Protein solubility plays an important role whether for biophysical and structural studies, or for production and delivery of therapeutic proteins. Poor solubility could lead to protein aggregation, which is an undesired physicochemical mechanism at any stage of recombinant proteins production. To date, more than half of all recombinant therapeutic proteins are produced in mammalian cells, mainly due to the high similarity of the final product to human protein structures. However, poor secretion can occur, due to misfolded proteins or aggregates leading to cellular stress and proteolysis. Another widely-used expression system is E. coli, which can offer a cost-efficient alternative. This system has an important limitation, since proteins tends to form insoluble protein aggregates in the cytoplasm upon heterologous overexpression. Several strategies are being implemented to improved soluble expression, ranging from culture conditions to solubility enhancing tags. However, there is no universal approach or technology that solves protein aggregation. In this thesis two recently published hypotheses from our group have been applied. One stated that soluble expression of proteins was inversely correlated with the size of the largest positively-charged patch on the protein surface. The second hypothesis (of protein solubility), arose from the finding that the relative content of lysine and arginine residues separated E. coli proteins by solubility. Both hypotheses arose from a study of an extensive dataset of experimental solubilities determined for cell-free expression of E. coli proteins. In combination with other widely used strategies, such as lowering expression temperature and inducer concentration, decreasing non-charged (hydrophobic) patches and addition of helical capping for increasing stability, a rational understanding for directed alteration of solubility in a variety of recombinant proteins has been explored. This includes three protein models to test: (i) recombinant human erythropoietin (rHuEPO) (one of the top selling therapeutics) (ii) recombinant 6-Phosphofructo-2-Kinase/fructose-2,6-bisphosphatase (rPFKFB3) (a product for which over-expression has been sought for characterisation and insight into possible cancer therapy) and (iii) a set of three selected E. coli proteins containing high ratios of lysines to arginines: thioredoxin-1 (TRX), cold shock-like protein cspB (cspB), and the histidine-containing phosphocarrier protein (HPr). It was found that single or multiple point mutations (changing amino acids from positive to negative charge or vice versa; or lysines to arginines) verified the predicted effect on rHuEPO, rPFKFB3, TRX, cspB, and HPr solubility (experimentally defined as the distribution between soluble and total fractions) for expression in E. coli. In addition, the redesigned set of rHuEPO transiently expressed in HEK 293-EBNA cells, suggesting that positively-charged patch size may also influence protein secretion. Further application of these computational and experimental approaches could provide a valuable tool in the design and engineering of proteins, with enhanced solubility, stability and secretion.

This thesis covers a range of methodologies to provide an account of the current (2010-2014) state of the art and to develop new methods for solubility prediction. We focus on predictions of intrinsic aqueous solubility, as this is a measure commonly used in many important industries including the pharmaceutical and agrochemical industries. These industries require fast and accurate methods, two objectives which are rarely complementary. We apply machine learning in chapters 4 and 5 suggesting methodologies to meet these objectives. In chapter 4 we look to combine machine learning, cheminformatics and chemical theory. Whilst in chapter 5 we look to predict related properties to solubility and apply them to a previously derived empirical equation. We also look at ab initio (from first principles) methods of solubility prediction. This is shown in chapter 3. In this chapter we present a proof of concept work that shows intrinsic aqueous solubility predictions, of sufficient accuracy to be used in industry, are now possible from theoretical chemistry using a small but diverse dataset. Chapter 6 provides a summary of our most recent research. We have begun to investigate predictions of sublimation thermodynamics. We apply quantum chemical, lattice minimisation and machine learning techniques in this chapter. In summary, this body of work concludes that currently, QSPR/QSAR methods remain the current state of the art for solubility prediction, although it is becoming possible for purely theoretical methods to achieve useful predictions of solubility. Theoretical chemistry can offer little useful additional input to informatics models for solubility predictions. However, theoretical chemistry will be crucial for enriching our understanding of the solvation process, and can have a beneficial impact when applied to informatics predictions of properties related to solubility.

