Understanding the regulation of protein synthesis in stress conditions.

Protein synthesis regulation primarily occurs at translation initiation, the first step of gene translation. However, the regulation of translation initiation under various conditions is not fully understood. Specifically, the reason why protein production from certain mRNAs remains resistant to stress while others do not show such resilience. Moreover, why is protein production enhanced from a few transcripts under stress conditions, whereas it is decreased in the majority of transcripts? We address them by developing a Monte-Carlo simulation model of protein synthesis and ribosome scanning. We find that mRNAs with strong Kozak contexts exhibit minimal reduction in translation initiation rate under stress conditions. Moreover, these transcripts exhibit even greater resilience to stress when the scanning speed of 43S ribosome subunit is slow, albeit at the cost of reduced initiation rate. This implies a trade-off between initiation rate and the ability of mRNA to withstand stress. We also show that mRNAs featuring an upstream ORF can act as a regulatory switch. This switch elevates protein production from the main ORF under stress conditions, however, minimal to no proteins are produced under the normal condition. Because, in stress, a larger fraction of 43S ribosomes bypasses the upstream ORF due to its weak Kozak context. This, in turn, increases the number of scanning ribosomes reaching the main ORF, whose strong Kozak context can convert them into 80S ribosomes, even in stress conditions. This switching allows an efficient use of cellular resources by producing proteins when they are required. Thus, our computational study provides valuable insights into our understanding of stress-responsive translation-initiation.

TIR predictor and optimizer: Web-tools for accurate prediction of translation initiation rate and precision gene design in Saccharomyces cerevisiae.

Translation initiation is the primary determinant of the rate of protein production. The variation in the rate with which this step occurs can cause up to three orders of magnitude differences in cellular protein levels. Several mRNA features, including mRNA stability in proximity to the start codon, coding sequence length, and presence of specific motifs in the mRNA molecule, have been shown to influence the translation initiation rate. These molecular factors acting at different strengths allow precise control of in vivo translation initiation rate and thus the rate of protein synthesis. However, despite the paramount importance of translation initiation rate in protein synthesis, accurate prediction of the absolute values of initiation rate remains a challenge. In fact, as of now, there is no available model for predicting the initiation rate in Saccharomyces cerevisiae. To address this, we train a machine learning model for predicting the in vivo initiation rate in S. cerevisiae transcripts. The model is trained using a diverse set of mRNA transcripts, enabling the comparison of initiation rates across different transcripts. Our model exhibited excellent accuracy in predicting the translation initiation rate and demonstrated its effectiveness with both endogenous and exogenous transcripts. Then, by combining the machine learning model with the Monte-Carlo search algorithm, we have also devised a method to optimize the nucleotide sequence of any gene to achieve a specific target initiation rate. The machine learning model we've developed for predicting translation initiation rates, along with the gene optimization method, are deployed as a web server. Both web servers are accessible for free at the following link: ajeetsharmalab.com/TIRPredictor. Thus, this research advances our fundamental understanding of translation initiation processes, with direct applications in biotechnology.

Decoding stoichiometric protein synthesis in E. coli through translation rate parameters.

E. coli is one of the most widely used organisms for understanding the principles of cellular and molecular genetics. However, we are yet to understand the origin of several experimental observations related to the regulation of gene expression in E. coli. One of the prominent examples in this context is the proportional synthesis in multiprotein complexes where all of their obligate subunits are produced in proportion to their stoichiometry. In this work, by combining the next-generation sequencing data with the stochastic simulations of protein synthesis, we explain the origin of proportional protein synthesis in multicomponent complexes. We find that the estimated initiation rates for the translation of all subunits in those complexes are proportional to their stoichiometry. This constraint on protein synthesis kinetics enforces proportional protein synthesis without requiring any feedback mechanism. We also find that the translation initiation rates in E. coli are influenced by the coding sequence length and the enrichment of A and C nucleotides near the start codon. Thus, this study rationalizes the role of conserved and nonrandom features of genes in regulating the translation kinetics and unravels a key principle of the regulation of protein synthesis.

Fundamental insights into the correlation between chromosome configuration and transcription.

