Pocket optimization and its application to identify small-molecule inhibitors of protein-protein interactions

Because of their ubiquitous nature in many cellular processes, modulating protein-protein interactions offers tremendous therapeutic potential. However, protein-protein interactions remain a difficult class of drug targets, as most protein interaction sites have not evolved to bind small molecules. Indeed, some protein interaction sites are thought to be simply not amenable to binding any small molecule at all. Other sites feature small molecule binding pockets that simply are not present in the unbound or protein-bound conformations, making structure-based drug discovery difficult. Sometimes, inhibitors bind to multiple family members with high affinity, causing toxicity. In this dissertation I seek to address many of these challenges, by developing methodologies to assess the druggability of a target, assess the selectivity of known inhibitors, identify conformations that are sampled uniquely by a single protein, and identify inhibitors of protein-protein interactions. To assess druggability, I developed the “pocket optimization” protocol which uses a biasing potential to create an ensemble of conformations that contain pockets at a specified location on the protein surface. I showed that low-resolution, low energy inhibitor shapes are encoded at druggable sites and sampled through low-energy fluctuations, whereas they are not present at random sites on protein surfaces. To assess selectivity and screen for inhibitors, I developed “exemplars”, representations of a pocket based on the perfect “non-physical” complementary ligand, allowing the comparison of pocket shapes independent of protein sequence. I predicted the selectivity of an array of inhibitors to a related family of proteins by comparing the exemplars from the known small-molecule bound conformation to the ensemble of exemplars from a “pocket optimized” ensemble. I identified distinct conformations that could be targeted for identifying selective inhibitors de novo by comparing ensembles of exemplars from related family members to one another. Finally, I developed a screening protocol that uses the speed of exemplar versus small molecule comparisons to screen very large compound libraries against ensembles of distinct, “pocket optimized” pocket conformations

The Microtubule Regulator Ringmaker Functions Downstream Of Rtca And The Rna Repair/splicing Pathway To Promote Axon Regeneration

Promoting axon regeneration in the central and peripheral nervous system is of clinical importance in neural injury and neurodegenerative diseases. Both pro- and anti-regeneration factors are being identified. We previously reported that the Rtca mediated RNA repair/splicing pathway restricts axon regeneration by inhibiting the nonconventional splicing of Xbp1 mRNA under cellular stress. Here, we describe the application of a computational screening pipeline used to identify small-molecule inhibitors of Rtca and a paradigm to test for efficacy. However, the downstream effectors of Rtca remain unknown. Through transcriptome profiling, we show that the tubulin polymerization promoting protein (TPPP) – ringmaker/ringer is dramatically increased in Rtca deficient Drosophila sensory neurons, which is dependent on Xbp1. Ringer is expressed in sensory neurons before and after injury, and is cell-autonomously required for axon regeneration. While loss of ringer abolishes the regeneration enhancement in Rtca mutants, its overexpression is sufficient to promote regeneration both in the peripheral and central nervous system. Ringer maintains microtubule stability/dynamics with the microtubule-associated protein – futsch/MAP1B, which is also required for axon regeneration. Furthermore, ringer lies downstream of and is negatively regulated by the microtubule-associated deacetylase – HDAC6, which functions as a regeneration inhibitor. Taken together, our findings suggest that ringer acts as a hub for microtubule regulators that relays cellular status information, such as cellular stress, to the integrity of microtubules in order to instruct neural regeneration

Ultra-High-Throughput Structure-Based Virtual Screening for Small-Molecule Inhibitors of Protein–Protein Interactions

