Rational Design of Small-Molecule Inhibitors of Protein-Protein Interactions: Application to the Oncogenic c-Myc/Max Interaction

Protein-protein interactions (PPIs) constitute an emerging class of targets for pharmaceutical intervention pursued by both industry and academia. Despite their fundamental role in many biological processes and diseases such as cancer, PPIs are still largely underrepresented in today's drug discovery. This dissertation describes novel computational approaches developed to facilitate the discovery/design of small-molecule inhibitors of PPIs, using the oncogenic c-Myc/Max interaction as a case study.First, we critically review current approaches and limitations to the discovery of small-molecule inhibitors of PPIs and we provide examples from the literature.Second, we examine the role of protein flexibility in molecular recognition and binding, and we review recent advances in the application of Elastic Network Models (ENMs) to modeling the global conformational changes of proteins observed upon ligand binding. The agreement between predicted soft modes of motions and structural changes experimentally observed upon ligand binding supports the view that ligand binding is facilitated, if not enabled, by the intrinsic (pre-existing) motions thermally accessible to the protein in the unliganded form.Third, we develop a new method for generating models of the bioactive conformations of molecules in the absence of protein structure, by identifying a set of conformations (from different molecules) that are most mutually similar in terms of both their shape and chemical features. We show how to solve the problem using an Integer Linear Programming formulation of the maximum-edge weight clique problem. In addition, we present the application of the method to known c-Myc/Max inhibitors.Fourth, we propose an innovative methodology for molecular mimicry design. We show how the structure of the c-Myc/Max complex was exploited to designing compounds that mimic the binding interactions that Max makes with the leucine zipper domain of c-Myc.In summary, the approaches described in this dissertation constitute important contributions to the fields of computational biology and computer-aided drug discovery, which combine biophysical insights and computational methods to expedite the discovery of novel inhibitors of PPIs

Identifying and Visualizing Macromolecular Flexibility in Structural Biology

Structural biology comprises a variety of tools to obtain atomic resolution data for the investigation of macromolecules. Conventional structural methodologies including crystallography, NMR and electron microscopy often do not provide sufficient details concerning flexibility and dynamics, even though these aspects are critical for the physiological functions of the systems under investigation. However, the increasing complexity of the molecules studied by structural biology (including large macromolecular assemblies, integral membrane proteins, intrinsically disordered systems, and folding intermediates) continuously demands in-depth analyses of the roles of flexibility and conformational specificity involved in interactions with ligands and inhibitors. The intrinsic difficulties in capturing often subtle but critical molecular motions in biological systems have restrained the investigation of flexible molecules into a small niche of structural biology. Introduction of massive technological developments over the recent years, which include time-resolved studies, solution X-ray scattering, and new detectors for cryo-electron microscopy, have pushed the limits of structural investigation of flexible systems far beyond traditional approaches of NMR analysis. By integrating these modern methods with powerful biophysical and computational approaches such as generation of ensembles of molecular models and selective particle picking in electron microscopy, more feasible investigations of dynamic systems are now possible. Using some prominent examples from recent literature, we review how current structural biology methods can contribute useful data to accurately visualize flexibility in macromolecular structures and understand its important roles in regulation of biological processes

Structural Prediction of Protein–Protein Interactions by Docking: Application to Biomedical Problems

A huge amount of genetic information is available thanks to the recent advances in sequencing technologies and the larger computational capabilities, but the interpretation of such genetic data at phenotypic level remains elusive. One of the reasons is that proteins are not acting alone, but are specifically interacting with other proteins and biomolecules, forming intricate interaction networks that are essential for the majority of cell processes and pathological conditions. Thus, characterizing such interaction networks is an important step in understanding how information flows from gene to phenotype. Indeed, structural characterization of protein–protein interactions at atomic resolution has many applications in biomedicine, from diagnosis and vaccine design, to drug discovery. However, despite the advances of experimental structural determination, the number of interactions for which there is available structural data is still very small. In this context, a complementary approach is computational modeling of protein interactions by docking, which is usually composed of two major phases: (i) sampling of the possible binding modes between the interacting molecules and (ii) scoring for the identification of the correct orientations. In addition, prediction of interface and hot-spot residues is very useful in order to guide and interpret mutagenesis experiments, as well as to understand functional and mechanistic aspects of the interaction. Computational docking is already being applied to specific biomedical problems within the context of personalized medicine, for instance, helping to interpret pathological mutations involved in protein–protein interactions, or providing modeled structural data for drug discovery targeting protein–protein interactions.Spanish Ministry of Economy grant number BIO2016-79960-R; D.B.B. is supported by a predoctoral fellowship from CONACyT; M.R. is supported by an FPI fellowship from the Severo Ochoa program. We are grateful to the Joint BSC-CRG-IRB Programme in Computational Biology.Peer ReviewedPostprint (author's final draft

