Optimization and Evaluation of Site-Identification by Ligand Competitive Saturation (SILCS) as a Tool for Target-Based Ligand Optimization

High-Throughput Ligand Dissociation Kinetics Predictions Using Site Identification by Ligand Competitive Saturation

The dissociation or off rate, koff, of a drug molecule has been shown to be more relevant to efficacy than affinity for selected systems, motivating the development of predictive computational methodologies. These are largely based on enhanced-sampling molecular dynamics (MD) simulations that come at a high computational cost limiting their utility for drug design where a large number of ligands need to be evaluated. To overcome this, presented is a combined physics- and machine learning (ML)-based approach that uses the physics-based site identification by ligand competitive saturation (SILCS) method to enumerate potential ligand dissociation pathways and calculate ligand dissociation free-energy profiles along those pathways. The calculated free-energy profiles along with molecular properties are used as features to train ML models, including tree and neural network approaches, to predict koff values. The protocol is developed and validated using 329 ligands for 13 proteins showing robustness of the ML workflow built upon the SILCS physics-based free-energy profiles. The resulting SILCS-Kinetics workflow offers a highly efficient method to study ligand dissociation kinetics, providing a powerful tool to facilitate drug design including the ability to generate quantitative estimates of atomic and functional groups contributions to ligand dissociation.

Potent and Selective Human Constitutive Androstane Receptor Activator DL5055 Facilitates Cyclophosphamide-Based Chemotherapies

Enhancement of the metabolic conversion of cyclophosphamide (CPA) increases its therapeutic effects. Activation of the human constitutive androstane receptor (hCAR) induces CYP2B6, a key enzyme responsible for CPA bioactivation. Based on our previous hCAR activator DL5016, we designed and synthesized a series of new hCAR activators. Compared to DL5016, three new compounds 6i, 6k (DL5055), and 7e, showed significantly improved activating potency for hCAR. Particularly, DL5055 activates hCAR with an EC50 of 0.35 μM and EMAX of 4.3, and does not activate hPXR and other related nuclear receptors. It induced the expression of CYP2B6 and caused the translocation of hCAR from the cytoplasm to the nucleus in human primary hepatocytes. DL5055 also induces the expression of Cyp2b10 (the mouse analog of human CYP2B6) in hCAR-transgenic mice. In addition, it significantly enhances the efficacy of CPA-based chemotherapy regimen, CHOP, in a coculture system and a mouse xenograft model in vivo.

Increasing the Accuracy and Robustness of the CHARMM General Force Field with an Expanded Training Set

Small molecule empirical force fields (FFs), including the CHARMM General Force Field (CGenFF), are designed to have wide coverage of organic molecules and to rapidly assign parameters to molecules not explicitly included in the FF. Assignment of parameters to new molecules in CGenFF is based on a trained bond-angle-dihedral charge increment linear interpolation scheme for the partial atomic charges along with bonded parameters assigned based on analogy using a rules-based penalty score scheme associated with atom types and chemical connectivity. Accordingly, the accuracy of CGenFF is related to the extent of the training set of available parameters. In the present study that training set is extended by 1390 molecules selected to represent connectivities new to CGenFF training compounds. Quantum mechanical (QM) data for optimized geometries, bond, valence angle, and dihedral angle potential energy scans, interactions with water, molecular dipole moments, and electrostatic potentials were used as target data. The resultant bonded parameters and partial atomic charges were used to train a new version of the CGenFF program, v5.0, which was used to generate parameters for a validation set of molecules, including drug-like molecules approved by the FDA, which were then benchmarked against both experimental and QM data. CGenFF v5.0 shows overall improvements with respect to QM intramolecular geometries, vibrations, dihedral potential energy scans, dipole moments and interactions with water. Tests of pure solvent properties of 216 molecules show small improvements versus the previous release of CGenFF v2.5.1 reflecting the high quality of the Lennard-Jones parameters that were explicitly optimized during the initial optimization of both the CGenFF and the CHARMM36 force field. CGenFF v5.0 represents an improvement that is anticipated to more accurately model intramolecular geometries and strain energies as well as noncovalent interactions of drug-like and other organic molecules.

Mapping the distribution and affinities of ligand interaction sites on human serum albumin

Ligands in many instances interact with a protein at multiple sites with a range of affinities. In this study, ligand-protein interaction sites on human serum albumin (HSA) are mapped using the site-identification by ligand competitive saturation (SILCS)-Biologics approach in conjunction with hydrogen-deuterium exchange (HDX)-mass spectrometry (MS) experiments. Ligands studied include known HSA binders, ibuprofen and ketoprofen, and compounds arginine, alanine, sucrose, and trehalose, excipients used in therapeutic formulations of protein-based drugs. In addition, the impact of excipient binding to HSA on its stability is investigated through temperature-ramp stability studies monitoring solution viscosity. For the studied ligands, interactions that correspond to known drug-binding sites (DSs) are identified. These include previously identified ibuprofen and ketoprofen interaction sites as well as additional sites and, in the case of the excipients, the ligands are shown to also bind at previously unidentified interaction sites, termed excipient sites (ESs) with 20 or more sites identified for the studied compounds. HDX-MS titrations were used to determine dissociation constants for a subset of the interaction sites for ibuprofen, ketoprofen, arginine, and sucrose, which exhibited Kd values in the low micromolar to millimolar range in satisfactory agreement with SILCS-Biologics predicted affinities, validating the computational approach to identify both high- and low-affinity interaction sites. The stability studies indicate the excipients offer protection at low excipient/protein ratios up to 66 with destabilization occurring at ratios above 132 with the exception of sucrose at the t0 time point, indicating that the more favorable affinities of sucrose seen in the SILCS-Biologics and HDX-MS analyses contribute to protein stabilization. These results indicate that ligands can bind to large numbers of interaction sites on proteins, with those interactions having implications for the development of formulations for therapeutic proteins.

Detection of Putative Ligand Dissociation Pathways in Proteins Using Site-Identification by Ligand Competitive Saturation

Drug efficacy often correlates better with dissociation kinetics than binding affinity alone. To study binding kinetics computationally, it is necessary to identify all of the possible ligand dissociation pathways. The site identification by ligand competitive saturation (SILCS) method involves the precomputation of a set of maps (FragMaps), which describe the free energy landscapes of typical chemical functionalities in and around a target protein or RNA. In the current work, we present and implement a method to use SILCS to identify ligand dissociation pathways, termed "SILCS-Pathway." The A* pathfinding algorithm is utilized to enumerate ligand dissociation pathways between the ligand binding site and the surrounding bulk solvent environment defined on evenly spaced points around the protein based on a Fibonacci lattice. The cost function for the A* algorithm is calculated using the SILCS exclusion maps and the SILCS grid free energy scores, thereby identifying paths that account for local protein flexibility and potential favorable interactions with the ligand. By traversing all evenly distributed bulk solvent points around the protein, we located all possible dissociation pathways and clustered them to identify general ligand unbinding pathways. The procedure is verified by using proteins studied previously with enhanced sampling molecular dynamics (MD) techniques and is shown to be capable of capturing important ligand dissociation routes in a highly computationally efficient manner. The identified pathways will act as the foundation for determining ligand dissociation kinetics using SILCS free energy profiles, which will be described in a subsequent article.

Discovery and Characterization of Two Selective Inhibitors for a Mu-Class Glutathione S-Transferase of 25 kDa from Taenia solium Using Computational and Bioinformatics Tools

Glutathione S-transferases (GSTs) are promising pharmacological targets for developing antiparasitic agents against helminths, as they play a key role in detoxifying cytotoxic xenobiotics and managing oxidative stress. Inhibiting GST activity can compromise parasite viability. This study reports the successful identification of two selective inhibitors for the mu-class glutathione S-transferase of 25 kDa (Ts25GST) from Taenia solium, named i11 and i15, using a computationally guided approach. The workflow involved modeling and refining the 3D structure from the sequence using the AlphaFold algorithm and all-atom molecular dynamics simulations with an explicit solvent. Representative structures from these simulations and a putative binding site with low conservation relative to human GSTs, identified via the SILCS methodology, were employed for virtual screening through ensemble docking against a commercial compound library. The two compounds were found to reduce the enzyme's activity by 50-70% under assay conditions, while showing a reduction of only 30-35% for human mu-class GSTM1, demonstrating selectivity for Ts25GST. Notable, i11 displayed competitive inhibition with CDNB, while i15 exhibited a non-competitive inhibition type.

Modeling Ligand Binding Site Water Networks with Site Identification by Ligand Competitive Saturation: Impact on Ligand Binding Orientations and Relative Binding Affinities

Appropriate treatment of water contributions to protein-ligand interactions is a very challenging problem in the context of adequately determining the number of waters to investigate and undertaking conformational sampling of the ligands, the waters, and the surrounding protein. In the present study, an extension of the Site Identification by Ligand Competitive Saturation-Monte Carlo (SILCS-MC) docking approach is presented that enables the determination of the location of water molecules in the binding pocket and their impact on the predicted ligand binding orientation and affinities. The approach, termed SILCS-WATER, involves MC sampling of the ligand along with explicit water molecules in a binding site followed by selection of a subset of waters within specified energetic and distance cutoffs that contribute to ligand binding and orientation. To allow for convergence of both the water and ligand orientations, SILCS-WATER is based on just the overlap of the ligand and water with the SILCS FragMaps and the interaction energy between the waters and ligand. Results show that the SILCS-WATER methodology can capture important waters and improve ligand binding orientations. For 6 of 10 multiple ligand-protein systems, the method improved relative binding affinity prediction against experimental results, with substantial improvements in five systems, when compared to standard SILCS-MC. Improved reproduction of crystallographic ligand binding orientations is shown to be an indicator of when SILCS-WATER will yield improved binding affinity correlations. The method also identifies waters interacting with ligands that occupy unfavorable locations with respect to the protein whose displacement through the appropriate ligand modifications should improve ligand binding affinity. Results are consistent with the binding affinity being modeled as a ligand-water complex interacting with the protein. The presented approach offers new possibilities in revealing water networks and their contributions to the binding orientation and affinity of a ligand for a protein and is anticipated to be of utility for computer-aided drug design.

