Development of a Novel Lead that Targets M. tuberculosis Polyketide Synthase 13.

Widespread resistance to first-line TB drugs is a major problem that will likely only be resolved through the development of new drugs with novel mechanisms of action. We have used structure-guided methods to develop a lead molecule that targets the thioesterase activity of polyketide synthase Pks13, an essential enzyme that forms mycolic acids, required for the cell wall of Mycobacterium tuberculosis. Our lead, TAM16, is a benzofuran class inhibitor of Pks13 with highly potent in vitro bactericidal activity against drug-susceptible and drug-resistant clinical isolates of M. tuberculosis. In multiple mouse models of TB infection, TAM16 showed in vivo efficacy equal to the first-line TB drug isoniazid, both as a monotherapy and in combination therapy with rifampicin. TAM16 has excellent pharmacological and safety profiles, and the frequency of resistance for TAM16 is ∼100-fold lower than INH, suggesting that it can be developed as a new antitubercular aimed at the acute infection. PAPERCLIP.

Drug discovery in the era of cryo-electron microscopy.

Structure-based drug discovery (SBDD) is an indispensable approach for the design and optimization of new therapeutic agents. Here, we highlight the rapid progress that has turned cryo-electron microscopy (cryoEM) into an exceptional SBDD tool, and the wealth of new structural information it is providing for high-value pharmacological targets. We review key advantages of a technique that directly images vitrified biomolecules without the need for crystallization; both in terms of a broader array of systems that can be studied and the different forms of information it can provide, including heterogeneity and dynamics. We discuss near- and far-future developments, working in concert towards achieving the resolution and throughput necessary for cryoEM to make a widespread impact on the SBDD pipeline.

A novel class of TMPRSS2 inhibitors potently block SARS-CoV-2 and MERS-CoV viral entry and protect human epithelial lung cells.

The host cell serine protease TMPRSS2 is an attractive therapeutic target for COVID-19 drug discovery. This protease activates the Spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and of other coronaviruses and is essential for viral spread in the lung. Utilizing rational structure-based drug design (SBDD) coupled to substrate specificity screening of TMPRSS2, we have discovered covalent small-molecule ketobenzothiazole (kbt) TMPRSS2 inhibitors which are structurally distinct from and have significantly improved activity over the existing known inhibitors Camostat and Nafamostat. Lead compound MM3122 (4) has an IC50 (half-maximal inhibitory concentration) of 340 pM against recombinant full-length TMPRSS2 protein, an EC50 (half-maximal effective concentration) of 430 pM in blocking host cell entry into Calu-3 human lung epithelial cells of a newly developed VSV-SARS-CoV-2 chimeric virus, and an EC50 of 74 nM in inhibiting cytopathic effects induced by SARS-CoV-2 virus in Calu-3 cells. Further, MM3122 blocks Middle East respiratory syndrome coronavirus (MERS-CoV) cell entry with an EC50 of 870 pM. MM3122 has excellent metabolic stability, safety, and pharmacokinetics in mice, with a half-life of 8.6 h in plasma and 7.5 h in lung tissue, making it suitable for in vivo efficacy evaluation and a promising drug candidate for COVID-19 treatment.

ChemFlow_py: a flexible toolkit for docking and rescoring.

The design of accurate virtual screening tools is an open challenge in drug discovery. Several structure-based methods have been developed at different levels of approximation. Among them, molecular docking is an established technique with high efficiency, but typically low accuracy. Moreover, docking performances are known to be target-dependent, which makes the choice of the docking program and corresponding scoring function critical when approaching a new protein target. To compare the performances of different docking protocols, we developed ChemFlow_py, an automated tool to perform docking and rescoring. Using four protein systems extracted from DUD-E with 100 known active compounds and 3000 decoys per target, we compared the performances of several rescoring strategies including consensus scoring. We found that the average docking results can be improved by consensus ranking, which emphasizes the relevance of consensus scoring when little or no chemical information is available for a given target. ChemFlow_py is a free toolkit to optimize the performances of virtual high-throughput screening (vHTS). The software is publicly available at https://github.com/IFMlab/ChemFlow_py .

A New Drug Discovery Platform: Application to DNA Polymerase Eta and Apurinic/Apyrimidinic Endonuclease 1.

The ability to quickly discover reliable hits from screening and rapidly convert them into lead compounds, which can be verified in functional assays, is central to drug discovery. The expedited validation of novel targets and the identification of modulators to advance to preclinical studies can significantly increase drug development success. Our SaXPyTM ("SAR by X-ray Poses Quickly") platform, which is applicable to any X-ray crystallography-enabled drug target, couples the established methods of protein X-ray crystallography and fragment-based drug discovery (FBDD) with advanced computational and medicinal chemistry to deliver small molecule modulators or targeted protein degradation ligands in a short timeframe. Our approach, especially for elusive or "undruggable" targets, allows for (i) hit generation; (ii) the mapping of protein-ligand interactions; (iii) the assessment of target ligandability; (iv) the discovery of novel and potential allosteric binding sites; and (v) hit-to-lead execution. These advances inform chemical tractability and downstream biology and generate novel intellectual property. We describe here the application of SaXPy in the discovery and development of DNA damage response inhibitors against DNA polymerase eta (Pol η or POLH) and apurinic/apyrimidinic endonuclease 1 (APE1 or APEX1). Notably, our SaXPy platform allowed us to solve the first crystal structures of these proteins bound to small molecules and to discover novel binding sites for each target.