Conformations and 3D pharmacophore searching

Several methods have been developed and published over the past years to generate sets of diverse and pharmacologically relevant conformations which can be used within 3D pharmacophore search protocols to increase the number of meaningful hits of such experiments. This review gives some insights into the general challenges and problems in the area of 3D structure and conformation generation and focuses on some available and recent software technologies and approaches applicable for this task. The methods, algorithms and philosophies behind the approaches are briefly described and discussed and some examples on the performance and results obtained with the different tools are given

Structure-based Development of Secondary Amines as Aspartic Protease Inhibitors

As novel promising scaffold for HIV protease inhibition pyrrolidine-derived inhibitors have recently been reported. In this thesis the stepwise improvement of this compound class to potent inhibitors of wildtype as well as selected mutant proteases utilizing rational drug discovery methods is reported. Based on the crystal structure of a (rac)-3,4-dimethyleneamino-pyrrolidine in complex with HIV-1 protease symmetric pyrrolidine-diesters possessing the same stereochemistry were synthesized following a chiral-pool approach. The most potent compounds of the series achieve one-digit micromolar inhibition towards wild type as well as two mutant proteases (Ile50Val and Ile84Val). The cocrystal structure of one derivative in complex with the Ile84Val HIV protease revealed that two inhibitor molecules are bound in the large active site cavity comprising an area encompassed by the catalytic dyad and the flaps in the open conformation. This is the first HIV protease cocrystal structure in which the open-flap conformation of the enzyme is stabilized by an inhibitor that concomitantly addresses the catalytic dyad. As an alternative approach towards HIV protease inhibitors, the development of symmetric 3,4-bis N-alkyl sulfonamide-pyrrolidines is described. The initial lead structure possessing benzene sulfonamide groups and benzyl substituents exhibited a Ki of 2.2 µM. The X-ray structure in complex with the HIV protease enabled the rational design of a second series of inhibitors and revealed three promising symmetric substitution patterns for further lead optimization: (A) Elongation of the P1/P1’-benzyl moieties with hydrophobic substituents in para-position, (B) ortho-substitution at the P2/P2’-phenyl ring systems, and (C) para-substitution at the P2/P2’-phenyl moieties. All three strategies were pursued and resulted in inhibitors with improved affinities up to 260 nM. To elucidate the underlying factors accounting for the SAR, the crystal structures of four representatives, at least one of each modification type, in complex with HIV protease were determined. These structures provided deeper insights into the protein–ligand interactions and the underlying principles of the SAR thus enabling to choose the most promising combination of substituents in the next design cycle. The combination of these substituents rendered a final inhibitor showing a significantly improved affinity of Ki = 74 nM and the cocrystal structure in complex with the HIV protease confirmed the successful application of the pursued optimization strategy. Subsequently the influence of the active site mutations Ile50Val and Ile84Val on these inhibitors is investigated by structural and kinetic analysis. Whereas the Ile50Val mutation leads to a significant decrease in affinity for all compounds in this series, they retain or even show increased affinity towards the crucial Ile84Val mutation. By detailed analysis of the crystal structures of two representatives in complex with wild-type and mutant proteases the structural basis of this phenomenon was elucidated. Inhibitors bearing smaller N-alkyl substituents revealed a selectivity profile not being explicable with the initial SAR. By cocrystallization of the most potent derivative of a small series with HIV-1 protease, astonishingly two different crystal forms, P2(1)2(1)2(1) and P6(1)22, were obtained. Structural analysis revealed two completely different binding modes, the interaction of the pyrrolidine nitrogen atom to the catalytic aspartates being the only similarity. Encouraged by the successful utilization of cyclic secondary amines as anchoring group in the development of HIV protease inhibitors, this strategy was expanded into a general approach for lead structure identification for aspartic proteases. An initial library comprising eleven inhibitors based on easily accessible achiral linear oligoamines was developed and screened against six selected aspartic proteases (HIV-1 protease, plasmepsin II, plasmepsin IV, renin, BACE-1, and pepsin). Several hits could be identified, among them selective as well as rather promiscuous inhibitors. The design concept was consecutively confirmed by determination of the crystal structure of two derivatives in complex with HIV-1 protease. The binding modes exhibit high similarity to the binding orientation of substrates as well as to that of peptidomimetic inhibitors. Using this information, a generalization of this binding situation to other aspartic proteases appears reasonable, thus providing a first insight into the observed structure-activity relationships

Lead optimization for new antimalarials and Successful lead identification for metalloproteinases: A Fragment-based approach Using Virtual Screening

