Johan Åqvist

The transcription factor IdeR regulates iron homeostasis in many gram-positive bacteria. It primarily functions as a repressor of iron uptake genes by binding to their promoters and blocking RNA polymerase access. IdeR recognizes a highly conserved DNA sequence consisting of four palindromic 5’-C ₁ C ₂ T ₃ A ₄ A ₅ repeats. For target site recognition, IdeR requires a thymine in position 3 of the repeat and at least one cytosine in the first two positions, while the identity of the bases in positions 4 and 5 is less important. We have previously shown that IdeR recognizes its target sequences through indirect or shape readout, rather than direct interactions with the bases. However, the mechanism of indirect recognition is unknown. Here we employed molecular dynamics simulations, X-ray crystallography, and DNA-binding assays to investigate the thermodynamic principles underlying accurate pyrimidine recognition by IdeR. We found that the recognition helix of IdeR forms a differential dielectric cleft that accommodates the C-H edge of the 5’-C ₁ C ₂ T ₃ sequence, with a polar pocket for the first two bases and a non-polar pocket for the third. The local dielectric environment of IdeR applies strain to non-cognate DNA, leading to non-productive binding and thereby ensuring the fidelity of target DNA reading. GRAPHICAL Alt text: Graphical abstract illustrating IdeR recognition of cognate DNA through a differential dielectric environment, with polar and non-polar pockets accommodating C ₁ C ₂ T ₃ , while non-cognate sequences incur energetic penalties and binding strain.

There is considerable interest in being able to address the temperature dependence of enzyme reactions by computer simulations. One reason for this is that enzymes that are adapted to different temperature regimes generally show distinct signatures in terms of their activation enthalpies and entropies, to the extent that it is basically possible to predict whether an enzyme is psychrophilic or mesophilic just by examining these activation parameters. The standard approach to this problem is to calculate reaction free energy profiles at a series of different temperatures. Computational Arrhenius plots can then be constructed from the data, analogous to the experimental procedure. This method has been shown to work well in a number of cases that have examined orthologous pairs of psychrophilic and mesophilic enzymes. The drawback is that the simulations have to be repeated at several temperatures. However, while multitemperature simulations may be computationally demanding they can be informative in revealing deviations from linear Arrhenius behavior. Another issue is that the calculated activation enthalpies obtained in this way cannot be readily decomposed into different energy terms. An alternative approach to the problem would be to just carry out free energy simulations at a single temperature and instead obtain the enthalpy profile by plain averaging of the total energy. The entropy term would then simply be calculated as the difference between free energy and enthalpy. This averaging approach was earlier considered unreliable due to convergence problems for the total energy, even for moderately sized systems. Here, we re-examine the performance of the averaging method for two solution reactions and one enzyme reaction and conclude that it works surprisingly well with sufficient data. This opens up new ways of analyzing nonlinearity of Arrhenius plots in terms of energetics, since the enthalpy is decomposable.

Cold‐active enzymes exhibit high catalytic activity at low temperatures and an anomalous temperature optimum occurring before the onset of protein unfolding. This study investigates this peculiar property by comparing the functional and structural features of a cold‐active glycoside hydrolase family 1 enzyme (M‐GH1) with those of its mesophilic counterpart (Pp‐GH1). Structural analysis and computational simulations reveal that the thermal profile of M‐GH1 is due to a combination of local unfolding and weakened enzyme‐substrate interactions. This behavior is attributed to the absence of an amino acid triad comprising M326‐W412‐F418, which stabilizes the loops surrounding the active site in the mesophilic enzyme. Introducing this triad into M‐GH1 through rational mutagenesis yielded a more thermostable variant, whereas reciprocal mutations in Pp‐GH1 resulted in a slight decrease of both the optimal temperature of catalysis and thermal stability. Phylogenetic analyses coupled with computational simulations suggest that this flexibility modulation mechanism is not universally conserved across the GH1 family but rather represents a targeted strategy for regulating the dynamics of the catalytic region. Overall, these findings identify a key structural determinant of temperature adaptation within the GH1 family.

Predicting how chemical modifications affect drug binding is central to rational drug design. Free energy perturbation (FEP) calculations provide accurate estimates of these binding affinity changes, but existing methods often require substantial computational resources and expert knowledge. Here, we present QligFEP v2.1.0, a flexible open-source workflow based on a graphical and command-line interface for calculating relative binding free energies using spherical boundary conditions, which dramatically reduces simulation system size by confining simulations to a focused region around the binding site. QligFEP features a configurable restraint algorithm that automatically handles diverse chemical transformations, streamlined setup procedures, and enhanced analysis tools. We validated the method using industry benchmarks comprising 16 protein targets and 639 ligand transformations. Statistical analysis demonstrates that QligFEP achieves comparable accuracy to established commercial and open-source alternatives while requiring only a fraction of the computational resources. The perturbation protocol simulates ∼6250 atoms per perturbation leg and completes transformation replicates in under 2 h on standard computational clusters. Unlike full-system simulations, QligFEP's modest computational requirements make FEP accessible for less than $1 on current AWS spot instances. The combination of accuracy, flexibility, and computational efficiency positions QligFEP as a practical solution for accelerating compound optimization in drug discovery, making rigorous binding affinity predictions accessible for large scale applications and to research groups with limited computational infrastructure.

