Copeland, M. M. | bioRxiv Science

bioRxiv2026

COB: a comprehensive database of chloroplast outer envelope beta-barrel proteins

Despite their central role in metabolite exchange, lipid trafficking, and protein import, chloroplast outer envelope beta-barrel proteins lack a dedicated comprehensive sequence database spanning many plant proteomes. Here we present the database COB (chloroplast outer-envelope beta-barrel), consisting of 16,586 beta-barrel sequences organized across ten protein categories and an uncharacterized group. COB was constructed using a machine learning classifier that identifies chloroplast beta-barrels based on features derived from evolutionary protein contact maps. Analysis of COB reveals that Streptophyta have more barrels overall and use a greater variety of solute transporters than Chlorophyta. Furthermore, we find considerable structural diversity across OEP categories, including variation in beta-strand count and a high prevalence of open barrel conformations not observed in bacterial outer membrane proteins. Structure predictions for Arabidopsis thaliana outer envelope proteins identified candidate hybrid barrel assemblies, with TOC159 family members emerging as universal interaction partners. We also report single-chain multi-barrel domain architectures in the chloroplast outer envelope, a topology previously described only in Gram-negative bacteria. Finally, we find chloroplast membrane barrels have more open topologies and shorter strands than bacterial membrane barrels. COB provides a comprehensive sequence resource for chloroplast outer envelope beta-barrels and establishes a foundation for investigating the evolution, structure, and function of this essential protein in chloroplast.

plant biology↗

bioRxiv2026

Simulation of cell-size systems at long timescales with flexible protein structures

Protein behavior inside cells is dominated by the crowded nature of the intracellular environment. Progress in structure determination of proteins and protein complexes, based on advances in Artificial Intelligence, provides an opportunity for structure-based modeling of cellular phenomena. Such modeling at the atomic resolution has been advanced by the traditional simulation techniques, e.g. molecular dynamics. A recently developed docking-based approach implements Markov Chain Monte Carlo sampling of intermolecular energy landscapes, offering several orders of magnitude faster simulation protocols. The approach allows addressing much longer trajectories of macromolecular systems in the crowded intracellular environment at atomic resolution. The sampling by design avoids low-probability (high-energy) states, which greatly accelerates the simulation process. A notable feature of this docking-based approach is the rigid body approximation of protein structures. The rigid-body approximation had been the primary direction in the protein docking field up until recent developments in deep learning. The rigid-body approach should be quite robust for the higher energy transient interactions that dominate the highly crowded cellular environment, as they likely involve relatively small conformational change. However, it is less applicable to the low-energy protein-protein complexes, especially those involving flexible regions. We addressed this problem by incorporating AlphaFold3 top models of the protein complexes in the mapping of the intermolecular energy landscape, as representative of the low-energy configurations of the protein assembly. By the nature of the AlphaFold predictions, these models involve appropriate conformational change between unbound and bound structures. These low-energy docking poses are combined with the rigid-body docking predictions that cover the multiplicity of the transient interactions. Such combination directly addresses the conformational flexibility of proteins upon binding along with the multiplicity of the transient protein encounters in the crowded cellular environment. SIGNIFICANCEProtein behavior inside cells is dominated by the crowded nature of intracellular environment. A recently developed approach allowed addressing long simulation trajectories of macromolecular systems in such environment at atomic resolution. A notable feature of this approach is the rigid body approximation in representation of the protein structures, which had been popular in the field up until the recent developments in artificial intelligence. However, such approximation is less applicable to stable protein-protein complexes, especially those involving flexible regions. We addressed this problem head-on by incorporating top deep learning-generated models of protein complexes. The new approach directly accounts for the flexibility of protein structures upon binding, along with the multiplicity of the transient protein encounters in the crowded cellular environment.

biophysics↗