Analyzing multitarget activity landscapes using protein-ligand interaction fingerprints: interaction cliffs.

This is the original submitted version, before peer review. The final peer-reviewed version is available from ACS at http://pubs.acs.org/doi/abs/10.1021/ci500721x.Activity landscape modeling is mostly a descriptive technique that allows rationalizing continuous and discontinuous SARs. Nevertheless, the interpretation of some landscape features, especially of activity cliffs, is not straightforward. As the nature of activity cliffs depends on the ligand and the target, information regarding both should be included in the analysis. A specific way to include this information is using protein-ligand interaction fingerprints (IFPs). In this paper we report the activity landscape modeling of 507 ligand-kinase complexes (from the KLIFS database) including IFP, which facilitates the analysis and interpretation of activity cliffs. Here we introduce the structure-activity-interaction similarity (SAIS) maps that incorporate information on ligand-target contact similarity. We also introduce the concept of interaction cliffs defined as ligand-target complexes with high structural and interaction similarity but have a large potency difference of the ligands. Moreover, the information retrieved regarding the specific interaction allowed the identification of activity cliff hot spots, which help to rationalize activity cliffs from the target point of view. In general, the information provided by IFPs provides a structure-based understanding of some activity landscape features. This paper shows examples of analyses that can be carried out when IFPs are added to the activity landscape model.M-L is very grateful to CONACyT (No. 217442/312933) and the Cambridge Overseas Trust for funding. AB thanks Unilever for funding and the European Research Council for a Starting Grant (ERC-2013- StG-336159 MIXTURE). J.L.M-F. is grateful to the School of Chemistry, Department of Pharmacy of the National Autonomous University of Mexico (UNAM) for support. This work was supported by a scholarship from the Secretariat of Public Education and the Mexican government

In depth analysis of kinase cross screening data to identify CaMKK2 inhibitory scaffolds

The calcium/calmodulin‐dependent protein kinase kinase 2 (CAMKK2) activates CAMK1, CAMK4, AMPK, and AKT, leading to numerous physiological responses. The deregulation of CAMKK2 is linked to several diseases, suggesting the utility of CAMKK2 inhibitors for oncological, metabolic and inflammatory indications. In this work, we demonstrate that STO‐609, frequently described as a selective inhibitor for CAMKK2, potently inhibits a significant number of other kinases. Through an analysis of literature and public databases, we have identified other potent CAMKK2 inhibitors and verified their activities in differential scanning fluorimetry and enzyme inhibition assays. These inhibitors are potential starting points for the development of selective CAMKK2 inhibitors and will lead to tools that delineate the roles of this kinase in disease biology.252CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP465651/2014-32013/50724-5; 2014/50897-0; 2019/14275-

Structure-Based Target-Specific Screening Leads to Small-Molecule CaMKII Inhibitors

Target-specific scoring methods are more commonly used to identify small-molecule inhibitors among compounds docked to a target of interest. Top candidates that emerge from these methods have rarely been tested for activity and specificity across a family of proteins. In this study we docked a chemical library into CaMKIIδ, a member of the Ca2+ /calmodulin (CaM)-dependent protein kinase (CaMK) family, and re-scored the resulting protein-compound structures using Support Vector Machine SPecific (SVMSP), a target-specific method that we developed previously. Among the 35 selected candidates, three hits were identified, such as quinazoline compound 1 (KIN-1; N4-[7-chloro-2-[(E)-styryl]quinazolin-4-yl]-N1,N1-diethylpentane-1,4-diamine), which was found to inhibit CaMKIIδ kinase activity at single-digit micromolar IC50 . Activity across the kinome was assessed by profiling analogues of 1, namely 6 (KIN-236; N4-[7-chloro-2-[(E)-2-(2-chloro-4,5-dimethoxyphenyl)vinyl]quinazolin-4-yl]-N1,N1-diethylpentane-1,4-diamine), and an analogue of hit compound 2 (KIN-15; 2-[4-[(E)-[(5-bromobenzofuran-2-carbonyl)hydrazono]methyl]-2-chloro-6-methoxyphenoxy]acetic acid), namely 14 (KIN-332; N-[(E)-[4-(2-anilino-2-oxoethoxy)-3-chlorophenyl]methyleneamino]benzofuran-2-carboxamide), against 337 kinases. Interestingly, for compound 6, CaMKIIδ and homologue CaMKIIγ were among the top ten targets. Among the top 25 targets of 6, IC50 values ranged from 5 to 22 μm. Compound 14 was found to be not specific toward CaMKII kinases, but it does inhibit two kinases with sub-micromolar IC50 values among the top 25. Derivatives of 1 were tested against several kinases including several members of the CaMK family. These data afforded a limited structure-activity relationship study. Molecular dynamics simulations with explicit solvent followed by end-point MM-GBSA free-energy calculations revealed strong engagement of specific residues within the ATP binding pocket, and also changes in the dynamics as a result of binding. This work suggests that target-specific scoring approaches such as SVMSP may hold promise for the identification of small-molecule kinase inhibitors that exhibit some level of specificity toward the target of interest across a large number of proteins

