The identification of protein complexes is important for the understanding of protein structure and function and the regulation of cellular processes. We used blue-native PAGE and tandem mass spectrometry to identify protein complexes systematically, and built a web database, the protein co-mi- gration database (PCoM-DB, http://pcomdb.lowtem.hokudai. ac.jp/proteins/top), to provide prediction tools for protein complexes. PCoM-DB provides migration profiles for any given protein of interest, and allows users to compare them with migration profiles of other proteins, showing the oligomeric states of proteins and thus identifying potential interaction partners. The initial version of PCoM-DB (launched in January 2013) included protein complex data for Synechocystis whole cells and Arabidopsis thaliana thyla- koid membranes. Here we report PCoM-DB version 2.0, which includes new data sets and analytical tools. Additional data are included from whole cells of the pelagic marine picocya- nobacterium Prochlorococcus marinus, the thermophilic cyanobacterium Thermosynechococcus elongatus, the unicel- lular green alga Chlamydomonas reinhardtii and the bryo- phyte Physcomitrella patens. The Arabidopsis protein data now include data for intact mitochondria, intact chloroplasts, chloroplast stroma and chloroplast envelopes. The new tools comprise a multiple-protein search form and a heat map viewer for protein migration profiles. Users can compare mi- gration profiles of a protein of interest among different or- ganelles or compare migration profiles among different proteins within the same sample. For Arabidopsis proteins, users can compare migration profiles of a protein of interest with putative homologous proteins from non-Arabidopsis or- ganisms. The updated PCoM-DB will help researchers find novel protein complexes and estimate their evolutionary changes in the green lineage.