Thermodynamics and solvent linkage of macromolecule-ligand interactions

Life in Multi-Extreme Environments: Brines, Osmotic and Hydrostatic Pressure─A Physicochemical View

Elucidating the details of the formation, stability, interactions, and reactivity of biomolecular systems under extreme environmental conditions, including high salt concentrations in brines and high osmotic and high hydrostatic pressures, is of fundamental biological, astrobiological, and biotechnological importance. Bacteria and archaea are able to survive in the deep ocean or subsurface of Earth, where pressures of up to 1 kbar are reached. The deep subsurface of Mars may host high concentrations of ions in brines, such as perchlorates, but we know little about how these conditions and the resulting osmotic stress conditions would affect the habitability of such environments for cellular life. We discuss the combined effects of osmotic (salts, organic cosolvents) and hydrostatic pressures on the structure, stability, and reactivity of biomolecular systems, including membranes, proteins, and nucleic acids. To this end, a variety of biophysical techniques have been applied, including calorimetry, UV/vis, FTIR and fluorescence spectroscopy, and neutron and X-ray scattering, in conjunction with high pressure techniques. Knowledge of these effects is essential to our understanding of life exposed to such harsh conditions, and of the physical limits of life in general. Finally, we discuss strategies that not only help us understand the adaptive mechanisms of organisms that thrive in such harsh geological settings but could also have important ramifications in biotechnological and pharmaceutical applications.

Protein Function Analysis through Machine Learning

Machine learning (ML) has been an important arsenal in computational biology used to elucidate protein function for decades. With the recent burgeoning of novel ML methods and applications, new ML approaches have been incorporated into many areas of computational biology dealing with protein function. We examine how ML has been integrated into a wide range of computational models to improve prediction accuracy and gain a better understanding of protein function. The applications discussed are protein structure prediction, protein engineering using sequence modifications to achieve stability and druggability characteristics, molecular docking in terms of protein–ligand binding, including allosteric effects, protein–protein interactions and protein-centric drug discovery. To quantify the mechanisms underlying protein function, a holistic approach that takes structure, flexibility, stability, and dynamics into account is required, as these aspects become inseparable through their interdependence. Another key component of protein function is conformational dynamics, which often manifest as protein kinetics. Computational methods that use ML to generate representative conformational ensembles and quantify differences in conformational ensembles important for function are included in this review. Future opportunities are highlighted for each of these topics.

Binding Thermodynamics and Interaction Patterns of Inhibitor-Major Urinary Protein-I Binding from Extensive Free-Energy Calculations: Benchmarking AMBER Force Fields

Mouse major urinary protein (MUP) plays a key role in the pheromone communication system. The one-end-closed β-barrel of MUP-I forms a small, deep, and hydrophobic central cavity, which could accommodate structurally diverse ligands. Previous computational studies employed old protein force fields and short simulation times to determine the binding thermodynamics or investigated only a small number of structurally similar ligands, which resulted in sampled regions far from the experimental structure, nonconverged sampling outcomes, and limited understanding of the possible interaction patterns that the cavity could produce. In this work, extensive end-point and alchemical free-energy calculations with advanced protein force fields were performed to determine the binding thermodynamics of a series of MUP-inhibitor systems and investigate the inter- and intramolecular interaction patterns. Three series of inhibitors with a total of 14 ligands were simulated. We independently simulated the MUP-inhibitor complexes under two advanced AMBER force fields. Our benchmark test showed that the advanced AMBER force fields including AMBER19SB and AMBER14SB provided better descriptions of the system, and the backbone root-mean-square deviation (RMSD) was significantly lowered compared with previous computational studies with old protein force fields. Surprisingly, although the latest AMBER force field AMBER19SB provided better descriptions of various observables, it neither improved the binding thermodynamics nor lowered the backbone RMSD compared with the previously proposed and widely used AMBER14SB. The older but widely used AMBER14SB actually achieved better performance in the prediction of binding affinities from the alchemical and end-point free-energy calculations. We further analyzed the protein-ligand interaction networks to identify important residues stabilizing the bound structure. Six residues including PHE38, LEU40, PHE90, ALA103, LEU105, and TYR120 were found to contribute the most significant part of protein-ligand interactions, and 10 residues were found to provide favorable interactions stabilizing the bound state. The two AMBER force fields gave extremely similar interaction networks, and the secondary structures also showed similar behavior. Thus, the intra- and intermolecular interaction networks described with the two AMBER force fields are similar. Therefore, AMBER14SB could still be the default option in free-energy calculations to achieve highly accurate binding thermodynamics and interaction patterns.

