On urea and temperature dependences of m-values

Computational Enzyme Redesign Enhances Tolerance to Denaturants for Peptide C-Terminal Amidation

The escalating demand for biocatalysts in pharmaceutical and biochemical applications underscores the critical imperative to enhance enzyme activity and durability under high denaturant concentrations. Nevertheless, the development of a practical computational redesign protocol for improving enzyme tolerance to denaturants is challenging due to the limitations of relying solely on model-driven approaches to adequately capture denaturant-enzyme interactions. In this study, we introduce an enzyme redesign strategy termed GRAPE_DA, which integrates multiple data-driven and model-driven computational methods to mitigate the sampling biases inherent in a single approach and comprehensively predict beneficial mutations on both the protein surface and backbone. To illustrate the methodology's effectiveness, we applied it to engineer a peptidylamidoglycolate lyase, resulting in a variant exhibiting up to a 24-fold increase in peptide C-terminal amidation activity under 2.5 M guanidine hydrochloride. We anticipate that this integrated engineering strategy will facilitate the development of enzymatic peptide synthesis and functionalization under denaturing conditions and highlight the role of engineering surface residues in governing protein stability.

Conformational Stability and Denaturation Processes of Proteins Investigated by Electrophoresis under Extreme Conditions

The functional structure of proteins results from marginally stable folded conformations. Reversible unfolding, irreversible denaturation, and deterioration can be caused by chemical and physical agents due to changes in the physicochemical conditions of pH, ionic strength, temperature, pressure, and electric field or due to the presence of a cosolvent that perturbs the delicate balance between stabilizing and destabilizing interactions and eventually induces chemical modifications. For most proteins, denaturation is a complex process involving transient intermediates in several reversible and eventually irreversible steps. Knowledge of protein stability and denaturation processes is mandatory for the development of enzymes as industrial catalysts, biopharmaceuticals, analytical and medical bioreagents, and safe industrial food. Electrophoresis techniques operating under extreme conditions are convenient tools for analyzing unfolding transitions, trapping transient intermediates, and gaining insight into the mechanisms of denaturation processes. Moreover, quantitative analysis of electrophoretic mobility transition curves allows the estimation of the conformational stability of proteins. These approaches include polyacrylamide gel electrophoresis and capillary zone electrophoresis under cold, heat, and hydrostatic pressure and in the presence of non-ionic denaturing agents or stabilizers such as polyols and heavy water. Lastly, after exposure to extremes of physical conditions, electrophoresis under standard conditions provides information on irreversible processes, slow conformational drifts, and slow renaturation processes. The impressive developments of enzyme technology with multiple applications in fine chemistry, biopharmaceutics, and nanomedicine prompted us to revisit the potentialities of these electrophoretic approaches. This feature review is illustrated with published and unpublished results obtained by the authors on cholinesterases and paraoxonase, two physiologically and toxicologically important enzymes.

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.

Linking thermodynamics and measurements of protein stability

We review the background, theory and general equations for the analysis of equilibrium protein unfolding experiments, focusing on denaturant and heat-induced unfolding. The primary focus is on the thermodynamics of reversible folding/unfolding transitions and the experimental methods that are available for extracting thermodynamic parameters. We highlight the importance of modelling both how the folding equilibrium depends on a perturbing variable such as temperature or denaturant concentration, and the importance of modelling the baselines in the experimental observables.

Global analysis of protein stability by temperature and chemical denaturation

The stability of a protein is a fundamental property that determines under which conditions, the protein is functional. Equilibrium unfolding with denaturants requires preparation of several samples and only provides the free energy of folding when performed at a single temperature. The typical sample requirement is around 0.5-1 mg of protein. If the stability of many proteins or protein variants needs to be determined, substantial protein production may be needed. Here we have determined the stability of acyl-coenzyme A binding protein at pH 5.3 and chymotrypsin inhibitor 2 at pH 3 and pH 6.25 by combined temperature and denaturant unfolding. We used a setup where tryptophan fluorescence is measured in quartz capillaries where only 10 μl is needed. Temperature unfolding of a series of 15 samples at increasing denaturant concentrations provided accurate and precise thermodynamic parameters. We find that the number of samples may be further reduced and less than 10 μg of protein in total are needed for reliable stability measurements. For assessment of stability of protein purified in small scale e.g. in micro plate format, our method will be highly applicable. The routine for fitting the experimental data is made available as a python notebook.

Dissecting the statistical properties of the linear extrapolation method of determining protein stability

The linear extrapolation method to determine protein stability from denaturant-induced unfolding experiments is based on the observation that the free energy of unfolding is often a linear function of the denaturant concentration. The value in the absence of denaturant is then estimated by extrapolation from this linear relationship. Parameters and their confidence intervals are typically estimated by nonlinear least-squares regression. We have compared different methods for calculating confidence intervals and found that a simple method based on linear theory gives accurate results. We have also compared three different parameterizations of the linear extrapolation method and show that the most commonly used form is problematic since the stability and m-value are correlated in the nonlinear least-squares analysis. Parameter correlation can in some cases causes problems in the estimation of confidence intervals and regions and should be avoided when possible. Two alternative parameterizations show much less correlation between parameters.