Protein–protein interactions play important roles in virtually all cellular processes, making them enticing targets for modulation by small-molecule therapeutics: specific examples have been well validated in diseases ranging from cancer and autoimmune disorders, to bacterial and viral infections. Despite several notable successes, however, overall these remain a very challenging target class. Protein interaction sites are especially challenging for computational approaches, because the target protein surface often undergoes a conformational change to enable ligand binding: this confounds traditional approaches for virtual screening. Through previous studies, we demonstrated that biased “pocket optimization” simulations could be used to build collections of low-energy pocket-containing conformations, starting from an unbound protein structure. Here, we demonstrate that these pockets can further be used to identify ligands that complement the protein surface. To do so, we first build from a given pocket its “exemplar”: a perfect, but nonphysical, pseudoligand that would optimally match the shape and chemical features of the pocket. In our previous studies, we used these exemplars to quantitatively compare protein surface pockets to one another. Here, we now introduce this exemplar as a template for pharmacophore-based screening of chemical libraries. Through a series of benchmark experiments, we demonstrate that this approach exhibits comparable performance as traditional docking methods for identifying known inhibitors acting at protein interaction sites. However, because this approach is predicated on ligand/exemplar overlays, and thus does not require explicit calculation of protein–ligand interactions, exemplar screening provides a tremendous speed advantage over docking: 6 million compounds can be screened in about 15 min on a single 16-core, dual-GPU computer. The extreme speed at which large compound libraries can be traversed easily enables screening against a “pocket-optimized” ensemble of protein conformations, which in turn facilitates identification of more diverse classes of active compounds for a given protein target

Activation and Inhibition of Biological Function through Design of Novel Protein-Ligand Interactions

Virtually every process within a cell involves a protein. They serve as cellular workhorses carrying out functions such as catalysis of essential metabolites, to regulating which genes get turned on or off, to forming the structural scaffolding to retain rigidity of a cell. Proteins form the link between the genetic information encoded in DNA to the observable phenotype of an organism. The way proteins communicate is by direct physical contact with another molecule that alters its shape and dynamics to carry out a particular function. For example, G protein-coupled receptors are membrane imbedded proteins that bind to a small molecule or peptide in the extracellular environment and translate the binding event into an internal signal to regulate processes such as heart rate and even mood. The ability to selectively modulate such fundamental systems offers huge potential with broad applications from the ability to interrogate unknown cellular mechanisms to developing therapeutics when these interactions become aberrant. The scope of this dissertation encompasses determining what properties dictate protein-ligand interactions and the application of these principles to the design of new ones. In particular, chapter 1 covers the design of a molecular switch that is turned on by small molecules. I follow this up in chapter 2 by investigating how to turn off protein function with small molecules in aberrant disease states. In chapter 3 we expand from the world of small molecule ligands to design a protein to turn off function of a protein involved in bacterial pathogenesis

Combining Computational And Experimental Approaches To Study Disordered And Aggregation Prone Proteins

Over the past two decades disordered proteins have become more widely recognized, challenging the canonical structure-function paradigm associated with proteins. These highly dynamic proteins have been identified across a wide range of species and play a variety of functional roles. Furthermore, the structural plasticity of these proteins gives way to their increased aggregation susceptibility, compared to canonical, well-folded proteins, placing disordered proteins at the center of many neurodegenerative diseases. Despite the increased recognition of the abundance and complexity of disordered proteins, their structural features and the mechanisms by which they transit between functional and pathological roles remains elusive. The efforts described herein focus on leveraging both experimental and computational approaches to study the structure and dynamics of these proteins. Fluorescence-based experiment have proven useful for studying these systems as the intrinsic heterogeneity of this class of proteins, which precludes the use of many traditional structural biochemistry techniques, can be accommodated. Therefore, initial efforts focused on developing new minimally perturbing fluorescence probes and coupling these probes with site-selective labeling strategies. Subsequent efforts focused on identifying methods which could predict where these probes would be tolerated to boost protein yield and avoid structural perturbation. These and other fluorescence probes were employed in Förster Resonance Energy Transfer (FRET) experiments, to study the conformational ensemble of α-synuclein, a disordered protein whose aggregation is implicated in Parkinson’s Disease pathogenesis. Experimental FRET data was paired with molecular modeling in PyRosetta to simulate the conformational ensembles of α-synuclein in the presence and absence of 2 M TMAO. The accuracy of the resultant ensembles was corroborated by comparison to other experimental data. Following this initial success using experimentally constrained simulations, attention was directed towards the development of algorithms capable of generating accurate structural representations of both disordered and ordered proteins de novo. Lastly, this work showcases the utility of a high-throughput in-silico screening approach in identifying a compound that binds selectively to α-synuclein fibrils with nanomolar affinity. Overall this work highlights several computational and experimental approaches which are broadly applicable to the study of disordered and aggregation prone protein