Effects of Osmolytes on Ligand Binding to Dihydropteroate Synthase from Bacillus anthracis

Osmolyte interactions with ligands can affect their affinity for proteins and are dependent upon the cosolute and the functional groups of the ligand. Here, we explored ligand binding to Bacillus anthracis dihydropteroate synthase (BaDHPS) under osmotic stress conditions. Osmolyte effects were specific to the cosolute and ligand, suggesting interaction of the osmolytes with the free ligands in solution. The association rates of pterin pyrophosphate were mostly unaffected by the osmolytes, except for a 2-fold decrease in the presence of 1 M trehalose, while the dissociation rates decreased in most osmolyte solutions. The viscosity and dielectric constant of the solution did not correlate with the effects of the osmolytes. Experimental results were compared with predicted preferential interaction coefficients (Δμ₂₃/RT) between the osmolytes and ligands. The Δμ₂₃/RT were able to predict the experimental data for most of the osmolytes. Trehalose and proline effects did not correlate with the predicted values, indicating that these two osmolytes may affect binding in more complex ways than simple preferential interactions. Additionally, osmolytes weakly interacted with the sulfa drug sulfathiazole, which altered its affinity for BaDHPS, suggesting that these types of weak interactions can also impact drug binding. As osmolytes affect ligands binding to two different folate cycle enzymes (DHFRs and DHPS), we predicted how ligand binding to other folate cycle enzymes will be altered by the presence of osmolytes.

Role of stable hydrogen isotope variations in water for drug dissolution managing

In the present work, we provide the results of defining by utilizing Laser diffraction spectroscopy, the kinetic isotopic effect of solvent and constant of dissolution rate κ, s−1 of аn active pharmaceutical ingredient (API) in water with a different content of a stable 2 1 H _2^1{\rm{H}} isotope on the basis of the laws of first-order kinetics. This approach is based on the analysis of the light scattering profile that occurs when the particles of the dispersion phase in the aquatic environment are covered with a collimated laser beam. For the first time, the dependence of the rate of dissolution is demonstrated not only on the properties of the pharmaceutical substance itself (water solubility mg/ml, octanol–water partition coefficient log P oct/water, topological polar surface area, Abraham solvation parameters, the lattice type), but also on the properties of the solvent, depending on the content of stable hydrogen isotope. We show that the rate constant of dissolution of a sparingly hydrophobic substance moxifloxacin hydrochloride (MF · HCl) in the Mili-Q water is: k=1.20±0.14∙10−2 s−1 at 293.15 K, while in deuterium depleted water, it is k=4.24±0.4∙10−2 s−1. Consequently, we have established the development of the normal kinetic isotopic effect (kH/kD >1) of the solvent. This effect can be explained both by the positions of the difference in the vibrational energy of zero levels in the initial and transition states, and from the position of water clusters giving volumetric effects of salvation, depending on the ratio D/H. The study of kinetic isotopic effects is a method that gives an indication of the mechanism of reactions and the nature of the transition state. The effect of increasing the dissolution of the API, as a function of the D/H ratio, we have discovered, can be used in the chemical and pharmaceutical industries in the study of API properties and in the drug production through improvement in soluble and pharmacokinetic characteristics.

