Impact of Urea on Monoclonal Antibodies: Multiple Destabilization and Aggregation Effects for Therapeutic Immunoglobulin G Proteins.

We performed nano differential scanning fluorimetry (nanoDSF) measurements of immunoglobulin G (IgG) in urea gradient solutions under thermal unfolding. Our results show that the denaturing effect of urea on individual IgG domains can be monitored via a linear mapping of thermal shift curves to the corresponding urea concentrations. Assignment of IgG domains to each thermal shift curve allows for a reliable differentiation of the underlying mechanisms. Further results show a decisive influence of salt-induced electrostatic screening effects. We are able to explain all findings by preferential binding mechanisms in combination with electrostatic effects. The results of our study shed more light on the complex interaction mechanisms between buffer solutions and complex proteins, which are important for improving the shelf life of protein therapeutic formulation.

Scientific Deep Machine Learning Concepts for the Prediction of Concentration Profiles and Chemical Reaction Kinetics: Consideration of Reaction Conditions.

Emerging concepts from scientific deep machine learning such as physics-informed neural networks (PINNs) enable a data-driven approach for the study of complex kinetic problems. We present an extended framework that combines the advantages of PINNs with the detailed consideration of experimental parameter variations for the simulation and prediction of chemical reaction kinetics. The approach is based on truncated Taylor series expansions for the underlying fundamental equations, whereby the external variations can be interpreted as perturbations of the kinetic parameters. Accordingly, our method allows for an efficient consideration of experimental parameter settings and their influence on the concentration profiles and reaction kinetics. A particular advantage of our approach, in addition to the consideration of univariate and multivariate parameter variations, is the robust model-based exploration of the parameter space to determine optimal reaction conditions in combination with advanced reaction insights. The benefits of this concept are demonstrated for higher-order chemical reactions including catalytic and oscillatory systems in combination with small amounts of training data. All predicted values show a high level of accuracy, demonstrating the broad applicability and flexibility of our approach.

Insights into Hildebrand Solubility Parameters - Contributions from Cohesive Energies or Electrophilicity Densities?

We introduce certain concepts and expressions from conceptual density functional theory (DFT) to study the properties of the Hildebrand solubility parameter. The original form of the Hildebrand solubility parameter is used to qualitatively estimate solubilities for various apolar and aprotic substances and solvents and is based on the square root of the cohesive energy density. Our results show that a revised expression allows the replacement of cohesive energy densities by electrophilicity densities, which are numerically accessible by simple DFT calculations. As an extension, the reformulated expression provides a deeper interpretation of the main contributions and, in particular, emphasizes the importance of charge transfer mechanisms. All calculated values of the Hildebrand parameters for a large number of common solvents are compared with experimental values and show good agreement for non- or moderately polar aprotic solvents in agreement with the original formulation of the Hildebrand solubility parameters. The observed deviations for more polar and protic solvents define robust limits from the original formulation which remain valid. Likewise, we show that the use of machine learning methods leads to only slightly better predictability.

Unraveling the Impact of Acetylation Patterns in Chitosan Oligomers on Cu2+ Ion Binding: Insights from DFT Calculations.

Chitosans are partially acetylated polymers of glucosamine, structurally characterized by their degree of polymerization as well as their fraction and pattern of acetylation. These parameters strongly influence the physico-chemical properties and biological activities of chitosans, but structure-function relationships are only poorly understood. As an example, we here investigated the influence of acetylation on chitosan-copper complexation using density functional theory. We investigated the electronic structures of completely deacetylated and partially acetylated chitosan oligomers and their copper-bound complexes. Frontier molecular orbital theory revealed bonding orbitals for electrophiles and antibonding orbitals for nucleophiles in fully deacetylated glucosamine oligomers, while partially acetylated oligomers displayed bonding orbitals for both electrophiles and nucleophiles. Our calculations showed that the presence of an acetylated subunit in a chitosan oligomer affects the structural and the electronic properties of the oligomer by generating new intramolecular interactions with the free amino group of neighboring deacetylated subunits, thereby influencing its polarity. Furthermore, the band gap energy calculated from the fully and partially deacetylated oligomers indicates that the mobility of electrons in partially acetylated chitosan oligomers is higher than in fully deacetylated oligomers. In addition, fully deacetylated oligomers form more stable complexes with higher bond dissociation energies with copper than partially acetylated ones. Interestingly, in partially acetylated oligomers, the strength of copper binding was found to be dependent on the pattern of acetylation. Our study provides first insight into the influence of patterns of acetylation on the electronic and ion binding properties of chitosans. Depending on the intended application, the obtained results can serve as a guide for the selection of the optimal chitosan for a specific purpose.

