Selectivity by Small-Molecule Inhibitors of Protein Interactions Can Be Driven by Protein Surface Fluctuations

Small-molecules that inhibit interactions between specific pairs of proteins have long represented a promising avenue for therapeutic intervention in a variety of settings. Structural studies have shown that in many cases, the inhibitor-bound protein adopts a conformation that is distinct from its unbound and its protein-bound conformations. This plasticity of the protein surface presents a major challenge in predicting which members of a protein family will be inhibited by a given ligand. Here, we use biased simulations of Bcl-2-family proteins to generate ensembles of low-energy conformations that contain surface pockets suitable for small molecule binding. We find that the resulting conformational ensembles include surface pockets that mimic those observed in inhibitor-bound crystal structures. Next, we find that the ensembles generated using different members of this protein family are overlapping but distinct, and that the activity of a given compound against a particular family member (ligand selectivity) can be predicted from whether the corresponding ensemble samples a complementary surface pocket. Finally, we find that each ensemble includes certain surface pockets that are not shared by any other family member: while no inhibitors have yet been identified to take advantage of these pockets, we expect that chemical scaffolds complementing these “distinct” pockets will prove highly selective for their targets. The opportunity to achieve target selectivity within a protein family by exploiting differences in surface fluctuations represents a new paradigm that may facilitate design of family-selective small-molecule inhibitors of protein-protein interactions.This work was supported by a grant from the National Institute of General Medical Sciences of the National Institutes of Health (R01GM099959), the National Science Foundation through XSEDE allocation MCB130049, and the Alfred P. Sloan Fellowship (JK). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Inhibition of protein interactions: co-crystalized protein–protein interfaces are nearly as good as holo proteins in rigid-body ligand docking

Modulating protein interaction pathways may lead to the cure of many diseases. Known protein–protein inhibitors bind to large pockets on the protein–protein interface. Such large pockets are detected also in the protein–protein complexes without known inhibitors, making such complexes potentially druggable. The inhibitor-binding site is primary defined by the side chains that form the largest pocket in the protein-bound conformation. Low-resolution ligand docking shows that the success rate for the protein-bound conformation is close to the one for the ligand-bound conformation, and significantly higher than for the apo conformation. The conformational change on the protein interface upon binding to the other protein results in a pocket employed by the ligand when it binds to that interface. This proof-of-concept study suggests that rather than using computational pocket-opening procedures, one can opt for an experimentally determined structure of the target co-crystallized protein–protein complex as a starting point for drug design

