Photosynthetic Antennas in a Computational Microscope: Training a new generation of computational scientists

Projektinformationen

PhotoCaM

ID Finanzhilfevereinbarung: 101119442

DOI

10.3030/101119442

EK-Unterschriftsdatum 20 Juni 2023

Startdatum 1 Januar 2024

Enddatum 31 Dezember 2027

Finanziert unter

• Marie Skłodowska-Curie Actions (MSCA)

Gesamtkosten

Keine Daten

EU-Beitrag

€ 2 589 847,20

Koordiniert durch

CONSTRUCTOR UNIVERSITY BREMEN GGMBH
Germany

Periodic Reporting for period 1 - PhotoCaM (Photosynthetic Antennas in a Computational Microscope: Training a new generation of computational scientists)

Berichtszeitraum: 2024-01-01 bis 2025-12-31

Zusammenfassung vom Kontext und den Gesamtzielen des Projekts

Photosynthesis is a natural process that relies on harvesting solar energy and transforming it into chemical energy. In addition to its ecological importance, obtaining a better molecular-level understanding of photosynthesis, particularly of the light-harvesting process, is important because it is of key significance. First, understanding the molecular basis of photosynthetic light harvesting and its regulation can advance our fundamental knowledge of nature's working principles and guide the rational design of organic solar cells. Second, controlling photosynthesis is a strategy for optimizing crop productivity using biotechnological solutions. A comprehensive understanding of photosynthesis requires strong multidisciplinary approaches, both experimental and computational. The project "Photosynthetic Antennas in a Computational Microscope: Training a New Generation of Computational Scientists” (PhotoCAM) focuses on theoretical and computational approaches, with a strong emphasis on calculating experimental observables to establish a clear connection to experiments.
There are common design principles in the organization of the photosynthetic apparatus, especially in green plants and algae like diatoms. Solar energy is first captured by pigments, such as chlorophyll and carotenoids, which are bound to light-harvesting complexes. These complexes are pigment-binding proteins embedded within the thylakoid membrane, a lipid membrane. Excitation energy from absorbed photons is converted into chemical energy within the photosystems. Ηigh-resolution techniques such as X-ray crystallography and cryogenic electron microscopy have provided atomistic details of the protein complexes involved in light harvesting.
This project aims to further develop, validate, and apply state-of-the-art, multi-scale methodologies that allow for simulations of photo-initiated exciton transfer in light-harvesting systems based purely on atomistic grounds. Work packages combining atomistic simulations that analyze conformational changes, quantum chemical and dynamics calculations that determine electronic excitations and their transfer in these systems, and the determination of spectroscopic properties that connect to experiments form a powerful computational toolbox for a detailed assessment of the underlying processes in light harvesting. Beyond advancing our understanding of the fascinating process of light harvesting and its variations in land and aquatic organisms, this knowledge could be applied by molecular biologists in academia and industry to modify plants and improve crop yields, helping to feed the growing global population.

Arbeit, die ab Beginn des Projekts bis zum Ende des durch den Bericht erfassten Berichtszeitraums geleistet wurde, und die wichtigsten bis dahin erzielten Ergebnisse

The PhotoCaM Doctoral Network has made substantial progress in understanding the structure-function relationships of light-harvesting complexes (LHCs) in plants and diatoms through an integrated computational approach. The research combines theoretical considerations with molecular dynamics (MD) simulations, Markov State Models, quantum chemical calculations of exciton energies, exciton coupling calculations, and machine learning methods. Based on the respective results (non-adiabtic) quantum dynamical calculations of the exciton dynamics were performed and spectroscopic signals determined.
The consortium has used a combined modeling approach linking LHC structure and function. MD simulations trace atomic motions over microsecond timescales. Exciton coupling calculations have been incorporated to directly connect structural states with pigment–pigment interactions and energy transfer efficiency, bridging the gap between structure and function. A focus in the ongoing research is on the atomic-level view of the conformational plasticity of light-harvesting complexes including those of LHCII from plants and FCPs from diatoms. The work highlights that these complexes are not static structures but dynamic systems that tune their shape and function depending on environmental conditions.
On the computational side, fast computational algorithms for conformational sampling have been employed, enabling the exploration of previously inaccessible timescales and system sizes. Machine learning methods have been integrated to identify key structural features distinguishing functional states, to predict behavior of unknown structures, but also to speed up simulations. Approaches have been further developed which quantitatively describe how LHCs switch between light-harvesting and photoprotective roles, particularly for complexes from various diatom species that remain unresolved experimentally.

Fortschritte, die über den aktuellen Stand der Technik hinausgehen und voraussichtliche potenzielle Auswirkungen (einschließlich der bis dato erzielten sozioökonomischen Auswirkungen und weiter gefassten gesellschaftlichen Auswirkungen des Projekts)

The results achieved in the project until now go significantly beyond the current state of the art by delivering a unified, multiscale and dynamically consistent description of how light-harvesting complexes (LHCs) regulate the balance between efficient energy capture and photoprotection across different photosynthetic organisms. The work integrates high-confidence structure prediction, long-timescale molecular dynamics, constant-pH simulations, and advanced free-energy and protonation analyses into a coherent workflow. This has enabled the reconstruction of fully pigmented, atomistic models of previously unresolved fucoxanthin–chlorophyll proteins (FCPs) in diatoms, the quantitative characterization of pH-dependent conformational landscapes in LHCs of plants and cyanobacteria, and the explicit identification of kinetically distinct macrostates associated with light-harvesting and quenched regimes for diatoms. Importantly, we demonstrate that photoprotection is not encoded in a single structural switch but in a tunable ensemble of conformations reshaped by pH, protein–protein interactions, and pigment composition.

A breakthrough is the establishment of a transferable methodological framework linking protonation equilibria to structural rearrangements and, ultimately, to excitonic and spectroscopic properties. Through the development of an approach for pKa prediction, we provide a rigorous way to decompose macroscopic pKa values into distinct local environments, thereby resolving a long-standing limitation in modeling proton-coupled conformational transitions in complex membrane proteins. By anchoring exciton dynamics and computational spectroscopy in structurally and thermodynamically consistent ensembles, the project has thus far delivered a mechanistic bridge between atomic-scale dynamics and experimentally observed quenching behavior. In parallel, the project has advanced the state of the art in ML-based exciton dynamics. By training machine-learning models on quantum chemical reference data, we have established models capable of predicting site energies, excitonic couplings, and spectral densities across thousands of molecular dynamics snapshots at a fraction of the original cost. This has enabled statistically robust sampling of exciton Hamiltonians and has opened the door to non-adiabatic molecular dynamics simulations of excited-state processes in realistic protein environments. The ML-accelerated workflows reproduce reference electronic-structure results while extending accessible timescales and system sizes well beyond conventional limits. As a result, we can now directly connect conformational variability, protonation patterns, and aggregation states to predicted absorption spectra, energy-transfer pathways, and quenching efficiencies, providing a predictive and scalable platform for computational spectroscopy of complex light-harvesting systems.