Linking thermodynamics and measurements of protein stability

Role of electrostatics in cold adaptation: A comparative study of eury- and stenopsychrophilic triose phosphate isomerase

Psychrophilic (cold-active) organisms have developed enzymes that facilitate sufficient metabolic activity at low temperatures to sustain life. This occurs through molecular adaptations that tend to increase protein flexibility at the expense of stability. However, psychrophiles also vary in their growth conditions. Eurypsychrophiles thrive over a wide temperature range and often prefer temperatures above 20 °C, while stenopsychrophiles grow optimally below 15 °C and are more narrowly adapted to cold temperatures. To elucidate differences between these two classes of enzymes, we here compare the stability and unfolding kinetics of two orthologues of the basal household enzyme triose phosphate isomerase, one from the stenopsychrophilic Antarctic permafrost bacterium Rhodonellum psychrophilum (sTPI) and the other from the eurypsychrophilic Greenland ikaite column bacterium Rhodococcus sp. JG-3 (eTPI). Remarkably, sTPI proved significantly more thermostable and resistant to chemical denaturation than its eurypsychrophilic counterpart, eTPI, in the absence of ionic components in solution, whereas inclusion of electrostatic screening agents in the form of sodium chloride or the charged denaturant guanidinium chloride largely cancelled out this difference. Thus, electrostatics play a prominent role in stabilizing the stenopsychrophilic sTPI, and a mandatory low-temperature growth environment does not preclude the development of considerable thermotolerance for individual enzymes. We were able to increase the thermostability of sTPI using an evolutionary machine learning model, which transferred several sTPI residues into the eTPI active site. While the stabilizing effect was modest, the combination of individual mutations was additive, underscoring the potential of combining multiple beneficial mutations to achieve enhanced enzyme properties.

An Active-Site Bro̷nsted Acid-Base Catalyst Destabilizes Mandelate Racemase and Related Subgroup Enzymes: Implications for Catalysis

Enzymes of the enolase superfamily (ENS) are mechanistically diverse, yet share a common partial reaction, i.e., the metal-assisted, Bro̷nsted base-catalyzed abstraction of the α-proton from a carboxylate substrate to form an enol(ate) intermediate. Although the catalytic machinery responsible for the initial deprotonation reaction has been conserved, divergent evolution has led to numerous ENS members that catalyze different overall reactions. Using differential scanning calorimetry, we examined the contribution of the Bro̷nsted acid-base catalysts to the thermostability (Tm) of four members of the mandelate racemase (MR)-subgroup of the ENS: MR, d-tartrate dehydratase, l-talarate/galactarate dehydratase, and l-fuconate dehydratase. Each enzyme contains an active-site Lys (part of a KxK motif) and His, which act as Bro̷nsted acid-base catalysts. The KxK → KxM substitutions increased the thermostability in all four enzymes with the effect being most prominent for MR (ΔTm = +8.6 °C). The KxK → MxK substitutions decreased the thermostability in all four enzymes, and the His → Asn substitution had a significant stabilizing effect only on MR. Thus, the active sites of MR-subgroup enzymes are destabilized by the Lys Bro̷nsted acid-base catalyst, suggesting that the destabilization energy may be used to drive a conformational change of the enzyme to yield a catalytically competent protonation state upon substrate binding.

Comprehensive Physicochemical Characterization and in Vitro Human Cell Culture Studies of an Innovative Biocompatible and Biodegradable Silk-Derived Protein Hydrolysate, SDP-4

SDP-4 is a soluble silk fibroin-derived protein hydrolysate extracted from the Bombyx mori silkworm cocoon and is a novel first-in-class biopolymer that is biodegradable, biocompatible, and shown to have regenerative properties. SDP-4 is currently used as a commercial wetting agent in topical eye drops, but it has also been shown to have anti-inflammatory properties that could be utilized in other biomedical applications. The purpose of this study was to comprehensively characterize the physicochemical properties that are necessary to design formulations and examine cell viability in response to varying doses of SDP-4 on different human cell types, with a particular attention toward respiratory applications. Lyophilized SDP-4 powder was characterized by scanning electron microscopy (SEM), energy-dispersive X-ray (EDX) spectroscopy, differential scanning calorimetry (DSC), hot-stage microscopy (HSM), Karl Fisher (KF) coulometric titration, Raman spectrometry, confocal Raman microscopy (CRM), and Fourier transform infrared microscopy. The lyophilized powder exhibited a nonuniform, angular glassy flake morphology with uniform chemical composition and minimal moisture uptake when tested under varying humidity conditions. Crystalline character was evident through birefringence at ambient temperature which changed during phase transitions, as evidenced through qualitative and quantitative assessments. Dose ranging SDP-4 biocompatibility studies on different human lung cells, nasal cells, skin cells, and brain cells was assessed by the in vitro cell viability assay. Assay results showed that cell viability was maintained at the various doses studied for different human cell types. The transepithelial resistance (TEER) assay showed that SDP-4 leads to transient fluctuations in cell membrane integrity and barrier tightness, followed by a recovery phase as cells adapt or repair the junctions. These findings demonstrate that SDP-4 is biocompatible with different types of human cells and safe at all of the doses studied. The unique physicochemical properties of SDP-4 revealed in this study demonstrate its favorable formulating ability for a variety of potential therapeutic applications.