Quantitative Solvation Energies from Gas-Phase Calculations: First-Principles Charge Transfer and Perturbation Approaches.

We present a first-principles approach for the calculation of solvation energies and enthalpies with respect to different ion pair combinations in various solvents. The method relies on the conceptual density functional theory (DFT) of solvation, from which detailed expressions for the solvation energies can be derived. In addition to fast and straightforward gas phase calculations, we also study the influence of modified chemical reactivity descriptors in terms of electronic perturbations. The corresponding phenomenological changes in molecular energy levels can be interpreted as the influence of continuum solvents. Our approach shows that the introduction of these modified expressions is essential for a quantitative agreement between the calculated and the experimental results.

Combination of explainable machine learning and conceptual density functional theory: applications for the study of key solvation mechanisms.

We present explainable machine learning approaches for the accurate prediction and understanding of solvation free energies, enthalpies, and entropies for different salts in various protic and aprotic solvents. As key input features, we use fundamental contributions from the conceptual density functional theory (DFT) of solutions. The most accurate models with the highest prediction accuracy for the experimental validation data set are decision tree-based approaches such as extreme gradient boosting and extra trees, which highlight the non-linear influence of feature values on target predictions. The detailed assessment of the importance of features in terms of Gini importance criteria as well as Shapley Additive Explanations (SHAP) and permutation and reduction approaches underlines the prominent role of anion and cation solvation effects in combination with fundamental electronic properties of the solvents. These results are reasonably consistent with previous assumptions and provide a solid rationale for more recent theoretical approaches.

Application of Fundamental Chemical Principles for Solvation Effects: A Unified Perspective for Interaction Patterns in Solution.

We demonstrate the utility of basic chemical principles like the "|Δµ| big is good" (DMB) rule for the study of solvation interactions between distinct solutes such as ions and solvents. The corresponding approach allows us to define relevant criteria for maximum solvation energies of ion pairs in different solvents in terms of electronegativities and chemical hardnesses. Our findings reveal that the DMB principle culminates into the strong and weak acids and bases concept as recently derived for specific ion effects in various solvents. The further application of the DMB approach highlights a similar condition for the chemical hardnesses with a reminiscence to the hard/soft acids and bases principle. Comparable conclusions can also be drawn with regard to the change of the solvent. We show that favorable solvent interactions are mainly driven by low chemical hardnesses as well as high electronegativity differences between the ions and the solvent. Our findings highlight that solvation interactions are governed by basic chemical principles, which demonstrates the close similarity between solvation mechanisms and chemical reactions.

Electronic properties of amino acids and nucleobases: similarity classes and pairing principles from chemical reactivity indices.

We present a new classification scheme for amino acids and nucleobases based on the electronic properties of the individual molecules. Using chemical reactivity indices such as electronegativity, electrophilicity, and chemical hardness, we can identify similarities and differences between each class of amino acids and nucleobases. Notable differences emerge in particular with regard to high, neutral or low electronegativity as well as different combinations of chemical hardness. Our approach allows us to relate these insights to the properties of the side groups in terms of a unique reference scheme. We further show that hydrophobic differences between amino acids are rather negligible in the context of electronic properties. Our classification scheme also rationalizes the occurrence of distinct stable nucleobase pairs and clearly emphasizes certain differences between individual molecules. The stability and abundant occurrence of Watson-Crick nucleobase pairs is further discussed in the context of the minimum electrophilicity principle.

