The Energy Landscape, Folding Pathways and the Kinetics of a Knotted Protein

The folding pathway and rate coefficients of the folding of a knotted protein are calculated for a potential energy function with minimal energetic frustration. A kinetic transition network is constructed using the discrete path sampling approach, and the resulting potential energy surface is visualized by constructing disconnectivity graphs. Owing to topological constraints, the low-lying portion of the landscape consists of three distinct regions, corresponding to the native knotted state and to configurations where either the N- or C-terminus is not yet folded into the knot. The fastest folding pathways from denatured states exhibit early formation of the N-terminus portion of the knot and a rate-determining step where the C-terminus is incorporated. The low-lying minima with the N-terminus knotted and the C-terminus free therefore constitute an off-pathway intermediate for this model. The insertion of both the N- and C-termini into the knot occur late in the folding process, creating large energy barriers that are the rate limiting steps in the folding process. When compared to other protein folding proteins of a similar length, this system folds over six orders of magnitude more slowly.Comment: 19 page

Monte Carlo simulations of densely-packed athermal polymers in the bulk and under confinement

We review the main results from extensive Monte Carlo (MC) simulations on athermal polymer packings in the bulk and under confinement. By employing the simplest possible model of excluded volume, macromolecules are represented as freely-jointed chains of hard spheres of uniform size. Simulations are carried out in a wide concentration range: from very dilute up to very high volume fractions, reaching the maximally random jammed (MRJ) state. We study how factors like chain length, volume fraction and flexibility of bond lengths affect the structure, shape and size of polymers, their packing efficiency and their phase behaviour (disorder–order transition). In addition, we observe how these properties are affected by confinement realized by flat, impenetrable walls in one dimension. Finally, by mapping the parent polymer chains to primitive paths through direct geometrical algorithms, we analyse the characteristics of the entanglement network as a function of packing density

TRANSLATING CHEMISTRY, STRUCTURE, AND PROCESSING TO THE SOLID-STATE MORPHOLOGY AND FUNCTION OF ORGANIC SEMICONDUCTORS THROUGH COMPUTATIONAL MODELING AND SIMULATIONS

The immense synthetic design space and material versatility have driven the exploration and development of organic semiconductors (OSC) over several decades. While many OSC designs focus on the chemistries of the molecular or polymer building blocks, a priori, multiscale control over the solid-state morphology is required for effective application of the active layer in a given technology. However, molecular assembly during solid-state formation is a complex function interconnecting the building block chemistry and the processing environment. Insufficient knowledge as to how these aspects engage, especially at the atomistic and molecular scales, has so far limited the ability to predict OSC solid-state morphology, leaving Edisonian approaches as the stalwart methods. Therefore, through multiscale simulations combining atomistic quantum scale modeling and state-of-the-art molecular dynamics (MD) techniques, we aim to establish first principles understanding required to synthetically regulate solid-state morphology of organic semiconductors (OSC) as a function of molecular chemistry and processing. In turn, we try to understand the deceivingly simple yet complex mechanisms behind molecular aggregation and crystallization of OSC. Simultaneously, we develop semi-to-fully automated high-throughput schemes to automate the complex and labor-intensive analyses to generate data based on various crystal structures in different crystallization environments. Ultimately, we aim to bridge molecular-scale information revealed on solid-state physical organization, understood in the context of chromophore chemistry and the molecular environment, with the macro scale properties to uncover useful guidelines for rational design and morphology regulation of OSC systems

Accelerated Computational Discovery of High-Performance Materials for Organic Photovoltaics by Means of Cheminformatics

