Development of Improved Torsional Potentials in Classical Force Field Models of Poly (Lactic Acid)

In this work, existing force field descriptions of poly (lactic acid), or PLA, were improved by modifying the torsional potential energy terms to more accurately model the bond rotational behavior of PLA. Extensive calculations were carried out using density functional theory (DFT), for small PLA molecules in vacuo, and also using DFT with a continuum model to approximate the electronic structure of PLA in its condensed phase. From these results, improved force field parameters were developed using a combination of the OPLS and CHARMM force fields. The new force field, PLAFF2, is an update to the previously developed PLAFF model developed in David Bruce\u27s group, and results in more realistic conformational distributions during simulation of bulk amorphous PLA. It is demonstrated that the PLAFF2 model retains the accuracy of the original PLAFF in simulating the crystalline α polymorph of PLA. The PLAFF2 model has superior performance to any other publicly available force field for use with PLA; hence, we recommend its use in future modeling studies on the material, whether in its crystalline or amorphous form

Absolute binding enthalpy calculations using molecular dynamics simulations

Computers play an essential role in drug discovery as advancements in technology, hardware, and algorithms have allowed for improved simulations of biomolecules. The field of drug discovery stands to benefit significantly from these developments. Currently, many innovative approaches to studying drug binding and predicting binding affinity are being explored. Using computational methods to predict thermodynamic components in drug design has become routine. While progress has been made in calculating free energy, the prediction of enthalpy and entropy remains an area that requires further investigation. These components reflect the interactions and dynamics between the ligand and protein. However, despite years of research, our understanding of these components still needs to be improved. Computing the enthalpy is particularly challenging, and even the achievable accuracy of these predictions is still not precise despite the apparent simplicity of the calculations per se. In my thesis, I conduct a series of studies to examine the potential utility of absolute binding enthalpy calculations using the direct method based on molecular dynamics simulations. In Chapter 3, I first assess the accuracy of water models and the host-guest force field in calculating the absolute binding enthalpy for 25 host-guest pairs. While actual protein-ligand or protein-protein data would be ideal for evaluating force fields, using very simplified test systems can be helpful for preliminary exploration of parameters. Then, in Chapter 4, I focus on predicting the binding enthalpies of small molecules to bromodomains, which are small protein modules involved in gene regulation linked to many diseases, such as cancer and inflammation. I evaluated the direct method for calculating absolute binding enthalpies by testing its ability to predict the binding enthalpies of 10 different ligands to BRD4-1. The results showed a strong correlation between the behaviour of the ZA loop and the predicted enthalpy. In Chapter 5, I extended the study by evaluating the method to include multiple protein-protein complexes essential in all cellular processes, ranging from signal transmission to enzyme activity. Understanding the thermodynamics of protein-peptide binding events is a significant challenge in computational chemistry. The complexity of both components having many degrees of freedom presents a substantial challenge for methods attempting to directly compute the enthalpic contribution to binding. Despite this, the method produced highly accurate and well-converged binding enthalpies for small protein-protein systems. Perhaps unsurprisingly, most inaccuracies can be attributed to poor conformational sampling. Nevertheless, I have shown that this can actually be used to highlight the possibility of hidden states. Overall, my work has shown that absolute enthalpy calculations using the direct method can be performed on protein-ligand and protein-protein systems with reasonable accuracy and that this is a useful contribution to computational drug design

A robust machine learning approach for the prediction of allosteric binding sites