Eukaryotic chromosomes exhibit a hierarchical organization that spans a spectrum of length scales, ranging from sub-regions known as loops, which typically comprise hundreds of base pairs, to much larger chromosome territories that can encompass a few mega base pairs. Chromosome conformation capture experiments that involve high-throughput sequencing methods combined with microscopy techniques have enabled a new understanding of inter- and intra-chromosomal interactions with unprecedented details. This information also provides mechanistic insights on the relationship between genome architecture and gene expression. In this article, we review the recent findings on three-dimensional interactions among chromosomes at the compartment, topologically associating domain, and loop levels and the impact of these interactions on the transcription process. We also discuss current understanding of various biophysical processes involved in multi-layer structural organization of chromosomes. Then, we discuss the relationships between gene expression and genome structure from perturbative genome-wide association studies. Furthermore, for a better understanding of how chromosome architecture and function are linked, we emphasize the role of epigenetic modifications in the regulation of gene expression. Such an understanding of the relationship between genome architecture and gene expression can provide a new perspective on the range of potential future discoveries and therapeutic research.

Optimization of ribosome utilization in Saccharomyces cerevisiae.

Resource optimization in protein synthesis is often looked at from the perspective of translation efficiency-the rate at which proteins are synthesized from a single transcript. The higher the rate of protein synthesis, the more efficiently a transcript is translated. However, the production of a ribosome consumes significantly more cellular resources than an mRNA molecule. Therefore, there should be a stronger selection pressure for optimizing ribosome usage than translation efficiency. This paper reports strong evidence of such optimization which becomes more prominent in highly expressed transcripts that consume a significant amount of cellular resources. The ribosome usage is optimized by the biases in codon usage and translation initiation rates. This optimization significantly reduces the ribosome requirement in Saccharomyces cerevisiae. We also find that a low ribosome density on mRNA transcripts helps optimize ribosome utilization. Therefore, protein synthesis occurs in a low ribosome density regime where translation-initiation is the rate-limiting step. Our results suggest that optimizing ribosome usage is one of the major forces shaping evolutionary selection pressure, and thus provide a new perspective to resource optimization in protein synthesis.

Combinations of slow-translating codon clusters can increase mRNA half-life in Saccharomyces cerevisiae.

The presence of a single cluster of nonoptimal codons was found to decrease a transcript's half-life through the interaction of the ribosome-associated quality control machinery with stalled ribosomes in Saccharomyces cerevisiae The impact of multiple nonoptimal codon clusters on a transcript's half-life, however, is unknown. Using a kinetic model, we predict that inserting a second nonoptimal cluster near the 5' end can lead to synergistic effects that increase a messenger RNA's (mRNA's) half-life in S. cerevisiae Specifically, the 5' end cluster suppresses the formation of ribosome queues, reducing the interaction of ribosome-associated quality control factors with stalled ribosomes. We experimentally validate this prediction by introducing two nonoptimal clusters into three different genes and find that their mRNA half-life increases up to fourfold. The model also predicts that in the presence of two clusters, the cluster closest to the 5' end is the primary determinant of mRNA half-life. These results suggest the "translational ramp," in which nonoptimal codons are located near the start codon and increase translational efficiency, may have the additional biological benefit of allowing downstream slow-codon clusters to be present without decreasing mRNA half-life. These results indicate that codon usage bias plays a more nuanced role in controlling cellular protein levels than previously thought.

Quantitative Modeling of Protein Synthesis Using Ribosome Profiling Data.

Quantitative prediction on protein synthesis requires accurate translation initiation and codon translation rates. Ribosome profiling data, which provide steady-state distribution of relative ribosome occupancies along a transcript, can be used to extract these rate parameters. Various methods have been developed in the past few years to measure translation-initiation and codon translation rates from ribosome profiling data. In the review, we provide a detailed analysis of the key methods employed to extract the translation rate parameters from ribosome profiling data. We further discuss how these approaches were used to decipher the role of various structural and sequence-based features of mRNA molecules in the regulation of gene expression. The utilization of these accurate rate parameters in computational modeling of protein synthesis may provide new insights into the kinetic control of the process of gene expression.

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.

Hierarchical Markov State Model Building to Describe Molecular Processes.