In this perspective we explore the use of strategies from drug discovery, pattern recognition, and machine learning in the context of computational materials science. We focus our discussion on the development of donor materials for organic photovoltaics by means of a cheminformatics approach. These methods enable the development of models based on molecular descriptors that can be correlated to the important characteristics of the materials. Particularly, we formulate empirical models, parametrized using a training set of donor polymers with available experimental data, for the important current–voltage and efficiency characteristics of candidate molecules. The descriptors are readily computed which allows us to rapidly assess key quantities related to the performance of organic photovoltaics for many candidate molecules. As part of the Harvard Clean Energy Project, we use this approach to quickly obtain an initial ranking of its molecular library with 2.6 million candidate compounds. Our method reveals molecular motifs of particular interest, such as the benzothiadiazole and thienopyrrole moieties, which are present in the most promising set of molecules.Chemistry and Chemical Biolog

Development of Computer-Aided Molecular Design Methods for Bioengineering Applications

Computer-aided molecular design (CAMD) offers a methodology for rational product design. The CAMD procedure consists of pre-design, design and post-design phases. CAMD was used to address two bioengineering problems: design of excipients for lyophilized protein formulations and design of ionic liquids for use in bioseparations. Protein stability remains a major concern during protein drug development. Lyophilization, or freeze-drying, is often sought to improve chemical stability. However, lyophilization can result in protein aggregation. Excipients, or additives, are included to stabilize proteins in lyophilized formulations. CAMD was used to rationally select or design excipients for lyophilized protein formulations. The use of solvents to aid separation is common in chemical processes. Ionic liquids offer a class of molecules with tunable properties that can be altered to find optimal solvents for a given application. CAMD was used to design ionic liquids for extractive distillation and in situ extractive fermentation processes. The pre-design phase involves experimental data gathering and problem formulation. When available, data was obtained from literature sources. For excipient design, data of percent protein monomer remaining post-lyophilization was measured for a variety of protein-excipient combinations. In problem formulation, the objective was to minimize the difference between the properties of the designed molecule and the target property values. Problem formulations resulted in either mixed-integer linear programs (MILPs) or mixed-integer non-linear programs (MINLPs). The design phase consists of the forward problem and the reverse problem. In the forward problem, linear quantitative structure-property relationships (QSPRs) were developed using connectivity indices. Chiral connectivity indices were used for excipient property models to improve fit and incorporate three-dimensional structural information. Descriptor selection methods were employed to find models that minimized Mallow's Cp statistic, obtaining models with good fit while avoiding overfitting. Cross-validation was performed to access predictive capabilities. Model development was also performed to develop group contribution models and non-linear QSPRs. A UNIFAC model was developed to predict the thermodynamic properties of ionic liquids. In the reverse problem of the design phase, molecules were proposed with optimal property values. Deterministic methods were used to design ionic liquids entrainers for azeotropic distillation. Tabu search, a stochastic optimization method, was applied to both ionic liquid and excipient design to provide novel molecular candidates. Tabu search was also compared to a genetic algorithm for CAMD applications. Tuning was performed using a test case to determine parameter values for both methods. After tuning, both stochastic methods were used with design cases to provide optimal excipient stabilizers for lyophilized protein formulations. Results suggested that the genetic algorithm provided a faster time to solution while the tabu search provides quality solutions more consistently. The post-design phase provides solution analysis and verification. Process simulation was used to evaluate the energy requirements of azeotropic separations using designed ionic liquids. Results demonstrated that less energy was required than processes using conventional entrainers or ionic liquids that were not optimally designed. Molecular simulation was used to guide protein formulation design and may prove to be a useful tool in post-design verification. Finally, prediction intervals were used for properties predicted from linear QSPRs to quantify the prediction error in the CAMD solutions. Overlapping prediction intervals indicate solutions with statistically similar property values. Prediction interval analysis showed that tabu search returns many results with statistically similar property values in the design of carbohydrate glass formers for lyophilized protein formulations. The best solutions from tabu search and the genetic algorithm were shown to be statistically similar for all design cases considered. Overall the CAMD method developed here provides a comprehensive framework for the design of novel molecules for bioengineering approaches