Molecular Dynamics Gives New Insights into the Glucose Tolerance and Inhibition Mechanisms on β-Glucosidases

β-Glucosidases are enzymes with high importance for many industrial processes, catalyzing the last and limiting step of the conversion of lignocellulosic material into fermentable sugars for biofuel production. However, β-glucosidases are inhibited by high concentrations of the product (glucose), which limits the biofuel production on an industrial scale. For this reason, the structural mechanisms of tolerance to product inhibition have been the target of several studies. In this study, we performed in silico experiments, such as molecular dynamics (MD) simulations, free energy landscape (FEL) estimate, Poisson-Boltzmann surface area (PBSA), and grid inhomogeneous solvation theory (GIST) seeking a better understanding of the glucose tolerance and inhibition mechanisms of a representative GH1 β-glucosidase and a GH3 one. Our results suggest that the hydrophobic residues Y180, W350, and F349, as well the polar one D238 act in a mechanism for glucose releasing, herein called "slingshot mechanism", dependent also on an allosteric channel (AC). In addition, water activity modulation and the protein loop motions suggest that GH1 β-Glucosidases present an active site more adapted to glucose withdrawal than GH3, in consonance with the GH1s lower product inhibition. The results presented here provide directions on the understanding of the molecular mechanisms governing inhibition and tolerance to the product in β-glucosidases and can be useful for the rational design of optimized enzymes for industrial interests.

Effects of Doxorubicin on the Liquid-Liquid Phase Change Properties of Elastin-Like Polypeptides

The lower critical solution temperature (LCST) of the thermo-responsive engineered elastin-like polypeptide (ELP) biopolymer is being exploited for the thermal targeted delivery of doxorubicin (Dox) to solid tumors. We examine the impact of Dox labeling on the thermodynamic and hydrodynamic behavior of an ELP drug carrier and how Dox influences the liquid-liquid phase separation (LLPS). Turbidity, dynamic light scattering (DLS), and differential scanning calorimetry measurements show that ELP undergoes a cooperative liquid-liquid phase separation from a soluble to insoluble coacervated state that is enhanced by Dox labeling. Circular dichroism measurements show that below the LCST ELP consists of both random coils and temperature-dependent β-turn structures. Labeling with Dox further enhances β-turn formation. DLS measurements reveal a significant increase in the hydrodynamic radius of ELP below the LCST consistent with weak self-association. Dox-labeled SynB1-ELP1 (Dox-ELP) has a significant increase in the hydrodynamic radius by DLS measurements that is consistent with stable oligomers and, at high Dox-ELP concentrations, micelle structures. Enhanced association by Dox-ELP is confirmed by sedimentation velocity analytical ultracentrifugation measurements. Both ELP self-association and the ELP inverse phase transition are entropically driven with positive changes in enthalpy and entropy. We show by turbidity and DLS that the ELP phase transition is monophasic, whereas mixtures of ELP and Dox-ELP are biphasic, with Dox-labeled ELP phase changing first and unlabeled ELP partitioning into the coacervate as the temperature is raised. DLS reveals a complex growth in droplet sizes consistent with coalescence and fusion of liquid droplets. Differential scanning calorimetry measurements show a -11 kcal/mol change in enthalpy for Dox-ELP coacervation relative to the unlabeled ELP, consistent with droplet formation being stabilized by favorable enthalpic interactions. We propose that the ELP phase change is initiated by ELP self-association, enhanced by increased Dox-ELP oligomer and micelle formation and stabilized by favorable enthalpic interactions in the liquid droplets.

Overview of the detection methods for equilibrium dissociation constant KD of drug-receptor interaction

Drug-receptor interaction plays an important role in a series of biological effects, such as cell proliferation, immune response, tumor metastasis, and drug delivery. Therefore, the research on drug-receptor interaction is growing rapidly. The equilibrium dissociation constant (KD) is the basic parameter to evaluate the binding property of the drug-receptor. Thus, a variety of analytical methods have been established to determine the KD values, including radioligand binding assay, surface plasmon resonance method, fluorescence energy resonance transfer method, affinity chromatography, and isothermal titration calorimetry. With the invention and innovation of new technology and analysis method, there is a deep exploration and comprehension about drug-receptor interaction. This review discusses the different methods of determining the KD values, and analyzes the applicability and the characteristic of each analytical method. Conclusively, the aim is to provide the guidance for researchers to utilize the most appropriate analytical tool to determine the KD values.

