ATOMISTIC SIMULATIONS OF INTRINSICALLY DISORDERED PROTEIN FOLDING AND DYNAMICS

Intrinsically disordered proteins (IDPs) are crucial in biology and human diseases, necessitating a comprehensive understanding of their structure, dynamics, and interactions. Atomistic simulations have emerged as a key tool for unraveling the molecular intricacies and establishing mechanistic insights into how these proteins facilitate diverse biological functions. However, achieving accurate simulations requires both an appropriate protein force field capable of describing the energy landscape of functionally relevant IDP conformations and sufficient conformational sampling to capture the free energy landscape of IDP dynamics. These factors are fundamental in comprehending potential IDP structures, dynamics, and interactions. I first conducted explicit solvent simulations to assess the performance of two state-of-the-art protein force fields, namely CHARMM36m and a99SB-disp, in capturing the stability of small protein-protein interactions. To evaluate their accuracy, I selected a set of 46 amino acid backbone and side chain pairs with representative configurations and computed the free energy profiles of their interactions. The results demonstrated that CHARMM36m consistently predicted stronger protein-protein interactions compared to a99SB-disp. Notably, the most significant overestimation in CHARMM36m occurred in charged pairs involving Arg and Glu side chains, with an overestimation of up to 2.9 kcal/mol. Through free energy decomposition analysis, I determined that these overestimations were primarily driven by protein-water electrostatic interactions rather than van der Waals (vdW) interactions. Consequently, these findings suggest that careful rebalancing of electrostatic interactions should be considered in the further optimization of protein force fields. In order to enhance the conformational sampling of IDPs, I developed an integrated approach that combines an improved implicit solvent model called Generalized Born with molecular volume and solvent accessible surface area (GBMV2/SA) with a multiscale enhanced sampling (MSES) technique. To make this approach more efficient, I implemented it as a standalone OpenMM plugin on Graphics Processing Units (GPUs). The results demonstrated that the GPU-GBMV2/SA model achieved numerical equivalence to the original CPU-GBMV2/SA models, while providing a remarkable ~60x speedup on a single NVIDIA TITAN X (Pascal) graphics card for molecular dynamic simulations of both folded and unstructured proteins. This significant acceleration greatly facilitated the application of the approach in biomolecular simulations. In addition, I conducted an evaluation of the reliability of GBMV2/SA models in simulating both folded and unfolded proteins. The results revealed that the GBMV2/SA model accurately describes small proteins, but its applicability is limited when it comes to larger proteins such as KID and p53-TAD proteins. This limitation can be attributed to the absence of long-range solute-solvent dispersion interactions in the model. To address this issue, I introduced a comprehensive treatment of nonpolar solvation free energy called GBMV2/NP model. Unfortunately, the GBMV2/NP model exhibited a destabilizing effect on well-folded proteins, particularly larger ones, due to an inaccurate representation of the repulsive solvent accessible surface area (SASA) model caused by the utilization of unphysical van der Waals volume. This observation highlights the need for further improvements in accurately describing the nonpolar term in the model

Targeting Intrinsically Disordered Proteins through Dynamic Interactions

Intrinsically disordered proteins (IDPs) are over-represented in major disease pathways and have attracted significant interest in understanding if and how they may be targeted using small molecules for therapeutic purposes. While most existing studies have focused on extending the traditional structure-centric drug design strategies and emphasized exploring pre-existing structure features of IDPs for specific binding, several examples have also emerged to suggest that small molecules could achieve specificity in binding IDPs and affect their function through dynamic and transient interactions. These dynamic interactions can modulate the disordered conformational ensemble and often lead to modest compaction to shield functionally important interaction sites. Much work remains to be done on further elucidation of the molecular basis of the dynamic small molecule–IDP interaction and determining how it can be exploited for targeting IDPs in practice. These efforts will rely critically on an integrated experimental and computational framework for disordered protein ensemble characterization. In particular, exciting advances have been made in recent years in enhanced sampling techniques, Graphic Processing Unit (GPU)-computing, and protein force field optimization, which have now allowed rigorous physics-based atomistic simulations to generate reliable structure ensembles for nontrivial IDPs of modest sizes. Such de novo atomistic simulations will play crucial roles in exploring the exciting opportunity of targeting IDPs through dynamic interactions

Atomistic Peptide Folding Simulations Reveal Interplay of Entropy and Long-Range Interactions in Folding Cooperativity

Understanding how proteins fold has remained a problem of great interest in biophysical research. Atomistic computer simulations using physics-based force fields can provide important insights on the interplay of different interactions and energetics and their roles in governing the folding thermodynamics and mechanism. In particular, generalized Born (GB)-based implicit solvent force fields can be optimized to provide an appropriate balance between solvation and intramolecular interactions and successfully recapitulate experimental conformational equilibria for a set of helical and β-hairpin peptides. Here, we further demonstrate that key thermodynamic properties and their temperature dependence obtained from replica exchange molecular dynamics simulations of these peptides are in quantitative agreement with experimental results. Useful lessons can be learned on how the interplay of entropy and sequentially long-range interactions governs the mechanism and cooperativity of folding. These results highlight the great potential of high-quality implicit solvent force fields for studying protein folding and large-scale conformational transitions

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 THEORETICAL INVESTIGATION EXAMINING DNA CONFORMATIONAL CHANGES AND THEIR EFFECTS ON GLYCOSYLASE FUNCTION