Predicting absolute protein folding stability using generative models

While there has been substantial progress in our ability to predict changes in protein stability due to amino acid substitutions, progress has been slower in methods to predict the absolute stability of a protein. Here, we show how a generative model for protein sequence can be leveraged to predict absolute protein stability. We benchmark our predictions across a broad set of proteins and find a mean error of 1.5 kcal/mol and a correlation coefficient of 0.7 for the absolute stability across a range of natural, small- to medium-sized proteins up to ca. 150 amino acid residues. We analyze current limitations and future directions including how such a model may be useful for predicting conformational free energies. Our approach is simple to use and freely available at an online implementation available via https://github.com/KULL-Centre/_2024_cagiada_stability.

HERMES: Holographic Equivariant neuRal network model for Mutational Effect and Stability prediction

Predicting the stability and fitness effects of amino-acid mutations in proteins is a cornerstone of biological discovery and engineering. Various experimental techniques have been developed to measure mutational effects, providing us with extensive datasets across a diverse range of proteins. By training on these data, machine learning approaches have advanced significantly in predicting mutational effects. Here, we introduce HERMES, a 3D rotationally equivariant structure-based neural network model for mutation effect prediction. Pre-trained to predict amino-acid propensities from their surrounding 3D structure atomic environments, HERMES can be efficiently fine-tuned to predict mutational effects, thanks to its symmetry-aware parameterization of the output space. Benchmarking against other models demonstrates that HERMES often outperforms or matches their performance in predicting mutation effects on stability, binding, and fitness, using either computationally or experimentally resolved protein structures. HERMES offers a versatile suit of tools for evaluating mutation effects and can be easily fine-tuned for specific predictive objectives using our open-source code.

Rapid protein stability prediction using deep learning representations

Predicting the thermodynamic stability of proteins is a common and widely used step in protein engineering, and when elucidating the molecular mechanisms behind evolution and disease. Here, we present RaSP, a method for making rapid and accurate predictions of changes in protein stability by leveraging deep learning representations. RaSP performs on-par with biophysics-based methods and enables saturation mutagenesis stability predictions in less than a second per residue. We use RaSP to calculate ∼ 230 million stability changes for nearly all single amino acid changes in the human proteome, and examine variants observed in the human population. We find that variants that are common in the population are substantially depleted for severe destabilization, and that there are substantial differences between benign and pathogenic variants, highlighting the role of protein stability in genetic diseases. RaSP is freely available-including via a Web interface-and enables large-scale analyses of stability in experimental and predicted protein structures.

Global Analysis of Multi-Mutants to Improve Protein Function

The identification of amino acid substitutions that both enhance the stability and function of a protein is a key challenge in protein engineering. Technological advances have enabled assaying thousands of protein variants in a single high-throughput experiment, and more recent studies use such data in protein engineering. We present a Global Multi-Mutant Analysis (GMMA) that exploits the presence of multiply-substituted variants to identify individual amino acid substitutions that are beneficial for the stability and function across a large library of protein variants. We have applied GMMA to a previously published experiment reporting on >54,000 variants of green fluorescent protein (GFP), each with known fluorescence output, and each carrying 1-15 amino acid substitutions (Sarkisyan et al., 2016). The GMMA method achieves a good fit to this dataset while being analytically transparent. We show experimentally that the six top-ranking substitutions progressively enhance GFP. More broadly, using only a single experiment as input our analysis recovers nearly all the substitutions previously reported to be beneficial for GFP folding and function. In conclusion, we suggest that large libraries of multiply-substituted variants may provide a unique source of information for protein engineering.

Slow conformational changes in the rigid and highly stable chymotrypsin inhibitor 2

Slow conformational changes are often directly linked to protein function. It is however less clear how such processes may perturb the overall folding stability of a protein. We previously found that the stabilizing double mutant L49I/I57V in the small protein chymotrypsin inhibitor 2 from barley led to distributed increased nanosecond and faster dynamics. Here we asked what effects the L49I and I57V substitutions, either individually or together, have on the slow conformational dynamics of CI2. We used ¹⁵ N CPMG spin relaxation dispersion experiments to measure the kinetics, thermodynamics and structural changes associated with slow conformational change in CI2. These changes result in an excited state that is populated to 4.3% at 1 °C. As the temperature is increased the population of the excited state decreases. Structural changes in the excited state are associated with residues that interact with water molecules that have well defined positions and are found at these positions in all crystal structures of CI2. The substitutions in CI2 have only little effect on the structure of the excited state whereas the stability of the excited state to some extent follows the stability of the main state. The minor state is thus most populated for the most stable CI2 variant and least populated for the least stable variant. We hypothesize that the interactions between the substituted residues and the well-ordered water molecules links subtle structural changes around the substituted residues to the region in the protein that experience slow conformational changes. This article is protected by copyright. All rights reserved.