Isolation of phytoconstituents from an extract of Murraya paniculata with cytotoxicity and antioxidant activities and in silico evaluation of their potential to bind to aldose reductase (AKR1B1)

The study on Murraya paniculata (Orange Jasmine) stem bark extract found it to have antioxidant and cytotoxic proper-ties. The structures of the isolated phytoconstituents were determined using NMR spectroscopy. Compounds were evaluated for their potential to be aldose reductase inhibitors using molecular docking and dynamics (MD) simulations. Phytochemical screening of methanolic crude extract was performed from which different fractions of the extract were screened for antioxidant activity using the DPPH radical scavenging method and cytotoxicity using the brine shrimp lethality bioassay. The aqueous fraction showed strong antioxidant activity as compared to the standard butylated hy-droxytoluene, whereas pet ether, dichloromethane, chloroform and methanolic extract exhibited moderate antioxidant activity. Activities in the DPPH assay ranged from 17 to 63 µg/ml and all fractions showed cytotoxic activity. Five identified phytochemical compounds (1-5) include ergosterol endoperoxide (1), the coumarin derivatives 7-methoxy-8-(3-methylbut-2-enyl)-1-benzopyran-2-one (2) and 5,7-dimethoxy-8-(3-methylbut-2-enyl)-1-benzopyran-2-one (3) and a mixture of β-sitosterol (4), and stigmasterol (5). Among them ergosterol endoperoxide has been isolated from the stem bark of the M. paniculata for the first time. MD simulations of the identified compounds indicated their potential to bind to the aldose reductase (AKR1B1) protein. Predicted binding affinities of the compounds based on the site identification the ligand competitive saturation (SILCS) technology was -15.04, -8.85, -9.83, -11.95, and -11.75 kcal/mol for 1 through 5, respectively. The present results are anticipated to lead to further study of the activities of the five compounds including experimental evaluation of their inter-actions with AKR1B1.

First-in-class mitogen-activated protein kinase (MAPK) p38α: MAPK-activated protein kinase 2 dual signal modulator with anti-inflammatory and endothelial-stabilizing properties

We previously identified a small molecule, UM101, predicted to bind to the substrate-binding groove of p38α mitogen-activated protein kinase (MAPK) near the binding site of its proinflammatory substrate, mitogen-activated protein kinase-activated protein kinase (MK)2. UM101 exhibited anti-inflammatory, endothelial-stabilizing, and lung-protective effects. To overcome its limited aqueous solubility and p38α binding affinity, we designed an analog of UM101, GEn-1124, with improved aqueous solubility, stability, and p38α-binding affinity. Compared with UM101, GEn-1124 has 18-fold greater p38α-binding affinity as measured by surface plasmon resonance, 11-fold greater aqueous solubility, enhanced barrier-stabilizing activity in thrombin-stimulated human pulmonary artery endothelial cells in vitro, and greater lung protection in vivo. GEn-1124 improved survival from 10%-40% in murine acute lung injury induced by combined exposure to intratracheal bacterial endotoxin lipopolysaccharide instillation and febrile-range hyperthermia and from 0% to 50% in a mouse influenza pneumonia model. Gene expression analysis by RNASeq in tumor necrosis factor α-treated human pulmonary artery endothelial cells showed that the gene-modifying effects of GEn-1124 were much more restricted to tumor necrosis factor α-inducible genes than those of the catalytic site p38 inhibitor, SB203580. Gene expression pathway analysis, confocal immunofluorescence analysis of p38α and MK2 subcellular trafficking, and surface plasmon resonance analysis of phosphorylated p38α:MK2 binding affinity supports a novel mechanism of action. GEn-1124 destabilizes the activated p38α:MK2 complex and dissociates nuclear export of MK2 and p38α, thereby promoting intranuclear retention and enhanced intranuclear signaling by phosphorylated p38α and accelerated inactivation of p38-free cytosolic MK2 by unopposed phosphatases. SIGNIFICANCE STATEMENT: We describe a novel analog of our first-in-class small molecule modulator of p38α/MK2 signaling targeted to a pocket near the glutamate-aspartate-containing substrate binding domain of p38α, which destabilizes the p38α:MK2 complex without blocking p38 catalytic activity or ablating downstream signaling. The result is a rebalancing of downstream proinflammatory and anti-inflammatory signaling, yielding anti-inflammatory, endothelial-stabilizing, and lung-protective effects with therapeutic potential in acute respiratory distress syndrome.

Combined Physics- and Machine-Learning-Based Method to Identify Druggable Binding Sites Using SILCS-Hotspots

Identifying druggable binding sites on proteins is an important and challenging problem, particularly for cryptic, allosteric binding sites that may not be obvious from X-ray, cryo-EM, or predicted structures. The Site-Identification by Ligand Competitive Saturation (SILCS) method accounts for the flexibility of the target protein using all-atom molecular simulations that include various small molecule solutes in aqueous solution. During the simulations, the combination of protein flexibility and comprehensive sampling of the water and solute spatial distributions can identify buried binding pockets absent in experimentally determined structures. Previously, we reported a method for leveraging the information in the SILCS sampling to identify binding sites (termed Hotspots) of small mono- or bicyclic compounds, a subset of which coincide with known binding sites of drug-like molecules. Here, we build on that physics-based approach and present a ML model for ranking the Hotspots according to the likelihood they can accommodate drug-like molecules (e.g., molecular weight >200 Da). In the independent validation set, which includes various enzymes and receptors, our model recalls 67% and 89% of experimentally validated ligand binding sites in the top 10 and 20 ranked Hotspots, respectively. Furthermore, we show that the model's output Decision Function is a useful metric to predict binding sites and their potential druggability in new targets. Given the utility the SILCS method for ligand discovery and optimization, the tools presented represent an important advancement in the identification of orthosteric and allosteric binding sites and the discovery of drug-like molecules targeting those sites.

Exploring Druggable Binding Sites on the Class A GPCRs Using the Residue Interaction Network and Site Identification by Ligand Competitive Saturation

G protein-coupled receptors (GPCRs) play a central role in cellular signaling and are linked to many diseases. Accordingly, computational methods to explore potential allosteric sites for this class of proteins to facilitate the identification of potential modulators are needed. Importantly, the availability of rich structural data providing the locations of the orthosteric ligands and allosteric modulators targeting different GPCRs allows for the validation of approaches to identify new allosteric binding sites. Here, we validate the combination of two computational techniques, the residue interaction network (RIN) model and the site identification by ligand competitive saturation (SILCS) method, to predict putative allosteric binding sites of class A GPCRs. RIN analysis identifies hub residues that mediate allosteric signaling within a receptor and have a high capacity to alter receptor dynamics upon ligand binding. The known orthosteric (and allosteric) binding sites of 18 distinct class A GPCRs were successfully predicted by RIN through a dataset of 105 crystal structures (91 ligand-bound, 14 unbound) with up to 77.8% (76.9%) sensitivity, 92.5% (95.3%) specificity, 51.9% (50%) precision, and 86.2% (92.4%) accuracy based on the experimental and theoretical binding site data. Moreover, graph spectral analysis of the residue networks revealed that the proposed sites were located at the interfaces of highly interconnected residue clusters with a high ability to coordinate the functional dynamics. Then, we employed the SILCS-Hotspots method to assess the druggability of the novel sites predicted for 7 distinct class A GPCRs that are critical for a variety of diseases. While the known orthosteric and allosteric binding sites are successfully explored by our approach, numerous putative allosteric sites with the potential to bind drug-like molecules are proposed. The computational approach presented here promises to be a highly effective tool to predict putative allosteric sites of GPCRs to facilitate the design of effective modulators.

Enhancing SILCS-MC via GPU Acceleration and Ligand Conformational Optimization with Genetic and Parallel Tempering Algorithms

In the domain of computer-aided drug design, achieving precise and accurate estimates of ligand-protein binding is paramount in the context of screening extensive drug libraries and performing ligand optimization. A fundamental aspect of the SILCS (site identification by ligand competitive saturation) methodology lies in the generation of comprehensive 3D free-energy functional group affinity maps (FragMaps), encompassing the entirety of the target molecule structure. These FragMaps offer an intricate landscape of functional group affinities across the protein, bilayer, or RNA, acting as the basis for subsequent SILCS-Monte Carlo (MC) simulations wherein ligands are docked to the target molecule. To augment the efficiency and breadth of ligand sampling capabilities, we implemented an improved SILCS-MC methodology. By harnessing the parallel computing capability of GPUs, our approach facilitates concurrent calculations over multiple ligands and binding sites, markedly enhancing the computational efficiency. Moreover, the integration of a genetic algorithm (GA) with MC allows us to employ an evolutionary approach to perform ligand sampling, assuring enhanced convergence characteristics. In addition, the potential utility of parallel tempering (PT) to improve sampling was investigated. Implementation of SILCS-MC on GPU architecture is shown to accelerate the speed of SILCS-MC calculations by over 2-orders of magnitude. Use of GA and PT yield improvements over Markov-chain MC, increasing the precision of the resultant docked orientations and binding free energies, though the extent of improvements is relatively small. Accordingly, significant improvements in speed are obtained through the GPU implementation with minor improvements in the precision of the docking obtained via the tested GA and PT algorithms.

