The Ribosome as an Optimal Decoder: A Lesson in Molecular Recognition

Life sets off a cascade of machines

Life is invasive, occupying all physically accessible scales, stretching between almost nothing (protons, electrons, and photons) and almost everything (the whole biosphere). Motivated by seventeenth-century insights into this infinity, this paper proposes a language to discuss life as an infinite double cascade of machines making machines. Using this simplified language, we first discuss the micro-cascade proposed by Leibniz, which describes how the self-reproducing machine of the cell is built of smaller submachines down to the atomic scale. In the other direction, we propose that a macro-cascade builds from cells larger, organizational machines, up to the scale of the biosphere. The two cascades meet at the critical point of 103 s in time and 1 micron in length, the scales of a microbial cell. We speculate on how this double cascade evolved once a self-replicating machine emerged in the salty water of prebiotic earth.

Reliable ligand discrimination in stochastic multistep kinetic proofreading: First passage time vs. product counting strategies

Cellular signaling, crucial for biological processes like immune response and homeostasis, relies on specificity and fidelity in signal transduction to accurately respond to stimuli amidst biological noise. Kinetic proofreading (KPR) is a key mechanism enhancing signaling specificity through time-delayed steps, although its effectiveness is debated due to intrinsic noise potentially reducing signal fidelity. In this study, we reformulate the theory of kinetic proofreading (KPR) by convolving multiple intermediate states into a single state and then define an overall "processing" time required to traverse these states. This simplification allows us to succinctly describe kinetic proofreading in terms of a single waiting time parameter, facilitating a more direct evaluation and comparison of KPR performance across different biological contexts such as DNA replication and T cell receptor (TCR) signaling. We find that loss of fidelity for longer proofreading steps relies on the specific strategy of information extraction and show that in the first-passage time (FPT) discrimination strategy, longer proofreading steps can exponentially improve the accuracy of KPR at the cost of speed. Thus, KPR can still be an effective discrimination mechanism in the high noise regime. However, in a product concentration-based discrimination strategy, longer proofreading steps do not necessarily lead to an increase in performance. However, by introducing activation thresholds on product concentrations, can we decompose the product-based strategy into a series of FPT-based strategies to better resolve the subtleties of KPR-mediated product discrimination. Our findings underscore the importance of understanding KPR in the context of how information is extracted and processed in the cell.

Channel capacity of the ribosome

Translation is one of the most fundamental processes in the biological cell. Because of the central role that translation plays across all domains of life, the enzyme that carries out this process, the ribosome, is required to process information with high accuracy. This accuracy often approaches values near unity experimentally. In this paper, we model the ribosome as an information channel and demonstrate mathematically that this biological machine has information-processing capabilities that have not been recognized previously. In particular, we calculate bounds on the ribosome's theoretical Shannon capacity and numerically approximate this capacity. Finally, by incorporating estimates on the ribosome's operation time, we show that the ribosome operates at speeds safely below its capacity, allowing the ribosome to process information with an arbitrary degree of error. Our results show that the ribosome achieves a high accuracy in line with purely information-theoretic means.

General Theory of Specific Binding: Insights from a Genetic-Mechano-Chemical Protein Model

Proteins need to selectively interact with specific targets among a multitude of similar molecules in the cell. However, despite a firm physical understanding of binding interactions, we lack a general theory of how proteins evolve high specificity. Here, we present such a model that combines chemistry, mechanics, and genetics and explains how their interplay governs the evolution of specific protein-ligand interactions. The model shows that there are many routes to achieving molecular discrimination-by varying degrees of flexibility and shape/chemistry complementarity-but the key ingredient is precision. Harder discrimination tasks require more collective and precise coaction of structure, forces, and movements. Proteins can achieve this through correlated mutations extending far from a binding site, which fine-tune the localized interaction with the ligand. Thus, the solution of more complicated tasks is enabled by increasing the protein size, and proteins become more evolvable and robust when they are larger than the bare minimum required for discrimination. The model makes testable, specific predictions about the role of flexibility and shape mismatch in discrimination, and how evolution can independently tune affinity and specificity. Thus, the proposed theory of specific binding addresses the natural question of "why are proteins so big?". A possible answer is that molecular discrimination is often a hard task best performed by adding more layers to the protein.

