Methods Development and Force Field Evaluation for Molecular Simulations of Iinteractions Between Structured Peptides and Functionalized Material Surfaces

The process of protein adsorption to material surfaces is highly complex and it is one of the most fundamental concepts upon which progress in the field of bioengineering is based. The strategic design of material surfaces for optimal utility in specific biological environments is absolutely dependent upon a thorough understanding of the mechanisms underlying protein adsorption, yet there is still a very limited understanding of these mechanisms. The primary reason for this lack of understanding is that protein adsorption is a dynamic process which occurs at the atomic and macromolecular scale, where experimental analyses provide a view that is static and too coarse to elucidate the stepwise processes behind this critical biochemical phenomenon. In recent years, continual improvements in speed and efficiency of computational hardware and simulation techniques have enabled the use of molecular simulation for studying systems of the size necessary for examining the mechanistic details of protein adsorption (tens to hundreds of thousands of atoms). Of the various forms of molecular simulation, all-atom empirical force field molecular dynamics (MD) simulation has shown the greatest potential for exploring the nature of protein adsorption because it offers a dynamic view of nanosecond-scale processes with atomistic detail. However, a shortcoming of the application of MD in studying protein adsorption is that the most widely used MD force fields (i.e., equations and parameter sets used for calculating structural and energetic properties) have been designed and validated for simulations of solvated molecular systems in the absence of solid surfaces. To address this shortcoming of an otherwise extremely powerful research tool, an initial evaluation of the applicability of existing MD force fields to model systems of structured peptides interacting with functionalized material surfaces is warranted. The work presented here encompasses that initial evaluation of force fields. Numerous detailed analyses of water, ions, and peptides were completed in order to provide the most accurate and comprehensive examination of simulated peptide adsorption available. As a result of this work, simulation methods for these unique systems were tested and determined to be appropriate for accurately representing experimental results. Also, a comparative evaluation of force field performance identified the force field that most consistently reflects experimental findings

The investigation of type-specific features of the copper coordinating AA9 proteins and their effect on the interaction with crystalline cellulose using molecular dynamics studies

AA9 proteins are metallo-enzymes which are crucial for the early stages of cellulose degradation. AA9 proteins have been suggested to cleave glycosidic bonds linking cellulose through the use of their Cu2+ coordinating active site. AA9 proteins possess different regioselectivities depending on the resulting cleavage they form and as result, are grouped accordingly. Type 1 AA9 proteins cleave the C1 carbon of cellulose while Type 2 AA9 proteins cleave the C4 carbon and Type 3 AA9 proteins cleave either C1 or C4 carbons. The steric congestion of the AA9 active site has been proposed to be a contributor to the observed regioselectivity. As such, a bioinformatics characterisation of type-specific sequence and structural features was performed. Initially AA9 protein sequences were obtained from the Pfam database and multiple sequence alignment was performed. The sequences were phylogenetically characterised and sequences were grouped into their respective types and sub-groups were identified. A selection analysis was performed on AA9 LPMO types to determine the selective pressure acting on AA9 protein residues. Motif discovery was then performed to identify conserved sequence motifs in AA9 proteins. Once type-specific sequence features were identified structural mapping was performed to assess possible effects on substrate interaction. Physicochemical property analysis was also performed to assess biochemical differences between AA9 LPMO types. Molecular dynamics (MD) simulations were then employed to dynamically assess the consequences of the discovered type-specific features on AA9-cellulose interaction. Due to the absence of AA9 specific force field parameters MD simulations were not readily applicable. As a result, Potential Energy Surface (PES) scans were performed to evaluate the force field parameters for the AA9 active site using the PM6 semi empirical approach and least squares fitting. A Type 1 AA9 active site was constructed from the crystal structure 4B5Q, encompassing only the Cu2+ coordinating residues, the Cu2+ ion and two water residues. Due to the similarity in AA9 active sites, the Type force field parameters were validated on all three AA9 LPMO types. Two MD simulations for each AA9 LPMO types were conducted using two separate Lennard-Jones parameter sets. Once completed, the MD trajectories were analysed for various features including the RMSD, RMSF, radius of gyration, coordination during simulation, hydrogen bonding, secondary structure conservation and overall protein movement. Force field parameters were successfully evaluated and validated for AA9 proteins. MD simulations of AA9 proteins were able to reveal the presence of unique type-specific binding modes of AA9 active sites to cellulose. These binding modes were characterised by the presence of unique type-specific loops which were present in Type 2 and 3 AA9 proteins but not in Type 1 AA9 proteins. The loops were found to result in steric congestion that affects how the Cu2+ ion interacts with cellulose. As a result, Cu2+ binding to cellulose was observed for Type 1 and not Type 2 and 3 AA9 proteins. In this study force field parameters have been evaluated for the Type 1 active site of AA9 proteins and this parameters were evaluated on all three types and binding. Future work will focus on identifying the nature of the reactive oxygen species and performing QM/MM calculations to elucidate the reactive mechanism of all three AA9 LPMO types