Fragment based Drug Discovery; Design and Validation of a Fragment Library; Computer-based Fragment Screening and Fragment-to-Lead Expansion

In recent years, fragment screening has become a popular approach to identify new lead structures. Fragments are usually defined by the Astex ‘rule of three’ (RO3). Surface Plasmon Resonance (SPR), Nuclear Magnetic Resonance spectroscopy (NMR), biochemical assays and X-ray crystallography are efficient screening techniques to discover prospective fragments as binders. However, these methods need an assembled fragment library. We designed an in-house fragment library, starting from approx. 380,000 commercially available fragments. During library design, we modified the RO3 and we did no strict filtering of physico-chemical properties during fragment enumeration (e.g. twice the number of H-bond acceptors was allowed). The fragments were stepwise reduced to 4,000 compounds. The last step was a visual inspection of the candidates, which lead to a final fragment library of 364 fragments. To validate the quality of the library, we screened it against endothiapepsin. The biochemical screening suggested 55 hits, which were entered into a crystallographic screen. Eleven complex crystal structures were determined, pointing out the remarkably high hit rate of the designed library. HotspotsX is a program which predicts (based on knowledge-based potentials) the probability of a certain atom type at a certain position in the binding pocket of a target enzyme. The eleven crystal structures obtained before were used to validate the program HotspotsX. Due to chemical diversity and the different binding modes of the fragments observed for the library examples we obtained binding through aromatic- , H-bond donor- , acceptor- , doneptor- and hydrophobic interactions. The calculated HotspotsX maps coincide remarkably well with the crystallographically determined fragment positions inside the binding pocket. The program HotspotsX has also been validated with crystal structures of molecular probes like phenol, urea and methylurea. Crystal structures of these molecular probes were determined with different targets. Overall, the experimental hotspot analysis coincided well with the computed contour maps. Thus, the calculated maps by HotspotsX have an excellent predictive power. Based on the binding modes of the molecular probe phenol to the cAMP-dependent protein kinase A (PKA), we started a fragment growing approach. In the latter complex, three phenol molecules are bound. Two are occupying the ATP binding site and one is sitting on top of the glycine-rich loop (G-loop). A virtual screening, using the hinge binding phenol as constraint, suggested a phenol derivative for which a crystal structure could be determined. Starting from this hit, a hotspot analysis was performed. This analysis indicates that growth in the direction of the G-loop, placing an aromatic portion under the G-loop and an acceptor functionality capable to address Lys72 is desired. The first compound of this de novo design had an affinity of 70 µM. In the following first design cycle, we were able to enhance the affinity to 6.5 µM. In the second design cycle an additional amino function was introduced, which did not improve affinity dramatically, but enhanced ligand efficiency to 0.38. In the last cycle, a spacer of one and two methylene groups was introduced and the affinity could be increased to about 110 nM for a diastereomeric mixture of four compounds. The phenol-PKA complex provides a putative allosteric site of PKA. The G-loop in this structure is in a closed state which is stabilized by two H-bonds. This G-loop conformation is probably induced by the phenol molecule sitting on top of the G-loop. Therefore, several molecular dynamics (MD) studies were performed, lacking different phenol molecules, to get insights into the G-loop opening. The MD studies suggest that after removal of the phenol sitting on top of the G-loop some first side chain movements are initiated that can indicate the first steps of the G-loop opening cascade. In a different project, a virtual screening approach was used to find new inhibitors for aldose reductase. A pre-filtered subset of the ZINC database was used as ligand dataset. For the best hit, a series of five compounds was synthesized. Among them one compound displayed an inhibition of 920 nM. The available assays to detect fragment hits are currently not sufficient. The challenges are the low affinity of the fragments and their poor solubility. Therefore, the known thermal shift assay was applied and adapted to detect fragment hits. To validate the method, it was used to characterize variant mutations of EctD. Lastly, a modeling study was used to get ideas about possible binding modes of arachidonic acid derivatives in a K+ ion channel. One predominant binding pose could not be suggested. The study proposes, however, that one arachidonic acid molecule can occupy the inner pore cavity, which is consistent with experimental data

