Molecular Mechanisms of Protein Thermal Stability

Organisms that thrive under extreme conditions, such as high salt concentration, low pH, or high temperature, provide an opportunity to investigate the molecular and cellular strategies these organisms have adapted to survive in their harsh environments. Thermophilic proteins, those extracted from organisms that live at high temperature, maintain their structure and function at much higher temperatures compared to their mesophilic counterparts, found in organisms that live near room temperature. Thermophilic and mesophilic homolog protein pairs have identical functionality, and show a high degree of structural and sequential similarity, but differ significantly in their response to high temperature. Addressing the principles of enhanced stability and structural resilience to high temperatures environments is important in furthering our understanding of protein folding and stability, and can be quite useful for protein engineering in industrial and biomedical arenas. Furthermore, understanding temperature dependent protein stability can provide valuable insights into aging and certain diseases. This work will present the observations from multiple large-scale studies that show meaningful general principles that can be a potential mechanism for thermophilic adaptation. First, from the analysis of the largest data set of thermodynamic data, the roles of reduced thermodynamic parameters upon unfolding, and their association with the unfolded state are discussed. Next, from a first-principle polymer physics model, the contribution from electrostatic interactions are shown to reduce the dimensionality of the unfolded state in thermophilic proteins. Finally, as a result of long time scale molecular dynamics simulations, electrostatic interactions are shown to be the key contributor in the stability of the folded state in thermal stable proteins. The combined results indicate that thermophilic proteins modify their amino acid content to increase the amount of charged side chains to utilize an adaptive strategy of enhancing favorable electrostatic energies. Molecular effects of protein mutations are observed in experimental measurements of protein thermodynamic values and enzymatic activity. However, modified proteins can also be quantitatively linked to cellular health and fitness. The consequences of modified thermodynamic traits seen in thermophilic proteins to the growth rate of several organisms will be discussed

Role of Proteome Physical Chemistry in Cell Behavior.

We review how major cell behaviors, such as bacterial growth laws, are derived from the physical chemistry of the cell's proteins. On one hand, cell actions depend on the individual biological functionalities of their many genes and proteins. On the other hand, the common physics among proteins can be as important as the unique biology that distinguishes them. For example, bacterial growth rates depend strongly on temperature. This dependence can be explained by the folding stabilities across a cell's proteome. Such modeling explains how thermophilic and mesophilic organisms differ, and how oxidative damage of highly charged proteins can lead to unfolding and aggregation in aging cells. Cells have characteristic time scales. For example, E. coli can duplicate as fast as 2-3 times per hour. These time scales can be explained by protein dynamics (the rates of synthesis and degradation, folding, and diffusional transport). It rationalizes how bacterial growth is slowed down by added salt. In the same way that the behaviors of inanimate materials can be expressed in terms of the statistical distributions of atoms and molecules, some cell behaviors can be expressed in terms of distributions of protein properties, giving insights into the microscopic basis of growth laws in simple cells

A Theoretical Method to Compute Sequence Dependent Configurational Properties in Charged Polymers and Proteins

A general formalism to compute configurational properties of proteins and other heteropolymers with an arbitrary sequence of charges and non-uniform excluded volume interaction is presented. A variational approach is utilized to predict average distance between any two monomers in the chain. The presented analytical model, for the first time, explicitly incorporates the role of sequence charge distribution to determine relative sizes between two sequences that vary not only in total charge composition but also in charge decoration (even when charge composition is fixed). Furthermore, the formalism is general enough to allow variation in excluded volume interactions between two monomers. Model predictions are benchmarked against the all-atom Monte Carlo studies of Das and Pappu [Proc. Natl. Acad. Sci. U. S. A. 110, 13392 (2013)] for 30 different synthetic sequences of polyampholytes. These sequences possess an equal number of glutamic acid (E) and lysine (K) residues but differ in the patterning within the sequence. Without any fit parameter, the model captures the strong sequence dependence of the simulated values of the radius of gyration with a correlation coefficient of R2 = 0.9. The model is then applied to real proteins to compare the unfolded state dimensions of 540 orthologous pairs of thermophilic and mesophilic proteins. The excluded volume parameters are assumed similar under denatured conditions, and only electrostatic effects encoded in the sequence are accounted for. With these assumptions, thermophilic proteins are found—with high statistical significance—to have more compact disordered ensemble compared to their mesophilic counterparts. The method presented here, due to its analytical nature, is capable of making such high throughput analysis of multiple proteins and will have broad applications in proteomic studies as well as in other heteropolymeric systems

Convergence of Molecular Dynamics Simulation of Protein Native States: Feasibility vs Self-Consistency Dilemma

