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Kinases are ubiquitous enzymes involved in the regulation of critical cellular pathways. However, in silico modelling of the conformational ensembles of these enzymes is difficult due to inherent limitations and the cost of computational approaches. Recent algorithmic advances combined with homology modelling and parallel simulations have enabled researchers to address this computational sampling bottleneck. Here, we present the results of molecular dynamics studies for seven Src family kinase (SFK) members: Fyn, Lyn, Lck, Hck, Fgr, Yes and Blk. We present a sequence invariant extension to Markov state models, which allows us to quantitatively compare the structural ensembles of the seven kinases. Our findings indicate that in the absence of their regulatory partners, SFK members have similar in silico dynamics with active state populations ranging from 4 to 40% and activation timescales in the hundreds of microseconds. Furthermore, we observe several potentially druggable intermediate states, including a pocket next to the adenosine triphosphate binding site that could potentially be targeted via a small-molecule inhibitor.

Protein allosteric pathways are investigated in the imidazole glycerol phosphate synthase heterodimer in an effort to elucidate how the effector (PRFAR, N'-[(5'-phosphoribulosyl)formimino]-5-aminoimidazole-4-carboxamide ribonucleotide) activates glutaminase catalysis at a distance of 25 Å from the glutamine-binding site. We apply solution NMR techniques and community analysis of dynamical networks, based on mutual information of correlated protein motions in the active and inactive enzymes. We find evidence that the allosteric pathways in the PRFAR bound enzyme involve conserved residues that correlate motion of the PRFAR binding loop to motion at the protein-protein interface, and ultimately at the glutaminase active site. The imidazole glycerol phosphate synthase bienzyme is an important branch point for the histidine and nucleotide biosynthetic pathways and represents a potential therapeutic target against microbes. The proposed allosteric mechanism and the underlying allosteric pathways provide fundamental insights for the design of new allosteric drugs and/or alternative herbicides.

Bruton tyrosine kinase (BTK) is a key enzyme in B-cell development whose improper regulation causes severe immunodeficiency diseases. Design of selective BTK therapeutics would benefit from improved, in-silico structural modeling of the kinase’s solution ensemble. However, this remains challenging due to the immense computational cost of sampling events on biological timescales. In this work, we combine multi-millisecond molecular dynamics (MD) simulations with Markov state models (MSMs) to report on the thermodynamics, kinetics, and accessible states of BTK’s kinase domain. Our conformational landscape links the active state to several inactive states, connected via a structurally diverse intermediate. Our calculations predict a kinome-wide conformational plasticity, and indicate the presence of several new potentially druggable BTK states. We further find that the population of these states and the kinetics of their inter-conversion are modulated by protonation of an aspartate residue, establishing the power of MD & MSMs in predicting effects of chemical perturbations.

Quadcopters are increasingly popular for robotics applications. Being able to efficiently calculate the set of positions reachable by a quadcopter within a time budget enables collision avoidance and pursuit-evasion strategies.This research computes the set of positions reachable by a quadcopter within a specified time limit using a simplified 2D model for quadcopter dynamics. This popular model is used to determine the set of candidate optimal control sequences to build the full 3D reachable set at final time T in (x, z, θ) phase space or rotated to form the set in (x, y, z) Cartesian space. We calculate the analytic equations that exactly bound the set of positions reachable in a given time horizon for all initial conditions. We use these bounds to: escape a bounded region, avoid a collision, find the collision set, determine the closest point to the reachable set, reach a goal (x, z) in the reachable set, and for drone countermeasures.

Enzymatic inhibition by product molecules is an important and widespread phenomenon. We describe an approach to study product inhibition at the single-molecule level. Individual HRP molecules are trapped within surface-tethered lipid vesicles, and their reaction with a fluorogenic substrate is probed. While the substrate readily penetrates into the vesicles, the charged product (resorufin) gets trapped and accumulates inside the vesicles. Surprisingly, individual enzyme molecules are found to stall when a few tens of product molecules accumulate. Bulk enzymology experiments verify that the enzyme is noncompetitively inhibited by resorufin. The initial reaction velocity of individual enzyme molecules and the number of product molecules required for their complete inhibition are broadly distributed and dynamically disordered. The two seemingly unrelated parameters, however, are found to be substantially correlated with each other in each enzyme molecule and over long times. These results suggest that, as a way to counter disorder, enzymes have evolved the means to correlate fluctuations at structurally distinct functional sites.

MRI-based navigation and propulsion of millirobots is a new and promising approach for minimally invasive therapies. The strong constant magnetic field inside the scanner precludes torque-based control. Consequently, prior propulsion techniques have been limited to gradient-based pulling through fluid-filled body lumens using the weaker gradient magnetic coils. Performing interventions requires techniques or mechanism to increase this weak magnetic pulling force. One technique is a self-assembling robotic tool designed by our lab called a Gauss gun. This thesis shows numerical analysis and results for optimizing the kinetic energy generated by a Gauss gun to penetrate tissue, deliver a drug or remove a clot. This analysis based on the equations of energy for an MRI Gauss gun. The numerical method used for this optimization is Nelder Mead, implemented in Mathematica software.

Bruton tyrosine kinase (BTK) is a key enzyme in B-cell development whose improper regulation causes severe immunodeficiency diseases. Design of selective BTK therapeutics would benefit from improved, in silico structural modeling of the kinase's solution ensemble. However, this remains challenging due to the immense computational cost of sampling events on biological timescales. In this work, we combine multi millisecond molecular dynamics (MD) simulations with Markov state models (MSMs) to report on the thermodynamics, kinetics, and accessible states of BTK's kinase domain. Our conformational landscape links the active state to several inactive states, connected via a structurally diverse intermediate. Our calculations predict a kinome-wide conformational plasticity, and indicate the presence of several new potentially druggable BTK states. We further find that the population of these states and the kinetics of their inter-conversion are modulated by protonation of an aspartate residue, establishing the power of MD & MSMs in predicting effects of chemical perturbations.

DNA-Encoded Library (DEL) has proven to be a powerful tool that utilizes combinatorially constructed small molecules to facilitate highly-efficient screening assays. These selection experiments, involving multiple stages of washing, elution, and identification of potent binders via unique DNA barcodes, often generate complex data. This complexity can potentially mask the underlying signals, necessitating the application of computational tools such as machine learning to uncover valuable insights. We introduce a compositional deep probabilistic model of DEL data, DEL-Compose, which decomposes molecular representations into their mono-synthon, di-synthon, and tri-synthon building blocks and capitalizes on the inherent hierarchical structure of these molecules by modeling latent reactions between embedded synthons. Additionally, we investigate methods to improve the observation models for DEL count data such as integrating covariate factors to more effectively account for data noise. Across two popular public benchmark datasets (CA-IX and HRP), our model demonstrates strong performance compared to count baselines, enriches the correct pharmacophores, and offers valuable insights via its intrinsic interpretable structure, thereby providing a robust tool for the analysis of DEL data.