TOWARDS THE RATIONAL DESIGN OF ORGANIC SEMICONDUCTORS THROUGH COMPUTATIONAL APPROACHES

Though organic semiconductors have illustrated potential as industry-relevant materials for electronics applications, there are few guidelines that can take one from molecular design to functional materials. This limitation is, in part, due to incomplete understanding as to how the atomic-scale construction of the π-conjugated molecules that comprise the organic semiconductors determines the nature and strength of both the noncovalent intramolecular interactions that govern molecular conformation and noncovalent intermolecular interactions that regulate the energetic preference for solid-state packing. Hence, there remain several fundamental questions that need to be resolved in order to design organic semiconductors from a priori knowledge, including: What is the relevance of the relatively weak noncovalent intramolecular interactions on determining molecular structure, are current hypotheses put forward as to important interactions valid, and how does chemical substitution as various positions along the π-conjugated backbone impact these interactions? How do the intermolecular noncovalent interactions regulate solid-state packing, are there features of the molecular structure – e.g. the π-conjugated backbone, heteroatoms, or pendent alkyl chains – that play a more important role? What connections can be made between the structures/properties of the π-conjugated molecules and the resulting organic semiconductors? In this dissertation, Chapter 1 provides an introductory discussion of these questions and a brief review of previous studies. Chapter 2 details the computational approaches that were implemented throughout the course of the thesis work. Chapter 3 describes the investigation of a series of pyrene-acene molecules to illustrate the importance of choosing the right molecular structure in π-conjugated chromophores. In Chapter 4, S...F noncovalent intramolecular interactions are systematically investigated in two separate cases to highlight the varied impact that these interactions can have on molecular and solid-state packing structures. Chapter 5 describes the investigation of an oscillatory crystal packing structure observed for a series of oligothiophenes that follow the odd-even carbon-atom counts of the pendant alkyl chains. In Chapter 6, the polymorphism of functionalized pentacene molecules is studied to reveal how seemingly simple atomic substitutions can drastically alter solid-state packing. To systematically address the aforementioned fundamental questions, Chapter 7 describes the construction and application of a database of crystalline molecular organic semiconductors. Finally, perspectives regarding future research are provided in Chapter 8

POLYMORPH FORMATION OF TOLFENAMIC ACID: AN INVESTIGATION OF PRE-NUCLEATION ASSOCIATION

The majority of pharmaceutical products are formulated as solids in the crystalline state. With the potential to exist in different crystalline modifications or polymorphs, each solid form bears its own physical and chemical properties, influencing directly bioavailability and manufacturability of the final dosage form. In view of the importance of crystalline form selection in the drug development process, it is imperative for pharmaceutical scientists to work arduously on various aspects of polymorphism, ranging from fundamental understanding of the phenomenon at the molecular level to practical utilization of a specific crystalline form. One common feature of organic crystals is the existence of distinct molecular conformations in different polymorphic structures, known as conformational polymorphism. Conformational polymorphs are routinely observed in drug development, produced when crystal growth conditions vary. Crystallization from solution involves nucleation and crystal growth, the mechanisms that influence the polymorphic outcome. The embryonic solute aggregate has been recognized to play a critical role in dictating the final crystal structure, and solution conditions are also known to drastically influence the self-association behavior of solute molecules during crystallization, affecting crystal packing of organic molecules. For the crystal growth of conformational polymorphs, changes in molecular conformation not only determine the growth kinetics, but also influence the nature and strength of interactions present in the crystal structures. How conformation and intermolecular interaction affect each other underlines the intricacy and the wonder of crystal growth of the organic. Thus, the overall goal of this research is to provide the fundamental understanding of the extent to which solution conditions influence the molecular conformation in the solid-state of a model drug, tolfenamic acid. By combining experimental studies with advanced computational tools, this dissertation offers novel insights into solution species during pre-nucleation and molecular packing of conformational polymorphs of tolfenamic acid. In-depth understanding of the underlying connection between molecular conformation and crystal packing will help advance the knowledge required for rational control of crystal growth