Genetic code degeneracy is established by the decoding center of the ribosome

The degeneracy of the genetic code confers a wide array of properties to coding sequences. Yet, its origin is still unclear. A structural analysis has shown that the stability of the Watson–Crick base pair at the second position of the anticodon–codon interaction is a critical parameter controlling the extent of non-specific pairings accepted at the third position by the ribosome, a flexibility at the root of degeneracy. Based on recent cryo-EM analyses, the present work shows that residue A1493 of the decoding center provides a significant contribution to the stability of this base pair, revealing that the ribosome is directly involved in the establishment of degeneracy. Building on existing evolutionary models, we show the evidence that the early appearance of A1493 and A1492 established the basis of degeneracy when an elementary kinetic scheme of translation was prevailing. Logical considerations on the expansion of this kinetic scheme indicate that the acquisition of the peptidyl transferase center was the next major evolutionary step, while the induced-fit mechanism, that enables a sharp selection of the tRNAs, necessarily arose later when G530 was acquired by the decoding center.

Diffusion control in biochemical specificity

Biochemical specificity is critical in enzyme function, evolution, and engineering. Here we employ an established kinetic model to dissect the effects of reactant geometry and diffusion on product formation speed and accuracy in the presence of cognate (correct) and near-cognate (incorrect) substrates. Using this steady-state model for spherical geometries, we find that, for distinct kinetic regimes, the speed and accuracy of the reactions are optimized on different regions of the geometric landscape. From this model we deduce that accuracy can be strongly dependent on reactant geometric properties even for chemically limited reactions. Notably, substrates with a specific geometry and reactivity can be discriminated by the enzyme with higher efficacy than others through purely diffusive effects. For similar cognate and near-cognate substrate geometries (as is the case for polymerases or the ribosome), we observe that speed and accuracy are maximized in opposing regions of the geometric landscape. We also show that, in relevant environments, diffusive effects on accuracy can be substantial even far from extreme kinetic conditions. Finally, we find how reactant chemical discrimination and diffusion can be related to simultaneously optimize steady-state flux and accuracy. These results highlight how diffusion and geometry can be employed to enhance reaction speed and discrimination, and similarly how they impose fundamental restraints on these quantities.

Constraints on error rate revealed by computational study of G•U tautomerization in translation

In translation, G•U mismatch in codon-anticodon decoding is an error hotspot likely due to transition of G•U from wobble (wb) to Watson-Crick (WC) geometry, which is governed by keto/enol tautomerization (wb-WC reaction). Yet, effects of the ribosome on the wb-WC reaction and its implications for decoding mechanism remain unclear. Employing quantum-mechanical/molecular-mechanical umbrella sampling simulations using models of the ribosomal decoding site (A site) we determined that the wb-WC reaction is endoergic in the open, but weakly exoergic in the closed A-site state. We extended the classical ‘induced-fit’ model of initial selection by incorporating wb-WC reaction parameters in open and closed states. For predicted parameters, the non-equilibrium exoergic wb-WC reaction is kinetically limited by the decoding rates. The model explains early observations of the WC geometry of G•U from equilibrium structural studies and reveals discrimination capacity for the working ribosome operating at non-equilibrium conditions. The equilibration of the exoergic wb-WC reaction counteracts the equilibration of the open-closed transition of the A site, constraining the decoding accuracy and potentially explaining the persistence of the G•U as an error hotspot. Our results unify structural and mechanistic views of codon-anticodon decoding and generalize the ‘induced-fit’ model for flexible substrates.