Bartender: Martini 3 Bonded Terms via Quantum Mechanics-Based Molecular Dynamics

Coarse-grained (CG) molecular dynamics (MD) simulations have grown in applicability over the years. The recently released version of the Martini CG force field (Martini 3) has been successfully applied to simulate many processes, including protein-ligand binding. However, the current ligand parametrization scheme is manual and requires an a priori reference all-atom (AA) simulation for benchmarking. For systems with suboptimal AA parameters, which are often unknown, this translates into a CG model that does not reproduce the true dynamical behavior of the underlying molecule. Here, we present Bartender, a quantum mechanics (QM)/MD-based parametrization tool written in Go. Bartender harnesses the power of QM simulations and produces reasonable bonded terms for Martini 3 CG models of small molecules in an efficient and user-friendly manner. For small, ring-like molecules, Bartender generates models whose properties are indistinguishable from the human-made models. For more complex, drug-like ligands, it is able to fit functional forms beyond simple harmonic dihedrals and thus better captures their dynamical behavior. Bartender has the power to both increase the efficiency and the accuracy of Martini 3-based high-throughput applications by producing numerically stable and physically realistic CG models.

Identifying and Assessing Putative Allosteric Sites and Modulators for CXCR4 Predicted through Network Modeling and Site Identification by Ligand Competitive Saturation

The chemokine receptor CXCR4 is a critical target for the treatment of several cancer types and HIV-1 infections. While orthosteric and allosteric modulators have been developed targeting its extracellular or transmembrane regions, the intramembrane region of CXCR4 may also include allosteric binding sites suitable for the development of allosteric drugs. To investigate this, we apply the Gaussian Network Model (GNM) to the monomeric and dimeric forms of CXCR4 to identify residues essential for its local and global motions located in the hinge regions of the protein. Residue interaction network (RIN) analysis suggests hub residues that participate in allosteric communication throughout the receptor. Mutual residues from the network models reside in regions with a high capacity to alter receptor dynamics upon ligand binding. We then investigate the druggability of these potential allosteric regions using the site identification by ligand competitive saturation (SILCS) approach, revealing two putative allosteric sites on the monomer and three on the homodimer. Two screening campaigns with Glide and SILCS-Monte Carlo docking using FDA-approved drugs suggest 20 putative hit compounds including antifungal drugs, anticancer agents, HIV protease inhibitors, and antimalarial drugs. In vitro assays considering mAB 12G5 and CXCL12 demonstrate both positive and negative allosteric activities of these compounds, supporting our computational approach. However, in vivo functional assays based on the recruitment of β-arrestin to CXCR4 do not show significant agonism and antagonism at a single compound concentration. The present computational pipeline brings a new perspective to computer-aided drug design by combining conformational dynamics based on network analysis and cosolvent analysis based on the SILCS technology to identify putative allosteric binding sites using CXCR4 as a showcase.

Preventing lipophilic aggregation in cosolvent molecular dynamics simulations with hydrophobic probes using Plumed Automatic Restraining Tool (PART)

Cosolvent molecular dynamics (MD) simulations are molecular dynamics simulations used to identify preferable locations of small organic fragments on a protein target. Most cosolvent molecular dynamics workflows make use of only water-soluble fragments, as hydrophobic fragments would cause lipophilic aggregation. To date the two approaches that allow usage of hydrophobic cosolvent molecules are to use a low (0.2 M) concentration of hydrophobic probes, with the disadvantage of a lower sampling speed, or to use force field modifications, with the disadvantage of a difficult and inflexible setup procedure. Here we present a third alternative, that does not suffer from low sampling speed nor from cumbersome preparation procedures. We have built an easy-to-use open source command line tool PART (Plumed Automatic Restraining Tool) to generate a PLUMED file handling all intermolecular restraints to prevent lipophilic aggregation. We have compared restrained and unrestrained cosolvent MD simulations, showing that restraints are necessary to prevent lipophilic aggregation at hydrophobic probe concentrations of 0.5 M. Furthermore, we benchmarked PART generated restraints on a test set of four proteins (Factor-Xa, HIV protease, P38 MAP kinase and RNase A), showing that cosolvent MD with PART generated restraints qualitatively reproduces binding features of cocrystallised ligands.

Non-β Lactam Inhibitors of the Serine β-Lactamase blaCTX-M15 in Drug-Resistant Salmonella typhi

Antibiotic resistance by bacterial pathogens against widely used β-lactam drugs is a major concern to public health worldwide, resulting in high healthcare cost. The present study aimed to extend previous research by investigating the potential activity of reported compounds against the S. typhi β-lactamase protein. 74 compounds from computational screening reported in our previous study against β-lactamase CMY-10 were subjected to docking studies against blaCTX-M15. Site-Identification by Ligand Competitive Saturation (SILCS)-Monte Carlo (SILCS-MC) was applied to the top two ligands selected from molecular docking studies to predict and refine their conformations for binding conformations against blaCTX-M15. The SILCS-MC method predicted affinities of -8.6 and -10.7 kcal/mol for Top1 and Top2, respectively, indicating low micromolar binding to the blaCTX-M15 active site. MD simulations initiated from SILCS-MC docked orientations were carried out to better characterize the dynamics and stability of the complexes. Important interactions anchoring the ligand within the active site include pi-pi stacked, amide-pi, and pi-alkyl interactions. Simulations of the Top2-blaCTX-M15 complex exhibited stability associated with a wide range of hydrogen-bond and aromatic interactions between the protein and the ligand. Experimental β-lactamase (BL) activity assays showed that Top1 has 0.1 u/mg BL activity, and Top2 has a BL activity of 0.038 u/mg with a minimum inhibitory concentration of 1 mg/mL. The inhibitors proposed in this study are non-β-lactam-based β-lactamase inhibitors that exhibit the potential to be used in combination with β-lactam antibiotics against multidrug-resistant clinical isolates. Thus, Top1 and Top2 represent lead compounds that increase the efficacy of β-lactam antibiotics with a low dose concentration.

Calpain-2 Inhibitors as Therapy for Traumatic Brain Injury

While calpains have long been implicated in neurodegeneration, no calpain inhibitor has been developed for the treatment of neurodegeneration. This is partly due to the lack of understanding of the specific functions of most of the 15 members of the calpain family. Work from our laboratory over the last 5-10 years has revealed that calpain-1 and calpain-2, two of the major calpain isoforms in the brain, play opposite roles in both synaptic plasticity/learning and memory and neuroprotection/neurodegeneration. Thus, calpain-1 activation is required for triggering certain forms of synaptic plasticity and for learning some types of information and is neuroprotective. In contrast, calpain-2 activation limits the extent of synaptic plasticity and of learning and is neurodegenerative. These results have been validated with the use of calpain-1 knock-out mice and mice with a selective calpain-2 deletion in excitatory neurons of the forebrain. Through a medicinal chemistry campaign, we have identified a number of selective calpain-2 inhibitors and shown that these inhibitors do facilitate learning of certain tasks and are neuroprotective in a number of animal models of acute neurodegeneration. One of these inhibitors, NA-184, is currently being developed for the treatment of traumatic brain injury, and clinical trials are being planned.

Combining SILCS and Artificial Intelligence for High-Throughput Prediction of the Passive Permeability of Drug Molecules

Membrane permeability of drug molecules plays a significant role in the development of new therapeutic agents. Accordingly, methods to predict the passive permeability of drug candidates during a medicinal chemistry campaign offer the potential to accelerate the drug design process. In this work, we combine the physics-based site identification by ligand competitive saturation (SILCS) method and data-driven artificial intelligence (AI) to create a high-throughput predictive model for the passive permeability of druglike molecules. In this study, we present a comparative analysis of four regression models to predict membrane permeabilities of small druglike molecules; of the tested models, Random Forest was the most predictive yielding an R2 of 0.81 for the independent data set. The input feature vector used to train the developed prediction model includes absolute free energy profiles of ligands through a POPC-cholesterol bilayer based on ligand grid free energy (LGFE) profiles obtained from the SILCS approach. The use of the membrane free energy profiles from SILCS offers information on the physical forces contributing to ligand permeability, while the use of AI yields a more predictive model trained on experimental PAMPA permeability data for a collection of 229 molecules. This combination allows for rapid estimations of ligand permeability at a level of accuracy beyond currently available predictive models while offering insights into the contributions of the functional groups in the ligands to the permeability barrier, thereby offering quantitative information to facilitate rational ligand design.

Nitro-benzylideneoxymorphone, a bifunctional mu and delta opioid receptor ligand with high mu opioid receptor efficacy

Introduction: There is a major societal need for analgesics with less tolerance, dependence, and abuse liability. Preclinical rodent studies suggest that bifunctional ligands with both mu (MOPr) and delta (DOPr) opioid peptide receptor activity may produce analgesia with reduced tolerance and other side effects. This study explores the structure-activity relationships (SAR) of our previously reported MOPr/DOPr lead, benzylideneoxymorphone (BOM) with C7-methylene-substituted analogs. Methods: Analogs were synthesized and tested in vitro for opioid receptor binding and efficacy. One compound, nitro-BOM (NBOM, 12) was evaluated for antinociceptive effects in the warm water tail withdrawal assay in C57BL/6 mice. Acute and chronic antinociception was determined, as was toxicologic effects on chronic administration. Molecular modeling experiments were performed using the Site Identification by Ligand Competitive Saturation (SILCS) method. Results: NBOM was found to be a potent MOPr agonist/DOPr partial agonist that produces high-efficacy antinociception. Antinociceptive tolerance was observed, as was weight loss; this toxicity was only observed with NBOM and not with BOM. Modeling supports the hypothesis that the increased MOPr efficacy of NBOM is due to the substituted benzylidene ring occupying a nonpolar region within the MOPr agonist state. Discussion: Though antinociceptive tolerance and non-specific toxicity was observed on repeated administration, NBOM provides an important new tool for understanding MOPr/DOPr pharmacology.

