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Structure/Function/Dynamics of Photosystem II Plastoquinone Binding Sites
摘要： Photosystem II (PSII) continuously attracts the attention of researchers aiming to unravel the riddle of its functioning and efficiency fundamental for all life on Earth. Besides, an increasing number of biotechnological applications have been envisaged exploiting and mimicking the unique properties of this macromolecular pigment-protein complex. The PSII organization and working principles have inspired the design of electrochemical water splitting schemes and charge separating triads in energy storage systems as well as biochips and sensors for environmental, agricultural and industrial screening of toxic compounds. An intriguing opportunity is the development of sensor devices, exploiting native or manipulated PSII complexes or ad hoc synthesized polypeptides mimicking the PSII reaction centre proteins as biosensing elements. This review offers a concise overview of the recent improvements in the understanding of structure and function of PSII donor side, with focus on the interactions of the plastoquinone cofactors with the surrounding environment and operational features. Furthermore, studies focused on photosynthetic proteins structure/function/dynamics and computational analyses aimed at rational design of high-quality bio-recognition elements in biosensor devices are discussed.
关键词： plastoquinone binding site,molecular dynamics simulations,plastoquinone,Molecular docking,protein dynamics,Photosystem II
更新于2025-09-23 15:22:29
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A plasmonic ellipse resonator possessing hybrid modes for ultracompact chipscale application
摘要： Data mining methods based on machine learning play an increasingly important role in drug design and discovery. In the current work, eight machine learning methods including decision trees, k- Nearest neighbor, support vector machines, random forests, extremely randomized trees, AdaBoost, gradient boosting trees, and XGBoost were evaluated comprehensively through a case study of ACC inhibitor data sets. Internal and external data sets were employed for cross- validation of the eight machine learning methods. Results showed that the extremely randomized trees model performed best and was adopted as the first step of virtual screening. Together with structure- based virtual screening in the second step, this combined strategy obtained desirable results. This work indicates that the combination of machine learning methods with traditional structure- based virtual screening can effectively strengthen the ability in finding potential hits from large compound database for a given target.
关键词： molecular docking,machine learning,extremely randomized trees,ACC inhibitors
更新于2025-09-11 14:15:04