A Code Within a Code: How Codons Fine-Tune Protein Folding in the Cell

The genetic code sets the correspondence between the sequence of a given nucleotide triplet in an mRNA molecule, called a codon, and the amino acid that is added to the growing polypeptide chain during protein synthesis. With four bases (A, G, U, and C), there are 64 possible triplet codons: 61 sense codons (encoding amino acids) and 3 nonsense codons (so-called, stop codons that define termination of translation). In most organisms, there are 20 common/standard amino acids used in protein synthesis; thus, the genetic code is redundant with most amino acids (with the exception of Met and Trp) are being encoded by more than one (synonymous) codon. Synonymous codons were initially presumed to have entirely equivalent functions, however, the finding that synonymous codons are not present at equal frequencies in mRNA suggested that the specific codon choice might have functional implications beyond coding for amino acid. Observation of nonequivalent use of codons in mRNAs implied a possibility of the existence of auxiliary information in the genetic code. Indeed, it has been found that genetic code contains several layers of such additional information and that synonymous codons are strategically placed within mRNAs to ensure a particular translation kinetics facilitating and fine-tuning co-translational protein folding in the cell via step-wise/sequential structuring of distinct regions of the polypeptide chain emerging from the ribosome at different points in time. This review summarizes key findings in the field that have identified the role of synonymous codons and their usage in protein folding in the cell.

Dimensional reduction in complex living systems: Where, why, and how

The unprecedented prowess of measurement techniques provides a detailed, multi-scale look into the depths of living systems. Understanding these avalanches of high-dimensional data-by distilling underlying principles and mechanisms-necessitates dimensional reduction. We propose that living systems achieve exquisite dimensional reduction, originating from their capacity to learn, through evolution and phenotypic plasticity, the relevant aspects of a non-random, smooth physical reality. We explain how geometric insights by mathematicians allow one to identify these genuine hallmarks of life and distinguish them from universal properties of generic data sets. We illustrate these principles in a concrete example of protein evolution, suggesting a simple general recipe that can be applied to understand other biological systems.

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.

From deterministic to fuzzy decision-making in artificial cells

Building autonomous artificial cells capable of homeostasis requires regulatory networks to gather information and make decisions that take time and cost energy. Decisions based on few molecules may be inaccurate but are cheap and fast. Realizing decision-making with a few molecules in artificial cells has remained a challenge. Here, we show decision-making by a bistable gene network in artificial cells with constant protein turnover. Reducing the number of gene copies from 10⁵ to about 10 per cell revealed a transition from deterministic and slow decision-making to a fuzzy and rapid regime dominated by small-number fluctuations. Gene regulation was observed at lower DNA and protein concentrations than necessary in equilibrium, suggesting rate enhancement by co-expressional localization. The high-copy regime was characterized by a sharp transition and hysteresis, whereas the low-copy limit showed strong fluctuations, state switching, and cellular individuality across the decision-making point. Our results demonstrate information processing with low-power consumption inside artificial cells.

Interferon-β exposure induces a fragile glioblastoma stem cell phenotype with a transcriptional profile of reduced migratory and MAPK pathway activity

Background

Type I interferons (IFN-α/β) are cytokines that are typically expressed in response to double-stranded RNA associated with viral infections. Glioblastomas are the most common malignant primary brain tumors, characterized by an infiltrative growth pattern and prominent angiogenic activity, and thought to be maintained by a subpopulation of glioma-initiating (stem-like) cells (GICs). The growth of human GIC lines is highly sensitive to IFN-β.

Methods

Repetitive pulse stimulation with IFN-β1a (IS) was used to generate IS sublines that had acquired resistance to IFN-β-induced suppression of sphere formation. These cell lines were characterized by analyses of type 1 IFN signaling, growth patterns, and transcriptomic profiles.

Results

Here we report that repetitive IFN-β1a stimulation (IS) induces a stable phenotype (referred to as IS) at the level of maintaining sphere formation, although classical IFN signaling defined by the expression of both IFN receptors, myxovirus resistance protein A (MxA) accumulation, and STAT1 induction is unaffected. Furthermore, this stably altered IS phenotype is characterized by constitutively decreased sphere formation capacity and morphological features of senescence and autophagy. Transcriptional profiling reveals increased type I IFN signaling in these IS cells, but decreased expression of genes involved in receptor signaling and cell migration.

Conclusions

Altogether, these data suggest a role for promoting IFN-β signaling in glioblastoma and might provide clues to design future therapeutic approaches.

