MCDB: A comprehensive curated mitotic catastrophe database for retrieval, protein sequence alignment, and target prediction

Abstract

Mitotic catastrophe (MC) is a form of programmed cell death induced by mitotic process disorders, which is very important in tumor prevention, development, and drug resistance. Because rapidly increased data for MC is vigorously promoting the tumor-related biomedical and clinical study, it is urgent for us to develop a professional and comprehensive database to curate MC-related data. Mitotic Catastrophe Database (MCDB) consists of 1214 genes/proteins and 5014 compounds collected and organized from more than 8000 research articles. Also, MCDB defines the confidence level, classification criteria, and uniform naming rules for MC-related data, which greatly improves data reliability and retrieval convenience. Moreover, MCDB develops protein sequence alignment and target prediction functions. The former can be used to predict new potential MC-related genes and proteins, and the latter can facilitate the identification of potential target proteins of unknown MC-related compounds. In short, MCDB is such a proprietary, standard, and comprehensive database for MC-relate data that will facilitate the exploration of MC from chemists to biologists in the fields of medicinal chemistry, molecular biology, bioinformatics, oncology and so on. The MCDB is distributed on http://www.combio-lezhang.online/MCDB/index_html/ .

Discovery of Tyrosinase Inhibitors: Structure-Based Virtual Screening and Biological Evaluation

“…In the first turn, small-molecule compound databases including SPECS, ACDB, and MCDB with more than 200,000 compounds were filtered by the Lipinski's rule of five. 19 It is well known that the molecule with the following features would be more likely to result in a real small-molecule drug: (1) a molecular mass less than 500 Da; (2) no more than 10 hydrogen bond acceptors; (3) no more than five hydrogen bond donors; and (4) log P (octanol–water partition coefficient) no greater than 5. 20 In addition, all of these compounds are commercially available or synthesized in laboratory, and could be picked out for further biological evaluation.…”

iPCD: A Comprehensive Data Resource of Regulatory Proteins in Programmed Cell Death

“…In this study, we first collected experimentally identified proteins from PubMed by using multiple keywords, such as 'apoptosis', 'apoptotic', 'anoikis', 'cytotoxic granulemediated cell death', 'oxeiptosis', 'necroptosis', 'necrosis', 'necrotic', 'pyroptosis', 'oncosis', 'oncotic', 'pyronecrosis', 'alkaliptosis', 'autophagic', 'autophagy', 'autosis', 'lysosomal cell death', 'autolysis', 'cornification', 'entosis', 'entotic', 'ferroptosis', 'hypersensitive response', 'mitoptosis', 'paraptosis', 'excitotoxicity', 'NETosis', 'netotic', 'parthanatos', 'phenoptosis', 'heterokaryon incompatibility', 'mitotic catastrophe', 'Wallerian degeneration', 'oxiapoptophagy', 'immunogenic cell death', 'phagoptosis', 'sarmoptosis' and 'PANoptosis'. In addition, we also integrated downloadable data resources from 11 PCD databases, including DeathBase [21], yApoptosis [22], CDP [23], Autophagy Database [24], HADb [25], ARN [26], HAMdb [27], ATdb [28], FerrDb [29], MCDB [30] and ncRDeathDB [32] (Figure 1A, Supplementary Methods). In order to ensure the quality of data, we carefully read the abstracts and the full text of papers.…”

“…Therefore, GPX4 protein is tagged with '−' as a negative regulator of ferroptosis (Figure 1A). In addition, we also integrated downloadable data resources from 11 PCD databases, including DeathBase [21], yApoptosis [22], CDP [23], Autophagy Database [24], HADb [25], ARN [26], HAMdb [27], ATdb [28], FerrDb [29], MCDB [30] and ncRDeathDB [32] (Figure 1A, Supplementary Methods). In order to ensure the quality of data, we carefully read the abstracts and the full text of papers.…”

“…Given the importance of different PCD forms in health and disease, tremendous efforts have been made to identify and integrate crucial PCD protein regulators. In the past two decades, multiple databases have been constructed for the collection of PCD regulators, including DeathBase [21], yApoptosis [22], Cell Death Proteomics database (CDP) [23], Autophagy Database [24], Human Autophagy Database (HADb) [25], Autophagy Regulatory Network (ARN) [26], Human Autophagy Modulator Database (HAMdb) [27], Autophagy and Tumor Database (ATdb) [28], FerrDb [29], MCDB [30], miRDeathDB [31] and ncRDeathDB [32] (Table S1). These data resources provide valuable clues for further deciphering PCD systems.…”

Developing an Embedding, Koopman and Autoencoder Technologies-Based Multi-Omics Time Series Predictive Model (EKATP) for Systems Biology research

“…Recently, future state prediction for a multi-omics time series has been widely studied by computational biologists. For genomic studies, we usually use a gene expression time series to develop gene regulatory networks ( Davidson and Levin 2005 ; Zhang et al, 2018 ; Xiao et al, 2020 ; Zhang et al, 2020 ; Xiao et al, 2021 ; Zhang et al, 2021a ). However, since the gene regulatory network is a complex high-dimensional nonlinear system ( Zhang et al, 2012a ), it often produces chaotic phenomena ( Levnajić and Tadić 2010 ), which not only play an important role in maintaining stable gene expression patterns ( Sevim and Rikvold 2008 ) but also are closely related to the occurrence of diseases ( Suzuki et al, 2016 ).…”