Markov state models can describe ensembles of pathways via kinetic networks but are difficult to create when large free-energy barriers limit unbiased sampling. Chain-of-states simulations allow sampling over large free-energy barriers but are often constructed using a single pathway that is unlikely to thermodynamically average over orthogonal degrees of freedom in complex systems. Here, we combine the advantages of these two approaches in the form of a Markov state model of Markov state models, which we call a Hierarchical Markov state model. In this approach, independent Markov models are constructed in regions of configuration space that are locally well sampled but are separated by large free-energy barriers from other regions. A string method is used to construct an ensemble of pathways connecting the states of these different local Markov models, and the rate through each pathway is then estimated. These rates are then combined with the rate information from the local Markov models in a master equation to predict global rates, fluxes, and populations. By applying this hierarchical approach to tractable systems, a toy potential and dipeptides, we demonstrate that it is more accurate than the conventional single-pathway description. The advantages of this approach are that it (i) is more realistic than the conventional chain-of-states approach, as an ensemble of pathways rather than a single pathway is used to describe processes in high-dimensional systems, and (ii) it resolves the issue of poor sampling in Markov State model building when large free-energy barriers are present. The divide-and-conquer strategy inherent to this approach should make this procedure straightforward to apply to more complex systems.

A chemical kinetic basis for measuring translation initiation and elongation rates from ribosome profiling data.

Analysis methods based on simulations and optimization have been previously developed to estimate relative translation rates from next-generation sequencing data. Translation involves molecules and chemical reactions, hence bioinformatics methods consistent with the laws of chemistry and physics are more likely to produce accurate results. Here, we derive simple equations based on chemical kinetic principles to measure the translation-initiation rate, transcriptome-wide elongation rate, and individual codon translation rates from ribosome profiling experiments. Our methods reproduce the known rates from ribosome profiles generated from detailed simulations of translation. By applying our methods to data from S. cerevisiae and mouse embryonic stem cells, we find that the extracted rates reproduce expected correlations with various molecular properties, and we also find that mouse embryonic stem cells have a global translation speed of 5.2 AA/s, in agreement with previous reports that used other approaches. Our analysis further reveals that a codon can exhibit up to 26-fold variability in its translation rate depending upon its context within a transcript. This broad distribution means that the average translation rate of a codon is not representative of the rate at which most instances of that codon are translated, and it suggests that translational regulation might be used by cells to a greater degree than previously thought.

Kinetic and structural comparison of a protein's cotranslational folding and refolding pathways.

Precise protein folding is essential for the survival of all cells, and protein misfolding causes a number of diseases that lack effective therapies, yet the general principles governing protein folding in the cell remain poorly understood. In vivo, folding can begin cotranslationally and protein quality control at the ribosome is essential for cellular proteostasis. We directly characterize and compare the refolding and cotranslational folding trajectories of the protein HaloTag. We introduce new techniques for both measuring folding kinetics and detecting the conformations of partially folded intermediates during translation in real time. We find that, although translation does not affect the rate-limiting step of HaloTag folding, a key aggregation-prone intermediate observed during in vitro refolding experiments is no longer detectable. This rerouting of the folding pathway increases HaloTag's folding efficiency and may serve as a general chaperone-independent mechanism of quality control by the ribosome.

Determinants of translation speed are randomly distributed across transcripts resulting in a universal scaling of protein synthesis times.

Ribosome profiling experiments have found greater than 100-fold variation in ribosome density along mRNA transcripts, indicating that individual codon elongation rates can vary to a similar degree. This wide range of elongation times, coupled with differences in codon usage between transcripts, suggests that the average codon translation-rate per gene can vary widely. Yet, ribosome run-off experiments have found that the average codon translation rate for different groups of transcripts in mouse stem cells is constant at 5.6 AA/s. How these seemingly contradictory results can be reconciled is the focus of this study. Here, we combine knowledge of the molecular factors shown to influence translation speed with genomic information from Escherichia coli, Saccharomyces cerevisiae and Homo sapiens to simulate the synthesis of cytosolic proteins in these organisms. The model recapitulates a near constant average translation rate, which we demonstrate arises because the molecular determinants of translation speed are distributed nearly randomly amongst most of the transcripts. Consequently, codon translation rates are also randomly distributed and fast-translating segments of a transcript are likely to be offset by equally probable slow-translating segments, resulting in similar average elongation rates for most transcripts. We also show that the codon usage bias does not significantly affect the near random distribution of codon translation rates because only about 10% of the total transcripts in an organism have high codon usage bias while the rest have little to no bias. Analysis of Ribo-Seq data and an in vivo fluorescent assay supports these conclusions.

Non-equilibrium coupling of protein structure and function to translation-elongation kinetics.