All-atom molecular dynamics simulations need convergence tests to evaluate the quality of data. The notion of “true” convergence is elusive, and one can only hope to satisfy self-consistency checks (SCC). There are multiple SCC criteria, and their assessment of all-atom simulations of the native state for real globular proteins is sparse. Here, we present a systematic study of different SCC algorithms, both in terms of their ability to detect the lack of self-consistency and their computational demand, for the all-atom native state simulations of four globular proteins (CSP, CheA, CheW, and BPTI). Somewhat surprisingly, we notice some of the most stringent SCC criteria, e.g., the criteria demanding similarity of the cluster probability distribution between the first and the second halves of the trajectory or the comparison of fluctuations between different blocks using covariance overlap measure, can require tens of microseconds of simulation even for proteins with less than 100 amino acids. We notice such long simulation times can sometimes be associated with traps, but these traps cannot be detected by some of the common SCC methods. We suggest an additional, and simple, SCC algorithm to quickly detect such traps by monitoring the constancy of the cluster entropy (CCE). CCE is a necessary but not sufficient criteria, and additional SCC algorithms must be combined with it. Furthermore, as seen in the explicit solvent simulation of 1 ms long trajectory of BPTI,1 passing self-consistency checks at an earlier stage may be misleading due to conformational changes taking place later in the simulation, resulting in different, but segregated regions of SCC. Although there is a hierarchy of complex SCC algorithms, caution must be exercised in their application with the knowledge of their limitations and computational expense

Convergence of Molecular Dynamics Simulation of Protein Native States: Feasibility vs Self-Consistency Dilemma

All-atom molecular dynamics simulations need convergence tests to evaluate the quality of data. The notion of “true” convergence is elusive, and one can only hope to satisfy self-consistency checks (SCC). There are multiple SCC criteria, and their assessment of all-atom simulations of the native state for real globular proteins is sparse. Here, we present a systematic study of different SCC algorithms, both in terms of their ability to detect the lack of self-consistency and their computational demand, for the all-atom native state simulations of four globular proteins (CSP, CheA, CheW, and BPTI). Somewhat surprisingly, we notice some of the most stringent SCC criteria, e.g., the criteria demanding similarity of the cluster probability distribution between the first and the second halves of the trajectory or the comparison of fluctuations between different blocks using covariance overlap measure, can require tens of microseconds of simulation even for proteins with less than 100 amino acids. We notice such long simulation times can sometimes be associated with traps, but these traps cannot be detected by some of the common SCC methods. We suggest an additional, and simple, SCC algorithm to quickly detect such traps by monitoring the constancy of the cluster entropy (CCE). CCE is a necessary but not sufficient criteria, and additional SCC algorithms must be combined with it. Furthermore, as seen in the explicit solvent simulation of 1 ms long trajectory of BPTI, passing self-consistency checks at an earlier stage may be misleading due to conformational changes taking place later in the simulation, resulting in different, but segregated regions of SCC. Although there is a hierarchy of complex SCC algorithms, caution must be exercised in their application with the knowledge of their limitations and computational expense

All-Atom Simulations Reveal Protein Charge Decoration in the Folded and Unfolded Ensemble Is Key in Thermophilic Adaptation

Thermophilic proteins denature at much higher temperature compared to their mesophilic homologues, in spite of high structural and sequential similarity. Computational approaches to understand this puzzle face three major challenges: (i) unfolded ensembles are usually neglected, (ii) simulation studies of the folded states are often too short, and (iii) the majority of investigations focus on a few protein pairs, obscuring the prevalence of different strategies across multiple protein systems. We address these concerns by carrying out all-atom simulations to characterize physicochemical properties of both the folded and the disordered ensemble in multiple (12) thermophilic–mesophilic homologous protein pairs. We notice two clear trends in most pairs (10 out of 12). First, specific distribution of charges in the native basinsampled from multimicrosecond long Molecular Dynamics (MD) simulation trajectoriesleads to more favorable electrostatic interaction energy in thermophiles compared to mesophiles. Next, thermophilic proteins have lowered electrostatic interaction in their unfolded stategenerated using Monte Carlo (MC) simulationcompared to their mesophilic counterparts. The net contribution of interaction energy to folding stability, however, remains more favorable in thermophiles compared to mesophiles. The overall contribution of electrostatics quantified by combining the net interaction energy and the solvation penalty of foldingdue to differential charge burial in the folded and the unfolded ensembleis also mostly favorable in thermophilic proteins compared to mesophiles. The systems that deviate from this trend provide interesting test cases to learn more about alternate design strategies when modification of charges is not viable due to functional reasons. The unequal contribution of the unfolded state to the stability in thermophiles and mesophiles highlights the importance of modeling the disordered ensemble to understand thermophilic adaptation as well as protein stability, in general. Our integrated approachcombining finite element analysis with MC and MDcan be useful in designing charge mutations to alter protein stability

