Bernhardt L. Trout

Design of therapeutic proteins with enhanced stability

Therapeutic proteins such as antibodies constitute the most rapidly growing class of pharmaceuticals for use in diverse clinical settings including cancer, chronic inflammatory diseases, kidney transplantation, cardiovascular medicine, and infectious diseases. Unfortunately, they tend to aggregate when stored under the concentrated conditions required in their usage. Aggregation leads to a decrease in antibody activity and could elicit an immunological response. Using full antibody atomistic molecular dynamics simulations, we identify the antibody regions prone to aggregation by using a technology that we developed called spatial aggregation propensity (SAP). SAP identifies the location and size of these aggregation prone regions, and allows us to perform target mutations of those regions to engineer antibodies for stability. We apply this method to therapeutic antibodies and demonstrate the significantly enhanced stability of our mutants compared with the wild type. The technology described here could be used to incorporate developability in a rational way during the screening of antibodies in the discovery phase for several diseases.

End-to-End Continuous Manufacturing of Pharmaceuticals: Integrated Synthesis, Purification, and Final Dosage Formation†

A series of tubes: The continuous manufacture of a finished drug product starting from chemical intermediates is reported. The continuous pilot-scale plant used a novel route that incorporated many advantages of continuous-flow processes to produce active pharmaceutical ingredients and the drug product in one integrated system.

Mechanisms of Protein Stabilization and Prevention of Protein Aggregation by Glycerol

The stability of proteins in aqueous solution is routinely enhanced by cosolvents such as glycerol. Glycerol is known to shift the native protein ensemble to more compact states. Glycerol also inhibits protein aggregation during the refolding of many proteins. However, mechanistic insight into protein stabilization and prevention of protein aggregation by glycerol is still lacking. In this study, we derive mechanisms of glycerol-induced protein stabilization by combining the thermodynamic framework of preferential interactions with molecular-level insight into solvent-protein interactions gained from molecular simulations. Contrary to the common conception that preferential hydration of proteins in polyol/water mixtures is determined by the molecular size of the polyol and the surface area of the protein, we present evidence that preferential hydration of proteins in glycerol/water mixtures mainly originates from electrostatic interactions that induce orientations of glycerol molecules at the protein surface such that glycerol is further excluded. These interactions shift the native protein toward more compact conformations. Moreover, glycerol preferentially interacts with large patches of contiguous hydrophobicity where glycerol acts as an amphiphilic interface between the hydrophobic surface and the polar solvent. Accordingly, we propose that glycerol prevents protein aggregation by inhibiting protein unfolding and by stabilizing aggregation-prone intermediates through preferential interactions with hydrophobic surface regions that favor amphiphilic interface orientations of glycerol. These mechanisms agree well with experimental data available in the literature, and we discuss the extent to which these mechanisms apply to other cosolvents, including polyols, arginine, and urea.

Obtaining reaction coordinates by likelihood maximization

We present a new approach for calculating reaction coordinates in complex systems. The new method is based on transition path sampling and likelihood maximization. It requires fewer trajectories than a single iteration of existing procedures, and it applies to both low and high friction dynamics. The new method screens a set of candidate collective variables for a good reaction coordinate that depends on a few relevant variables. The Bayesian information criterion determines whether additional variables significantly improve the reaction coordinate. Additionally, we present an advantageous transition path sampling algorithm and an algorithm to generate the most likely transition path in the space of collective variables. The method is demonstrated on two systems: a bistable model potential energy surface and nucleation in the Ising model. For the Ising model of nucleation, we quantify for the first time the role of nuclei surface area in the nucleation reaction coordinate. Surprisingly, increased surface area increases the stability of nuclei in two dimensions but decreases nuclei stability in three dimensions.

A new approach for studying nucleation phenomena using molecular simulations: Application to CO2 hydrate clathrates

We use an order-parameter formulation, in conjunction with non-Boltzmann sampling to study the nucleation of clathrate hydrates from water–CO2 mixtures, using computer simulations. A set of order parameters are defined: Φigg (i=1,2,…,n and gg for guest–guest), which characterize the spatial and orientational order of the CO2 molecules, and Φihh (hh for host–host), which govern the ordering of the water molecules. These are bond-orientational order parameters based on the average geometrical distribution of nearest-neighbor bonds. The free-energy hypersurface as a function of the order parameters is calculated using the Landau–Ginzburg approach. The critical cluster size that leads to the nucleation of the clathrate phase is determined accurately by analyzing the free energy surface. We find that the nucleation proceeds via “the local structuring mechanism,” i.e., a thermal fluctuation causing the local ordering of CO2 molecules leads to the nucleation of the clathrate, and not by the current conceptual pi...

A super-linear minimization scheme for the nudged elastic band method

In this article, we present a superlinear minimization scheme for the nudged elastic band (NEB) method, which determines a minimum-energy path (MEP) of a reaction via connecting intermediate “replicas” between the reactant and the product. The minimization scheme is based on a quasi-Newton method: the adopted basis Newton–Raphson (ABNR) minimization scheme. In each step of ABNR minimization, the Newton–Raphson procedure is performed in a subspace of a user-defined dimension. The tangent directions of the path at a new Newton–Raphson step are determined self-consistently in the subspace. The acceleration of the proposed scheme over the quenched molecular-dynamic minimization, the current practice for minimizing a path using NEB, is demonstrated in three nontrivial test cases: isomerization of an alanine dipeptide, α-helix to π-helix transition of an alanine decapeptide, and oxidation of dimethyl sulfide. New features are also added such that the distances between replicas can be defined in the root of mean...