Integrated Covalent Drug Design Workflow Using Site Identification by Ligand Competitive Saturation

Covalent drug design is an important component in drug discovery. Traditional drugs interact with their target in a reversible equilibrium, while irreversible covalent drugs increase the drug-target interaction duration by forming a covalent bond with targeted residues and thus may offer a more effective therapeutic approach. To facilitate the design of this class of ligands, computational methods can be used to help identify reactive nucleophilic residues, frequently cysteines, on a target protein for covalent binding, to test various warhead groups for their potential reactivities, and to predict noncovalent contributions to binding that can facilitate drug-target interactions that are important for binding specificity. To further aid covalent drug design, we extended a functional group mapping approach based on explicit solvent all-atom molecular simulations (SILCS: site identification by ligand competitive saturation) that intrinsically considers protein flexibility, functional group, and protein desolvation along with functional group-protein interactions. Through docking of a library of representative warhead fragments using SILCS-Monte Carlo (SILCS-MC), reactive cysteines can be correctly identified for proteins being tested. Furthermore, a machine learning model was trained to quantify the effectiveness of various warhead groups for proteins using metrics from SILCS-MC as well as experimental model compound warhead reactivity data. The ability to rank covalent molecular binders with similar warheads using SILCS ligand grid free energy (LGFE) ranking was also tested for several proteins. Based on these tools, an integrated SILCS-based workflow was developed, named SILCS-Covalent, which can both qualitatively and quantitatively inform covalent drug discovery.

Site Identification by Ligand Competitive Saturation-Biologics Approach for Structure-Based Protein Charge Prediction

Protein-based therapeutics typically require high concentrations of the active protein, which can lead to protein aggregation and high solution viscosity. Such solution behaviors can limit the stability, bioavailability, and manufacturability of protein-based therapeutics and are directly influenced by the charge of a protein. Protein charge is a system property affected by its environment, including the buffer composition, pH, and temperature. Thus, the charge calculated by summing the charges of each residue in a protein, as is commonly done in computational methods, may significantly differ from the effective charge of the protein as these calculations do not account for contributions from bound ions. Here, we present an extension of the structure-based approach termed site identification by ligand competitive saturation-biologics (SILCS-Biologics) to predict the effective charge of proteins. The SILCS-Biologics approach was applied on a range of protein targets in different salt environments for which membrane-confined electrophoresis-determined charges were previously reported. SILCS-Biologics maps the 3D distribution and predicted occupancy of ions, buffer molecules, and excipient molecules bound to the protein surface in a given salt environment. Using this information, the effective charge of the protein is predicted such that the concentrations of the ions and the presence of excipients or buffers are accounted for. Additionally, SILCS-Biologics also produces 3D structures of the binding sites of ions on the proteins, which enable further analyses such as the characterization of protein surface charge distribution and dipole moments in different environments. Notable is the capability of the method to account for competition between salts, excipients, and buffers on the calculated electrostatic properties in different protein formulations. Our study demonstrates the ability of the SILCS-Biologics approach to predict the effective charge of proteins and its applicability in uncovering protein-ion interactions and their contributions to protein solubility and function.

Endocannabinoids enhance hKV7.1/KCNE1 channel function and shorten the cardiac action potential and QT interval

BACKGROUND

Genotype-positive patients who suffer from the cardiac channelopathy Long QT Syndrome (LQTS) may display a spectrum of clinical phenotypes, with often unknown causes. Therefore, there is a need to identify factors influencing disease severity to move towards an individualized clinical management of LQTS. One possible factor influencing the disease phenotype is the endocannabinoid system, which has emerged as a modulator of cardiovascular function. In this study, we aim to elucidate whether endocannabinoids target the cardiac voltage-gated potassium channel K_V7.1/KCNE1, which is the most frequently mutated ion channel in LQTS.

METHODS

We used two-electrode voltage clamp, molecular dynamics simulations and the E4031 drug-induced LQT2 model of ex-vivo guinea pig hearts.

FINDINGS

We found a set of endocannabinoids that facilitate channel activation, seen as a shifted voltage-dependence of channel opening and increased overall current amplitude and conductance. We propose that negatively charged endocannabinoids interact with known lipid binding sites at positively charged amino acids on the channel, providing structural insights into why only specific endocannabinoids modulate K_V7.1/KCNE1. Using the endocannabinoid ARA-S as a prototype, we show that the effect is not dependent on the KCNE1 subunit or the phosphorylation state of the channel. In guinea pig hearts, ARA-S was found to reverse the E4031-prolonged action potential duration and QT interval.

INTERPRETATION

We consider the endocannabinoids as an interesting class of hK_V7.1/KCNE1 channel modulators with putative protective effects in LQTS contexts.

FUNDING

ERC (No. 850622), Canadian Institutes of Health Research, Canada Research Chairs and Compute Canada, Swedish National Infrastructure for Computing.

Structure-Based Design of Potent Iminosugar Inhibitors of Endoplasmic Reticulum α-Glucosidase I with Anti-SARS-CoV-2 Activity

Enveloped viruses depend on the host endoplasmic reticulum (ER) quality control (QC) machinery for proper glycoprotein folding. The endoplasmic reticulum quality control (ERQC) enzyme α-glucosidase I (α-GluI) is an attractive target for developing broad-spectrum antivirals. We synthesized 28 inhibitors designed to interact with all four subsites of the α-GluI active site. These inhibitors are derivatives of the iminosugars 1-deoxynojirimycin (1-DNJ) and valiolamine. Crystal structures of ER α-GluI bound to 25 1-DNJ and three valiolamine derivatives revealed the basis for inhibitory potency. We established the structure-activity relationship (SAR) and used the Site Identification by Ligand Competitive Saturation (SILCS) method to develop a model for predicting α-GluI inhibition. We screened the compounds against SARS-CoV-2 in vitro to identify those with greater antiviral activity than the benchmark α-glucosidase inhibitor UV-4. These host-targeting compounds are candidates for investigation in animal models of SARS-CoV-2 and for testing against other viruses that rely on ERQC for correct glycoprotein folding.

Computer-Aided Drug Design: An Update

Computer-aided drug design (CADD) approaches are playing an increasingly important role in understanding the fundamentals of ligand-receptor interactions and helping medicinal chemists design therapeutics. About 5 years ago, we presented a chapter devoted to an overview of CADD methods and covered typical CADD protocols including structure-based drug design (SBDD) and ligand-based drug design (LBDD) approaches that were frequently used in the antibiotic drug design process. Advances in computational hardware and algorithms and emerging CADD methods are enhancing the accuracy and ability of CADD in drug design and development. In this chapter, an update to our previous chapter is provided with a focus on new CADD approaches from our laboratory and other peers that can be employed to facilitate the development of antibiotic therapeutics.

CryoEM structures of anion exchanger 1 capture multiple states of inward- and outward-facing conformations

Anion exchanger 1 (AE1, band 3) is a major membrane protein of red blood cells and plays a key role in acid-base homeostasis, urine acidification, red blood cell shape regulation, and removal of carbon dioxide during respiration. Though structures of the transmembrane domain (TMD) of three SLC4 transporters, including AE1, have been resolved previously in their outward-facing (OF) state, no mammalian SLC4 structure has been reported in the inward-facing (IF) conformation. Here we present the cryoEM structures of full-length bovine AE1 with its TMD captured in both IF and OF conformations. Remarkably, both IF-IF homodimers and IF-OF heterodimers were detected. The IF structures feature downward movement in the core domain with significant unexpected elongation of TM11. Molecular modeling and structure guided mutagenesis confirmed the functional significance of residues involved in TM11 elongation. Our data provide direct evidence for an elevator-like mechanism of ion transport by an SLC4 family member.

Spatial requirements for ITAM signaling in an intracellular natural killer cell model membrane

FcγRIIIa-FcεRIγ complexes, upon stimulation by antibodies, cluster to initiate intracellular signaling and activate natural killer (NK) cells. Intracellular signaling involves Lck phosphorylation of ITAMs of each monomer of a FcεRIγ homodimer in a FcγRIIIa-FcεRIγ complex and subsequent binding of two phosphotyrosines (pY) in tandem by a Syk family kinase. However, how FcR clustering triggers ITAM signaling is not resolved. Molecular modeling and dynamics (MD) simulations are applied to generate ensembles of structures of the FcγRIIIa and FcεRIγ homodimeric cytoplasmic tails of FcγRIIIa-FcεRIγ complexes based on the transmembrane helices and cytoplasmic tails spaced 120, 80, and 50 Å apart to model different extents of clustering. Site-identification by ligand competitive saturation method with Monte Carlo sampling (SILCS-MC) is used to model how Lck could phosphorylate a diversity of ITAM conformations. At 80 Å separation between FcγRIIIa-FcεRIγ complexes, Lck can perform multiple phosphorylations on individual and multiple ITAMs across complexes, including potential sequential phosphorylation events. Syk may then potentially bind the two pYs within a single ITAM in tandem in isolated FcγRIIIa-FcεRIγ complexes, as observed in CD3ε and ζ chains of T cell receptors by the Syk family kinase ZAP-70. In addition, at 50 Å separation between complexes, unique to natural killer cells over T cells, Syk could potentially bind in tandem to pYs in different ITAMs across FcγRIIIa-FcεRIγ complexes. Thus, we predict that an ensemble of spatial orientations of the ITAMS of FcγRIIIa-FcεRIγ complexes that occur upon clustering lead to ITAM phosphorylation by Lck and subsequent Syk activity thereby facilitating downstream signaling.

