A pan-plant protein complex map reveals deep conservation and novel assemblies

Abstract

Plants are foundational to global ecological and economic systems, yet most plant proteins remain uncharacterized. Protein interaction networks often suggest protein functions and open new avenues to characterize genes and proteins. We therefore systematically determined protein complexes from 13 plant species of scienti ic and agricultural importance, greatly expanding the known repertoire of stable protein complexes in plants. Using co-fractionation mass spectrometry, we recovered known complexes, con irmed complexes predicted to occur in plants, and identi ied novel interactions conserved over 1.1 billion years of green plant evolution. Several novel complexes are involved in vernalization and pathogen defense, traits critical to agriculture. We also uncovered plant analogs of animal complexes with distinct molecular assemblies, including a megadalton-scale tRNA multi-synthetase complex. The resulting map offers the irst cross-species view of conserved, stable protein assemblies shared across plant cells and provides a mechanistic, biochemical framework for interpreting plant genetics and mutant phenotypes.

A systematic, label-free method for identifying RNA-associated proteinsin vivoprovides insights into vertebrate ciliary beating

“…To overcome these limitations, we applied an orthology-based approach that we recently developed for proteomic comparisons across polyploid plant species (McWhite et al, 2019), in which we collapse highly related protein sequences into EggNOG vertebrate orthology groups (Huerta-Cepas et al, 2016)(Supp. Figure 1, see methods).…”

Predicted landscape of RETINOBLASTOMA‐RELATED LxCxE‐mediated interactions across the Chloroplastida

“…POLA1 and ORC1) contain LxCxE-like SLiMs (L /I xCxE /D ), which recent work demonstrates that, at least in the case for the LxCxD-motif, retain the ability to bind both pRB (Ramanujan et al, 2021) and RBR (Zhao et al, 2017). Analysis of protein-protein interactions and complexes detected through co-fractionation mass spectrometry (McWhite et al, 2020) revealed that, indeed, RBR interacts (likely in a transient way) with MCM6 and POLD, thus further supporting our in silico approach. In animals, pRB interaction with both POLa and POLh stimulates their replicative activity (Krucher et al, 2000;Takemura et al, 1997).…”

A machine learning-based approach to identify reliable gold standards for protein complex composition prediction

“…Negative interactions are created from proteins not present in the positive interaction set. These positive and negative sets have been used for training and testing computation methods of protein complex predictions (9,18,20,26). This approach assumes that CORUM complexes are fully assembled in the cell extracts that are used as the input for CFMS experiments.…”

“…Many are achieved by using multiple metrics from external data sources that could inform multimerization behaviors. Examples include using mRNA co-expression or co-citation information via machine learning classifiers (3,9,(18)(19)(20) or integration of existing protein interaction predictions from orthogonal approaches (4-6, 16, 21-24). There is no clear-cut agreement on the effectiveness of those strategies for protein complex predictions (18).…”