Protein folding research has been dominated by the assumption that thermodynamics determines protein structure and function. And that when the folding process is compromised in vivo the proteostasis machinery-chaperones, deaggregases, the proteasome-work to restore proteins to their soluble, functional form or degrade them to maintain the cellular pool of proteins in a quasi-equilibrium state. During the past decade, however, more and more proteins have been identified for which altering only their speed of synthesis alters their structure and function, the efficiency of the down-stream processes they take part in, and cellular phenotype. Indeed, evidence has emerged that evolutionary selection pressures have encoded translation-rate information into mRNA molecules to coordinate diverse co-translational processes. Thus, non-equilibrium physics can play a fundamental role in influencing nascent protein behavior, mRNA sequence evolution, and disease. Here, we discuss how our understanding of this phenomenon is being advanced by the application of theoretical tools from the physical sciences.

Increasing Protein Production Rates Can Decrease the Rate at Which Functional Protein Is Produced and Their Steady-State Levels.

The rate at which soluble, functional protein is produced by the ribosome has recently been found to vary in complex and unexplained ways as various translation-associated rates are altered through synonymous codon substitutions. To understand this phenomenon, here, we combine a well-established ribosome-traffic model with a master-equation model of cotranslational domain folding to explore the scenarios that are possible for the protein production rate, J, and the functional-nascent protein production rate, F, as the rates of various translation processes are altered for five different E. coli proteins. We find that while J monotonically increases as the rates of translation-initiation, -elongation, and -termination increase, F can either increase or decrease. We show that F's nonmonotonic behavior arises within the model from two opposing trends: the tendency for increased translation rates to produce more total protein but less cotranslationally folded protein. We further demonstrate that under certain conditions these nonmonotonic changes in F can result in nonmonotonic variations in post-translational, steady-state levels of functional protein. These results provide a potential explanation for recent experimental observations in which the specific activity of enzymatic proteins decreased with increased synthesis rates. Additionally our model has the potential to be used to rationally design transcripts to maximize the production of functional nascent protein by simultaneously optimizing translation initiation, elongation, and termination rates.

Accurate prediction of cellular co-translational folding indicates proteins can switch from post- to co-translational folding.

The rates at which domains fold and codons are translated are important factors in determining whether a nascent protein will co-translationally fold and function or misfold and malfunction. Here we develop a chemical kinetic model that calculates a protein domain's co-translational folding curve during synthesis using only the domain's bulk folding and unfolding rates and codon translation rates. We show that this model accurately predicts the course of co-translational folding measured in vivo for four different protein molecules. We then make predictions for a number of different proteins in yeast and find that synonymous codon substitutions, which change translation-elongation rates, can switch some protein domains from folding post-translationally to folding co-translationally--a result consistent with previous experimental studies. Our approach explains essential features of co-translational folding curves and predicts how varying the translation rate at different codon positions along a transcript's coding sequence affects this self-assembly process.

Physical Origins of Codon Positions That Strongly Influence Cotranslational Folding: A Framework for Controlling Nascent-Protein Folding.

An emerging paradigm in the field of in vivo protein biophysics is that nascent-protein behavior is a type of nonequilibrium phenomenon, where translation-elongation kinetics can be more important in determining nascent-protein behavior than the thermodynamic properties of the protein. Synonymous codon substitutions, which change the translation rate at select codon positions along a transcript, have been shown to alter cotranslational protein folding, suggesting that evolution may have shaped synonymous codon usage in the genomes of organisms in part to increase the amount of folded and functional nascent protein. Here, we develop a Monte Carlo-master-equation method that allows for the control of nascent-chain folding during translation through the rational design of mRNA sequences to guide the cotranslational folding process. We test this framework using coarse-grained molecular dynamics simulations and find it provides optimal mRNA sequences to control the simulated, cotranslational folding of a protein in a user-prescribed manner. With this approach we discover that some codon positions in a transcript can have a much greater impact on nascent-protein folding than others because they tend to be positions where the nascent chain populates states that are far from equilibrium, as well as being dependent on a complex ratio of time scales. As a consequence, different cotranslational profiles of the same protein can have different critical codon positions and different numbers of synonymous mRNA sequences that encode for them. These findings explain that there is a fundamental connection between the nonequilibrium nature of cotranslational processes, nascent-protein behavior, and synonymous codon usage.

Quantitative Connection between Ensemble Thermodynamics and Single-Molecule Kinetics: A Case Study Using Cryogenic Electron Microscopy and Single-Molecule Fluorescence Resonance Energy Transfer Investigations of the Ribosome.

