Million-atom molecular dynamics simulations reveal the interfacial interactions and assembly of plant PSII-LHCII supercomplex

Structure of cryptophyte photosystem II-light-harvesting antennae supercomplex

Cryptophytes are ancestral photosynthetic organisms evolved from red algae through secondary endosymbiosis. They have developed alloxanthin-chlorophyll a/c2-binding proteins (ACPs) as light-harvesting complexes (LHCs). The distinctive properties of cryptophytes contribute to efficient oxygenic photosynthesis and underscore the evolutionary relationships of red-lineage plastids. Here we present the cryo-electron microscopy structure of the Photosystem II (PSII)-ACPII supercomplex from the cryptophyte Chroomonas placoidea. The structure includes a PSII dimer and twelve ACPII monomers forming four linear trimers. These trimers structurally resemble red algae LHCs and cryptophyte ACPI trimers that associate with Photosystem I (PSI), suggesting their close evolutionary links. We also determine a Chl a-binding subunit, Psb-γ, essential for stabilizing PSII-ACPII association. Furthermore, computational calculation provides insights into the excitation energy transfer pathways. Our study lays a solid structural foundation for understanding the light-energy capture and transfer in cryptophyte PSII-ACPII, evolutionary variations in PSII-LHCII, and the origin of red-lineage LHCIIs.

A Vision for the Future of Multiscale Modeling

The rise of modern computer science enabled physical chemistry to make enormous progresses in understanding and harnessing natural and artificial phenomena. Nevertheless, despite the advances achieved over past decades, computational resources are still insufficient to thoroughly simulate extended systems from first principles. Indeed, countless biological, catalytic and photophysical processes require ab initio treatments to be properly described, but the breadth of length and time scales involved makes it practically unfeasible. A way to address these issues is to couple theories and algorithms working at different scales by dividing the system into domains treated at different levels of approximation, ranging from quantum mechanics to classical molecular dynamics, even including continuum electrodynamics. This approach is known as multiscale modeling and its use over the past 60 years has led to remarkable results. Considering the rapid advances in theory, algorithm design, and computing power, we believe multiscale modeling will massively grow into a dominant research methodology in the forthcoming years. Hereby we describe the main approaches developed within its realm, highlighting their achievements and current drawbacks, eventually proposing a plausible direction for future developments considering also the emergence of new computational techniques such as machine learning and quantum computing. We then discuss how advanced multiscale modeling methods could be exploited to address critical scientific challenges, focusing on the simulation of complex light-harvesting processes, such as natural photosynthesis. While doing so, we suggest a cutting-edge computational paradigm consisting in performing simultaneous multiscale calculations on a system allowing the various domains, treated with appropriate accuracy, to move and extend while they properly interact with each other. Although this vision is very ambitious, we believe the quick development of computer science will lead to both massive improvements and widespread use of these techniques, resulting in enormous progresses in physical chemistry and, eventually, in our society.

Perspective on Integrative Simulations of Bioenergetic Domains

Bioenergetic processes in cells, such as photosynthesis or respiration, integrate many time and length scales, which makes the simulation of energy conversion with a mere single level of theory impossible. Just like the myriad of experimental techniques required to examine each level of organization, an array of overlapping computational techniques is necessary to model energy conversion. Here, a perspective is presented on recent efforts for modeling bioenergetic phenomena with a focus on molecular dynamics simulations and its variants as a primary method. An overview of the various classical, quantum mechanical, enhanced sampling, coarse-grained, Brownian dynamics, and Monte Carlo methods is presented. Example applications discussed include multiscale simulations of membrane-wide electron transport, rate kinetics of ATP turnover from electrochemical gradients, and finally, integrative modeling of the chromatophore, a photosynthetic pseudo-organelle.

A new paradigm for molecular dynamics databases: the COVID-19 database, the legacy of a titanic community effort

Molecular dynamics (MD) simulations are keeping computers busy around the world, generating a huge amount of data that is typically not open to the scientific community. Pioneering efforts to ensure the safety and reusability of MD data have been based on the use of simple databases providing a limited set of standard analyses on single-short trajectories. Despite their value, these databases do not offer a true solution for the current community of MD users, who want a flexible analysis pipeline and the possibility to address huge non-Markovian ensembles of large systems. Here we present a new paradigm for MD databases, resilient to large systems and long trajectories, and designed to be compatible with modern MD simulations. The data are offered to the community through a web-based graphical user interface (GUI), implemented with state-of-the-art technology, which incorporates system-specific analysis designed by the trajectory providers. A REST API and associated Jupyter Notebooks are integrated into the platform, allowing fully customized meta-analysis by final users. The new technology is illustrated using a collection of trajectories obtained by the community in the context of the effort to fight the COVID-19 pandemic. The server is accessible at https://bioexcel-cv19.bsc.es/#/. It is free and open to all users and there are no login requirements. It is also integrated into the simulations section of the BioExcel-MolSSI COVID-19 Molecular Structure and Therapeutics Hub: https://covid.molssi.org/simulations/ and is part of the MDDB effort (https://mddbr.eu).