Kinetic proofreading and the limits of thermodynamic uncertainty

To mitigate errors induced by the cell's heterogeneous noisy environment, its main information channels and production networks utilize the kinetic proofreading (KPR) mechanism. Here, we examine two extensively studied KPR circuits, DNA replication by the T7 DNA polymerase and translation by the E. coli ribosome. Using experimental data, we analyze the performance of these two vital systems in light of the fundamental bounds set by the recently discovered thermodynamic uncertainty relation (TUR), which places an inherent trade-off between the precision of a desirable output and the amount of energy dissipation required. We show that the DNA polymerase operates close to the TUR lower bound, while the ribosome operates ∼5 times farther from this bound. This difference originates from the enhanced binding discrimination of the polymerase which allows it to operate effectively as a reduced reaction cycle prioritizing correct product formation. We show that approaching this limit also decouples the thermodynamic uncertainty factor from speed and error, thereby relaxing the accuracy-speed trade-off of the system. Altogether, our results show that operating near this reduced cycle limit not only minimizes thermodynamic uncertainty, but also results in global performance enhancement of KPR circuits.

AAAA-DDDD Quadruple H-Bond-Assisted Ionic Interactions: Robust Bis(guanidinium)/Dicarboxylate Heteroduplexes in Water

The use of geminal di(guanidinium) and acridin-9(10H)-one-derived di(carboxylate) derivatives (1a-c and 2a-e, respectively) allows stabilization of heterodimers characterized by high binding affinities in water (maximum ΔG < -7 kcal mol^-1, K_a > 10⁵ M^-1) as inferred from UV-vis spectroscopic titrations and ITC measurements, therefore rivaling or surpassing the interaction energy between the strongest DNA or RNA triplet pairs. These duplexes are readily accessible and are structurally modifiable, rendering them attractive as building blocks for creating heteroduplex constructs. Incorporating poly(ethylene glycol)-decorated benzyl groups into the dicarboxylate, allows formation of hydrogels in the case of 1b-2c.

Assembly of heteropolymers via a network of reaction coordinates

In biochemistry, heteropolymers encoding biological information are assembled out of equilibrium by sequentially incorporating available monomers found in the environment. Current models of polymerization treat monomer incorporation as a sequence of discrete chemical reactions between intermediate metastable states. In this paper, we use ideas from reaction rate theory and describe nonequilibrium assembly of a heteropolymer via a continuous reaction coordinate. Our approach allows for estimating the copy error and incorporation speed from the Gibbs free energy landscape of the process. We apply our theory to several examples from a simple reaction characterized by a free energy barrier to more complex cases incorporating error correction mechanisms, such as kinetic proofreading.

Biological Pathway Specificity in the Cell-Does Molecular Diversity Matter?

Biology arises from the crowded molecular environment of the cell, rendering it a challenge to understand biological pathways based on the reductionist, low-concentration in vitro conditions generally employed for mechanistic studies. Recent evidence suggests that low-affinity interactions between cellular biopolymers abound, with still poorly defined effects on the complex interaction networks that lead to the emergent properties and plasticity of life. Mass-action considerations are used here to underscore that the sheer number of weak interactions expected from the complex mixture of cellular components significantly shapes biological pathway specificity. In particular, on-pathway-i.e., "functional"-become those interactions thermodynamically and kinetically stable enough to survive the incessant onslaught of the many off-pathway ("nonfunctional") interactions. Consequently, to better understand the molecular biology of the cell a further paradigm shift is needed toward mechanistic experimental and computational approaches that probe intracellular diversity and complexity more directly. Also see the video abstract here https://youtu.be/T19X_zYaBzg.

Error-Speed Correlations in Biopolymer Synthesis

Synthesis of biopolymers such as DNA, RNA, and proteins are biophysical processes aided by enzymes. The performance of these enzymes is usually characterized in terms of their average error rate and speed. However, because of thermal fluctuations in these single-molecule processes, both error and speed are inherently stochastic quantities. In this Letter, we study fluctuations of error and speed in biopolymer synthesis and show that they are in general correlated. This means that, under equal conditions, polymers that are synthesized faster due to a fluctuation tend to have either better or worse errors than the average. The error-correction mechanism implemented by the enzyme determines which of the two cases holds. For example, discrimination in the forward reaction rates tends to grant smaller errors to polymers with faster synthesis. The opposite occurs for discrimination in monomer rejection rates. Our results provide an experimentally feasible way to identify error-correction mechanisms by measuring the error-speed correlations.