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

Molecular dynamics and virtual screening approaches in drug discovery

Computer-aided drug discovery (CADD) methods are now routinely used in the preclinical phase of drug development. Powerful high-performance computing facilities and the extremely fast CADD methods constantly scale up the coverage of drug-like chemical space achievable in rational drug development. In this thesis, CADD approaches were applied to address several early-phase drug discovery problems. Namely, small molecule binding site detection on a novel target protein, virtual screening (VS) of molecular databases, and characterization of small molecule interactions with metabolic enzymes were studied. Various CADD methods, including molecular dynamics (MD) simulations in mixed solvents, molecular docking, and binding free energy calculations, were employed. Co-solvent MD simulations detected biologically relevant binding sites and provided guidance for screening potential protein-protein interaction inhibitors for a crucial protein of the SARS-CoV-2. VS with fragment- and negative image-based (F-NIB) models identified three active and structurally novel inhibitors of the putative drug target phosphodiesterase 10A. MD simulations and docking provided detailed insights on the effects of active site structural flexibility and variation on the binding and resultant metabolism of small molecules with the cytochrome P450 enzymes. The results presented in this thesis contribute to the increasing evidence that supports employment and further development of CADD approaches in drug discovery. Ultimately, rational drug development coupled with CADD may enable higher quality drug candidates to the human studies in the future, reducing the risk of financially and temporally costly clinical failure. KEYWORDS: Structure-based drug development, Computer-aided drug discovery (CADD), Molecular dynamics (MD) simulation, Virtual screening (VS), Fragmentand negative image-based (F-NIB) model, Structure-activity relationship (QSAR), Cytochrome P450 ligand binding predictionMolekyylidynamiikka- ja virtuaaliseulontamenetelmät lääkeaine-etsinnässä Tietokoneavusteista lääkeaine-etsintää käytetään nykyisin yleisesti prekliinisessä lääketutkimuksessa. Suurteholaskenta ja äärimmäisen nopeat tietokoneavusteiset lääkeaine-etsintämenetelmät mahdollistavat jatkuvasti kattavamman lääkkeenkaltaisten molekyylien kemiallisen avaruuden seulonnan. Tässä väitöskirjassa tietokonepohjaisia menetelmiä hyödynnettiin lääketutkimuksen prekliiniseen vaiheeseen liittyvissä tyypillisissä tutkimusongelmissa. Työhön kuului pienmolekyylien sitoutumisalueiden tunnistus uuden kohdeproteiinin rakenteesta, molekyylitietokantojen virtuaaliseulonta sekä pienmolekyylien ja metabolian entsyymien välisten vuorovaikutusten tietokonemallinnus. Työssä käytettiin useita tietokoneavusteisen lääkeaine-etsinnän menetelmiä, sisältäen molekyylidynamiikkasimulaatiot (MD-simulaatiot) vaihtuvissa liuottimissa, molekulaarisen telakoinnin ja sitoutumisenergian laskennan. Orgaanisen liuottimen ja veden sekoituksessa tehdyt MD-simulaatiot tunnistivat biologisesti merkittäviä sitoutumisalueita SARS-CoV-2:n tärkeästä proteiinista ja ohjasivat infektioon liittyvän proteiini-proteiinivuorovaikutuksen potentiaalisten estäjien etsintää. Virtuaaliseulonnalla tunnistettiin kolme rakenteellisesti uudenlaista tunnetun lääkekehityskohteen, fosfodiesteraasi 10A:n, estäjää hyödyntäen fragmentti- ja negatiivikuvamalleja. MD-simulaatiot ja telakointi tuottivat yksityiskohtaista tietoa sytokromi P450 entsyymien aktiivisen kohdan rakenteen jouston ja muutosten vaikutuksesta pienmolekyylien sitoutumiseen ja metaboliaan. Tämän väitöskirjan tulokset tukevat kasvavaa todistusaineistoa tietokoneavusteisen lääkeaine-etsinnän käytön ja kehityksen hyödyllisyydestä prekliinisessä lääketutkimuksessa. Tietokoneavusteinen lääkeaine-etsintä voi lopulta mahdollistaa korkeampilaatuisten lääkekandidaattien päätymisen ihmiskokeisiin, pienentäen taloudellisesti ja ajallisesti kalliin kliinisen tutkimuksen epäonnistumisen riskiä. AVAINSANAT: Rakennepohjainen lääkeainekehitys, Tietokoneavusteinen lääkeaine-etsintä, Molekyylidynamiikkasimulaatio (MD-simulaatio), Virtuaaliseulonta, Fragmentti- ja negatiivikuvamalli, Rakenne-aktiivisuussuhde, Sytokromi P450 ligandien sitoutumisen ennustu

Predicting the Most Tractable Protein Surfaces in the Human Proteome for Developing New Therapeutics

A critical step in the target identification phase of drug discovery is evaluating druggability, i.e., whether a protein can be targeted with high affinity using drug-like ligands. The overarching goal of my PhD thesis is to build a machine learning model that predicts the binding affinity that can be attained when addressing a given protein surface. I begin by examining the lead optimization phase of drug development, where I find that in a test set of 297 examples, 41 of these (14%) change binding mode when a ligand is elaborated. My analysis shows that while certain ligand physiochemical properties predispose changes in binding mode, particularly those properties that define fragments, simple structure-based modeling proves far more effective for identifying substitutions that alter the binding mode. My proposed measure of RMAC (rmsd after minimization of the aligned complex) can help determine whether a given ligand can be reliably elaborated without changing binding mode, thus enabling straightforward interpretation of the resulting structure-activity relationships. Moving forward, I next noted that a very popular machine learning algorithm for regression tasks, random forest, has a systematic bias in the predictions it generates; this bias is present in both real-world datasets and synthetic datasets. To address this, I define a numerical transformation that can be applied to the output of random forest models. This transformation fully removes the bias in the resulting predictions, and yields improved predictions across all datasets. Finally, taking advantage of this improved machine learning approach, I describe a model that predicts the “attainable binding affinity” for a given binding pocket on a protein surface. This model uses 13 physiochemical and structural features calculated from the protein structure, without any information about the ligand. While details of the ligand must (of course) contribute somewhat to the binding affinity, I find that this model still recapitulates the binding affinity for 848 different protein-ligand complexes (across 230 different proteins) with correlation coefficient 0.57. I further find that this model is not limited to “traditional” drug targets, but rather that it works just as well for emerging “non-traditional” drug targets such as inhibitors of protein-protein interactions. Collectively, I anticipate that the tools and insights generated in the course of my PhD research will play an important role in facilitating the key target selection phase of drug discovery projects