Structural Cheminformatics for Kinase-Centric Drug Design

Drug development is a long, expensive, and iterative process with a high failure rate, while patients wait impatiently for treatment. Kinases are one of the main drug targets studied for the last decades to combat cancer, the second leading cause of death worldwide. These efforts resulted in a plethora of structural, chemical, and pharmacological kinase data, which are collected in the KLIFS database. In this thesis, we apply ideas from structural cheminformatics to the rich KLIFS dataset, aiming to provide computational tools that speed up the complex drug discovery process. We focus on methods for target prediction and fragment-based drug design that study characteristics of kinase binding sites (also called pockets). First, we introduce the concept of computational target prediction, which is vital in the early stages of drug discovery. This approach identifies biological entities such as proteins that may (i) modulate a disease of interest (targets or on-targets) or (ii) cause unwanted side effects due to their similarity to on-targets (off-targets). We focus on the research field of binding site comparison, which lacked a freely available and efficient tool to determine similarities between the highly conserved kinase pockets. We fill this gap with the novel method KiSSim, which encodes and compares spatial and physicochemical pocket properties for all kinases (kinome) that are structurally resolved. We study kinase similarities in the form of kinome-wide phylogenetic trees and detect expected and unexpected off-targets. To allow multiple perspectives on kinase similarity, we propose an automated and production-ready pipeline; user-defined kinases can be inspected complementarily based on their pocket sequence and structure (KiSSim), pocket-ligand interactions, and ligand profiles. Second, we introduce the concept of fragment-based drug design, which is useful to identify and optimize active and promising molecules (hits and leads). This approach identifies low-molecular-weight molecules (fragments) that bind weakly to a target and are then grown into larger high-affinity drug-like molecules. With the novel method KinFragLib, we provide a fragment dataset for kinases (fragment library) by viewing kinase inhibitors as combinations of fragments. Kinases have a highly conserved pocket with well-defined regions (subpockets); based on the subpockets that they occupy, we fragment kinase inhibitors in experimentally resolved protein-ligand complexes. The resulting dataset is used to generate novel kinase-focused molecules that are recombinations of the previously fragmented kinase inhibitors while considering their subpockets. The KinFragLib and KiSSim methods are published as freely available Python tools. Third, we advocate for open and reproducible research that applies FAIR principles ---data and software shall be findable, accessible, interoperable, and reusable--- and software best practices. In this context, we present the TeachOpenCADD platform that contains pipelines for computer-aided drug design. We use open source software and data to demonstrate ligand-based applications from cheminformatics and structure-based applications from structural bioinformatics. To emphasize the importance of FAIR data, we dedicate several topics to accessing life science databases such as ChEMBL, PubChem, PDB, and KLIFS. These pipelines are not only useful to novices in the field to gain domain-specific skills but can also serve as a starting point to study research questions. Furthermore, we show an example of how to build a stand-alone tool that formalizes reoccurring project-overarching tasks: OpenCADD-KLIFS offers a clean and user-friendly Python API to interact with the KLIFS database and fetch different kinase data types. This tool has been used in this thesis and beyond to support kinase-focused projects. We believe that the FAIR-based methods, tools, and pipelines presented in this thesis (i) are valuable additions to the toolbox for kinase research, (ii) provide relevant material for scientists who seek to learn, teach, or answer questions in the realm of computer-aided drug design, and (iii) contribute to making drug discovery more efficient, reproducible, and reusable