Modified Ligand-Exchange for Efficient Solubilization of CdSe/ZnS Quantum Dots in Water: A Procedure Guided by Computational Studies

One of the methods to render CdSe/ZnS core-shell quantum dots(QDots) water-soluble is to functionalize the surface with carboxylate groups by the use of heterobifunctional ligands such as 3-mercaptopropionic acid, where the thiolic end binds onto the outer ZnS shell. However, currently available ligand-exchange procedures starting with TOPO-capped quantum dots often lead to significant loss of quantum yields and poor stability of the colloids in water. As part of our efforts to overcome these problems, we used computational methods to understand the nature of binding between alkyl thiols and ZnS wurtzite surfaces. Guided by the computational results, we modified the ligand-exchange method and increased the reactivity of 3-mercaptopropionic acid toward the ZnS surface in chloroform. The functionlization reaction required only mild reaction conditions and led to QDot nanoparticles that were individually dispersed in water with good colloidal stability. Importantly, the photoluminescence performance of the QDots was highly preserved.

Surface design for controlled crystallization: the role of surface chemistry and nanoscale pores in heterogeneous nucleation.

Current industrial practice for control of primary nucleation (nucleation from a system without pre-existing crystalline matter) during crystallization from solution involves control of supersaturation generation, impurity levels, and solvent composition. Nucleation behavior remains largely unpredictable, however, due to the presence of container surfaces, dust, dirt, and other impurities that can provide heterogeneous nucleation sites, thus making the control and scale-up of processes that depend on primary nucleation difficult. To develop a basis for the rational design of surfaces to control nucleation during crystallization from solution, we studied the role of surface chemistry and morphology of various polymeric substrates on heterogeneous nucleation using aspirin as a model compound. Nucleation induction time statistics were utilized to investigate and quantify systematically the effectiveness of polymer substrates in inducing nucleation. The nucleation induction time study revealed that poly(4-acryloylmorpholine) and poly(2-carboxyethyl acrylate), each cross-linked by divinylbenzene, significantly lowered the nucleation induction time of aspirin while the other polymers were essentially inactive. In addition, we found the presence of nanoscopic pores on certain polymer surfaces led to order-of-magnitude faster aspirin nucleation rates when compared with surfaces without pores. We studied the preferred orientation of aspirin crystals on polymer films and found the nucleation-active polymer surfaces preferentially nucleated the polar facets of aspirin, guided by hydrogen bonds. A model based on interfacial free energies was also developed which predicted the same trend of polymer surface nucleation activities as indicated by the nucleation induction times.

Interaction of arginine with proteins and the mechanism by which it inhibits aggregation.

Aqueous arginine solutions are used extensively for inhibiting protein aggregation. There are several theories proposed to explain the effect of arginine on protein stability, but the exact mechanism is still not clear. To understand the mechanism of protein cosolvent interaction, the intraprotein, protein-solvent, and intrasolvent interactions have to be understood. Molecular dynamics simulations of aqueous arginine solutions were carried out for experimentally accessible concentrations and temperature ranges to study the structure of the solution and its energetic properties and obtain insight into the mechanism by which arginine inhibits protein aggregation. Simulations of proteins (α-chymotrypsinogen A and melittin) were performed. Structurally, the most striking feature of the aqueous arginine solutions is the self-association of arginine molecules. Arginine shows a marked tendency to form clusters with head to tail hydrogen bonding. Due to the presence of the three charged groups, there are several possible configurations in which arginine molecules interact. At relatively high concentrations, these arginine clusters associate with other clusters and monomeric arginine molecules to form large clusters. The hydrogen bonds between arginine molecules were found to be stronger than those between arginine and water, which makes the process of self-association enthalpically favorable. From the simulation of the proteins in aqueous arginine solution, arginine is found to interact with the aromatic and charged side chains of surface residues. A probable mechanism of the effect of arginine on protein stability consistent with our findings is proposed. In particular, arginine interacts with aromatic and charged residues due to cation-π interaction and salt-bridge formation, respectively, to stabilize the partially unfolded intermediates. The self-interaction of arginine leads to the formation of clusters which, due to their size, crowd out the protein-protein interaction. The mechanisms proposed in the literature are analyzed on the basis of the simulation results reported in this paper and recent experimental data.

Aggregation-prone motifs in human immunoglobulin G.

Therapeutic antibodies of many different IgG subclasses (IgG1, IgG2 and IgG4) are used in the treatment of various cancers, rheumatoid arthritis and other inflammatory and infectious diseases. These antibodies are stored for long durations under high concentrations as required in the disease treatment. Unfortunately, these antibodies aggregate under these storage conditions, leading to a decrease in antibody activity and raising concerns about causing an immunological response. Thus, there is a tremendous need to identify the aggregation-prone regions in different classes of antibodies. We use the SAP (spatial-aggregation-propensity) technology based on molecular simulations to determine the aggregation-prone motifs in the constant regions of IgG1 classes of antibodies. Mutations engineered on these aggregation-prone motif regions led to antibodies of enhanced stability. Fourteen aggregation-prone motifs are identified, with each motif containing one to seven residues. While some of these motifs contain residues that are neighbors in primary sequence, others contain residues that are far apart in primary sequence but are close together in the tertiary structure. Comparison of the IgG1 sequence with those of other subclasses (IgG2, IgG3 and IgG4) showed that these aggregation-prone motifs are largely preserved among all IgG subclasses. Other broader classes of antibodies (IgA1, IgD, IgE and IgM), however, differed in these motif regions. The aggregation-prone motifs identified were therefore common to all IgG subclasses, but differ from those of non-IgG classes. Moreover, since the motifs identified are in the constant regions, they are applicable for all antibodies within the IgG class irrespective of the variable region. Thus, the motif regions identified could be modified on all IgGs to yield antibodies of enhanced stability.