Energy transfer from two luteins to chlorophylls in light-harvesting complex II study by using exciton models with phase correction

In light-harvesting complex II of plants, the two lutein pigments (LUT1 and LUT2) are always paired and an energy transfer pathway between them is believed to exist. However, it remains unclear whether this pathway is essential for the energy transfer between carotenoids and chlorophylls. In this work, we performed hybrid quantum mechanics/molecular mechanics simulations with Frenkel exciton models to investigate this energy transfer. The results show that the energy transfer pathways between the S2 state of LUT1 and CLAs are not affected by LUT2 S2. The energy transfer between LUT and chlorophyll-a (CLA) also follows a resonance mechanism. The two LUTs have different energy transfer pathways according to their energy gaps and coupling strengths with each CLA. The present work sheds light on the energy transfer pathways involved in the two LUTs.

Energetic driving force for LHCII clustering in plant membranes

Plants capture and convert solar energy in a complex network of membrane proteins. Under high light, the luminal pH drops and induces a reorganization of the protein network, particularly clustering of the major light-harvesting complex (LHCII). While the structures of the network have been resolved in exquisite detail, the thermodynamics that control the assembly and reorganization had not been determined, largely because the interaction energies of membrane proteins have been inaccessible. Here, we describe a method to quantify these energies and its application to LHCII. Using single-molecule measurements, LHCII proteoliposomes, and statistical thermodynamic modeling, we quantified the LHCII-LHCII interaction energy as ~-5 kBT at neutral pH and at least -7 kBT at acidic pH. These values revealed an enthalpic thermodynamic driving force behind LHCII clustering. Collectively, this work captures the interactions that drive the organization of membrane protein networks from the perspective of equilibrium statistical thermodynamics, which has a long and rich tradition in biology.

Key ingredients in Verbena officinalis and determination of their anti-atherosclerotic effect using a computer-aided drug design approach

Lipid metabolism disorders may considerably contribute to the formation and development of atherosclerosis (AS). Traditional Chinese medicine has received considerable attention in recent years owing to its ability to treat lipid metabolism disorders using multiple components and targets. Verbena officinalis (VO), a Chinese herbal medicine, exhibits anti-inflammatory, analgesic, immunomodulatory, and neuroprotective effects. Evidence suggests that VO regulates lipid metabolism; however, its role in AS remains unclear. In the present study, an integrated network pharmacology approach, molecular docking, and molecular dynamics simulation (MDS) were applied to examine the mechanism of VO against AS. Analysis revealed 209 potential targets for the 11 main ingredients in VO. Further, 2698 mechanistic targets for AS were identified, including 147 intersection targets between VO and AS. Quercetin, luteolin, and kaempferol were considered key ingredients for the treatment of AS based on a potential ingredient target-AS target network. GO analysis revealed that biological processes were primarily associated with responses to xenobiotic stimuli, cellular responses to lipids, and responses to hormones. Cell components were predominantly focused on the membrane microdomain, membrane raft, and caveola nucleus. Molecular functions were mainly focused on DNA-binding transcription factor binding, RNA polymerase II-specific DNA-binding transcription factor binding, and transcription factor binding. KEGG pathway enrichment analysis identified pathways in cancer, fluid shear stress, and atherosclerosis, with lipid and atherosclerosis being the most significantly enriched pathways. Molecular docking revealed that three key ingredients in VO (i.e., quercetin, luteolin, and kaempferol) strongly interacted with three potential targets (i.e., AKT1, IL-6, and TNF-α). Further, MDS revealed that quercetin had a stronger binding affinity for AKT1. These findings suggest that VO has beneficial effects on AS via these potential targets that are closely related to the lipid and atherosclerosis pathways. Our study utilized a new computer-aided drug design to identify key ingredients, potential targets, various biological processes, and multiple pathways associated with the clinical roles of VO in AS, which provides a comprehensive and systemic pharmacological explanation for the anti-atherosclerotic activity of VO.