Glycosylase enzymes initiate the process of base excision repair (BER) in order to prevent the irreversible modification of the genome. In the BER process a damaged DNA base is recognized, removed from the DNA sequence, and then the remaining abasic site is repaired. Glycosylase enzymes are responsible for the base recognition mechanism and catalysis of the base excision. One of the most studied glycosylase superfamilies is uracil DNA glycosylase (UDG). The UDG superfamily has demonstrated specificity for excising uracil, which is the deamination product of cytosine, from DNA sequences of prokaryotes and eukaryotes. Mismatch-specific uracil DNA glycosylase (MUG) is a member of the UDG superfamily, and interestingly has shown specificity for both uracil and xanthine bases. The following dissertation provides an anlaysis on the recognition mechanism of E. coli MUG for deaminated DNA bases. Glycosylase enzymes require the damaged base to be flipped out of the base stack, and into an active site for catalysis of the N-glycosidic cleavage. Typically, recognition of substrates by enzymes is characterized by binding affinities, but in the following work the binding of E.Coli MUG is broken down into contributions from the base flipping and enzyme binding equilibria. Since DNA conformational changes play a large role in UDG systems, the robustness of molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) free energy method was evaluated for a DNA conformational change. The A-form to B-form DNA conformational free energy differences were calculated using MM/PBSA, and compared with free energy differences determined with a more rigorous umbrella sampling method. MM/PBSA calculations of the free energy difference between A-form and B-form DNA are shown to be in very close agreement with the PMF result determined using an umbrella sampling approach. The sensitivity to solvent model and force field used during conformational sampling was also established for the MM/PBSA free energies. In order to determine the influence of base flipping conformational changes on the MUG recognition process, PMF profiles were generated for each of the damaged bases (uracil, xanthine, oxanine, inosine). Agreement was displayed between the base pair stability trends from the umbrella sampling, and the enzyme activities from experiment. Interaction energies and structural analyses were used to examine the MUG enzyme, which revealed regions of the active site critical for binding xanthine and uracil substrates. Site-directed mutagenesis experiments were performed on MUG to determine the role of specific amino acids in the recognition mechanism. Mutations were studied further through modeling and molecular dynamics (MD) simulations of the unbound and bound proteins

Intrinsically Disordered Proteins and Chronic Diseases

This book is an embodiment of a series of articles that were published as part of a Special Issue of Biomolecules. It is dedicated to exploring the role of intrinsically disordered proteins (IDPs) in various chronic diseases. The main goal of the articles is to describe recent progress in elucidating the mechanisms by which IDPs cause various human diseases, such as cancer, cardiovascular disease, amyloidosis, neurodegenerative diseases, diabetes, and genetic diseases, to name a few. Contributed by leading investigators in the field, this compendium serves as a valuable resource for researchers, clinicians as well as postdoctoral fellows and graduate student

Towards Improving The Accuracy of Implicit Solvent Models and Understanding Electrostatic Catalysis in Complex Solvent Environment

This thesis develops improved protocols for studying reactions in solution and uses them to explore the possibility of harnessing complex non-standard solvent environments to catalyse chemical reactions. The thesis covers different but related topics: Improving the accuracy of implicit solvent models. Implicit solvent models are simple cost-effective strategies for modelling solvent as a polarizable continuum. However, the accuracy of this approach can be quite variable. Herein, we examine approaches to improving their accuracy through cavity scaling, the choice of theoretical level and the inclusion of explicit solvent molecules. For SMD, we show that the best performance is achieved when cavity scaling is not employed, while for PCM we present a series of electrostatic scale factors that are radii, solvent and ion type dependent. For both families of method, we also highlight the importance choosing an appropriate level of theory, and identify when explicit solvent molecules are required.. Modelling electrostatic catalysis in complex solvent environment. Recent nanoscale experiments have shown that electric fields are capable of catalysing and controlling chemical reactions, but experimental platforms for scaling these effects remain elusive. Herein, two different approaches to addressing this challenge are explored. The first is using the internal electric field of ordered solvents and ionic liquids, the second is using the electric fields that form naturally at the gas-water interface. A multi-scale modelling approach was developed using polarizable force field based molecular dynamic simulation, post-HF, DFT and semi-empirical quantum chemical calculations. We showed that after exposure to an external electric field, ensembles of solvent or ionic liquid molecules become ordered and this ordering can generate an internal electric field, which persists even after the external potential is removed. Experimental collaborators subsequently detected this field as an open-circuit potential that is strong and long-lived. Computationally we showed that this field is enough to lower reaction barriers by as much as 20 kcal mol-1, and we also developed a predictive structure-reactivity model to choose ionic liquids that optimize this field. In the second approach, we harnessed the electric fields of the gas-water interface. A collaborator showed that in the presence of static, inert gas bubbles, the oxidation potential of HO anion/HO radical was dramatically lowered (by more than 0.5V), much more than any subtle concentration effects predicted by the Nernst equation. Further experiments showed that a high unbalanced concentration of HO- ions (as much as 5M) accumulate at the interface. Our multi-scale modelling calculations showed that this reduction in potential was due to the mutual repulsion of the HO- ions and as little as 1M unbalanced excess was enough to explain the experimental results. The work raises opportunities in reducing the cost of electrochemical processes, and points to electrostatic effects contributing to the well-known catalytic effects of "on water" reactions. Works in this thesis are expected to be useful in the future studies of solution-phase pKa, redox potential, electrostatic catalysis and ionic liquids-based electrochemical devices