Transcriptomic Responses of Two Ecologically Divergent Populations of Japanese Mantis Shrimp (Oratosquilla oratoria) under Thermal Stress

Simple Summary

Rising ocean temperature would change the seawater chemistry and affect the external and internal physiology of crustaceans due to their lack of certain efficient temperature regulators. In addition, the infraspecific populations of crustaceans might also have different response strategies to the rising of temperature. Therefore, we identified the transcriptomic variations to the same thermal stress between ecologically divergent populations of Oratosquilla oratoria. The aim of this study was to investigate the population-specific function genes and relevant pathways in response to thermal stress in O. oratoria. The results showed that gene-expressed variation was in a population-specific pattern, which indicated that the local environment could lead to the evolvement of changes in gene regulation, ultimately leading to adaptive divergences. Additionally, we found several genes with large pleiotropic effects in the Zhoushan population, which might indicate that the regulation mechanisms of the Zhoushan population were more efficient than those of the Qingdao population under same thermal stress. The results provided some novel insights into the local adaptive differences of the infraspecific populations of O. oratoria and other crustaceans.

Abstract

Crustaceans are generally considered more sensitive to ocean warming due to their lack of certain efficient regulators. However, the alterations in the physiology and behavior of crustaceans in response to thermal stress differ vastly even among the infraspecific populations of heterogeneous landscapes. Consequently, understanding the impact of temperature fluctuation on crustacean infraspecific populations might be essential for maintaining a sustainable persistence of populations at existing locations. In the present study, we chose the Japanese mantis shrimp (Oratosquilla oratoria) as the representative crustacean population, and conducted transcriptome analyses in two divergent O. oratoria populations (the Zhoushan and Qingdao populations) under same thermal stress (20–28 °C) to identify the population-specific expression response to thermal stress. The results showed significant differences in gene expressions, GO terms and metabolic pathways between the two populations. We hypothesized that intraspecific mutations in the same or different genes might lead to thermal adaptive divergences. Temperature increases from 20–28 °C produced significant enrichment in GO terms and altered the metabolic pathways in the Zhoushan population despite the lack of differentially expressed unigenes. Therefore, several functional genes with large pleiotropic effects may underlie the response to thermal stress in the Zhoushan population. Furthermore, the most significantly enriched biological processes of the Qingdao population were associated with the state or activity of cells and its significant enriched pathways with genetic information processing as well as immune and environmental information processing. In contrast, the differentially regulated unigenes of the Zhoushan population were primarily involved in the regulatory cellular and transcription processes and the most significant pathways found were metabolic and digestive. Consequently, the regulatory mechanisms of the Zhoushan population are probably more efficient than those of the Qingdao population under the same thermal stress.

The Origin of Prebiotic Information System in the Peptide/RNA World: A Simulation Model of the Evolution of Translation and the Genetic Code