Lead optimization for new antimalarials and Successful lead identification for metalloproteinases: A Fragment-based approach Using Virtual Screening Computer-aided drug design is an essential part of the modern medicinal chemistry, and has led to the acceleration of many projects. The herein described thesis presents examples for its application in the field of lead optimization and lead identification for three metalloproteins. DOXP-reductoisomerase (DXR) is a key enzyme of the mevalonate independent isoprenoid biosynthesis. Structure-activity relationships for 43 DXR inhibitors are established, derived from protein-based docking, ligand-based 3D QSAR and a combination of both approaches as realized by AFMoC. As part of an effort to optimize the properties of the established inhibitor Fosmidomycin, analogues have been synthesized and tested to gain further insights into the primary determinants of structural affinity. Unfortunately, these structures still leave the active Fosmidomycin conformation and detailed reaction mechanism undetermined. This fact, together with the small inhibitor data set provides a major challenge for presently available docking programs and 3D QSAR tools. Using the recently developed protein tailored scoring protocol AFMoC precise prediction of binding affinities for related ligands as well as the capability to estimate the affinities of structurally distinct inhibitors has been achieved. Farnesyltransferase is a zinc-metallo enzyme that catalyzes the posttranslational modification of numerous proteins involved in intracellular signal transduction. The development of farnesyltransferase inhibitors is directed towards the so-called non-thiol inhibitors because of adverse drug effects connected to free thiols. A first step on the way to non-thiol farnesyltransferase inhibitors was the development of an CAAX-benzophenone peptidomimetic based on a pharmacophore model. On its basis bisubstrate analogues were developed as one class of non-thiol farnesyltransferase inhibitors. In further studies two aryl binding and two distinct specificity sites were postulated. Flexible docking of model compounds was applied to investigate the sub-pockets and design highly active non-thiol farnesyltransferase inhibitor. In addition to affinity, special attention was paid towards in vivo activity and species specificity. The second part of this thesis describes a possible strategy for computer-aided lead discovery. Assembling a complex ligand from simple fragments has recently been introduced as an alternative to traditional HTS. While frequently applied experimentally, only a few examples are known for computational fragment-based approaches. Mostly, computational tools are applied to compile the libraries and to finally assess the assembled ligands. Using the metalloproteinase thermolysin (TLN) as a model target, a computational fragment-based screening protocol has been established. Starting with a data set of commercially available chemical compounds, a fragment library has been compiled considering (1) fragment likeness and (2) similarity to known drugs. The library is screened for target specificity, resulting in 112 fragments to target the zinc binding area and 75 fragments targeting the hydrophobic specificity pocket of the enzyme. After analyzing the performance of multiple docking programs and scoring functions forand the most 14 candidates are selected for further analysis. Soaking experiments were performed for reference fragment to derive a general applicable crystallization protocol for TLN and subsequently for new protein-fragment complex structures. 3-Methylsaspirin could be determined to bind to TLN. Additional studies addressed a retrospective performance analysis of the applied scoring functions and modification on the screening hit. Curios about the differences of aspirin and 3-methylaspirin, 3-chloroaspirin has been synthesized and affinities could be determined to be 2.42 mM; 1.73 mM und 522 μM respectively. The results of the thesis show, that computer aided drug design approaches could successfully support projects in lead optimization and lead identification. fragments in general, the fragments derived from the screening are docke

Advances in the Development of Shape Similarity Methods and Their Application in Drug Discovery

Molecular similarity is a key concept in drug discovery. It is based on the assumption that structurally similar molecules frequently have similar properties. Assessment of similarity between small molecules has been highly effective in the discovery and development of various drugs. Especially, two-dimensional (2D) similarity approaches have been quite popular due to their simplicity, accuracy and efficiency. Recently, the focus has been shifted toward the development of methods involving the representation and comparison of three-dimensional (3D) conformation of small molecules. Among the 3D similarity methods, evaluation of shape similarity is now gaining attention for its application not only in virtual screening but also in molecular target prediction, drug repurposing and scaffold hopping. A wide range of methods have been developed to describe molecular shape and to determine the shape similarity between small molecules. The most widely used methods include atom distance-based methods, surface-based approaches such as spherical harmonics and 3D Zernike descriptors, atom-centered Gaussian overlay based representations. Several of these methods demonstrated excellent virtual screening performance not only retrospectively but also prospectively. In addition to methods assessing the similarity between small molecules, shape similarity approaches have been developed to compare shapes of protein structures and binding pockets. Additionally, shape comparisons between atomic models and 3D density maps allowed the fitting of atomic models into cryo-electron microscopy maps. This review aims to summarize the methodological advances in shape similarity assessment highlighting advantages, disadvantages and their application in drug discovery