Multilocation proteins in organelle communication: Based on protein–protein interactions

Abstract

Protein-protein interaction (PPI) plays a crucial role in most biological processes, including signal transduction and cell apoptosis. Importantly, the knowledge of PPIs can be useful for identification of multimeric protein complexes and elucidation of uncharacterized protein functions. Arabidopsis thaliana, the best-characterized dicotyledonous plant, the steadily increasing amount of information on the levels of its proteome and signaling pathways is progressively enabling more researchers to construct models for cellular processes for the plant, which in turn encourages more experimental data to be generated. In this study, we performed an overview analysis of the 10 major organelles and their associated proteins of the dicotyledonous model plant Arabidopsis thaliana via PPI network, and found that PPI may play an important role in organelle communication. Further, multilocation proteins, especially phosphorylation-related multilocation proteins, can function as a "needle and thread" via PPIs and play an important role in organelle communication. Similar results were obtained in a monocotyledonous model crop, rice. Furthermore, we provide a research strategy for multilocation proteins by LOPIT technique, proteomics, and bioinformatics analysis and also describe their potential role in the field of plant science. The results provide a new view that the phosphorylation-related multilocation proteins play an important role in organelle communication and provide new insight into PPIs and novel directions for proteomic research. The research of phosphorylationrelated multilocation proteins may promote the development of organelle communication and provide an important theoretical basis for plant responses to external stress.

A combined lipidomic and proteomic profiling ofArabidopsis thalianaplasma membrane

“…In agreement with the results of digital immunoblotting (Figure 2), new AtPM proteins only consisted of a small set (18.7%) of proteins exclusively localized to the PM when compared to the 330 multilocalizing proteins (MLPs) recorded according to experimental evidence codes (Supplemental Table S8). The recurrent identification of these proteins in the three AtPM fractions of this study indicated that these MLPs should not be discarded as contaminants, but rather argued for proteins shuttling or operating across organelles to regulate metabolic reactions or to transmit information from the surrounding environment (66). Within this line, the extracellular space and the Golgi apparatus were the two major co-localizing cellular components for the 112 MLPs, each accounting for 74 and 69 proteins, respectively.…”

“…The further in silico analyses we performed on the 2165 PM protein set fitted typical traits of the plant PM, as inferred from functional grouping, membrane-anchoring mechanisms, hydrophobicity pattern, pI distribution, and PM-characteristic features of bitopic proteins ( Figures 3, 4 ). They also highlighted that the AtPM proteome mostly consisted of MLPs, as inferred from experimental evidence codes ( Tables S6, S7, S8 ), arguing for proteins shuttling or operating across organelles to regulate metabolic reactions or to transmit information from the surrounding environment (73). The plant PM is notably known to form ER–PM membrane contact sites (MCSs), defined as tight junctions tethered by specific proteins (74,75).…”

A Review for Artificial Intelligence Based Protein Subcellular Localization

“…Recent studies have also shown the remarkable significance of multilocation proteins in cell growth and development [41]. For instance, phosphorylation-related multilocation proteins can function as a "needle and thread" via protein-protein interactions (PPI), thus playing an important role in organelle communication and regulating plant growth [42]. Under these circumstances, there are mainly two ways for predicting multi-location proteins based on conventional classifiers: algorithm adaption and problem transformation.…”

The soybean ubiquitin‐proteasome system: Current knowledge and future perspective

“…The genes of 2,429 ubiquitin-related proteins in the UniPro-tKB database were searched in the iSoybean mutant library, and 2,078 genes with 22,067 nonsynonymous or stop gain mutations were retrieved (Supplemental Table S1). Among them, 373 E3 ubiquitin ligases were used for protein-protein interaction network predictive analysis using the STRING 11.0 program (https://cn.string-db.org/) (Gong et al, 2021;Xiong et al, 2022Xiong et al, , 2021. The network showed that the E3 ubiquitin ligases GmARI1, GmARI2, GmARI3, and GmARI7 have multiple interacting proteins, which may be involved in multiple pathways.…”