Information is the currency of life, but the origin of prebiotic information remains a mystery. We propose transitional pathways from the cosmic building blocks of life to the complex prebiotic organic chemistry that led to the origin of information systems. The prebiotic information system, specifically the genetic code, is segregated, linear, and digital, and it appeared before the emergence of DNA. In the peptide/RNA world, lipid membranes randomly encapsulated amino acids, RNA, and peptide molecules, which are drawn from the prebiotic soup, to initiate a molecular symbiosis inside the protocells. This endosymbiosis led to the hierarchical emergence of several requisite components of the translation machine: transfer RNAs (tRNAs), aminoacyl-tRNA synthetase (aaRS), messenger RNAs (mRNAs), ribosomes, and various enzymes. When assembled in the right order, the translation machine created proteins, a process that transferred information from mRNAs to assemble amino acids into polypeptide chains. This was the beginning of the prebiotic information age. The origin of the genetic code is enigmatic; herein, we propose an evolutionary explanation: the demand for a wide range of protein enzymes over peptides in the prebiotic reactions was the main selective pressure for the origin of information-directed protein synthesis. The molecular basis of the genetic code manifests itself in the interaction of aaRS and their cognate tRNAs. In the beginning, aminoacylated ribozymes used amino acids as a cofactor with the help of bridge peptides as a process for selection between amino acids and their cognate codons/anticodons. This process selects amino acids and RNA species for the next steps. The ribozymes would give rise to pre-tRNA and the bridge peptides to pre-aaRS. Later, variants would appear and evolution would produce different but specific aaRS-tRNA-amino acid combinations. Pre-tRNA designed and built pre-mRNA for the storage of information regarding its cognate amino acid. Each pre-mRNA strand became the storage device for the genetic information that encoded the amino acid sequences in triplet nucleotides. As information appeared in the digital languages of the codon within pre-mRNA and mRNA, and the genetic code for protein synthesis evolved, the prebiotic chemistry then became more organized and directional with the emergence of the translation and genetic code. The genetic code developed in three stages that are coincident with the refinement of the translation machines: the GNC code that was developed by the pre-tRNA/pre-aaRS /pre-mRNA machine, SNS code by the tRNA/aaRS/mRNA machine, and finally the universal genetic code by the tRNA/aaRS/mRNA/ribosome machine. We suggest the coevolution of translation machines and the genetic code. The emergence of the translation machines was the beginning of the Darwinian evolution, an interplay between information and its supporting structure. Our hypothesis provides the logical and incremental steps for the origin of the programmed protein synthesis. In order to better understand the prebiotic information system, we converted letter codons into numerical codons in the Universal Genetic Code Table. We have developed a software, called CATI (Codon-Amino Acid-Translator-Imitator), to translate randomly chosen numerical codons into corresponding amino acids and vice versa. This conversion has granted us insight into how the genetic code might have evolved in the peptide/RNA world. There is great potential in the application of numerical codons to bioinformatics, such as barcoding, DNA mining, or DNA fingerprinting. We constructed the likely biochemical pathways for the origin of translation and the genetic code using the Model-View-Controller (MVC) software framework, and the translation machinery step-by-step. While using AnyLogic software, we were able to simulate and visualize the entire evolution of the translation machines, amino acids, and the genetic code.

Green function of correlated genes in a minimal mechanical model of protein evolution

The function of proteins arises from cooperative interactions and rearrangements of their amino acids, which exhibit large-scale dynamical modes. Long-range correlations have also been revealed in protein sequences, and this has motivated the search for physical links between the observed genetic and dynamic cooperativity. We outline here a simplified theory of protein, which relates sequence correlations to physical interactions and to the emergence of mechanical function. Our protein is modeled as a strongly coupled amino acid network with interactions and motions that are captured by the mechanical propagator, the Green function. The propagator describes how the gene determines the connectivity of the amino acids and thereby, the transmission of forces. Mutations introduce localized perturbations to the propagator that scatter the force field. The emergence of function is manifested by a topological transition when a band of such perturbations divides the protein into subdomains. We find that epistasis-the interaction among mutations in the gene-is related to the nonlinearity of the Green function, which can be interpreted as a sum over multiple scattering paths. We apply this mechanical framework to simulations of protein evolution and observe long-range epistasis, which facilitates collective functional modes.

Fundamental principles in bacterial physiology-history, recent progress, and the future with focus on cell size control: a review

Bacterial physiology is a branch of biology that aims to understand overarching principles of cellular reproduction. Many important issues in bacterial physiology are inherently quantitative, and major contributors to the field have often brought together tools and ways of thinking from multiple disciplines. This article presents a comprehensive overview of major ideas and approaches developed since the early 20th century for anyone who is interested in the fundamental problems in bacterial physiology. This article is divided into two parts. In the first part (sections 1-3), we review the first 'golden era' of bacterial physiology from the 1940s to early 1970s and provide a complete list of major references from that period. In the second part (sections 4-7), we explain how the pioneering work from the first golden era has influenced various rediscoveries of general quantitative principles and significant further development in modern bacterial physiology. Specifically, section 4 presents the history and current progress of the 'adder' principle of cell size homeostasis. Section 5 discusses the implications of coarse-graining the cellular protein composition, and how the coarse-grained proteome 'sectors' re-balance under different growth conditions. Section 6 focuses on physiological invariants, and explains how they are the key to understanding the coordination between growth and the cell cycle underlying cell size control in steady-state growth. Section 7 overviews how the temporal organization of all the internal processes enables balanced growth. In the final section 8, we conclude by discussing the remaining challenges for the future in the field.