SILCS-RNA: Toward a Structure-Based Drug Design Approach for Targeting RNAs with Small Molecules

RNA molecules can act as potential drug targets in different diseases, as their dysregulated expression or misfolding can alter various cellular processes. Noncoding RNAs account for ∼70% of the human genome, and these molecules can have complex tertiary structures that present a great opportunity for targeting by small molecules. In the present study, the site identification by ligand competitive saturation (SILCS) computational approach is extended to target RNA, termed SILCS-RNA. Extensions to the method include an enhanced oscillating excess chemical potential protocol for the grand canonical Monte Carlo calculations and individual simulations of the neutral and charged solutes from which the SILCS functional group affinity maps (FragMaps) are calculated for subsequent binding site identification and docking calculations. The method is developed and evaluated against seven RNA targets and their reported small molecule ligands. SILCS-RNA provides a detailed characterization of the functional group affinity pattern in the small molecule binding sites, recapitulating the types of functional groups present in the ligands. The developed method is also shown to be useful for identification of new potential binding sites and identifying ligand moieties that contribute to binding, granular information that can facilitate ligand design. However, limitations in the method are evident including the ability to map the regions of binding sites occupied by ligand phosphate moieties and to fully account for the wide range of conformational heterogeneity in RNA associated with binding of different small molecules, emphasizing inherent challenges associated with applying computer-aided drug design methods to RNA. While limitations are present, the current study indicates how the SILCS-RNA approach may enhance drug discovery efforts targeting RNAs with small molecules.

Antimicrobial Activity of Rhenium Di- and Tricarbonyl Diimine Complexes: Insights on Membrane-Bound S. aureus Protein Binding

Antimicrobial resistance is one of the major human health threats, with significant impacts on the global economy. Antibiotics are becoming increasingly ineffective as drug-resistance spreads, imposing an urgent need for new and innovative antimicrobial agents. Metal complexes are an untapped source of antimicrobial potential. Rhenium complexes, amongst others, are particularly attractive due to their low in vivo toxicity and high antimicrobial activity, but little is known about their targets and mechanism of action. In this study, a series of rhenium di- and tricarbonyl diimine complexes were prepared and evaluated for their antimicrobial potential against eight different microorganisms comprising Gram-negative and -positive bacteria. Our data showed that none of the Re dicarbonyl or neutral tricarbonyl species have either bactericidal or bacteriostatic potential. In order to identify possible targets of the molecules, and thus possibly understand the observed differences in the antimicrobial efficacy of the molecules, we computationally evaluated the binding affinity of active and inactive complexes against structurally characterized membrane-bound S. aureus proteins. The computational analysis indicates two possible major targets for this class of compounds, namely lipoteichoic acids flippase (LtaA) and lipoprotein signal peptidase II (LspA). Our results, consistent with the published in vitro studies, will be useful for the future design of rhenium tricarbonyl diimine-based antibiotics.

hERG Blockade Prediction by Combining Site Identification by Ligand Competitive Saturation and Physicochemical Properties

Human ether-a-go-go-related gene (hERG) potassium channel is well-known contributor to drug-induced cardiotoxicity and therefore an extremely important target when performing safety assessments of drug candidates. Ligand-based approaches in connection with quantitative structure active relationships (QSAR) analyses have been developed to predict hERG toxicity. Availability of the recent published cryogenic electron microscopy (cryo-EM) structure for the hERG channel opened the prospect for using structure-based simulation and docking approaches for hERG drug liability predictions. In recent time, the idea of combining structure- and ligand-based approaches for modeling hERG drug liability has gained momentum offering improvements in predictability when compared to ligand-based QSAR practices alone. The present article demonstrates uniting the structure-based SILCS (site-identification by ligand competitive saturation) approach in conjunction with physicochemical properties to develop predictive models for hERG blockade. This combination leads to improved model predictability based on Pearson's R and percent correct (represents rank-ordering of ligands) metric for different validation sets of hERG blockers involving diverse chemical scaffold and wide range of pIC50 values. The inclusion of the SILCS structure-based approach allows determination of the hERG region to which compounds bind and the contribution of different chemical moieties in the compounds to blockade, thereby facilitating the rational ligand design to minimize hERG liability.

Accelerated Ligand-Mapping Molecular Dynamics Simulations for the Detection of Recalcitrant Cryptic Pockets and Occluded Binding Sites

The identification and characterization of binding sites is a critical component of structure-based drug design (SBDD). Probe-based/cosolvent molecular dynamics (MD) methods that allow for protein flexibility have been developed to predict ligand binding sites. However, cryptic pockets that appear only upon ligand binding and occluded binding sites with no access to the solvent pose significant challenges to these methods. Here, we report the development of accelerated ligand-mapping MD (aLMMD), which combines accelerated MD with LMMD, for the detection of these challenging binding sites. The method was validated on five proteins with what we term "recalcitrant" cryptic pockets, which are deeply buried pockets that require extensive movement of the protein backbone to expose, and three proteins with occluded binding sites. In all the cases, aLMMD was able to detect and sample the binding sites. Our results suggest that aLMMD could be used as a general approach for the detection of such elusive binding sites in protein targets, thus providing valuable information for SBDD.

Application of Site-Identification by Ligand Competitive Saturation in Computer-Aided Drug Design

Site Identification by Ligand Competitive Saturation (SILCS) is a molecular simulation approach that uses diverse small solutes in aqueous solution to obtain functional group affinity patterns of a protein or other macromolecule. This involves employing a combined Grand Canonical Monte Carlo (GCMC)-molecular dynamics (MD) method to sample the full 3D space of the protein, including deep binding pockets and interior cavities from which functional group free energy maps (FragMaps) are obtained. The information content in the maps, which include contributions from protein flexibilty and both protein and functional group desolvation contributions, can be used in many aspects of the drug discovery process. These include identification of novel ligand binding pockets, including allosteric sites, pharmacophore modeling, prediction of relative protein-ligand binding affinities for database screening and lead optimization efforts, evaluation of protein-protein interactions as well as in the formulation of biologics-based drugs including monoclonal antibodies. The present article summarizes the various tools developed in the context of the SILCS methodology and their utility in computer-aided drug design (CADD) applications, showing how the SILCS toolset can improve the drug-development process on a number of fronts with respect to both accuracy and throughput representing a new avenue of CADD applications.

Efficient Hit-to-Lead Searching of Kinase Inhibitor Chemical Space via Computational Fragment Merging

In early-stage drug discovery, the hit-to-lead optimization (or "hit expansion") stage entails starting from a newly identified active compound and improving its potency or other properties. Traditionally, this process relies on synthesizing and evaluating a series of analogues to build up structure-activity relationships. Here, we describe a computational strategy focused on kinase inhibitors, intended to expedite the process of identifying analogues with improved potency. Our protocol begins from an inhibitor of the target kinase and generalizes the synthetic route used to access it. By searching for commercially available replacements for the individual building blocks used to make the parent inhibitor, we compile an enumerated library of compounds that can be accessed using the same chemical transformations; these huge libraries can exceed many millions─or billions─of compounds. Because the resulting libraries are much too large for explicit virtual screening, we instead consider alternate approaches to identify the top-scoring compounds. We find that contributions from individual substituents are well described by a pairwise additivity approximation, provided that the corresponding fragments position their shared core in precisely the same way relative to the binding site. This key insight allows us to determine which fragments are suitable for merging into single new compounds and which are not. Further, the use of pairwise approximation allows interaction energies to be assigned to each compound in the library without the need for any further structure-based modeling: interaction energies instead can be reliably estimated from the energies of the component fragments, and the reduced computational requirements allow for flexible energy minimizations that allow the kinase to respond to each substitution. We demonstrate this protocol using libraries built from six representative kinase inhibitors drawn from the literature, which target five different kinases: CDK9, CHK1, CDK2, EGFR^T790M, and ACK1. In each example, the enumerated library includes additional analogues reported by the original study to have activity, and these analogues are successfully prioritized within the library. We envision that the insights from this work can facilitate the rapid assembly and screening of increasingly large libraries for focused hit-to-lead optimization. To enable adoption of these methods and to encourage further analyses, we disseminate the computational tools needed to deploy this protocol.

Cryo-EM structure of the sodium-driven chloride/bicarbonate exchanger NDCBE

SLC4 transporters play significant roles in pH regulation and cellular sodium transport. The previously solved structures of the outward facing (OF) conformation for AE1 (SLC4A1) and NBCe1 (SLC4A4) transporters revealed an identical overall fold despite their different transport modes (chloride/bicarbonate exchange versus sodium-carbonate cotransport). However, the exact mechanism determining the different transport modes in the SLC4 family remains unknown. In this work, we report the cryo-EM 3.4 Å structure of the OF conformation of NDCBE (SLC4A8), which shares transport properties with both AE1 and NBCe1 by mediating the electroneutral exchange of sodium-carbonate with chloride. This structure features a fully resolved extracellular loop 3 and well-defined densities corresponding to sodium and carbonate ions in the tentative substrate binding pocket. Further, we combine computational modeling with functional studies to unravel the molecular determinants involved in NDCBE and SLC4 transport.