At equilibrium, thermodynamic and kinetic information can be extracted from biomolecular energy landscapes by many techniques. However, while static, ensemble techniques yield thermodynamic data, often only dynamic, single-molecule techniques can yield the kinetic data that describe transition-state energy barriers. Here we present a generalized framework based upon dwell-time distributions that can be used to connect such static, ensemble techniques with dynamic, single-molecule techniques, and thus characterize energy landscapes to greater resolutions. We demonstrate the utility of this framework by applying it to cryogenic electron microscopy (cryo-EM) and single-molecule fluorescence resonance energy transfer (smFRET) studies of the bacterial ribosomal pre-translocation complex. Among other benefits, application of this framework to these data explains why two transient, intermediate conformations of the pre-translocation complex, which are observed in a cryo-EM study, may not be observed in several smFRET studies.

Distribution of lifetimes of kinetochore-microtubule attachments: interplay of energy landscape, molecular motors and microtubule (de-)polymerization.

Before a cell divides into two daughter cells, chromosomes are replicated resulting in two sister chromosomes embracing each other. Each sister chromosome is bound to a separate proteinous structure, called kinetochore (kt), that captures the tip of a filamentous protein, called microtubule (MT). Two oppositely oriented MTs pull the two kts attached to two sister chromosomes, thereby pulling the two sisters away from each other. Here we theoretically study an even simpler system, namely an isolated kt coupled to a single MT; this system mimics an in vitro experiment where a single kt-MT attachment is reconstituted using purified extracts from budding yeast. Our models not only account for the experimentally observed 'catch-bond-like' behavior of the kt-MT coupling, but also make new predictions on the probability distribution of the lifetimes of the attachments. In principle, our new predictions can be tested by analyzing the data collected in the in vitro experiments, provided that the experiment is repeated a sufficiently large number of times. Our theory provides a deep insight into the effects of (a) size, (b) energetics, and (c) stochastic kinetics of the kt-MT coupling on the distribution of the lifetimes of these attachments.

Does incorporation of thromboelastography improve bleeding prediction following adult cardiac surgery?

Cardiopulmonary bypass (CPB) coagulopathy increases utilization of allogenic blood/blood products, which can negatively affect patient outcomes. Thromboelastography (TEG) is a point-of-care measurement of clot formation and fibrinolysis. We investigated whether the addition of TEG parameters to a clinically based bleeding model would improve the predictability of postoperative bleeding. A total of 439 patients' charts were retrospectively investigated for 8-h chest tube output (CTO) postoperatively. For model 1, the variables recorded were patient age, gender, body surface area, clopidogrel use, CPB time, first post-CPB fibrinogen serum level, first post-CPB platelet count, first post-CPB international normalized ratio, the total amount of intraoperative cell saver blood transfused, and postoperative first ICU hematocrit level. Model 2 had the model 1 variables, TEG angle, and maximum amplitude. The outcome was defined as 0-8-h CTO. The predictor variables were placed into a forward stepwise regression model for continuous outcomes. Analysis of variance with adjusted R was used to assess the goodness-of-fit of both predictive models. The predictive accuracy of the model was examined using CTO as a dichotomous variable (75th percentile, 480 ml) and receiver operating characteristic curves for both models. Advanced age, male gender, preoperative clopidogrel use for 5 days or less, greater cell saver blood utilization, and lower postoperative hematocrit levels were associated with increased 8-h CTO (P < 0.05). Adding TEG angle and maximum amplitude to model 1 did not improve CTO predictability. When TEG angle and maximum amplitude were added as predictor factors, the predictability of the bleeding model did not improve.

First-passage problems in DNA replication: effects of template tension on stepping and exonuclease activities of a DNA polymerase motor.

A DNA polymerase (DNAP) replicates a template DNA strand. It also exploits the template as the track for its own motor-like mechanical movement. In the polymerase mode it elongates the nascent DNA by one nucleotide in each step. However, whenever it commits an error by misincorporating an incorrect nucleotide, it can switch to an exonuclease mode. In the latter mode it excises the wrong nucleotide before switching back to its polymerase mode. We develop a stochastic kinetic model of DNA replication that mimics an in vitro experiment where single-stranded DNA, subjected to a mechanical tension F, is converted to double-stranded DNA by a single DNAP. The F-dependence of the average rate of replication, which depends on the rates of both polymerase and exonuclease activities of the DNAP, is in good qualitative agreement with the corresponding experimental results. We introduce nine novel distinct conditional dwell times of a DNAP. Using the method of first-passage times, we also derive the exact analytical expressions for the probability distributions of these conditional dwell times. The predicted F-dependences of these distributions are, in principle, accessible to single-molecule experiments.