Previously held under moratorium from 28 March 2017 until 28 March 2022Allosteric regulatory sites are highly prized targets in drug discovery. They remain difficult to detect by conventional methods, with the vast majority of known examples being found serendipitously. Herein, a rigorous, wholly-computational protocol is presented for the prediction of allosteric sites. Previous attempts to predict the location of allosteric sites by computational means drew on only a small amount of data. Moreover, no attempt was made to modify the initial crystal structure beyond the in silico deletion of the allosteric ligand. This behaviour can leave behind a conformation with a significant structural deformation, often betraying the location of the allosteric binding site. Despite this artificial advantage, modest success rates are observed at best. This work addresses both of these issues. A set of 60 protein crystal structures with known allosteric modulators was collected. To remove the imprint on protein structure caused by the presence of bound modulators, molecular dynamics was performed on each protein prior to analysis. A wide variety of analytical techniques were then employed to extract meaningful data from the trajectories. Upon fusing them into a single, coherent dataset, random forest - a machine learning algorithm - was applied to train a high performance classification model. After successive rounds of optimisation, the final model presented in this work correctly identified the allosteric site for 72% of the proteins tested. This is not only an improvement over alternative strategies in the literature; crucially, this method is unique among site prediction tools in that is does not abuse crystal structures containing imprints of bound ligands - of key importance when making live predictions, where no allosteric regulatory sites are known.Allosteric regulatory sites are highly prized targets in drug discovery. They remain difficult to detect by conventional methods, with the vast majority of known examples being found serendipitously. Herein, a rigorous, wholly-computational protocol is presented for the prediction of allosteric sites. Previous attempts to predict the location of allosteric sites by computational means drew on only a small amount of data. Moreover, no attempt was made to modify the initial crystal structure beyond the in silico deletion of the allosteric ligand. This behaviour can leave behind a conformation with a significant structural deformation, often betraying the location of the allosteric binding site. Despite this artificial advantage, modest success rates are observed at best. This work addresses both of these issues. A set of 60 protein crystal structures with known allosteric modulators was collected. To remove the imprint on protein structure caused by the presence of bound modulators, molecular dynamics was performed on each protein prior to analysis. A wide variety of analytical techniques were then employed to extract meaningful data from the trajectories. Upon fusing them into a single, coherent dataset, random forest - a machine learning algorithm - was applied to train a high performance classification model. After successive rounds of optimisation, the final model presented in this work correctly identified the allosteric site for 72% of the proteins tested. This is not only an improvement over alternative strategies in the literature; crucially, this method is unique among site prediction tools in that is does not abuse crystal structures containing imprints of bound ligands - of key importance when making live predictions, where no allosteric regulatory sites are known

Classical and reactive molecular dynamics: Principles and applications in combustion and energy systems

Molecular dynamics (MD) has evolved into a ubiquitous, versatile and powerful computational method for fundamental research in science branches such as biology, chemistry, biomedicine and physics over the past 60 years. Powered by rapidly advanced supercomputing technologies in recent decades, MD has entered the engineering domain as a first-principle predictive method for material properties, physicochemical processes, and even as a design tool. Such developments have far-reaching consequences, and are covered for the first time in the present paper, with a focus on MD for combustion and energy systems encompassing topics like gas/liquid/solid fuel oxidation, pyrolysis, catalytic combustion, heterogeneous combustion, electrochemistry, nanoparticle synthesis, heat transfer, phase change, and fluid mechanics. First, the theoretical framework of the MD methodology is described systemically, covering both classical and reactive MD. The emphasis is on the development of the reactive force field (ReaxFF) MD, which enables chemical reactions to be simulated within the MD framework, utilizing quantum chemistry calculations and/or experimental data for the force field training. Second, details of the numerical methods, boundary conditions, post-processing and computational costs of MD simulations are provided. This is followed by a critical review of selected applications of classical and reactive MD methods in combustion and energy systems. It is demonstrated that the ReaxFF MD has been successfully deployed to gain fundamental insights into pyrolysis and/or oxidation of gas/liquid/solid fuels, revealing detailed energy changes and chemical pathways. Moreover, the complex physico-chemical dynamic processes in catalytic reactions, soot formation, and flame synthesis of nanoparticles are made plainly visible from an atomistic perspective. Flow, heat transfer and phase change phenomena are also scrutinized by MD simulations. Unprecedented details of nanoscale processes such as droplet collision, fuel droplet evaporation, and CO2 capture and storage under subcritical and supercritical conditions are examined at the atomic level. Finally, the outlook for atomistic simulations of combustion and energy systems is discussed in the context of emerging computing platforms, machine learning and multiscale modelling