Predicting how chemical modifications affect drug binding is central to rational drug design. Free Energy Perturbation (FEP) calculations provide accurate estimates of these binding affinity changes, but existing methods often require substantial computational resources and expert knowledge. Here we present QligFEP v2.1.0, an automated open-source platform for calculating relative binding free energies using spherical boundary conditions (SBC), which dramatically reduces computational cost by confining simulations to a focused region around the binding site. QligFEP features a configurable restraint algorithm that automatically handles diverse chemical transformations, streamlined setup procedures, and enhanced analysis tools. We validated the method using industry benchmarks comprising 16 protein targets and 639 ligand transformations. Statistical analysis demonstrates that QligFEP achieves comparable accuracy to established commercial and open-source alternatives, while requiring only a fraction of the computational resources. The perturbation protocol simulates ~6250 atoms per perturbation leg and completes transformation replicates in under 2 hours on standard computational clusters, which is substantially faster than traditional full-system simulations, and currently would cost less than $1 on current AWS spot instances. The combination of accuracy, automation, and computational efficiency positions QligFEP as a practical solution for accelerating compound optimization in drug discovery, making rigorous binding affinity predictions accessible for large scale applications and to research groups with limited computational infrastructure.

Parkinson's disease is a debilitating neurodegenerative disorder currently affecting ten million people worldwide. l-DOPA, or levodopa, is a crucial drug in addressing this issue, being a precursor of dopamine, a neurotransmitter which regulates motor functions, relieving the tremor symptom of Parkinson's. However, l-DOPA comes with side effects that are concerning for long-term treatment. Like dopamine, which can autoxidize to dopaquinone by entering a redox cycle, l-DOPA can also be converted to dopaquinone by the same mechanism, thus becoming a continuous source of hydrogen peroxide. Furthermore, because it is structurally similar to the amino acid tyrosine, it can also get incorporated into the proteins' sequence, thus becoming an additional source of oxidative stress for patients undergoing l-DOPA treatment. The rate-limiting step in the process of l-DOPA autoxidation is water protolysis, which yields hydroxide and hydronium ions. A similar rate-limiting step was observed in carbonic anhydrase II. In addition, the mechanism by which a hydroxide ion is transferred from bulk water was also considered. The next step, involving a nucleophilic attack by a hydroxide ion on a neutral amino group, along with cyclization, is not rate limiting. Using the Empirical Valence Bond (EVB) method, we computed the free-energy profiles for the reaction of l-DOPA incorporated into MAO A, replacing Tyr407. The calculated barrier of 33.93 kcal mol^-1 is approximately 6 kcal mol^-1 higher than the experimental barrier of 27.55 kcal mol^-1 for l-DOPA in aqueous solution. The findings from our previous study of l-DOPA autoxidation in aqueous solution are critically discussed in the context of the rate-limiting step. The slow autoxidation kinetics of l-DOPA-containing proteins suggest that the main pathway through which l-DOPA induces oxidative stress is likely either the autoxidation of l-DOPA in aqueous solution or its decarboxylation, followed by dopamine autoxidation. However, a significant source of l-DOPA-induced oxidative stress may be zinc- and calcium-dependent proteins present in the central nervous system.

In this review we discuss the development of methodology for calculating the temperature dependence and thermodynamic activation parameters for chemical reactions in solution and in enzymes, from computer simulations. We outline how this is done by combining the empirical valence bond method with molecular dynamics free energy simulations. In favorable cases it turns out that such simulations can even capture temperature optima for the catalytic rate. The approach turns out be very useful both for addressing questions regarding the roles of enthalpic and entropic effects in specific enzymes and also for attacking evolutionary problems regarding enzyme adaptation to different temperature regimes. In the latter case, we focus on cold-adaptation of enzymes from psychrophilic species and show how computer simulations have revealed the basic mechanisms behind such adaptation. Understanding these mechanisms also opens up the possibility of designing the temperature dependence, and we highlight a recent example of this.

Heat capacity effects in protein-ligand binding as measured by calorimetric experiments have recently attracted considerable attention, particularly in the field of enzyme inhibitor design. A significant negative heat capacity change upon ligand binding implies a marked temperature dependence of the binding enthalpy, which is of high relevance for attempts to optimize protein-ligand interactions. In this work, we address the question of how well such heat capacity changes can be predicted by computer simulations. We examine a series of human thrombin inhibitors that all bind with ΔC_p values of about -0.4 kcal/mol/K and calculate heat capacity changes from plain molecular dynamics simulations of the bound and free states of the enzyme and ligand. The results show that accurate ΔC_p estimates within a few tenths of a kcal/mol/K of the experimental values can be obtained with this approach. This allows us to address the structural and energetic origin of the negative heat capacity changes for the thrombin inhibitors, and it is found that conformational equilibria of the free ligands in solution make a major contribution to the observed effect.

Computer simulations of the temperature dependence of enzyme reactions using the empirical valence bond (EVB) method have proven to give very accurate results in terms of the thermodynamic activation parameters. Here, we analyze the reasons for why such simulations are able to correctly capture activation enthalpies and entropies and how sensitive these quantities are to parametrization of the reactive potential energy function. We examine first the solution reference reaction for the enzyme ketosteroid isomerase, which corresponds to the acetate catalyzed deprotonation of the steroid in water. The experimentally determined activation parameters for this reaction turn out to be remarkably well reproduced by the calculations. By modifying the EVB potential so that the activation and reaction free energies become significantly shifted, we show that the activation entropy is basically invariant to such changes and that Delta S-double dagger is instead determined by the specific mixture of the underlying force fields in the transition state region. The coefficients of this mixture do not change appreciably when the EVB potential is modified within reasonable limits, and hence, the estimate of Delta S double dagger becomes very robust. This is further verified by examining a more complex concerted hydride and proton transfer reaction in the enzyme hydroxybutyrate dehydrogenase.