Design and characterisation of tool inhibitors of DNA damage response proteins

As a result of the high cost of drug discovery, it is imperative that promising targets with strong disease association are identified and validated before embarking on costly molecule discovery and development phases. Chemical inhibitors provide an excellent tool for target validation. This project was initiated with the aim of designing and characterising tool inhibitors of proteins involved in the DNA damage response, to enable detailed mechanistic biological investigation, disease validation and initiation of translational drug discovery projects. The SMC complexes are critically important in coordinating chromosome condensation, sister chromatid cohesion, DNA repair, homologous recombination and transcriptional regulation. They represent interesting oncology targets and are compelling targets for tool inhibitor development. The core SMC proteins and NSE subunits of SMC5/6 and cohesin SMC1 and SMC3 were modelled and their druggabilities assessed, with an aim to advance to inhibitor development for any of the proteins that proved druggable. Unfortunately, the SMC5/6 and cohesin complexes were established as not druggable. BAF180 represents a major clear cell renal cell carcinoma (ccRCC) cancer gene, exhibiting truncating mutations in 41% of samples in a series of primary ccRCCs.1 It has therefore been highlighted as a promising opportunity to target ccRCC using a synthetic lethal therapeutic approach.2 Hopkins et al. utilised a novel screening technique to identify several genes that are synthetic lethal with BAF180.3 In this work, the druggabilities of three of these genes were assessed, and KAT2A was identified as a promising target for tool inhibitor development. Druggable pockets were identified at the KAT2A histone acetyltransferase (HAT) domain and bromodomain. The catalytically active HAT domain was prioritised as the preferred target. Potential KAT2A HAT domain inhibitors were available from a high-throughput screen (HTS), which utilised a fluorescence based activity assay.4,5 Unfortunately, this assay format proved prohibitively unreliable, and moreover, after discounting the hits from the HTS as likely false positives, the inventory of KAT2A inhibitors was quickly exhausted and the target abandoned. Focus turned to the KAT2A bromodomain. Available X-ray crystal structures were utilised in a computational drug design effort, and Tm shift, TR-FRET, ITC and X-ray crystallography techniques were optimised and established in-house to enable characterisation of prospective KAT2A tool inhibitors. Over 3000 small molecules and fragments were screened, and an assortment of novel KAT2A bromodomain binders were identified. The optimised assays, novel chemical matter and ligand-bound crystal structures afford an exciting opportunity to develop potent and selective KAT2A bromodomain tool inhibitors

Cheminformatics Approaches to Structure Based Virtual Screening: Methodology Development and Applications

Structure-based virtual screening (VS) using 3D structures of protein targets has become a popular in silico drug discovery approach. The success of VS relies on the quality of underlying scoring functions. Despite of the success of structure-based VS in several reported cases, target-dependent VS performance and poor binding affinity predictions are well-known drawbacks in structure-based scoring functions. The goal of my dissertation is to use cheminformatics approaches to address above problems of the existing structure-based scoring methods. In Aim 1, cheminformatics practices are applied to those problems which conventional structure-based scoring functions find difficult (anti-bacterial leads efflux study) or fail to address (AmpC β-lactamase study). Predictive binary classification QSAR models can be constructed to classify complex efflux properties (low vs. high) and to differentiate AmpC β-lactamase binders from binding decoys (i.e., the false positives generated by scoring functions). The above models are applied to virtual screening and many computational hits are experimentally confirmed. In Aim 2, novel statistical binding and pose scoring functions (or pose filter in Aim 3) are developed, to accurately predict protein-ligand binding affinity and to discriminate native-like poses of ligands from pose decoys respectively. In my approach, the proteinligand interface is represented at the atomic level resolution and transformed via a special computational geometry approach called Delaunay tessellation to a collection of atom quadruplet motifs. And individual atom members of the motifs are characterized by conceptual Density Functional Theory (DFT)-based atomic properties. The binding scoring function shows acceptable prediction accuracy towards Community Structure-Activity Resources (CSAR) data sets with diverse protein families. In Aim 3, a two-step scoring protocol for target-specific virtual screening is developed and validated using the challenging Directory of Useful Decoys (DUD) data sets. In the first step our target-specific pose (-scoring) filter developed in Aim 2 is used to filter out/penalize putative pose decoys for every compound. Then in the second step the remaining putative native-like poses are scored with MedusaScore, which is a conventional force-field-based scoring function. This novel screening protocol can consistently improve MedusaScore VS performance, suggesting it possible applications to practical pharmaceutically relevant targets