Elucidating interplay of speed and accuracy in biological error correction

One of the most fascinating features of biological systems is the ability to sustain high accuracy of all major cellular processes despite the stochastic nature of underlying chemical processes. It is widely believed that such low error values are the result of the error-correcting mechanism known as kinetic proofreading. However, it is usually argued that enhancing the accuracy should result in slowing down the process, leading to the so-called speed-accuracy trade-off. We developed a discrete-state stochastic framework that allowed us to investigate the mechanisms of the proofreading using the method of first-passage processes. With this framework, we simultaneously analyzed the speed and accuracy of the two fundamental biological processes, DNA replication and tRNA selection during the translation. The results indicate that these systems tend to optimize speed rather than accuracy, as long as the error level is tolerable. Interestingly, for these processes, certain kinetic parameters lay in the suboptimal region where their perturbations can improve both speed and accuracy. Additional constraints due to the energetic cost of proofreading also play a role in the error correcting process. Our theoretical findings provide a microscopic picture of how complex biological processes are able to function so fast with high accuracy.

Biophysical comparison of ATP-driven proton pumping mechanisms suggests a kinetic advantage for the rotary process depending on coupling ratio

ATP-driven proton pumps, which are critical to the operation of a cell, maintain cytosolic and organellar pH levels within a narrow functional range. These pumps employ two very different mechanisms: an elaborate rotary mechanism used by V-ATPase H⁺ pumps, and a simpler alternating access mechanism used by P-ATPase H⁺ pumps. Why are two different mechanisms used to perform the same function? Systematic analysis, without parameter fitting, of kinetic models of the rotary, alternating access and other possible mechanisms suggest that, when the ratio of protons transported per ATP hydrolyzed exceeds one, the one-at-a-time proton transport by the rotary mechanism is faster than other possible mechanisms across a wide range of driving conditions. When the ratio is one, there is no intrinsic difference in the free energy landscape between mechanisms, and therefore all mechanisms can exhibit the same kinetic performance. To our knowledge all known rotary pumps have an H⁺:ATP ratio greater than one, and all known alternating access ATP-driven proton pumps have a ratio of one. Our analysis suggests a possible explanation for this apparent relationship between coupling ratio and mechanism. When the conditions under which the pump must operate permit a coupling ratio greater than one, the rotary mechanism may have been selected for its kinetic advantage. On the other hand, when conditions require a coupling ratio of one or less, the alternating access mechanism may have been selected for other possible advantages resulting from its structural and functional simplicity.

Biophysical comparison of ATP synthesis mechanisms shows a kinetic advantage for the rotary process

The ATP synthase (F-ATPase) is a highly complex rotary machine that synthesizes ATP, powered by a proton electrochemical gradient. Why did evolution select such an elaborate mechanism over arguably simpler alternating-access processes that can be reversed to perform ATP synthesis? We studied a systematic enumeration of alternative mechanisms, using numerical and theoretical means. When the alternative models are optimized subject to fundamental thermodynamic constraints, they fail to match the kinetic ability of the rotary mechanism over a wide range of conditions, particularly under low-energy conditions. We used a physically interpretable, closed-form solution for the steady-state rate for an arbitrary chemical cycle, which clarifies kinetic effects of complex free-energy landscapes. Our analysis also yields insights into the debated "kinetic equivalence" of ATP synthesis driven by transmembrane pH and potential difference. Overall, our study suggests that the complexity of the F-ATPase may have resulted from positive selection for its kinetic advantage.

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

We examined [Y. Savir and T. Tlusty, Cell 153, 471 (2013)10.1016/j.cell.2013.03.032] the decoding performance of tRNA by the ribosome. For this purpose, we specified the kinetics of tRNA decoding and the corresponding energy landscape, from which we calculated the steady-state decoding rate R_{C}. Following our work, Xie reexamined [P. Xie, Phys. Rev. E 92, 022716 (2015)10.1103/PhysRevE.92.022716] the energy landscape of tRNA decoding. His analysis relies on an alternative expression for R_{C}, while claiming that the expression we use is missing some terms. In this Comment we rederive in detail our expression for the steady-state decoding rate R_{C}, show they hold, explain why the alternative expression for R_{C} is inaccurate, and discuss the underlying intuition.