Fully Atomistic Modelling of Collagen Cross-linking

The extracellular matrix (ECM) undergoes progressive age-related stiffening and loss of proteolytic digestibility due to an increase in concentration of advanced glycation end products (AGEs). Detrimental collagen stiffening properties are believed to play a significant role in several age-related diseases such as osteoporosis and cardiovascular disease. Currently little is known of the potential location of covalently cross-linked AGEs formation within collagen molecules; neither are there reports on how the respective cross-link sites affect the physical and biochemical properties of collagen. Using fully atomistic molecular dynamics simulations (MD) we have identified preferential sites for exothermic formation of two lysine-arginine derived AGEs, glucosepane and DOGDIC. Identification of these favourable sites enables us to align collagen cross-linking with experimentally observed changes to the ECM. For example, formation of both AGEs were found to be energetically favourable within close proximity of the Matrix Metalloproteinase-1 (MMP1) binding site, which could potentially disrupt collagen degradation. With the aid of a number of dynamic analysis techniques we have provided an explanation for the site specificity of the two AGE cross-links. The mechanical properties of collagen were also investigated through the use of steered MD to determine the effect of the cross-links presence. Additionally the effect of the sequence on the collagen mechanical properties was also investigated, owing to the heterogeneous response of collagen to an applied load. A homology model for the Homo sapiens sequence was developed from the crystal structure of the Rattus norvegicus structure that was shown to produce stable simulations. Through the use of the homology model and implementation of a novel simulation technique we attempted to ascertain the orientations of the collagen molecules within a fibril, that is currently below the resolution limit of experimental techniques

Structure and Activity of Antimicrobial Peptoids

This thesis concerns complementary experimental and computational investigations into the relationship between the primary sequence and secondary structure of peptoids. Peptoids are a class of peptide mimetic molecules with applications as novel antimicrobial agents. The antimicrobial properties of peptoids are linked to their interactions with lipid bilayers in cell membranes, which in turn are linked to their helical secondary structure, making understanding sequence to structure relationships crucial to the design of functional sequences. Here we investigate a library of linear, cationic peptoid sequences with structural variations in the proportion and positioning of helix inducing residues and the chemical nature of the cationic side chains. We use circular dichroism spectroscopy to characterise the peptoids in aqueous and organic solvent and also to investigate structural changes upon binding to lipid bilayers designed to mimic mammalian and bacterial membranes. We present a new set of force field parameters, derived from GAFF and quantum mechanical calculations, that accurately capture the backbone torsional preferences of peptoids. Subsequently we use the modified force field to perform atomistic MD simulations of our library of peptoid sequences, using Hamiltonian replica exchange to improve sampling at less computational expense than traditional replica exchange methods. The CD spectra reveal that the peptoids adopt characteristically helical secondary structures with variations depending on primary sequence. The intensity of helical features increases upon increasing the proportion of helix inducing residues, switching from an aqueous to an organic environment and as extra methylene groups are added to the cationic side chains, increasing their length. The length and proportion of cationic side chains also influences the folded hydrophobicity of the peptoids, though this does not correlate to their antimicrobial activity. Modelling the binding of the peptoids to lipids as a two state system enables us to estimate, in some cases, the free energy of transfer into the bilayer, where the length of the cationic side chain is also influential. MD simulations do not reveal a clear distinction in peptoid backbone conformation depending on cationic side chain length however it is clear that the peptoid backbone is more flexible and deviates more from a perfect helical conformation in aqueous than organic solvent. Ultimately these findings may aid in the rational design of new sequences

Hardware and software aspects of parallel computing

Part 1 (Chapters 2,3 and 4) is concerned with the development of hardware for multiprocessor systems. Some of the concepts used in digital hardware design are introduced in Chapter 2. These include the fundamentals of digital electronics such as logic gates and flip-flops as well as the more complicated topics of rom and programmable logic. It is often desirable to change the network topology of a multiprocessor machine to suit a particular application. The third chapter describes a circuit switching scheme that allows the user to alter the network topology prior to computation. To achieve this, crossbar switches are connected to the nodes, and the host processor (a PC) programs the crossbar switches to make the desired connections between the nodes. The hardware and software required for this system is described in detail. Whilst this design allows the topology of a multiprocessor system to be altered prior to computation, the topology is still fixed during program run-time. Chapter 4 presents a system that allows the topology to be altered during run-time. The nodes send connection requests to a control processor which programs a crossbar switch connected to the nodes. This system allows every node in a parallel computer to communicate directly with every other node. The hardware interface between the nodes and the control processor is discussed in detail, and the software on the control processor is also described. Part 2 (Chapters 5 and 6) of this thesis is concerned with the parallelisation of a large molecular mechanics program. Chapter 5 describes the fundamentals of molecular mechanics such as the steric energy equation and its components, force field parameterisation and energy minimisation. The implementation of a novel programming (COMFORT) and hardware (the BB08) environment into a parallel molecular mechanics (MM) program is presented in Chapter 6. The structure of the sequential version of the MM program is detailed, before discussing the implementation of the parallel version using COMFORT and the BB08