Species-Specific Endotoxin Stimulus Determines Toll-Like Receptor 4- and Caspase 11-Mediated Pathway Activation Characteristics

The innate immune system is the body’s first line of defense against pathogens and its protection against infectious diseases. On the surface of host myeloid cells, Toll-like receptor 4 (TLR4) senses lipopolysaccharide (LPS), the major outer membrane component of Gram-negative bacteria. Intracellularly, LPS is recognized by caspase 11 through the noncanonical inflammasome to induce pyroptosis—an inflammatory form of lytic cell death. While TLR4-mediated signaling perturbations result in secretion of cytokines and chemokines that help clear infection and facilitate adaptive immunity, caspase 11-mediated pyroptosis leads to the release of damage-associated molecular patterns and inflammatory mediators. Although the core signaling events and many associated proteins in the TLR4 signaling pathway are known, the complex signaling events and protein networks within the noncanonical inflammasome pathway remain obscure. Moreover, there is mounting evidence for pathogen-specific innate immune tuning. We characterized the major LPS structures from two different pathogens, modeled their binding to the surface receptors, systematically examined macrophage inflammatory responses to these LPS molecules, and surveyed the temporal differences in global protein secretion resulting from TLR4 and caspase 11 activation in macrophages using mass spectrometry (MS)-based quantitative proteomics. This integrated strategy, spanning functional activity assays, top-down structural elucidation of endotoxins, and secretome analysis of stimulated macrophages, allowed us to identify crucial differences in TLR4- and caspase 11-mediated protein secretion in response to two Gram-negative bacterial endotoxins.

IMPORTANCE Macrophages and monocytes are innate immune cells playing an important role in orchestrating the initial innate immune response to bacterial infection and the tissue damage. This response is facilitated by specific receptors on the cell surface and intracellularly. One of the bacterial molecules recognized is a Gram-negative bacteria cell wall component, lipopolysaccharide (LPS). The structure of LPS differs between different species. We have characterized the innate immune responses to the LPS molecules from two bacteria, Escherichia coli and Bordetella pertussis, administered either extracellularly or intracellularly, whose structures we first determined. We observed marked differences in the temporal dynamics and amounts of proteins secreted by the innate immune cells stimulated by any of these molecules and routes. This suggests that there is specificity in the first line of response to different Gram-negative bacteria that can be explored to tailor specific therapeutic interventions.

Insights into substrate recognition and specificity for IgG by Endoglycosidase S2

Antibodies bind foreign antigens with high affinity and specificity leading to their neutralization and/or clearance by the immune system. The conserved N-glycan on IgG has significant impact on antibody effector function, with the endoglycosidases of Streptococcus pyogenes deglycosylating the IgG to evade the immune system, a process catalyzed by the endoglycosidase EndoS2. Studies have shown that two of the four domains of EndoS2, the carbohydrate binding module (CBM) and the glycoside hydrolase (GH) domain are critical for catalytic activity. To yield structural insights into contributions of the CBM and the GH domains as well as the overall flexibility of EndoS2 to the proteins’ catalytic activity, models of EndoS2-Fc complexes were generated through enhanced-sampling molecular-dynamics (MD) simulations and site-identification by ligand competitive saturation (SILCS) docking followed by reconstruction and multi-microsecond MD simulations. Modeling results predict that EndoS2 initially interacts with the IgG through its CBM followed by interactions with the GH yielding catalytically competent states. These may involve the CBM and GH of EndoS2 simultaneously interacting with either the same Fc CH2/CH3 domain or individually with the two Fc CH2/CH3 domains, with EndoS2 predicted to assume closed conformations in the former case and open conformations in the latter. Apo EndoS2 is predicted to sample both the open and closed states, suggesting that either complex can directly form following initial IgG-EndoS2 encounter. Interactions of the CBM and GH domains with the IgG are predicted to occur through both its glycan and protein regions. Simulations also predict that the Fc glycan can directly transfer from the CBM to the GH, facilitating formation of catalytically competent complexes and how the 734 to 751 loop on the CBM can facilitate extraction of the glycan away from the Fc CH2/CH3 domain. The predicted models are compared and consistent with Hydrogen/Deuterium Exchange data. In addition, the complex models are consistent with the high specificity of EndoS2 for the glycans on IgG supporting the validity of the predicted models.

The pathogen Streptococcus pyogenes uses the endoglycosidases S and S2 to cleave the glycans on the Fc portion of IgG antibodies, leading to a decreased cytotoxicity of the antibodies, thereby evading the host immune response. To identify potential structures of the complex of EndoS2 with IgG that could lead to the catalytic hydrolysis of the IgG glycan, molecular modeling and molecular dynamics simulations were applied. The resulting structural models predict that EndoS2 initially interacts through its carbohydrate binding module (CBM) with the IgG with subsequent interactions with the catalytic glycoside hydrolase (GH) domain yielding stable complexes. In the modeled complexes the CBM and the GH interact either simultaneously with the same Fc CH2/CH3 domain or with the two individual Fc CH2/CH3 domains separately to yield potentially catalytically competent species. In addition, apo EndoS2 is shown to assume both open and closed conformations allowing it to directly form either type of complex from which deglycosylation of either mono- or diglycosylated IgG species may occur.

Rapid and accurate estimation of protein-ligand relative binding affinities using site-identification by ligand competitive saturation

Predicting relative protein-ligand binding affinities is a central pillar of lead optimization efforts in structure-based drug design. The site identification by ligand competitive saturation (SILCS) methodology is based on functional group affinity patterns in the form of free energy maps that may be used to compute protein-ligand binding poses and affinities. Presented are results obtained from the SILCS methodology for a set of eight target proteins as reported originally in Wang et al. (J. Am. Chem. Soc., 2015, 137, 2695-2703) using free energy perturbation (FEP) methods in conjunction with enhanced sampling and cycle closure corrections. These eight targets have been subsequently studied by many other authors to compare the efficacy of their method while comparing with the outcomes of Wang et al. In this work, we present results for a total of 407 ligands on the eight targets and include specific analysis on the subset of 199 ligands considered previously. Using the SILCS methodology we can achieve an average accuracy of up to 77% and 74% when considering the eight targets with their 199 and 407 ligands, respectively, for rank-ordering ligand affinities as calculated by the percent correct metric. This accuracy increases to 82% and 80%, respectively, when the SILCS atomic free energy contributions are optimized using a Bayesian Markov-chain Monte Carlo approach. We also report other metrics including Pearson's correlation coefficient, Pearlman's predictive index, mean unsigned error, and root mean square error for both sets of ligands. The results obtained for the 199 ligands are compared with the outcomes of Wang et al. and other published works. Overall, the SILCS methodology yields similar or better-quality predictions without a priori need for known ligand orientations in terms of the different metrics when compared to current FEP approaches with significant computational savings while additionally offering quantitative estimates of individual atomic contributions to binding free energies. These results further validate the SILCS methodology as an accurate, computationally efficient tool to support lead optimization and drug discovery.

EXPRORER: Rational Cosolvent Set Construction Method for Cosolvent Molecular Dynamics Using Large-Scale Computation

Cosolvent molecular dynamics (CMD) simulations involve an MD simulation of a protein in the presence of explicit water molecules mixed with cosolvent molecules to perform hotspot detection, binding site identification, and binding energy estimation, while other existing methods (e.g., MixMD, SILCS, and MDmix) utilize small molecules that represent functional groups of compounds. However, the cosolvent selections employed in these methods differ and there are only a few cosolvents that are commonly used in these methods. In this study, we proposed a systematic method for constructing a set of cosolvents for drug discovery, termed the EXtended PRObes set construction by REpresentative Retrieval (EXPRORER). First, we extracted typical substructures from FDA-approved drugs, generated 138 cosolvent structures, and for each cosolvent molecule, we conducted CMD simulations to generate a spatial probability distribution map of cosolvent atoms (PMAP). Analyses of PMAP similarity revealed that a cosolvent pair with a PMAP similarity greater than 0.70-0.75 shared similar structural features. We present a method for the construction of a cosolvent subset that satisfies a similarity threshold for all cosolvents, and we tested the constructed sets for four proteins. To our knowledge, this is the first study to include a systematic proposal for cosolvent set construction, and thus, the EXPRORER cosolvents will provide deeper insights into ligand binding sites of various proteins.

Identification of multiple substrate binding sites in SLC4 transporters in the outward-facing conformation: Insights into the transport mechanism

Solute carrier family 4 (SLC4) transporters mediate the transmembrane transport of HCO₃^-, CO₃^2-, and Cl^- necessary for pH regulation, transepithelial H⁺/base transport, and ion homeostasis. Substrate transport with varying stoichiometry and specificity is achieved through an exchange mechanism and/or through coupling of the uptake of anionic substrates to typically co-transported Na⁺. Recently solved outward-facing structures of two SLC4 members (human anion exchanger 1 [hAE1] and human electrogenic sodium bicarbonate cotransporter 1 [hNBCe1]) with different transport modes (Cl^-/HCO₃^- exchange versus Na⁺-CO₃^2- symport) revealed highly conserved three-dimensional organization of their transmembrane domains. However, the exact location of the ion binding sites and their protein-ion coordination motifs are still unclear. In the present work, we combined site identification by ligand competitive saturation mapping and extensive molecular dynamics sampling with functional mutagenesis studies which led to the identification of two substrate binding sites (entry and central) in the outward-facing states of hAE1 and hNBCe1. Mutation of residues in the identified binding sites led to impaired transport in both proteins. We also showed that R730 in hAE1 is crucial for anion binding in both entry and central sites, whereas in hNBCe1, a Na⁺ acts as an anchor for CO₃^2- binding to the central site. Additionally, protonation of the central acidic residues (E681 in hAE1 and D754 in hNBCe1) alters the ion dynamics in the permeation cavity and may contribute to the transport mode differences in SLC4 proteins. These results provide a basis for understanding the functional differences between hAE1 and hNBCe1 and may facilitate potential drug development for diseases such as proximal and distal renal tubular acidosis.