<p>Aqueous solubility is a key parameter influencing the bioavailability of drugs and drug candidates. In this thesis computational models for the prediction of aqueous drug solubility were explored. High quality experimental solubility data for drugs were generated using a standardised protocol and models were developed using multivariate data analysis tools and calculated molecular descriptors. In addition, structural features associated with either solid-state limited or solvation limited solubility of drugs were identified.</p><p>Solvation, as represented by the octanol-water partition coefficient (log<i>P</i>), was found to be the dominant factor limiting the solubility of drugs, with solid-state properties being the second most important limiting factor.</p><p>The relationship between the chemical structure of drugs and the strength of their crystal lattice was studied for a dataset displaying log<i>P</i>-independent solubility. Large, rigid and flat molecules with an extended ring-structure and a large number of conjugated π-bonds were found to be more likely to have their solubility limited by a strong crystal lattice than were small, spherically shaped molecules with flexible side-chains.</p><p>Finally, the relationship between chemical structure and drug solvation was studied using computer simulated values of the free energy of hydration. Drugs exhibiting poor hydration were found to be large and flexible, to have low polarisability and few hydrogen bond acceptors and donors.</p><p>The relationship between the structural features of drugs and their aqueous solubility discussed in this thesis provide new rules-of-thumb that could guide decision-making in early drug discovery.</p>

Aqueous solubility is a key parameter influencing the bioavailability of drugs and drug candidates. In this thesis computational models for the prediction of aqueous drug solubility were explored. High quality experimental solubility data for drugs were generated using a standardised protocol and models were developed using multivariate data analysis tools and calculated molecular descriptors. In addition, structural features associated with either solid-state limited or solvation limited solubility of drugs were identified. Solvation, as represented by the octanol-water partition coefficient (logP), was found to be the dominant factor limiting the solubility of drugs, with solid-state properties being the second most important limiting factor. The relationship between the chemical structure of drugs and the strength of their crystal lattice was studied for a dataset displaying logP-independent solubility. Large, rigid and flat molecules with an extended ring-structure and a large number of conjugated π-bonds were found to be more likely to have their solubility limited by a strong crystal lattice than were small, spherically shaped molecules with flexible side-chains. Finally, the relationship between chemical structure and drug solvation was studied using computer simulated values of the free energy of hydration. Drugs exhibiting poor hydration were found to be large and flexible, to have low polarisability and few hydrogen bond acceptors and donors. The relationship between the structural features of drugs and their aqueous solubility discussed in this thesis provide new rules-of-thumb that could guide decision-making in early drug discovery.

Solubility prediction usually refers to prediction of the intrinsic aqueous solubility, which is the concentration of an unionised molecule in a saturated aqueous solution at thermodynamic equilibrium at a given temperature. Solubility is determined by structural and energetic components emanating from solid-phase structure and packing interactions, solute–solvent interactions, and structural reorganisation in solution. An overview of the most commonly used methods for solubility prediction is given in Chapter 1. In this thesis, we investigate various approaches to solubility prediction and solvation model development, based on informatics and incorporation of empirical and experimental data. These are of a knowledge-based nature, and specifically incorporate information from the Cambridge Structural Database (CSD). A common problem for solubility prediction is the computational cost associated with accurate models. This issue is usually addressed by use of machine learning and regression models, such as the General Solubility Equation (GSE). These types of models are investigated and discussed in Chapter 3, where we evaluate the reliability of the GSE for a set of structures covering a large area of chemical space. We find that molecular descriptors relating to specific atom or functional group counts in the solute molecule almost always appear in improved regression models. In accordance with the findings of Chapter 3, in Chapter 4 we investigate whether radial distribution functions (RDFs) calculated for atoms (defined according to their immediate chemical environment) with water from organic hydrate crystal structures may give a good indication of interactions applicable to the solution phase, and justify this by comparison of our own RDFs to neutron diffraction data for water and ice. We then apply our RDFs to the theory of the Reference Interaction Site Model (RISM) in Chapter 5, and produce novel models for the calculation of Hydration Free Energies (HFEs).