Protein stability [determination] problems

Human health depends on the correct folding of proteins, for misfolding and aggregation lead to diseases. An unfolded (denatured) protein can refold to its original folded state. How does this occur is known as the protein folding problem. One of several related questions to this problem is that how much more stable is the folded state than the unfolded state. There are several measures of protein stability. In this article, protein stability is given a thermodynamic definition and is measured by Gibbs free energy change ( Δ G D 0 ) associated with the equilibrium, native (N) conformation ↔ denatured (D) conformation under the physiological condition usually taken as dilute buffer (or water) at 25 °C. We show that this thermodynamic quantity ( Δ G D 0 ), where subscript D represents transition between N and D states, and superscript 0 (zero) represents the fact that the transition occurs in the absence of denaturant, can be neither measured nor predicted under physiological conditions. However, Δ G D can be measured in the presence of strong chemical denaturants such as guanidinium chloride and urea which are shown to destroy all noncovalent interactions responsible for maintaining the folded structure. A problem with this measurement is that the estimate of Δ G D 0 comes from the analysis of the plot of Δ G D versus denaturant concentration, which requires a long extrapolation of values of Δ G D , and all the three methods of extrapolation give three different values of Δ G D 0 for a protein. Thus, our confidence in the authentic value of Δ G D 0 is eroded. Another problem with this in vitro measurement of Δ G D 0 is that it is done on the pure protein sample in dilute buffer which is a very large extrapolation of the in vivo conditions, for the crowding effect on protein stability is ignored.

Denaturation of proteins: electrostatic effects vs. hydration

The unfolding transition of proteins in aqueous solution containing various salts or uncharged solutes is a classical subject of biophysics. In many cases, this transition is a well-defined two-stage equilibrium process which can be described by a free energy of transition ΔG u and a transition temperature T m. For a long time, it has been known that solutes can change T m profoundly. Here we present a phenomenological model that describes the change of T m with the solute concentration c s in terms of two effects: (i) the change of the number of correlated counterions Δn ci and (ii) the change of hydration expressed through the parameter Δw and its dependence on temperature expressed through the parameter dΔc p/dc s. Proteins always carry charges and Δn ci describes the uptake or release of counterions during the transition. Likewise, the parameter Δw measures the uptake or release of water during the transition. The transition takes place in a reservoir with a given salt concentration c s that defines also the activity of water. The parameter Δn ci is a measure for the gain or loss of free energy because of the release or uptake of ions and is related to purely entropic effects that scale with ln c s. Δw describes the effect on ΔG u through the loss or uptake of water molecules and contains enthalpic as well as entropic effects that scale with c s. It is related to the enthalpy of transition ΔH u through a Maxwell relation: the dependence of ΔH u on c s is proportional to the dependence of Δw on temperature. While ionic effects embodied in Δn ci are independent of the kind of salt, the hydration effects described through Δw are directly related to Hofmeister effects of the various salt ions. A comparison with literature data underscores the general validity of the model.

Structural and functional adaptation in extremophilic microbial α-amylases

Maintaining stable native conformation of a protein under a given ecological condition is the prerequisite for survival of organisms. Extremophilic bacteria and archaea have evolved to adapt under extreme conditions of temperature, pH, salt, and pressure. Molecular adaptations of proteins under these conditions are essential for their survival. These organisms have the capability to maintain stable, native conformations of proteins under extreme conditions. The enzymes produced by the extremophiles are also known as extremozyme, which are used in several industries. Stability and functionality of extremozymes under varying temperature, pH, and solvent conditions are the most desirable requirement of industry. α-Amylase is one of the most important enzymes used in food, pharmaceutical, textile, and detergent industries. This enzyme is produced by diverse microorganisms including various extremophiles. Therefore, understanding its stability is important from fundamental as well as an applied point of view. Each class of extremophiles has a distinctive set of dominant non-covalent interactions which are important for their stability. Static information obtained by comparative analysis of amino acid sequence and atomic resolution structure provides information on the prevalence of particular amino acids or a group of non-covalent interactions. Protein folding studies give the information about thermodynamic and kinetic stability in order to understand dynamic aspect of molecular adaptations. In this review, we have summarized information on amino acid sequence, structure, stability, and adaptability of α-amylases from different classes of extremophiles.

Transient exposure of a buried phosphorylation site in an autoinhibited protein

Autoinhibition is a mechanism used to regulate protein function, often by making functional sites inaccessible through the interaction with a cis-acting inhibitory domain. Such autoinhibitory domains often display a substantial degree of structural disorder when unbound, and only become structured in the inhibited state. These conformational dynamics make it difficult to study the structural origin of regulation, including effects of regulatory post-translational modifications. Here, we study the autoinhibition of the Dbl Homology domain in the protein Vav1 by the so-called acidic inhibitory domain. We use molecular simulations to study the process by which a mostly unstructured inhibitory domain folds upon binding and how transient exposure of a key buried tyrosine residue makes it accessible for phosphorylation. We show that the inhibitory domain, which forms a helix in the bound and inhibited stated, samples helical structures already before binding and that binding occurs via a molten-globule-like intermediate state. Together, our results shed light on key interactions that enable the inhibitory domain to sample a finely tuned equilibrium between an inhibited and a kinase-accessible state.