Quality control by a mobile molecular workshop: quality versus quantity

Translational autoregulation of RF2 protein in E. coli through programmed frameshifting

Various feedback mechanisms regulate the expression of different genes to ensure the required protein levels inside a cell. In this paper, we develop a kinetic model for one such mechanism that autoregulates RF2 protein synthesis in E. coli through programmed frameshifting. The model finds that the programmed frameshifting autoregulates RF2 protein synthesis by two independent mechanisms. First, it increases the rate of RF2 synthesis from each mRNA transcript at low RF2 concentration. Second, programmed frameshifting can dramatically increase the lifetime of RF2 transcripts when RF2 protein levels are lower than a threshold. This sharp increase in mRNA lifetime is caused by a first-order phase transition from a low to a high ribosome density on an RF2 transcript. The high ribosome density prevents the transcript's degradation by shielding it from nucleases, which increases its average lifetime and hence RF2 protein levels. Our study identifies this quality control mechanism that regulates the cellular protein levels by breaking the hierarchy of processes involved in gene expression.

Stochastic thermodynamics and modes of operation of a ribosome: A network theoretic perspective

The ribosome is one of the largest and most complex macromolecular machines in living cells. It polymerizes a protein in a step-by-step manner as directed by the corresponding nucleotide sequence on the template messenger RNA (mRNA) and this process is referred to as "translation" of the genetic message encoded in the sequence of mRNA transcript. In each successful chemomechanical cycle during the (protein) elongation stage, the ribosome elongates the protein by a single subunit, called amino acid, and steps forward on the template mRNA by three nucleotides called a codon. Therefore, a ribosome is also regarded as a molecular motor for which the mRNA serves as the track, its step size is that of a codon and two molecules of GTP and one molecule of ATP hydrolyzed in that cycle serve as its fuel. What adds further complexity is the existence of competing pathways leading to distinct cycles, branched pathways in each cycle, and futile consumption of fuel that leads neither to elongation of the nascent protein nor forward stepping of the ribosome on its track. We investigate a model formulated in terms of the network of discrete chemomechanical states of a ribosome during the elongation stage of translation. The model is analyzed using a combination of stochastic thermodynamic and kinetic analysis based on a graph-theoretic approach. We derive the exact solution of the corresponding master equations. We represent the steady state in terms of the cycles of the underlying network and discuss the energy transduction processes. We identify the various possible modes of operation of a ribosome in terms of its average velocity and mean rate of GTP hydrolysis. We also compute entropy production as functions of the rates of the interstate transitions and the thermodynamic cost for accuracy of the translation process.

Structural conditions on complex networks for the Michaelis-Menten input-output response

The Michaelis-Menten (MM) fundamental formula describes how the rate of enzyme catalysis depends on substrate concentration. The familiar hyperbolic relationship was derived by timescale separation for a network of three reactions. The same formula has subsequently been found to describe steady-state input-output responses in many biological contexts, including single-molecule enzyme kinetics, gene regulation, transcription, translation, and force generation. Previous attempts to explain its ubiquity have been limited to networks with regular structure or simplifying parametric assumptions. Here, we exploit the graph-based linear framework for timescale separation to derive general structural conditions under which the MM formula arises. The conditions require a partition of the graph into two parts, akin to a "coarse graining" into the original MM graph, and constraints on where and how the input variable occurs. Other features of the graph, including the numerical values of parameters, can remain arbitrary, thereby explaining the formula's ubiquity. For systems at thermodynamic equilibrium, we derive a necessary and sufficient condition. For systems away from thermodynamic equilibrium, especially those with irreversible reactions, distinct structural conditions arise and a general characterization remains open. Nevertheless, our results accommodate, in much greater generality, all examples known to us in the literature.