Probing the distribution of ionic liquid mixtures at charged and neutral interfaces via simulations and lattice-gas theory.

Room temperature ionic liquid solutions confined between neutral and charged surfaces are investigated by means of atomistic Molecular Dynamics simulations. We study 1-ethyl-3-methylimidazolium dicyanamide ([EMIm]+[DCA]-) in water or dimethylsulfoxide (DMSO) mixtures in confinement between two interfaces. The analysis is based on the comparison of the molecular species involved and the charged state of the surfaces. Focus is given on the influence of different water/DMSO concentrations on the microstructuring and accumulation of each species. Thermodynamic aspects, such as the entropic contributions in the observed trends are obtained from the simulations using a lattice-gas theory. The results clearly underline the differences in these properties for the water and DMSO mixtures and unravel the underlying mechanisms and inherent details. We were able to pinpoint the importance of the size and the relative permittivity of the molecules in guiding their microstructuring in the vicinity of the surfaces, as well as their interactions with the latter, i.e. the solute-surface interactions. The influence of water and DMSO on the overscreening at charged interfaces is also discussed. The analysis on the molecular accumulation at the interfaces allows us to predict whether the accumulation is entropy or enthalpy driven, which has an impact in the removal of the molecular species from the surfaces. We discuss the impact of this work in providing an essential understanding towards a careful design of electrochemical elements.

Specific Ion Effects in Different Media: Current Status and Future Challenges.

We discuss the current state of research as well as the future challenges for a deeper understanding of specific ion effects in protic and aprotic solvents as well as various additional media. Despite recent interest in solute or interfacial effects, we focus exclusively on the specific properties of ions in bulk electrolyte solutions. Corresponding results show that many mechanisms remain unknown for these simple media, although theoretical, computational, and experimental studies have provided some insights into explaining individual observations. In particular, the importance of local interactions and electronic properties is emphasized, which enabled a more consistent interpretation of specific ion effects over the past years. Despite current insufficient knowledge, we also discuss future challenges in relation to dynamic properties as well as the influence of different concentrations, different solvents, and solute contributions to gain a deeper understanding of specific ion effects for technological applications.

Electronic Properties of Protein Destabilizers and Stabilizers: Implications for Preferential Binding and Exclusion Mechanisms.

We study the electronic properties of low-weight organic co-solutes by means of conceptual density functional theory calculations. Our results highlight the important role of certain chemical reactivity descriptors such as chemical hardness, electronegativity, nucleofugality, and the electrofugality as important criteria to classify protein stabilizers and destabilizers. Our results imply Lewis basic properties with lower chemical hardness for stabilizers, while destabilizers show higher Lewis acidity with higher chemical hardness. Further consideration of analytical calculations in terms of transfer energies reveals the crucial role of co-solute-protein interactions which significantly change the interaction pattern of the stabilizing or destabilizing species. The corresponding outcomes connect statistical thermodynamics with the electronic properties of co-solutes and also allow us to define general principles for strong stabilizers and destabilizers.

Stabilization of DPPC lipid bilayers in the presence of co-solutes: molecular mechanisms and interaction patterns.

We study the interactions between dipalmitoylphosphatidylcholine (DPPC) lipid bilayers in the gel and the fluid phase with ectoine, amino ectoine and water molecules by means of atomistic molecular dynamics (MD) simulations and conceptual density functional theory (DFT) calculations. Our results reveal a pronounced preferential exclusion of both co-solutes from the DPPC lipid bilayer which is stronger for the fluid phase. The corresponding outcomes can be brought into relation with the Kirkwood-Buff theory of solutions in order to provide a thermodynamic rationale for the experimentally observed stabilization of the gel phase. Closely related to preferential exclusion of both co-solutes, our simulations also highlight a preferential hydration behavior as manifested by an increased number of hydrogen bonds between water and DPPC molecules. All results are rationalized by conceptual DFT calculations with regard to differences in the electronic properties between ectoine and amino ectoine.

