Evolutionary Processes and Biophysical Mechanisms: Revisiting Why Evolved Proteins Are Marginally Stable

Temporal Quantitative Proteomic and Phosphoproteomic Profiling of SH-SY5Y and IMR-32 Neuroblastoma Cells during All-Trans-Retinoic Acid-Induced Neuronal Differentiation

The human neuroblastoma cell lines SH-SY5Y and IMR-32 can be differentiated into neuron-like phenotypes through treatment with all-trans-retinoic acid (ATRA). After differentiation, these cell lines are extensively utilized as in vitro models to study various aspects of neuronal cell biology. However, temporal and quantitative profiling of the proteome and phosphoproteome of SH-SY5Y and IMR-32 cells throughout ATRA-induced differentiation has been limited. Here, we performed relative quantification of the proteomes and phosphoproteomes of SH-SY5Y and IMR-32 cells at multiple time points during ATRA-induced differentiation. Relative quantification of proteins and phosphopeptides with subsequent gene ontology analysis revealed that several biological processes, including cytoskeleton organization, cell division, chaperone function and protein folding, and one-carbon metabolism, were associated with ATRA-induced differentiation in both cell lines. Furthermore, kinase-substrate enrichment analysis predicted altered activities of several kinases during differentiation. Among these, CDK5 exhibited increased activity, while CDK2 displayed reduced activity. The data presented serve as a valuable resource for investigating temporal protein and phosphoprotein abundance changes in SH-SY5Y and IMR-32 cells during ATRA-induced differentiation.

Protein Stability─Analysis of Heat and Cold Denaturation without and with Unfolding Models

Protein stability is important in many areas of life sciences. Thermal protein unfolding is investigated extensively with various spectroscopic techniques. The extraction of thermodynamic properties from these measurements requires the application of models. Differential scanning calorimetry (DSC) is less common, but is unique as it measures directly a thermodynamic property, that is, the heat capacity C_p(T). The analysis of C_p(T) is usually performed with the chemical equilibrium two-state model. This is not necessary and leads to incorrect thermodynamic consequences. Here we demonstrate a straightforward model-independent evaluation of heat capacity experiments in terms of protein unfolding enthalpy ΔH(T), entropy ΔS(T), and free energy ΔG(T)). This now allows the comparison of the experimental thermodynamic data with the predictions of different models. We critically examined the standard chemical equilibrium two-state model, which predicts a positive free energy for the native protein, and diverges distinctly from the experimental temperature profiles. We propose two new models which are equally applicable to spectroscopy and calorimetry. The Θ_U(T)-weighted chemical equilibrium model and the statistical-mechanical two-state model provide excellent fits of the experimental data. They predict sigmoidal temperature profiles for enthalpy and entropy, and a trapezoidal temperature profile for the free energy. This is illustrated with experimental examples for heat and cold denaturation of lysozyme and β-lactoglobulin. We then show that the free energy is not a good criterion to judge protein stability. More useful parameters are discussed, including protein cooperativity. The new parameters are embedded in a well-defined thermodynamic context and are amenable to molecular dynamics calculations.

Is Protein Folding a Thermodynamically Unfavorable, Active, Energy-Dependent Process?

The prevailing current view of protein folding is the thermodynamic hypothesis, under which the native folded conformation of a protein corresponds to the global minimum of Gibbs free energy G. We question this concept and show that the empirical evidence behind the thermodynamic hypothesis of folding is far from strong. Furthermore, physical theory-based approaches to the prediction of protein folds and their folding pathways so far have invariably failed except for some very small proteins, despite decades of intensive theory development and the enormous increase of computer power. The recent spectacular successes in protein structure prediction owe to evolutionary modeling of amino acid sequence substitutions enhanced by deep learning methods, but even these breakthroughs provide no information on the protein folding mechanisms and pathways. We discuss an alternative view of protein folding, under which the native state of most proteins does not occupy the global free energy minimum, but rather, a local minimum on a fluctuating free energy landscape. We further argue that ΔG of folding is likely to be positive for the majority of proteins, which therefore fold into their native conformations only through interactions with the energy-dependent molecular machinery of living cells, in particular, the translation system and chaperones. Accordingly, protein folding should be modeled as it occurs in vivo, that is, as a non-equilibrium, active, energy-dependent process.

About the Protein Space Vastness

An accurate estimation of the Protein Space size, in light of the factors that govern it, is a long-standing problem and of paramount importance in evolutionary biology, since it determines the nature of protein evolvability. A simple analysis will enable us to, firstly, reduce an unrealistic Protein Space size of ~ 10¹³⁰ sequences, for a 100-residues polypeptide chain, to ~ 10⁹ functional proteins and, secondly, estimate a robust average-mutation rate per amino acid (ξ ~ 1.23) and infer from it, in light of the protein marginal stability, that only a fraction of the sequence will be available at any one time for a functional protein to evolve. Although this result does not solve the Protein Space vastness problem frames it in a more rational one and illustrates the impact of the marginal stability on protein evolvability.