Functional Group Distributions, Partition Coefficients, and Resistance Factors in Lipid Bilayers Using Site Identification by Ligand Competitive Saturation

Small molecules such as metabolites and drugs must pass through the membrane of the cell, a barrier primarily comprising phospholipid bilayers and embedded proteins. To better understand the process of passive diffusion, knowledge of the ability of various functional groups to partition across bilayers and the associated energetics would be of utility. In the present study, the site identification by ligand competitive saturation (SILCS) methodology has been applied to sample the distributions of a diverse set of chemical solutes representing the functional groups of small molecules across phospholipid bilayers composed of 0.9:0.1 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine/cholesterol and a mixture of 0.52:0.18:0.3 1,2-dioleoyl-sn-glycero-3-phospho-l-serine/1,2-dioleoyl-sn-glycero-3-phosphocholine/cholesterol used in parallel artificial membrane permeability assay experiments. A combination of oscillating chemical potential grand canonical Monte Carlo and molecular dynamics in the SILCS simulations was applied to achieve solute sampling through the bilayers and surrounding aqueous environment from which the distribution of solutes and the functional groups they represent were obtained. Results show differential distribution of aliphatic versus aromatic groups with the former having increased sampling in the center of the bilayers versus in the region of the glycerol linker for the latter. Variations in the distribution of different polar groups are evident, with large differences between negative acetate and positive methylammonium with accumulation of the polar-neutral and acetate solutes above the bilayer head groups. Conversion of the distributions to absolute free energies allows for a detailed understanding of energetics of functional groups in different regions of the bilayers and for calculation of absolute free-energy profiles of multifunctional drug-like molecules across the bilayers from which partition coefficients and resistance factors suitable for insertion into the homogenous solubility-diffusion equation for calculation of permeability were obtained. Comparisons of the calculated bilayer/solution partition coefficients with 1-octanol/water experimental data for both drug-like molecules and the solutes show overall good agreement, validating the calculated distributions and associated absolute free-energy profiles.

Insights into Glucose-6-phosphate Allosteric Activation of β-Glucosidase A

Second-generation ethanol production involves the use of agricultural and forestry waste as feedstock, being an alternative to the first-generation technology as it relies on low-cost abundant residues and does not affect food agriculture. However, the success of second-generation biorefineries relies on energetically efficient processes and effective enzyme cocktails to convert cellulose into fermentable sugars. β-glucosidases catalyze the last step on the enzymatic hydrolysis of cellulose; however, they are often inhibited by glucose. Previous studies demonstrated that glucose-6-phosphate (G6P) is a positive allosteric modulator of Bacillus polymyxa β-glucosidase A, improving enzymatic efficiency, providing thermoresistance, and imparting glucose tolerance. However, the precise molecular details of G6P-β-glucosidase A interactions have not yet been described so far. We investigated the molecular details of G6P binding into B. polymyxa β-glucosidase A through in silico docking using the site identification by ligand competitive saturation technology followed by site-directed mutagenesis studies, from which an allosteric binding site for G6P was identified. In addition, a mechanistic shift toward the transglycosylation reaction as opposed to hydrolysis was observed in the presence of G6P, suggesting a new role of G6P allosteric modulation of the catalytic activity of β-glucosidase A.

Profiling the Tox21 Chemical Collection for Acetylcholinesterase Inhibition

BACKGROUND

Inhibition of acetylcholinesterase (AChE), a biomarker of organophosphorous and carbamate exposure in environmental and occupational human health, has been commonly used to identify potential safety liabilities. So far, many environmental chemicals, including drug candidates, food additives, and industrial chemicals, have not been thoroughly evaluated for their inhibitory effects on AChE activity. AChE inhibitors can have therapeutic applications (e.g., tacrine and donepezil) or neurotoxic consequences (e.g., insecticides and nerve agents).

OBJECTIVES

The objective of the current study was to identify environmental chemicals that inhibit AChE activity using in vitro and in silico models.

METHODS

To identify AChE inhibitors rapidly and efficiently, we have screened the Toxicology in the 21st Century (Tox21) 10K compound library in a quantitative high-throughput screening (qHTS) platform by using the homogenous cell-based AChE inhibition assay and enzyme-based AChE inhibition assays (with or without microsomes). AChE inhibitors identified from the primary screening were further tested in monolayer or spheroid formed by SH-SY5Y and neural stem cell models. The inhibition and binding modes of these identified compounds were studied with time-dependent enzyme-based AChE inhibition assay and molecular docking, respectively.

RESULTS

A group of known AChE inhibitors, such as donepezil, ambenonium dichloride, and tacrine hydrochloride, as well as many previously unreported AChE inhibitors, such as chelerythrine chloride and cilostazol, were identified in this study. Many of these compounds, such as pyrazophos, phosalone, and triazophos, needed metabolic activation. This study identified both reversible (e.g., donepezil and tacrine) and irreversible inhibitors (e.g., chlorpyrifos and bromophos-ethyl). Molecular docking analyses were performed to explain the relative inhibitory potency of selected compounds.

CONCLUSIONS

Our tiered qHTS approach allowed us to generate a robust and reliable data set to evaluate large sets of environmental compounds for their AChE inhibitory activity. https://doi.org/10.1289/EHP6993.

Current and Future Challenges in Modern Drug Discovery

Drug discovery is an expensive, time-consuming, and risky business. To avoid late-stage failure, learnings from past projects and the development of new approaches are crucial. New modalities and emerging new target spaces allow the exploration of unprecedented indications or to address so far undrugable targets. Late-stage attrition is usually attributed to the lack of efficacy or to compound-related safety issues. Efficacy has been shown to be related to a strong genetic link to human disease, a better understanding of the target biology, and the availability of biomarkers to bridge from animals to humans. Compound safety can be improved by ligand optimization, which is becoming increasingly demanding for difficult targets. Therefore, new strategies include the design of allosteric ligands, covalent binders, and other modalities. Design methods currently heavily rely on artificial intelligence and advanced computational methods such as free energy calculations and quantum chemistry. Especially for quantum chemical methods, a more detailed overview is given in this chapter.

Simple Synthesis of a Heterocyclophane Exhibiting Anti-c-Met Activity by Acting as a Hatch Blocking Access to the Active Site*

A simple approach to the synthesis of heterocyclophane consisting of two 4,4'-bithiazoles has been developed in mild conditions. The heterocyclophane with two short chains was conveniently prepared by Hantzsch thiazoles synthesis using the reaction of 3-tert-butoxycarbonyl-3-azapentanethiocarboxamide with 1,4-dibromobutane-2,3-dione in methanol under reflux for only 15 min. Amino groups at the linkers of this heterocyclophane can be functionalized to give acylated and carbamate derivatives. Their properties as protein kinase inhibitors were investigated, and one of the heterocyclophanes exhibited specific anti-activity for c-mesenchymal epithelial transition factor (IC50 =603 nm), among seven types of protein kinases investigated. The computational site identification by ligand competitive saturation method was used to determine why the one heterocyclophane exhibited strong anti-activity for c-mesenchymal epithelial transition factor.

Discovery of beta-lactamase CMY-10 inhibitors for combination therapy against multi-drug resistant Enterobacteriaceae

β-lactam antibiotics are the most widely used antimicrobial agents since the discovery of benzylpenicillin in the 1920s. Unfortunately, these life-saving antibiotics are vulnerable to inactivation by continuously evolving β-lactamase enzymes that are primary resistance determinants in multi-drug resistant pathogens. The current study exploits the strategy of combination therapeutics and aims at identifying novel β-lactamase inhibitors that can inactivate the β-lactamase enzyme of the pathogen while allowing the β-lactam antibiotic to act against its penicillin-binding protein target. Inhibitor discovery applied the Site-Identification by Ligand Competitive Saturation (SILCS) technology to map the functional group requirements of the β-lactamase CMY-10 and generate pharmacophore models of active site. SILCS-MC, Ligand-grid Free Energy (LGFE) analysis and Machine-learning based random-forest (RF) scoring methods were then used to screen and filter a library of 700,000 compounds. From the computational screens 74 compounds were subjected to experimental validation in which β-lactamase activity assay, in vitro susceptibility testing, and Scanning Electron Microscope (SEM) analysis were conducted to explore their antibacterial potential. Eleven compounds were identified as enhancers while 7 compounds were recognized as inhibitors of CMY-10. Of these, compound 11 showed promising activity in β-lactamase activity assay, in vitro susceptibility testing against ATCC strains (E. coli, E. cloacae, E. agglomerans, E. alvei) and MDR clinical isolates (E. cloacae, E. alvei and E. agglomerans), with synergistic assay indicating its potential as a β-lactam enhancer and β-lactamase inhibitor. Structural similarity search against the active compound 11 yielded 28 more compounds. The majority of these compounds also exhibited β-lactamase inhibition potential and antibacterial activity. The non-β-lactam-based β-lactamase inhibitors identified in the current study have the potential to be used in combination therapy with lactam-based antibiotics against MDR clinical isolates that have been found resistant against last-line antibiotics.