Generic and specific recurrent neural network models: Applications for large and small scale biopharmaceutical upstream processes.

The calculation of temporally varying upstream process outcomes is a challenging task. Over the last years, several parametric, semi-parametric as well as non-parametric approaches were developed to provide reliable estimates for key process parameters. We present generic and product-specific recurrent neural network (RNN) models for the computation and study of growth and metabolite-related upstream process parameters as well as their temporal evolution. Our approach can be used for the control and study of single product-specific large-scale manufacturing runs as well as generic small-scale evaluations for combined processes and products at development stage. The computational results for the product titer as well as various major upstream outcomes in addition to relevant process parameters show a high degree of accuracy when compared to experimental data and, accordingly, a reasonable predictive capability of the RNN models. The calculated values for the root-mean squared errors of prediction are significantly smaller than the experimental standard deviation for the considered process run ensembles, which highlights the broad applicability of our approach. As a specific benefit for platform processes, the generic RNN model is also used to simulate process outcomes for different temperatures in good agreement with experimental results. The high level of accuracy and the straightforward usage of the approach without sophisticated parameterization and recalibration procedures highlight the benefits of the RNN models, which can be regarded as promising alternatives to existing parametric and semi-parametric methods.

Validation Is Not Verification: Precise Terminology and Scientific Methods in Bioprocess Modeling.

Advanced statistical approaches and new modeling procedures for biopharmaceutical development and manufacturing have received increasing attention. With this forum article, we would like to highlight the need for a consistent terminology and the necessity for appropriate model validation strategies.

Interactions of a DNA G-quadruplex with TMAO and urea: a molecular dynamics study on co-solute compensation mechanisms.

We study the individual and combined influence of TMAO and urea on a basket-type DNA G-quadruplex by means of atomistic molecular dynamics (MD) simulations. In combination with the Kirkwood-Buff theory of solutions, we propose a simple mechanism to elucidate the impact of TMAO and urea on the G-quadruplex. Our results reveal the importance of the molecular accumulation around the DNA in terms of stabilizing or destabilizing effects. The results for mixtures show only a weak interaction between both co-solutes, which highlights the additivity of contributions. Despite the fact, that TMAO can to some extent compensate the adverse impact of urea on the G-quadruplex structure, the destabilizing influence is not completely eliminated. This observation opens the door for further research on selective stabilization of DNA G-quadruplexes by modulating the concentrations of TMAO and urea in solution.

Artificial neural networks for the prediction of solvation energies based on experimental and computational data.

The knowledge of thermodynamic properties for novel electrolyte formulations is of fundamental interest for industrial applications as well as academic research. Herewith, we present an artificial neural networks (ANN) approach for the prediction of solvation energies and entropies for distinct ion pairs in various protic and aprotic solvents. The considered feed-forward ANN is trained either by experimental data or computational results from conceptual density functional theory calculations. The proposed concept of mapping computed values to experimental data lowers the amount of time-consuming and costly experiments and helps to overcome certain limitations. Our findings reveal high correlation coefficients between predicted and experimental values which demonstrate the validity of our approach.

Theoretical Insights into Specific Ion Effects and Strong-Weak Acid-Base Rules for Ions in Solution: Deriving the Law of Matching Solvent Affinities from First Principles.

We present a detailed study of specific ion effects, volcano plots and the law of matching solvent affinities by means of a conceptual density functional theory (DFT) approach. Our results highlight that specific ion effects and the corresponding implications on the solvation energy are mainly due to differences in the electric chemical potentials and chemical hardnesses of the ions and the solvent. Our approach can be further used to identify reliable criteria for the validity of the law of matching solvent affinities. Basic expressions are derived, which allow us to study the limiting conditions for this empirical observation with regard to matching chemical reactivity indices. Moreover, we show that chaotropic and kosmotropic concepts and their implications for the stability of ion pairs are directly related to a generalized strong and weak acids and bases (SWAB) principle for ions in solution, which is also applicable to rationalize the shape of volcano plots for different solvents. In contrast to previous assumptions, all empirical findings can be explained by the properties of local solvent-ion complexes which dominate the specific global behavior of ion pairs in solution.