Ribosome utilizes the minimum free energy changes to achieve the highest decoding rate and fidelity

The performance of ribosome translation can be characterized by two factors, the translation rate and fidelity. Here, we provide analytical studies of the effect of the near-cognate tRNAs on the two factors. It is shown that the increase of the concentration of the near-cognate tRNAs relative to that of the cognate tRNA has negative effects on the ribosome translation by reducing both the translation rate and the translation fidelity. The effect of the near-cognate ternary complexes on the translation rate results mainly from the initial selection phase, whereas the proofreading phase has a minor effect. By contrast, the effect of the near-cognate ternary complexes on the fidelity results almost equally from the two phases. By using two successive phases, the initial selection and the proofreading, the ribosome can achieve higher translation fidelity than the product of the fidelity when only the initial selection is included and when only the proofreading is included, especially at the large ratio of the concentration of the near-cognate tRNAs compared to that of the cognate tRNA. Moreover, we study the changes of the free energy landscape in the tRNA decoding step. It is found that the rate constants of the tRNA decoding step measured experimentally give the minimum energy changes for the ribosomal complex to attain the optimal performance with both the highest decoding rate and fidelity and/or with the maximum value of the decoding fitness function. This suggests that the ribosome has evolved to utilize the minimum free energy changes gained from the conformational changes of the ribosome, EF-Tu, and tRNA to achieve the optimal performance in the tRNA decoding.

Mathematical and Computational Modelling of Ribosomal Movement and Protein Synthesis: an overview

Translation or protein synthesis consists of a complex system of chemical reactions, which ultimately result in decoding of the mRNA and the production of a protein. The complexity of this reaction system makes it difficult to quantitatively connect its input parameters (such as translation factor or ribosome concentrations, codon composition of the mRNA, or energy availability) to output parameters (such as protein synthesis rates or ribosome densities on mRNAs). Mathematical and computational models of translation have now been used for nearly five decades to investigate translation, and to shed light on the relationship between the different reactions in the system. This review gives an overview over the principal approaches used in the modelling efforts, and summarises some of the major findings that were made.

Genome-scale analysis of translation elongation with a ribosome flow model

We describe the first large scale analysis of gene translation that is based on a model that takes into account the physical and dynamical nature of this process. The Ribosomal Flow Model (RFM) predicts fundamental features of the translation process, including translation rates, protein abundance levels, ribosomal densities and the relation between all these variables, better than alternative ('non-physical') approaches. In addition, we show that the RFM can be used for accurate inference of various other quantities including genes' initiation rates and translation costs. These quantities could not be inferred by previous predictors. We find that increasing the number of available ribosomes (or equivalently the initiation rate) increases the genomic translation rate and the mean ribosome density only up to a certain point, beyond which both saturate. Strikingly, assuming that the translation system is tuned to work at the pre-saturation point maximizes the predictive power of the model with respect to experimental data. This result suggests that in all organisms that were analyzed (from bacteria to Human), the global initiation rate is optimized to attain the pre-saturation point. The fact that similar results were not observed for heterologous genes indicates that this feature is under selection. Remarkably, the gap between the performance of the RFM and alternative predictors is strikingly large in the case of heterologous genes, testifying to the model's promising biotechnological value in predicting the abundance of heterologous proteins before expressing them in the desired host.

Distribution of dwell times of a ribosome: effects of infidelity, kinetic proofreading and ribosome crowding

Ribosome is a molecular machine that polymerizes a protein where the sequence of the amino acid residues, the monomers of the protein, is dictated by the sequence of codons (triplets of nucleotides) on a messenger RNA (mRNA) that serves as the template. The ribosome is a molecular motor that utilizes the template mRNA strand also as the track. Thus, in each step the ribosome moves forward by one codon and, simultaneously, elongates the protein by one amino acid. We present a theoretical model that captures most of the main steps in the mechanochemical cycle of a ribosome. The stochastic movement of the ribosome consists of an alternating sequence of pause and translocation; the sum of the durations of a pause and the following translocation is the time of dwell of the ribosome at the corresponding codon. We derive the analytical expression for the distribution of the dwell times of a ribosome in our model. Wherever experimental data are available, our theoretical predictions are consistent with those results. We suggest appropriate experiments to test the new predictions of our model, particularly the effects of the quality control mechanism of the ribosome and that of their crowding on the mRNA track.