Specificity of Molecular Fragments Binding to S100B versus S100A1 as Identified by NMR and Site Identification by Ligand Competitive Saturation (SILCS)

S100B, a biomarker of malignant melanoma, interacts with the p53 protein and diminishes its tumor suppressor function, which makes this S100 family member a promising therapeutic target for treating malignant melanoma. However, it is a challenge to design inhibitors that are specific for S100B in melanoma versus other S100-family members that are important for normal cellular activities. For example, S100A1 is most similar in sequence and structure to S100B, and this S100 protein is important for normal skeletal and cardiac muscle function. Therefore, a combination of NMR and computer aided drug design (CADD) was used to initiate the design of specific S100B inhibitors. Fragment-based screening by NMR, also termed "SAR by NMR," is a well-established method, and was used to examine spectral perturbations in 2D [1H, 15N]-HSQC spectra of Ca2+-bound S100B and Ca2+-bound S100A1, side-by-side, and under identical conditions for comparison. Of the 1000 compounds screened, two were found to be specific for binding Ca2+-bound S100A1 and four were found to be specific for Ca2+-bound S100B, respectively. The NMR spectral perturbations observed in these six data sets were then used to model how each of these small molecule fragments showed specificity for one S100 versus the other using a CADD approach termed Site Identification by Ligand Competitive Saturation (SILCS). In summary, the combination of NMR and computational approaches provided insight into how S100A1 versus S100B bind small molecules specifically, which will enable improved drug design efforts to inhibit elevated S100B in melanoma. Such a fragment-based approach can be used generally to initiate the design of specific inhibitors for other highly homologous drug targets.

Assessing hERG1 Blockade from Bayesian Machine-Learning-Optimized Site Identification by Ligand Competitive Saturation Simulations

Drug-induced cardiotoxicity is a potentially lethal and yet one of the most common side effects with the drugs in clinical use. Most of the drug-induced cardiotoxicity is associated with an off-target pharmacological blockade of K+ currents carried out by the cardiac Human-Ether-a-go-go-Related (hERG1) potassium channel. There is a compulsory preclinical stage safety assessment for the hERG1 blockade for all classes of drugs, which adds substantially to the cost of drug development. The availability of a high-resolution cryogenic electron microscopy (cryo-EM) structure for the channel in its open/depolarized state solved in 2017 enabled the application of molecular modeling for rapid assessment of drug blockade by molecular docking and simulation techniques. More importantly, if successful, in silico methods may allow a path to lead-compound salvaging by mapping out key block determinants. Here, we report the blind application of the site identification by the ligand competitive saturation (SILCS) protocol to map out druggable/regulatory hotspots in the hERG1 channel available for blockers and activators. The SILCS simulations use small solutes representative of common functional groups to sample the chemical space for the entire protein and its environment using all-atom simulations. The resulting chemical maps, FragMaps, explicitly account for receptor flexibility, protein-fragment interactions, and fragment desolvation penalty allowing for rapid ranking of potential ligands as blockers or nonblockers of hERG1. To illustrate the power of the approach, SILCS was applied to a test set of 55 blockers with diverse chemical scaffolds and pIC50 values measured under uniform conditions. The original SILCS model was based on the all-atom modeling of the hERG1 channel in an explicit lipid bilayer and was further augmented with a Bayesian-optimization/machine-learning (BML) stage employing an independent literature-derived training set of 163 molecules. BML approach was used to determine weighting factors for the FragMaps contributions to the scoring function. pIC50 predictions from the combined SILCS/BML approach to the 55 blockers showed a Pearson correlation (PC) coefficient of >0.535 relative to the experimental data. SILCS/BML model was shown to yield substantially improved performance as compared to commonly used rigid and flexible molecular docking methods for a well-established cohort of hERG1 blockers, where no correlation with experimental data was recorded. SILCS/BML results also suggest that a proper weighting of protonation states of common blockers present at physiological pH is essential for accurate predictions of blocker potency. The precalculated and optimized SILCS FragMaps can now be used for the rapid screening of small molecules for their cardiotoxic potential as well as for exploring alternative binding pockets in the hERG1 channel with applications to the rational design of activators.

Structural Fluctuations of Aromatic Residues in an Apo-Form Reveal Cryptic Binding Sites: Implications for Fragment-Based Drug Design

Cryptic sites are binding pockets that are transiently formed in an apo form or that are induced by ligand binding. The investigation of cryptic sites is crucial for drug discovery, since these sites are ubiquitous in disease-related human proteins, and targeting them expands the number of drug targets greatly. However, although many computational studies have attempted to identify cryptic sites, the detection remains challenging. Here, we aimed to characterize and detect cryptic sites in terms of structural fluctuations in an apo form, investigating proteins each of which possesses a cryptic site. From their X-ray structures, we saw that aromatic residues tended to be found in cryptic sites. To examine structural fluctuations of the apo forms, we performed molecular dynamics (MD) simulations, producing probability distributions of the solvent-accessible surface area per aromatic residue. To detect aromatic residues in cryptic sites, we have proposed a "cryptic-site index" based on the distribution, demonstrating the performance via several measures, such as recall and specificity. Besides, we found that high-ranking aromatic residues were likely to probe concaves in a cryptic site. This implies that such fluctuations provide a profile of scaffolds of compounds with the potential to bind to a particular cryptic site.

Computational Characterization of Antibody-Excipient Interactions for Rational Excipient Selection Using the Site Identification by Ligand Competitive Saturation-Biologics Approach

Protein therapeutics typically require a concentrated protein formulation, which can lead to self-association and/or high viscosity due to protein-protein interaction (PPI). Excipients are often added to improve stability, bioavailability, and manufacturability of the protein therapeutics, but the selection of excipients often relies on trial and error. Therefore, understanding the excipient-protein interaction and its effect on non-specific PPI is important for rational selection of formulation development. In this study, we validate a general workflow based on the site identification by ligand competitive saturation (SILCS) technology, termed SILCS-Biologics, that can be applied to protein therapeutics for rational excipient selection. The National Institute of Standards and Technology monoclonal antibody (NISTmAb) reference along with the CNTO607 mAb is used as model antibody proteins to examine PPIs, and NISTmAb was used to further examine excipient-protein interactions, in silico. Metrics from SILCS include the distribution and predicted affinity of excipients, buffer interactions with the NISTmAb Fab, and the relation of the interactions to predicted PPI. Comparison with a range of experimental data showed multiple SILCS metrics to be predictive. Specifically, the number of favorable sites to which an excipient binds and the number of sites to which an excipient binds that are involved in predicted PPIs correlate with the experimentally determined viscosity. In addition, a combination of the number of binding sites and the predicted binding affinity is indicated to be predictive of relative protein stability. Comparison of arginine, trehalose, and sucrose, all of which give the highest viscosity in combination with analysis of B₂₂ and k_D and the SILCS metrics, indicates that higher viscosities are associated with a low number of predicted binding sites, with lower binding affinity of arginine leading to its anomalously high impact on viscosity. The present study indicates the potential for the SILCS-Biologics approach to be of utility in the rational design of excipients during biologics formulation.

Toward Biotherapeutics Formulation Composition Engineering using Site-Identification by Ligand Competitive Saturation (SILCS)

Formulation of protein-based therapeutics employ advanced formulation and analytical technologies for screening various parameters such as buffer, pH, and excipients. At a molecular level, physico-chemical properties of a protein formulation depend on self-interaction between protein molecules, protein-solvent and protein-excipient interactions. This work describes a novel in silico approach, SILCS-Biologics, for structure-based modeling of protein formulations. SILCS Biologics is based on the Site-Identification by Ligand Competitive Saturation (SILCS) technology and enables modeling of interactions among different components of a formulation at an atomistic level while accounting for protein flexibility. It predicts potential hotspot regions on the protein surface for protein-protein and protein-excipient interactions. Here we apply SILCS-Biologics on a Fab domain of a monoclonal antibody (mAbN) to model Fab-Fab interactions and interactions with three amino acid excipients, namely, arginine HCl, proline and lysine HCl. Experiments on 100 mg/ml formulations of mAbN showed that arginine increased, lysine reduced, and proline did not impact viscosity. We use SILCS-Biologics modeling to explore a structure-based hypothesis for the viscosity modulating effect of these excipients. Current efforts are aimed at further validation of this novel computational framework and expanding the scope to model full mAb and other protein therapeutics.

Rationally Designed Polypharmacology: α-Helix Mimetics as Dual Inhibitors of the Oncoproteins Mcl-1 and HDM2

Protein-protein interactions (PPIs), many of which are dominated by α-helical recognition domains, play key roles in many essential cellular processes, and the dysregulation of these interactions can cause detrimental effects. For instance, aberrant PPIs involving the Bcl-2 protein family can lead to several diseases including cancer, neurodegenerative diseases, and diabetes. Interactions between Bcl-2 pro-life proteins, such as Mcl-1, and pro-death proteins, such as Bim, regulate the intrinsic pathway of apoptosis. p53, a tumor-suppressor protein, also has a pivotal role in apoptosis and is negatively regulated by its E3 ubiquitin ligase HDM2. Both Mcl-1 and HDM2 are upregulated in numerous cancers, and, interestingly, there is crosstalk between both protein pathways. Recently, synergy has been observed between Mcl-1 and HDM2 inhibitors. Towards the development of new anticancer drugs, we herein describe a polypharmacology approach for the dual inhibition of Mcl-1 and HDM2 by employing three densely functionalized isoxazoles, pyrazoles, and thiazoles as mimetics of key α-helical domains of their partner proteins.