Search arXivSearch

arXiv · 2607.17096

Network Characteristics of Individual Pigments in Cyanobacterial Photosystem II Core Complexes

Abstract

Part of the excitation energy transfer (EET) characteristics of the photosystem II (PSII) comes from the interconnection between pigments. To understand the correlation between the EET and the pigments' interaction structure, we construct a network from the EET rates, which are related to both the distance between the pigments (chlorophylls and pheophytins) and their spatial orientations. Especially, we investigate how well the PS II core complex's EET functionality can be explained by using only the network topology in Thermosynechococcus vulcanus 1.9 Å. Starting from the Förster theory, we construct a network of EET pathways. For an analysis of the network structure, we calculate common network-structural measures like betweenness centrality, eigenvector centrality and weighted clustering. These measures can reflect the role of individual pigments in the EET network. In our work, we found that some well-known properties were reproduced by the network analysis of the simplified network, which means that the topology of the network encodes functionally relevant information. For example, from the network structural analysis, we can infer that most of the chlorophyll molecules (chlorophylls) in the pigment-protein complex CP47 have a heightened probability of transferring energy compared with other chlorophylls. We also see that the active branch chlorophylls in the reaction center are characterized by a high eigenvector centrality, a high betweenness centrality, and a low weighted clustering coefficient. This is indicative of functionally important vertices.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Eun Lee, Petter Holme. 2026-07-19. Network Characteristics of Individual Pigments in Cyanobacterial Photosystem II Core Complexes. https://arxiv.org/abs/2607.17096

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

In Vivo Length Distributions as Mechanistic Fingerprints of Pathological Protein Aggregation

Modern imaging techniques can resolve individual pathological protein aggregates in postmortem human samples, providing detailed measurements of aggregate size distributions that are inaccessible with conventional bulk approaches. These distributions represent mechanistic fingerprints of the microscopic processes that generated the observed pathology, but extracting this mechanistic information requires a quantitative theoretical framework. Here, we develop the mathematical tools needed to interpret aggregate length distributions in living systems, where aggregate growth competes with active removal. We show that, across a class of models, the length distribution of sufficiently large aggregates approaches a geometric decay. Crucially, the decay rate is determined by the balance between aggregate elongation and removal, providing a direct quantitative readout of these competing processes from a single time point measurement. This enables mechanistic comparisons between healthy and diseased human samples without requiring longitudinal measurements of aggregate dynamics. We further analyse how additional aggregation and removal processes modify the observed length distributions. Together, these results establish the mathematical foundations and tools to use aggregate length distributions as an experimentally accessible route for inferring microscopic aggregation dynamics directly from human tissue.

q-bio.BM

Decoding enzyme-substrate interaction topology reveals principles underlying catalytic efficiency and mutational outcomes

The enzyme turnover number (kcat) defines catalytic efficiency and constrains quantitative models of metabolism, yet the molecular determinants governing kcat and its response to mutation remain poorly understood. Measurements are sparse and labor-intensive, and most computational approaches provide numerical predictions without explaining how enzyme-substrate interactions shape catalytic outcomes. A central challenge is therefore to identify the topological principles that determine where mutations act and how their functional outcomes are encoded within the enzyme-substrate interaction network. Here, we show that catalytic efficiency and mutational effects can be interpreted through enzyme-substrate interaction topology. We developed Interkcat, an interpretable bidirectional cross-attention framework that captures reciprocal coordination between protein residues and substrate atoms. Optimized on a unified benchmark, Interkcat achieves state-of-the-art predictive performance (R2 = 0.701). From its learned representations, we derive an Interaction Topology Score (ITS) that identifies sequence regions statistically enriched for mutation-sensitive sites without explicit structural inputs. We further demonstrate that higher-order topological features distinguish opposing mutational outcomes: lethal mutations disrupt coordinated networks, whereas activity-preserving or enhancing mutations retain sparse, globally organized coupling. These findings establish interaction topology as a unifying principle linking enzyme sequence, catalytic efficiency, and evolutionary perturbation.

q-bio.BM

Adapting Boltz-2 with limited experimental activity data improves early enrichment in virtual screening

Virtual screening aims to prioritize active compounds from large chemical libraries within a limited experimental budget. When applying Boltz-2 to virtual screening, a key challenge is how to use limited experimental data from the target assay to improve the prioritization of active compounds. We investigated whether fine-tuning the Boltz-2 affinity heads with a small number of binary activity labels could improve early enrichment of active compounds in hit discovery. We compared fine-tuning with 40-300 labels in a retrospective evaluation on eight MF-PCBA targets. With 300 activity measurements, fine-tuning increased the number of actives in the top 1% by a geometric mean of 1.77-fold across the eight targets and improved average precision (AP) by 2.14-fold relative to the control without fine-tuning. We also investigated whether rescoring a subset of candidates could retain the improvement in hit recovery by reranking only the top-ranked Boltz-2 candidates with the fine-tuned head. Restricting rescoring to approximately 10% of the evaluation set retained hit recovery comparable to full rescoring. These findings show that affinity-head fine-tuning with limited activity labels improves early enrichment with Boltz-2 and that this benefit can be retained when rescoring a restricted set of candidates.

q-bio.BM