Role of Proteome Physical Chemistry in Cell Behavior

We review how major cell behaviors, such as bacterial growth laws, are derived from the physical chemistry of the cell’s proteins. On one hand, cell actions depend on the individual biological functionalities of their many genes and proteins. On the other hand, the common physics among proteins can be as important as the unique biology that distinguishes them. For example, bacterial growth rates depend strongly on temperature. This dependence can be explained by the folding stabilities across a cell’s proteome. Such modeling explains how thermophilic and mesophilic organisms differ, and how oxidative damage of highly charged proteins can lead to unfolding and aggregation in aging cells. Cells have characteristic time scales. For example, E. coli can duplicate as fast as 2–3 times per hour. These time scales can be explained by protein dynamics (the rates of synthesis and degradation, folding, and diffusional transport). It rationalizes how bacterial growth is slowed down by added salt. In the same way that the behaviors of inanimate materials can be expressed in terms of the statistical distributions of atoms and molecules, some cell behaviors can be expressed in terms of distributions of protein properties, giving insights into the microscopic basis of growth laws in simple cells

Role of Proteome Physical Chemistry in Cell Behavior

[Image: see text] We review how major cell behaviors, such as bacterial growth laws, are derived from the physical chemistry of the cell’s proteins. On one hand, cell actions depend on the individual biological functionalities of their many genes and proteins. On the other hand, the common physics among proteins can be as important as the unique biology that distinguishes them. For example, bacterial growth rates depend strongly on temperature. This dependence can be explained by the folding stabilities across a cell’s proteome. Such modeling explains how thermophilic and mesophilic organisms differ, and how oxidative damage of highly charged proteins can lead to unfolding and aggregation in aging cells. Cells have characteristic time scales. For example, E. coli can duplicate as fast as 2–3 times per hour. These time scales can be explained by protein dynamics (the rates of synthesis and degradation, folding, and diffusional transport). It rationalizes how bacterial growth is slowed down by added salt. In the same way that the behaviors of inanimate materials can be expressed in terms of the statistical distributions of atoms and molecules, some cell behaviors can be expressed in terms of distributions of protein properties, giving insights into the microscopic basis of growth laws in simple cells

Structural and Biochemical Characterization of Human Adenylosuccinate Lyase (ADSL) and the R303C ADSL Deficiency-Associated Mutation

Adenylosuccinate lyase (ADSL) deficiency is a rare autosomal recessive disorder, which causes a defect in purine metabolism resulting in neurological and physiological symptoms. ADSL executes two nonsequential steps in the de novo synthesis of AMP: the conversion of phosphoribosylsuccinyl-aminoimidazole carboxamide (SAICAR) to phosphoribosylaminoimidazole carboxamide, which occurs in the de novo synthesis of IMP, and the conversion of adenylosuccinate to AMP, which occurs in the de novo synthesis of AMP and also in the purine nucleotide cycle, using the same active site. Mutation of ADSL’s arginine 303 to a cysteine is known to lead to ADSL deficiency. Interestingly, unlike other mutations leading to ADSL deficiency, the R303C mutation has been suggested to more significantly affect the enzyme’s ability to catalyze the conversion of succinyladenosine monophosphate than that of SAICAR to their respective products. To better understand the causation of disease due to the R303C mutation, as well as to gain insights into why the R303C mutation potentially has a disproportional decrease in activity toward its substrates, the wild type (WT) and the R303C mutant of ADSL were investigated enzymatically and thermodynamically. Additionally, the X-ray structures of ADSL in its apo form as well as with the R303C mutation were elucidated, providing insight into ADSL’s cooperativity. By utilizing this information, a model for the interaction between ADSL and SAICAR is proposed

How Do Thermophilic Proteins and Proteomes Withstand High Temperature?

We attempt to understand the origin of enhanced stability in thermophilic proteins by analyzing thermodynamic data for 116 proteins, the largest data set achieved to date. We compute changes in entropy and enthalpy at the convergence temperature where different driving forces are maximally decoupled, in contrast to the majority of previous studies that were performed at the melting temperature. We find, on average, that the gain in enthalpy upon folding is lower in thermophiles than in mesophiles, whereas the loss in entropy upon folding is higher in mesophiles than in thermophiles. This implies that entropic stabilization may be responsible for the high melting temperature, and hints at residual structure or compactness of the denatured state in thermophiles. We find a similar trend by analyzing a homologous set of proteins classified based only on the optimum growth temperature of the organisms from which they were extracted. We find that the folding free energy at the temperature of maximal stability is significantly more favorable in thermophiles than in mesophiles, whereas the maximal stability temperature itself is similar between these two classes. Furthermore, we extend the thermodynamic analysis to model the entire proteome. The results explain the high optimal growth temperature in thermophilic organisms and are in excellent quantitative agreement with full thermal growth rate data obtained in a dozen thermophilic and mesophilic organisms