Steric interactions lead to collective tilting motion in the ribosome during mRNA-tRNA translocation

Translocation of mRNA and tRNA through the ribosome is associated with large-scale rearrangements of the head domain in the 30S ribosomal subunit. To elucidate the relationship between 30S head dynamics and mRNA–tRNA displacement, we apply molecular dynamics simulations using an all-atom structure-based model. Here we provide a statistical analysis of 250 spontaneous transitions between the A/P–P/E and P/P–E/E ensembles. Consistent with structural studies, the ribosome samples a chimeric ap/P–pe/E intermediate, where the 30S head is rotated ∼18°. It then transiently populates a previously unreported intermediate ensemble, which is characterized by a ∼10° tilt of the head. To identify the origins of head tilting, we analyse 781 additional simulations in which specific steric features are perturbed. These calculations show that head tilting may be attributed to specific steric interactions between tRNA and the 30S subunit (PE loop and protein S13). Taken together, this study demonstrates how molecular structure can give rise to large-scale collective rearrangements.

During protein elongation, the translocation of mRNA and tRNA molecules across the 30S ribosomal subunit is associated with large-scale motions of the 30S head domain. Here the authors carry out MD simulations to probe the associated steric interactions and identify novel tilting motions during the late stages of translocation.

Stochastic Proofreading Mechanism Alleviates Crosstalk in Transcriptional Regulation

Gene expression is controlled primarily by interactions between transcription factor proteins (TFs) and the regulatory DNA sequence, a process that can be captured well by thermodynamic models of regulation. These models, however, neglect regulatory crosstalk: the possibility that noncognate TFs could initiate transcription, with potentially disastrous effects for the cell. Here, we estimate the importance of crosstalk, suggest that its avoidance strongly constrains equilibrium models of TF binding, and propose an alternative nonequilibrium scheme that implements kinetic proofreading to suppress erroneous initiation. This proposal is consistent with the observed covalent modifications of the transcriptional apparatus and predicts increased noise in gene expression as a trade-off for improved specificity. Using information theory, we quantify this trade-off to find when optimal proofreading architectures are favored over their equilibrium counterparts. Such architectures exhibit significant super-Poisson noise at low expression in steady state.

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.

A cellular solution to an information-processing problem

Signaling receptors on the cell surface are mobile and have evolved to efficiently sense and process mechanical or chemical information. We pose the problem of identifying the optimal strategy for placing a collection of distributed and mobile sensors to faithfully estimate a signal that varies in space and time. The optimal strategy has to balance two opposing objectives: the need to locally assemble sensors to reduce estimation noise and the need to spread them to reduce spatial error. This results in a phase transition in the space of strategies as a function of sensor density and efficiency. We show that these optimal strategies have been arrived at multiple times in diverse cell biology contexts, including the stationary lattice architecture of receptors on the bacterial cell surface and the active clustering of cell-surface signaling receptors in metazoan cells.

Structure-based energetics of mRNA decoding on the ribosome

The origin of high fidelity in bacterial protein synthesis on the ribosome remains a fundamental unsolved problem despite available three-dimensional structures of different stages of the translation process. However, these structures open up the possibility of directly computing the energetics of tRNA selection that is required for an authentic understanding of fidelity in decoding. Here, we report extensive computer simulations that allow us to quantitatively calculate tRNA discrimination and uncover the energetics underlying accuracy in code translation. We show that the tRNA-mRNA interaction energetics varies drastically along the path from initial selection to peptide bond formation. While the selection process is obviously controlled by kinetics, the underlying thermodynamics explains the origin of the high degree of accuracy. The existence of both low- and high-selectivity states provides an efficient mechanism for initial selection and proofreading that does not require codon-dependent long-range structural signaling within the ribosome. It is instead the distinctly unequal population of the high-selectivity states for cognate and noncognate substrates that is the key discriminatory factor. The simulations reveal the essential roles played both by the 30S subunit conformational switch and by the common tRNA modification at position 37 in amplifying the accuracy.