Multiscale Simulations of Intrinsically Disordered Proteins

Intrinsically disordered proteins (IDPs) lack stable secondary and/or tertiary structures under physiological conditions. The have now been recognized to play important roles in numerous biological processes, particularly cellular signaling and regulation. Mutation of IDPs are frequently associated with human diseases, such as cancers and neuron degenerative diseases. Therefore, it is important to understand the structure, dynamics, and interactions of IDPs, so as to establish the mechanistic basis of how intrinsic disorder mediates versatile functions and how such mechanisms may fail in human diseases. However, the heterogeneous structural ensembles of IDPs are not amenable to high resolution characterization solely through experimental measurements, and molecular modelling and simulation are required to study IDP structures, dynamics, and interactions at the atomistic levels. Here, we first applied the state-of-the-art explicit solvent atomistic simulations to an anti-apoptotic protein Bcl-xL and demonstrated how inherent structural disorder may provide a physical basis of protein regulated unfolding in signaling transduction. We have also constructed a series of efficient coarse-grained models to directly simulate the interactions between IDPs and unveiled how the preexisting structural elements accelerate binding of ACTR to NCBD by promoting efficient folding upon encounter. These studies shed important light on how IDPs perform functions in the cellular regulatory network, but also reveal the necessity of new sampling techniques for more efficient simulations of IDPs. We have thus developed a novel sampling technique, called multiscale enhanced sampling (MSES). MSES couples the atomistic model with coarse-grained ones, to accelerate the sampling of atomistic conformational space. Bias from coupling to a coarse-grained model can be removed using Hamiltonian replica exchange. To achieve the best possible efficiency of MSES simulations, we have developed a new hybrid resolution protein model that could capture the essential features of IDP structures, so as to generate local and long-range fluctuations that are largely consistent with those at the atomistic level. We have also developed an advanced replica exchange protocol, to allow the fast conformational transitions observed in the coupled conditions to be rapidly exchanged to the unbiased limit. Application of these strategies to characterize the structural ensembles of a few non-trivial IDPs shows that faster convergence rate can be achieved, demonstrating the great potential of MSES for atomistic simulations of larger and more complex IDPs

A New Method for Ligand-supported Homology Modelling of Protein Binding Sites: Development and Application to the neurokinin-1 receptor

In this thesis, a novel strategy (MOBILE (Modelling Binding Sites Including Ligand Information Explicitly)) was developed that models protein binding-sites simultaneously considering information about the binding mode of bioactive ligands during the homology modelling process. As a result, protein binding-site models of higher accuracy and relevance can be generated. Starting with the (crystal) structure of one or more template proteins, in the first step several preliminary homology models of the target protein are generated using the homology modelling program MODELLER. Ligands are then placed into these preliminary models using different strategies depending on the amount of experimental information about the binding mode of the ligands. (1.) If a ligand is known to bind to the target protein and the crystal structure of the protein-ligand complex with the related template protein is available, it can be assumed that the ligand binding modes are similar in the target and template protein. Accordingly, ligands are then transferred among these structures keeping their orientation as a restraint for the subsequent modelling process. (2.) If no complex crystal structure with the template is available, the ligand(s) can be placed into the template protein structure by docking, and the resulting orientation can then be used to restrain the following protein modelling process. Alternatively, (3.) in cases where knowledge about the binding mode cannot be inferred by the template protein, ligand docking is performed into an ensemble of homology models. The ligands are placed into a crude binding-site representation via docking into averaged property fields derived from knowledge-based potentials. Once the ligands are placed, a new set of homology models is generated. However, in this step, ligand information is considered as additional restraint in terms of the knowledge-based DrugScore protein-ligand atom pair potentials. Consulting a large ensemble of produced models exhibiting di erent side-chain rotamers for the binding-site residues, a composite picture is assembled considering the individually best scored rotamers with respect to the ligand. After a local force-field optimisation, the obtained binding-site models can be used for structure-based drug design