An Overview of Current Methods to Confirm Protein-Protein Interactions

BACKGROUND

The research field of protein-protein interactions is interdisciplinary and specialized field that spans all aspects of biology, physics and chemistry. Therefore, in order to discuss the protein-protein interaction in detail and rigorously, it is desirable to integrate knowledge and methods of many related fields including boundary areas such as biochemistry, biophysics and physical chemistry in addition to biology, physics and chemistry.

OBJECTIVE

The purpose of this review is to overview current methods to confirm protein-protein interactions. Furthermore, I discuss future prospects of methodology based on current status.

RESULTS

It is often necessary to integrate, combine and validate multiple results from various methods to understand protein-protein interactions in detail.

CONCLUSION

It might be desirable for the addition of tags, labeling, and immobilization to solid phases to be unnecessary, and to obtain information on affinity, kinetics, and structure via the analytical method for protein-protein interactions. Therefore, I argue that novel methods based on principles that have already been sufficiently studied in physics or chemistry, but insufficiently applied to the life sciences, should be established to further develop the study of protein-protein interactions.

Hot-Spotting with Thermal Scanning: A Ligand- and Structure-Independent Assessment of Target Ligandability

Evaluating the ligandability of a protein target is a key component when defining hit-finding strategies or when prioritize among drug targets. Computational as well as biophysical approaches based on nuclear magnetic resonance (NMR) fragment screening are powerful approaches but suffer from specific constraints that limit their usage. Here, we demonstrate the applicability of high-throughput thermal scanning (HTTS) as a simple and generic biophysical fragment screening method to reproduce assessments from NMR-based screening. By applying this method to a large set of proteins we can furthermore show that the assessment is predictive of the success of high-throughput screening (HTS). The few divergences for targets of low ligandability originate from the sensitivity differences of the orthogonal biophysical methods. We thus applied a new strategy making use of modulations in the solvent structure to improve assay sensitivity. This novel approach enables improved ligandability assessments in accordance with NMR-based assessments and more importantly positions the methodology as a valuable option for biophysical fragment screening.

Aspects of Weak Interactions between Folate and Glycine Betaine

Folate, or vitamin B9, is an important compound in one-carbon metabolism. Previous studies have found weaker binding of dihydrofolate to dihydrofolate reductase in the presence of osmolytes. In other words, osmolytes are more difficult to remove from the dihydrofolate solvation shell than water; this shifts the equilibrium toward the free ligand and protein species. This study uses vapor-pressure osmometry to explore the interaction of folate with the model osmolyte, glycine betaine. This method yields a preferential interaction potential (μ23/RT value). This value is concentration-dependent as folate dimerizes. The μ23/RT value also tracks the deprotonation of folate's N3-O4 keto-enol group, yielding a pKa of 8.1. To determine which folate atoms interact most strongly with betaine, the interaction of heterocyclic aromatic compounds (as well as other small molecules) with betaine was monitored. Using an accessible surface area approach coupled with osmometry measurements, deconvolution of the μ23/RT values into α values for atom types was achieved. This allows prediction of μ23/RT values for larger molecules such as folate. Molecular dynamics simulations of folate show a variety of structures from extended to L-shaped. These conformers possess μ23/RT values from -0.18 to 0.09 m-1, where a negative value indicates a preference for solvation by betaine and a positive value indicates a preference for water. This range of values is consistent with values observed in osmometry and solubility experiments. As the average predicted folate μ23/RT value is near zero, this indicates folate interacts almost equally well with betaine and water. Specifically, the glutamate tail prefers to interact with water, while the aromatic rings prefer betaine. In general, the more protonated species in our small molecule survey interact better with betaine as they provide a source of hydrogens (betaine is not a hydrogen bond donor). Upon deprotonation of the small molecule, the preference swings toward water interaction because of its hydrogen bond donating capacities.