CPLM 4.0: an updated database with rich annotations for protein lysine modifications

Abstract

Here, we reported the compendium of protein lysine modifications (CPLM 4.0, http://cplm.biocuckoo.cn/), a data resource for various post-translational modifications (PTMs) specifically occurred at the side-chain amino group of lysine residues in proteins. From the literature and public databases, we collected 450 378 protein lysine modification (PLM) events, and combined them with the existing data of our previously developed protein lysine modification database (PLMD 3.0). In total, CPLM 4.0 contained 592 606 experimentally identified modification events on 463 156 unique lysine residues of 105 673 proteins for up to 29 types of PLMs across 219 species. Furthermore, we carefully annotated the data using the knowledge from 102 additional resources that covered 13 aspects, including variation and mutation, disease-associated information, protein-protein interaction, protein functional annotation, DNA & RNA element, protein structure, chemical-target relation, mRNA expression, protein expression/proteomics, subcellular localization, biological pathway annotation, functional domain annotation, and physicochemical property. Compared to PLMD 3.0 and other existing resources, CPLM 4.0 achieved a >2-fold increase in collection of PLM events, with a data volume of ∼45GB. We anticipate that CPLM 4.0 can serve as a more useful database for further study of PLMs.

Dynamic regulation and key roles of ribonucleic acid methylation

“…The following are the emerged databases of methylation modifications associated with some specific diseases and fields: (1) After m6A methylation-related genes based on The Cancer Genome Atlas (TCGA) were used to predict the prognosis of hepatocellular carcinoma ( Group et al, 2020 ; Liu et al, 2020b ; Li et al, 2021b ), cancer-related methylation modification databases have begun to emerge, including, Lnc2Cancer 3.0 and OncoDB ( Gao et al, 2021 ; Tang et al, 2022 ); (2) Osteoarthritis-omics and molecular biomarkers (OAOB), which are a group of database containing differential molecular biomarkers related to osteoarthritis ( Li et al, 2022a ); (3) Other than disease, Pan et al (2022) established an integrative multi-omic database (iMOMdb) of Asian pregnant women providing the first blood-based multi-omic analysis of pregnant women in Asia. This database contains high-resolution genotypes, DNA methylation, and transcriptome profiles, and fills the knowledge gap of complex traits in populations of Asian ancestry; (4) Gao et al (2022) developed AgingBank, an experimentally supported multiomics database of information related to aging in multiple species; (5) ProMetheusDB, a database generated by analyzing and sorting cell culture experiments data using ML tools from the protein perspective ( Massignani et al, 2022 ); (6) compendium of protein lysine modifications (CPLM 4.0), a post-translational modification (PTMs) database ( Zhang et al, 2022 ); (7) tRNA-related databases containing high-throughput tRNA sequencing data ( Sajek et al, 2020 ); (8) RNAWRE and RM2 Target are two databases focusing on information of writers, readers, and erasers ( Nie et al, 2020 ; Bao et al, 2022 ); and (9) SyStemCell, a multiple-levels experimental database for stem cell research ( Yu et al, 2012 ). With the emergence of these specialized and multi-angle RNA methylation related databases, the traditional databases are also constantly updated and developed.…”

“…• RMDisease V2.0 (Song et al, 2022b), AgingBank (Gao et al, 2022), CPLM 4.0 (Zhang et al, 2022), OncoDB (Tang et al, 2022), ASMdb (Zhou et al, 2022), iMOMdb (Pan et al, 2022), OAOB (Li et al, 2022a), ProMetheusDB (Massignani et al, 2022), RM2Target (Bao et al, 2022(Bao et al, ) (2022 performance based on its SpinalNet architecture that was inspired by the human somatosensory system.…”

“…(5) ProMetheusDB, a database generated by analyzing and sorting cell culture experiments data using ML tools from the protein perspective (Massignani et al, 2022); (6) compendium of protein lysine modifications (CPLM 4.0), a post-translational modification (PTMs) database (Zhang et al, 2022); (7) tRNArelated databases containing high-throughput tRNA sequencing data (Sajek et al, 2020); (8) RNAWRE and RM2 Target are two databases focusing on information of writers, readers, and erasers (Nie et al, 2020;Bao et al, 2022); and (9) SyStemCell, a multiple-levels experimental database for stem cell research (Yu et al, 2012). With the emergence of these specialized and multi-angle RNA methylation related databases, the traditional databases are also constantly updated and developed.…”

AnimalTFDB 4.0: a comprehensive animal transcription factor database updated with variation and expression annotations

“…Next, we integrated 190 627 and 8 294 851 variation records for TFs and TcoFs from ClinVar ( 16 ) and COSMIC (v96) ( 17 ), respectively. PTM information of TFs and TcoFs for eight species was obtained from CPLM ( 29 ) and EPSD ( 30 ), containing 131 378 phosphorylation sites and 38 943 lysine modification sites (including acetylation, methylation and ubiquitination). In addition, we accessed information from THANATOS ( 31 ) on whether a TF or TcoF is involved in regulating autophagy-related processes (autophagy, apoptosis, and necrosis).…”

“…Growing evidence shows that the PTMs of TFs have positive and negative consequences on transcription ( 48 ). Here, we parsed 38 943 lysine modification sites (including 14 041 acetylation, 1169 methylation and 23 733 ubiquitination) from CPLM ( 29 ) database and 131 378 phosphorylation sites from EPSD ( 30 ) database in eight model species ( H. sapiens , M. musculus , R. norvegicus , C. elegans , B. taurus , Cavia porcellus , Gallus and D. melanogaster ). There are 2941 TFs and 2343 TcoFs with PTM information containing 1588 human TFs, 1013 human TcoFs, 980 mouse TFs and 835 mouse TcoFs, as well as 373 TFs and 494 TcoFs in the remaining six species ( Supplementary Table S7 ).…”