Preprotein translocation in Escherichia coli is mediated by translocase, a multimeric membrane protein complex with SecA as the peripheral ATPase and SecYEG as the translocation pore. Unique cysteines were introduced into transmembrane segment (TMS) 2 of SecY and TMS 3 of SecE to probe possible sites of interaction between the integral membrane subunits. The SecY and SecE single-Cys mutants were cloned individually and in pairs into a secYEG expression vector and functionally overexpressed. Oxidation of the single-Cys pairs revealed periodic contacts between SecY and SecE that are confined to a specific alpha-helical face of TMS 2 and 3, respectively. A Cys at the opposite alpha-helical face of TMS 3 of SecE was found to interact with a neighboring SecE molecule. Formation of this SecE dimer did not affect the high-affinity binding of SecA to SecYEG and ATP hydrolysis, but blocked preprotein translocation and thus uncouples the SecA ATPase activity from translocation. Conditions that prevent membrane deinsertion of SecA markedly stimulated the interhelical contact between the SecE molecules. The latter demonstrates a SecA-mediated modulation of the protein translocation channel that is sensed by SecE.
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Sci Rep
January 2025
School of Electrical Engineering, VIT University, Tamilnadu, 632014, India.
Curr Biol
August 2024
Unité d'Ecologie Systématique et Evolution, CNRS, Université Paris-Saclay, AgroParisTech, 91190 Gif-sur-Yvette, France. Electronic address:
Micromachines (Basel)
June 2024
Internet of Things Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511400, China.
A frequency up-conversion piezoelectric energy harvester (FUC-PEH) consists of a force amplifier, a piezoelectric stack, a low-frequency oscillator (LFO), and a stop limiter. The force amplifier generates the amplification of stress on the piezoelectric stack. The LFO, comprising a spring and a mass block, impacts the stop limiter during vibration to induce high-frequency oscillations within the piezoelectric stack.
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June 2024
State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China.
Accurate identification of spatial domains is essential in the analysis of spatial transcriptomics data in order to elucidate tissue microenvironments and biological functions. However, existing methods only perform domain segmentation based on local or global spatial relationships between spots, resulting in an underutilization of spatial information. To this end, we propose SECE, a deep learning-based method that captures both local and global relationships among spots and aggregates their information using expression similarity and spatial similarity.
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June 2024
Molecular Biophysics Unit, Indian Institute of Science, Bangalore 560012, India; Dr.Reddy's Institute of Life Science, Hyderabad 500046, India. Electronic address:
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