What Does Ectoine Do to DNA? A Molecular-Scale Picture of Compatible Solute-Biopolymer Interactions.

Compatible solutes accumulate in the cytoplasm of halophilic microorganisms, enabling their survival in a high-salinity environment. Ectoine is such a compatible solute. It is a zwitterionic molecule that strongly interacts with surrounding water molecules and changes the dynamics of the local hydration shell. Ectoine interacts with biomolecules such as lipids, proteins, and DNA. The molecular interaction between ectoine and biomolecules, in particular the interaction between ectoine and DNA, is far from being understood. In this paper, we describe molecular aspects of the interaction between ectoine and double-stranded DNA (dsDNA). Two 20 base pairs-long dsDNA fragments were immobilized on a gold surface via a thiol-tether. The interaction between the dsDNA monolayers with diluted and concentrated ectoine solutions was examined by means of X-ray photoelectron and polarization modulation infrared reflection absorption spectroscopies (PM IRRAS). Experimental results indicate that the ability of ectoine to bind water reduces the strength of hydrogen bonds formed to the ribose-phosphate backbone in the dsDNA. In diluted (0.1 M) ectoine solution, DNA interacts predominantly with water molecules. The sugar-phosphate backbone is involved in the formation of strong hydrogen bonds to water, which, over time, leads to a reorientation of the planes of nucleic acid bases. This reorientation destabilizes the strength of hydrogen bonds between the bases and leads to a partial dehybridization of the dsDNA. In concentrated ectoine solution (2.5 M), almost all water molecules interact with ectoine. Under this condition, ectoine is able to interact directly with DNA. Density functional theory (DFT) calculations demonstrate that the direct interaction involves the nitrogen atoms in ectoine and phosphate groups in the DNA molecule. The results of the quantum-chemical calculations show that rearrangements in the ribose-phosphate backbone, caused by a direct interaction with ectoine, facilitates contacts between the O atom in the phosphate group and H atoms in a nucleic acid base. In the PM IRRA spectra, an increase in the number of IR absorption modes in the base pair frequency region proves that the hydrogen bonds between bases become weaker. Thus, a sequence of reorientations caused by interaction with ectoine leads to a breakdown of hydrogen bonds between bases in the double helix.

Raman Spectroscopic Signature of Ectoine Conformations in Bulk Solution and Crystalline State.

Recent crystallographic results revealed conformational changes of zwitterionic ectoine upon hydration. By means of confocal Raman spectroscopy and density functional theory calculations, we present a detailed study of this transformation process as part of a Fermi resonance analysis. The corresponding findings highlight that all resonant couplings are lifted upon exposure to water vapor as a consequence of molecular binding processes. The importance of the involved molecular groups for water binding and conformational changes upon hydration is discussed. Our approach further shows that the underlying rapid process can be reversed by carbon dioxide saturated atmospheres. For the first time, we also confirm that the conformational state of ectoine in aqueous bulk solution coincides with crystalline ectoine in its dihydrate state, thereby highlighting the important role of a few bound water molecules.

Towards a Digital Bioprocess Replica: Computational Approaches in Biopharmaceutical Development and Manufacturing.

Quantitative unit operation models for the optimization and refinement of modern late-stage biopharmaceutical drug manufacturing processes have recently attracted increasing attention. The supplementary benefits of these models include increased process robustness and control in combination with a more stringent design of the bioprocess due to a reduced number of exploratory experiments. In addition to unit operations, further efforts also focus on digital bioprocess replicas, which are straightforward combinations of unit operation and process models from inoculum to the fill and finish phase. In this review, we shed more light on digital bioprocess replicas in addition to standard unit operation models and discuss their strengths and weaknesses. We comment on the current usage of these approaches for late stage processes and outline the associated benefits, challenges and limitations.