Dynamic relationships between ribosomal conformational and RNA positional changes during ribosomal translocation

Abstract

Ribosomal translocation catalyzed by EF-G hydrolyzing GTP entails multiple conformational changes of ribosome and positional changes of tRNAs and mRNA in the ribosome. However, the detailed dynamic relations among these changes and EF-G sampling are not clear. Here, based on our proposed pathway of ribosomal translocation, we study theoretically the dynamic relations among these changes exhibited in the single molecule data and those exhibited in the ensemble kinetic data. It is shown that the timing of these changes in the single molecule data and that in the ensemble kinetic data show very different. The theoretical results are in agreement with both the available ensemble kinetic experimental data and the single molecule experimental data.

ATP-Concentration- and Force-Dependent Chemomechanical Coupling of Kinesin Molecular Motors

“…At low ATP, the pathway (Figure ) is so complex that it is difficult to obtain analytical solutions for the motor dynamics. Here, we use the Monte Carlo (MC) algorithm to study numerically the dynamics, as done before. ,,, The MC simulation procedure is as follows. ATP can bind, with constant second-order binding rate k b , to the ϕ-head in any position (whether it is in the leading, INT, or trailing position).…”

Transcriptomic reprogramming in soybean seedlings under salt stress

“…Translational control is one possible mechanism in salt stress responses (Park et al, ). Ribosomal proteins have been reported to be transcriptionally affected under abiotic stresses, and it has been proposed that this leads to changes in ribosomal compositions (Sormani, Masclaux‐Daubresse, Daniele‐Vedele, & Chardon, ; Xie, ). Our data revealed that most of the ribosomal protein genes were induced in the leaf in the late phase (module L‐M1) whereas the expressions of ribosomal proteins S10, L29, L24, S13, and L35 (L‐M16) showed a reduction in 24 hr (Table and Table S2).…”

Processivity of dimeric kinesin‐1 molecular motors

“…Using Equations (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20), we can simulate the mechanical step of the movement of a kinesin head following P i release relative to the other MT-bound kinesin head and the dissociation of the dimer from MT using a stochastic Runge-Kutta algorithm, as done before [35,37,38,50]. Then, we can simulate processive movement of the dimer by also considering continuous ATPase activities, which can be simulated using a Monte Carlo algorithm, as used before [37,38,51]. In the Monte Carlo simulations, during each time step Dt (Dt = 10 À4 s in our simulation), a random number ran is generated with uniform probability between 0 and 1. being the second-order rate constant for ATP binding and [ATP] being the ATP concentration, k H represents the rate constant of ATP hydrolysis, k c represents the rate constant of P i release, k NL represents the rate constant of NL docking into the motor domain of MT-bound head in the ATP or ADP.P i state when the detached ADP-head is in the intermediate position, and k -D represents the rate constant of ADP releasing from ADP-head.…”

“…Using Equations (1-20), we can simulate the mechanical step of the movement of a kinesin head following P i release relative to the other MT-bound kinesin head and the dissociation of the dimer from MT using a stochastic Runge-Kutta algorithm, as done before [35,37,38,50]. Then, we can simulate processive movement of the dimer by also considering continuous ATPase activities, which can be simulated using a Monte Carlo algorithm, as used before [37,38,51]. In the Monte Carlo simulations, during each time step Dt (Dt = 10 À4 s in our simulation), a random number ran is generated with uniform probability between 0 and 1.…”