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 onl...
Description / Details
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.
Source: arXiv:2607.17096v1 - http://arxiv.org/abs/2607.17096v1 PDF: https://arxiv.org/pdf/2607.17096v1 Original Link: http://arxiv.org/abs/2607.17096v1
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Jul 21, 2026
Pharmaceutical Research
Biochemistry
0