Machine Learning and Solvation Theory for Drug Discovery

Drug discovery is a notoriously expensive and time-consuming process; hence, developing computational methods to facilitate the discovery process and lower the associated costs is a long-sought goal of computational chemists. Protein-ligand binding, which provides the physical and chemical basis for the mechanism of action of most drugs, occurs in an aqueous environment, and binding affinity is determined not only by atomic interactions between the protein and ligand but also by changes in their interactions with surrounding water molecules that occur upon binding. Thus, a quantitative understanding of the roles water molecules play in the protein-ligand binding process is an essential foundation for developing computational methods and tools to aid the drug discovery process. Grid inhomogeneous solvation theory (GIST) is a tool that measures the thermodynamic and structural properties of water molecules on protein surfaces. Since its implementation, GIST has been used to study water behavior upon protein-ligand binding and to account for solvent effects in scoring functions used in virtual screening. This thesis is comprised of two research projects that extend the applications and functionality of GIST. In the first project, we investigated whether the water properties measured by GIST could improve the performance of machine learning models, specifically, convolutional neural networks (CNN) applied to virtual screening (GIST-CNN project). In the second project, we implemented the particle mesh Ewald (PME) algorithm for energy calculation in GIST, enabling GIST to become a more accurate and more efficient tool for end-state free energy calculation (PME-GIST project). The GIST-CNN project arose in response to reports indicating that convolutional neural network (CNN) models were able to outperform classical scoring functions in virtual screening. We noticed that all the reported machine learning models had been trained only by protein-ligand structures, while water molecules were completely neglected. Given that water molecules play essential roles in protein-ligand binding, we hypothesized that we could further improve the performance of CNN models in terms of enrichment efficiency by adding water features, measured by GIST, to the data used to train the model. Contrary to our hypothesis, we found that adding water features could not further improve the performance of a CNN model trained by protein-ligand structures, which was already very high. However, further investigation revealed that the high performance and reported enrichment efficiency of a CNN model trained by protein-ligand information was solely attributable to biases in the Database of Useful Decoys-Enhanced (DUD-E), which was used to train and test the model. In this project, we also established a suite of methods to investigate what a model learns from the input during training and argued that machine learning models should be thoroughly validated before being applied in real drug discovery projects. The motivations for the PME-GIST project were twofold. First, although GIST provides the statistical thermodynamic framework for thermodynamic end-state free energy calculation, inconsistencies in energy calculations between the previous GIST implementation (GIST-2016) and modern molecular dynamics engines prevent precise comparison of the GIST end-state method to other reference free energy calculation methods such as thermodynamic integration (TI). Second, the O(N2) nonbonded energy calculation is the most expensive step in the entire GIST calculation process. By implementation of the PME algorithm into GIST, we aimed to achieve GIST energy calculations consistent with those of modern molecular dynamic engines and to accelerate the energy calculation to O(NlogN), which is highly desirable when applying GIST to the measurement of water properties across an entire protein surface. In addition to implementing PME, we derived a simple empirical estimator for high order entropies, which are truncated in GIST. After incorporating PME-based energy calculation and the high order entropy estimator, we used PME-GIST to calculate end-state solvation free energy for a wide range of small molecules and achieved results highly consistent with TI (= 0.99, mean unsigned difference = 0.44 kcal/mol). The PME-GIST code we developed in this project was integrated into the open-source molecular dynamics analysis software CPPTRAJ for easy access by others in the drug discovery community. In summary, in this thesis, we explored the potential of adding solvation thermodynamics to machine learning-based virtual screening and found that the high performance reported for machine learning models in this application reflected biases in the dataset used construct and test them rather than successfully generalization of the physical principles that govern molecular interactions. We also addressed the inconsistent energy calculation between GIST and modern molecular simulation engines by developing PME-GIST. We hope the research work presented in this thesis will further expand and accelerate the application of GIST to drug discovery

VIRTUAL SCREENING FOR JNK INHIBITORS AND PREDICTION OF PXR ACTIVITY USING COMPUTATIONAL MODELS

Ph.DDOCTOR OF PHILOSOPH