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Updated: Jun 25, 2026

Evaluation of Photosynthetic Efficiency in Photorespiratory Mutants by Chlorophyll Fluorescence Analysis
Published on: December 9, 2022
In silico approaches to improved understanding of herbicides targeting photosystem II (PSII)
B Praneet Prabhanjan1, Nandni Kumari1, Abilashni Arthiswaran1
1Bioclues.org, Hyderabad 501511, India.
Introduction:
Photosystem II (PSII) is a crucial component of the photosynthetic machinery in plants, responsible for initiating electron transport and sustaining carbon fixation. Within PSII, the D1 and D2 proteins serve as central reaction core subunits, directly participating in light energy and driving electron flow, thereby making them key molecular targets for herbicidal intervention.
Methods:
In this study, we carried out an analysis of chloroplast-associated genes across three plant species, Taxus brevifolia, Manihot esculenta, and Azadirachta indica, to identify molecular determinants regulating PSII activity and stability. Gene function prediction, together with protein-protein interaction networks, was performed using GeneMANIA and STRING, with the networks further visualized via Cytoscape, enabling the identification of hub genes, interaction clusters, and critical PSII-associated nodes. A total of 2988 ligand analogs, derived from five known PSII-inhibiting herbicides, were subjected to ADME-based screening using KNIME workflows and Lipinski's rule of five to filter compounds with favorable pharmacokinetic properties. Molecular docking simulations were then conducted with AutoDock4 on the QB-binding site of the D1 protein (PDB ID: 5XNL), and predicted binding affinities, hydrogen bonding, and hydrophobic interactions were analyzed to assess inhibitory potential.
Results And Discussion:
From the screening, 48 promising analogs were identified, with five herbicides-Diuron, Metribuzin, Bentazon, Terbuthylazine, and Metobromuron-showing strong predicted binding to PSII proteins. These results provide mechanistic insight into potential herbicide binding modes and structure-activity relationships, demonstrating the utility of integrating bioinformatics, ADME filtering, and molecular docking as an in silico screening framework to prioritize D1-targeting compounds.
Conclusions:
The findings are intended to guide future experimental validation rather than establish confirmed herbicidal activity.
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