Simulations of conformational changes and enzyme-substrate interactions in protein drug targets
Authors:   , Tesmer Laura 
Published: January 2023

Finding new drugs is a difficult, time-consuming, and costly challenge, with only a small success rate along the drug discovery pipeline of far less than 10%. The high failure rate of drug discovery projects motivates the integration of computational tools throughout the whole drug discovery pipeline, from target identification to clinical trials. Target identification is the first step in the process. A biological target, e.g., a protein that plays a role in disease, is identified and its molecular mechanism in the disease is studied. Further, a potential binding site on the target, where therapeutic molecules can bind and modulate the target’s activity, needs to be characterized. Computational tools can contribute to improving the initial molecular target elucidation and assessment. In this thesis, I use computational, physics-based approaches to characterize binding sites of drug targets and to decipher enzyme-substrate interactions, which play a role in disease mechanisms. Molecular dynamics (MD) simulations were applied to study the dynamics of molecules in solution at high temporal and spatial resolution. The method generates time-resolved trajectories of the particles in a system of interest by integrating Newton’s equations of motion numerically, starting from a set of coordinates and velocities. In MD simulations, all atoms of a chosen system, including solvent, are represented explicitly. Atomistic simulations are especially well-suited to study detailed interactions that depend on intermolecular interactions, such as hydration effects, hydrogen bonding, hydrophobic interactions, or subtle chemical differences. System properties are inferred from the trajectories, provided that the force fields, describing the interactions between the particles in the system, have a high accuracy. The bonded and non-bonded interactions are parametrized on experimental and quantum chemical data. The purpose of MD simulations can be to gain insight into the behavior of complex biological systems at molecular level, which often cannot be observed in experiments at the same resolution. With recent advances in computer hardware and simulation software, molecular systems of increasing size and simulation length can be investigated. In the first part of the thesis, I investigated the conformational ensemble of various protein drug targets. Proteins are dynamic biomacromolecules that can have diverse and nearly isoenergetic conformational states. Ligand binding can shift the equilibrium of this conformational ensemble and can uncover binding sites, called cryptic sites. Cryptic sites only emerge upon small molecule binding and are often flat and featureless, and thus not easily recognized in crystal structures without bound ligands. If new binding sites including cryptic sites are detected, they can potentially be exploited for binding to ligands and enable a druggable target. Druggability is the ability of a protein to bind small, drug-like molecules, which is the basis for rational drug design. In this thesis, I used state-of-the-art physics-based, computational approaches to investigate the conformational ensembles of binding sites. In all studied systems, it is known from experiment that a specific group of ligands can induce conformational changes. The aim is to sample the conformational space made accessible upon ligand binding, yet without using the specific ligand structures or details about their interactions. We are interested in sampling the pocket conformational states and identifying the respective pocket opening mechanism. For some cases, I additionally assessed whether the observed flexibility is a feature of the protein family, or specific to the protein under consideration. The first studied system is factor VIIa (FVIIa). FVIIa is an essential part of the coagulation cascade and hence a potential drug target for thrombotic diseases. In addition, I investigated various other trypsin-like serine proteases from the same protein family. The binding pocket of trypsin-like serine proteases is called S1 pocket. An X-ray crystal structure solved by our collaborators reveals that a b-sheet structure in the S1 pocket is distorted by a bound ligand. I resolved the conformational change with MD simulations, starting from the unbound protein structure solvated in water and ions. I observed multiple spontaneous transition events. In 7 out of 22 simulations with the b-sheet as starting structure, the S1 pocket eventually rearranged into a distorted loop structure. These transitions occurred spontaneously and were mediated by water molecules probing the backbone hydrogen bonds. The conformational change studied here controls the onset of substrate binding and catalysis. Furthermore, I used metadynamics simulation, an enhanced-sampling method, to estimate the free energy barrier of this conformational change..