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

Discovery of Molecules that Modulate Protein-Protein Interactions in the Context of Human Proliferating Cell Nuclear Antigen-Associated Processes of DNA Replication and Damage Repair

Integral to cell viability is the homotrimeric protein complex Proliferating Cell Nuclear Antigen (PCNA) that encircles chromatin-bound DNA and functionally acts as a DNA clamp that provides topological sites for recruitment of proteins necessary for DNA replication and damage repair. PCNA has critical roles in the survival and proliferation of cells, as disease-associated dysregulation of associated functions can have dire effects on genome stability, leading to the formation of various malignancies ranging from non-Hodgkin’s lymphoma to skin, laryngeal, ocular, prostate and breast cancers. Here, a strategy was explored with PCNA as a drug target that may have wider implications for targeting protein-protein interactions (PPIs) as well as for fragment-based drug design. A design platform using peptidomimetic small molecules was developed that maps ideal surface binding interaction sites at a PPI interface before considering detailed conformations of an optimal ligand. A novel in silico multi-fragment, combinatorial screening approach was used to guide the selection and subsequent synthesis of tripeptoid ligands, which were evaluated in a PCNA-based competitive displacement assay. From the results, some of the peptoid-based compounds that were synthesized displayed the ability to disrupt the interaction between PCNA and a PIP box-containing peptide. The IC50 values of these compounds had similar or improved affinity to that of T2AA, an established inhibitor of PCNA-PIP box interactions. The information gained here could be useful for subsequent drug lead candidate identification

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

Computational approaches to identify small-molecule inhibitors of non-traditional drug targets

Non-traditional targets for therapeutic intervention are those proteins that have not evolved to bind small molecules, but have instead evolved to bind other macromolecules. Such targets include protein–protein interaction sites, protein–RNA interaction sites and protein–DNA interaction sites. Modulating these biologically important targets will allow us as a community to develop novel therapeutics, but still remains a major challenge. In this thesis, I describe two different computational approaches that I have developed: one for identifying small-molecule inhibitors of protein–protein interactions, and the other for identifying small-molecule inhibitors of protein–RNA interactions. To specifically target protein interaction sites, I have developed a docking method called DARC (Docking Approach using Ray-Casting). This method quantitatively measures the complementarity between the protein surface and a ligand, by using ray-casting to map and compare their shapes. I have applied DARC to carry out a virtual screen against the protein interaction site of the protein Mcl 1, allowing us to identify 6 new inhibitors of this exciting target. To specifically target protein-RNA interactions, I have developed a mimicry-inspired strategy that extracts a “hotspot pharmacophore” from the structure of a protein-RNA complex, and then uses this as a template for ligand-based virtual screening. I have applied this strategy to screen for compounds that inhibit the Musashi-1 / NUMB mRNA interaction, allowing us to identify a new class of compounds that inhibit this interaction in both biochemical and cell-based assays. This thesis is outlined as follows. In the first chapter, I will compare the structural features of inhibitor-bound complexes of traditional versus non-traditional protein targets. In the second chapter, I will present the DARC method and its application to Mcl-1. In the third chapter, I will present various enhancements to DARC method that result in both speed and performance improvements. Finally, in the Fourth chapter I will present the “hotspot mimicry” approach for targeting protein-RNA interactions and application of this approach in identification of inhibitors for Musashi 1 / NUMB mRNA interaction