes were searched in the Traditional Chinese Medicine Database and Analysis Platform (TCMSP). Gene ontology functional analysis was performed to identify biological processes related to the collected genes, and a compound-target network was visualized using Cytoscape 3.7.2.[Results]A total of 21 ginseng active compounds were detected, and 110 targets regulated by 17 active substances were identified. We found that the active compound protein was involved in the biological process of adrenergic receptor activity in 80%, G-protein-coupled neurotransmitter in 10%, and leucocyte adhesion to arteries in 10%. Additionally, the biological response centered on adrenergic receptor activity showed a close relationship with G protein through the beta-1 adrenergic receptor gene reactivity.[Conclusion]According to bioavailability analysis, ginseng comprises 21 active compounds. Furthermore, we investigated the ginseng-stimulated gene activation using ontology analysis. GPCR, a gene upregulated by ginseng, is positively correlated to exercise. Therefore, if a study on this factor is conducted, it will provide useful basic data for improving exercise performance and health.     

MOET: a web-based gene set enrichment tool at the Rat Genome Database for multiontology and multispecies analyses

Biological interpretation of a large amount of gene or protein data is complex. Ontology analysis tools are imperative in finding functional similarities through overrepresentation or enrichment of terms associated with the input gene or protein lists. However, most tools are limited by their ability to do ontology-specific and species-limited analyses. Furthermore, some enrichment tools are not updated frequently with recent information from databases, thus giving users inaccurate, outdated or uninformative data. Here, we present MOET or the Multi-Ontology Enrichment Tool (v.1 released in April 2019 and v.2 released in May 2021), an ontology analysis tool leveraging data that the Rat Genome Database (RGD) integrated from in-house expert curation and external databases including the National Center for Biotechnology Information (NCBI), Mouse Genome Informatics (MGI), The Kyoto Encyclopedia of Genes and Genomes (KEGG), The Gene Ontology Resource, UniProt-GOA, and others. Given a gene or protein list, MOET analysis identifies significantly overrepresented ontology terms using a hypergeometric test and provides nominal and Bonferroni corrected P-values and odds ratios for the overrepresented terms. The results are shown as a downloadable list of terms with and without Bonferroni correction, and a graph of the P-values and number of annotated genes for each term in the list. MOET can be accessed freely from https://rgd.mcw.edu/rgdweb/enrichment/start.html.     

The electric dipole moment of DNA-binding HU protein calculated by the use of NMR database

Electric birefringence measurements indicated the presence of a large permanent dipole moment in HU protein–DNA complex. In order to substantiate this observation, numerical computation of the dipole moment of HU protein homodimer was carried out by using NMR protein databases. The dipole moments of globular proteins have hitherto been calculated with X-ray databases and NMR data have never been used before. The advantages of NMR databases are: (a) NMR data are obtained, unlike X-ray databases, using protein solutions. Accordingly, this method eliminates the bothersome question as to the possible alteration of the protein structure due to the transition from the crystalline state to the solution state. This question is particularly important for proteins such as HU protein which has considerable internal flexibility’s; (b) the three dimensional coordinates of hydrogen atoms in protein molecules can be determined with a sufficient resolution and this enables the N–H as well as C=O bond moments to be calculated. Since the NMR database of HU protein from Bacillus stearothermophilus consists of 25 models, the surface charge as well as the core dipole moments were computed for each of these structures. The results of these calculations show that the net permanent dipole moments of HU protein homodimer is approximately 500–530 D (1 D=3.33×10⁻³⁰ Cm) at pH 7.5 and 600–630 D at the isoelectric point (pH 10.5). These permanent dipole moments are unusually large for a small protein of the size of 19.5 kDa. Nevertheless, the result of numerical calculations is compatible with the electro-optical observation, confirming a very large dipole moment in this protein.     

PDBsum extras: SARS‐CoV‐2 and AlphaFold models

The PDBsum web server provides structural analyses of the entries in the Protein Data Bank (PDB). Two recent additions are described here. The first is the detailed analysis of the SARS‐CoV‐2 virus protein structures in the PDB. These include the variants of concern, which are shown both on the sequences and 3D structures of the proteins. The second addition is the inclusion of the available AlphaFold models for human proteins. The pages allow a search of the protein against existing structures in the PDB via the Sequence Annotated by Structure (SAS) server, so one can easily compare the predicted model against experimentally determined structures. The server is freely accessible to all at http://www.ebi.ac.uk/pdbsum.     

基于知识库的植物害虫鉴别计算机辅助系统

在收集大量国内和外来害虫资料及图谱的基础上,采用Visual Basic6.0作为系统开发语言,模拟专家鉴别害虫的思路,建立了3个适合于害虫鉴别的计算机辅助鉴别模式,分别为计算机二叉分类检索表模式、外部特征组合鉴别模式和图谱显示辅助鉴别模式。