Crystal structures of the catalytic domain of human PARP15 in complex with small molecule inhibitors.

PARP15, or ARTD7, is an enzyme carrying out mono-ADP-ribosylation and regulating activities of a range of cellular proteins. This enzyme belongs to the family of the poly(ADP-ribose) polymerases (PARPs), which comprises of proteins with various potential disease indications. Due to their involvement in a number of cellular processes and important role in DNA repair and regulation, PARPs have been considered attractive therapeutic targets over the past few years. The pursuit of small molecule PARP inhibitors has resulted in several FDA approved drugs for multiple cancers so far. As the use of PARP inhibitors as drug scaffolds is actively explored recently, there is increasing interest in the design of selective inhibitors based on the structural features of the PARP proteins. Here, we solved high-resolution crystal structures of the human PARP15 catalytic domain in complex with three marketed drugs of PARP inhibitors, which includes compounds 3-AB, iniparib and niraparib. The structures reported here contribute to our understanding of the ligand binding modes and structural features in the PARP15 catalytic domain, which can be employed to guide the rational design of selective inhibitors of PARPs.

Big data and artificial intelligence (AI) methodologies for computer-aided drug design (CADD).

There have been numerous advances in the development of computational and statistical methods and applications of big data and artificial intelligence (AI) techniques for computer-aided drug design (CADD). Drug design is a costly and laborious process considering the biological complexity of diseases. To effectively and efficiently design and develop a new drug, CADD can be used to apply cutting-edge techniques to various limitations in the drug design field. Data pre-processing approaches, which clean the raw data for consistent and reproducible applications of big data and AI methods are introduced. We include the current status of the applicability of big data and AI methods to drug design areas such as the identification of binding sites in target proteins, structure-based virtual screening (SBVS), and absorption, distribution, metabolism, excretion and toxicity (ADMET) property prediction. Data pre-processing and applications of big data and AI methods enable the accurate and comprehensive analysis of massive biomedical data and the development of predictive models in the field of drug design. Understanding and analyzing biological, chemical, or pharmaceutical architectures of biomedical entities related to drug design will provide beneficial information in the biomedical big data era.

Neuropeptides, New Ligands of SARS-CoV-2 Nucleoprotein, a Potential Link between Replication, Inflammation and Neurotransmission.

This work identifies new ligands of the nucleoprotein N of SARS-CoV-2 by in silico screening, which used a new model of N, built from an Alphafold model refined by molecular dynamic simulations. The ligands were neuropeptides, such as substance P (1-7) and enkephalin, bound at a large site of the C-terminal or associated with the N-terminal ß-sheet. The BA4 and BA5 Omicron variants of N also exhibited a large site as in wt N, and an increased flexibility of the BA5 variant, enabling substance P binding. The binding sites of some ligands deduced from modeling in wt N were assessed by mutation studies in surface plasmon resonance experiments. Dynamic light scattering showed that the ligands impeded RNA binding to N, which likely inhibited replication. We suggest that the physiological role of these neuropeptides in neurotransmission, pain and vasodilation for cholecystokinin and substance P could be altered by binding to N. We speculate that N may link between viral replication and multiple pathways leading to long COVID-19 symptoms. Therefore, N may constitute a "danger hub" that needs to be inhibited, even at high cost for the host. Antivirals targeted to N may therefore reduce the risk of brain fog and stroke, and improve patients' health.

Structure based design and synthesis of novel Toll-like Receptor 2 (TLR 2) lipid antagonists.

Toll-like receptors (TLRs) play key role in innate immune response to Damage Associated Molecular Patterns (DAMPs) and Pathogen Associated Molecular Patterns (PAMPs). DAMP/PAMP-mediated activation of TLRs triggers NFκB signaling resulting in pro-inflammatory cytokine release. Using TLR2-Pam2CSK4 agonist co-crystal structure information, we designed and synthesized a novel series of Toll-like Receptor 2 (TLR2) lipid antagonists and identified compounds 14, 15 and 17 with sub-micromolar potency. TLR2 antagonists that we identified are stable for > 1.0 h in both gastric juice and PBS buffer and could be used as research tools.

The critical role of QM/MM X-ray refinement and accurate tautomer/protomer determination in structure-based drug design.

Conventional protein:ligand crystallographic refinement uses stereochemistry restraints coupled with a rudimentary energy functional to ensure the correct geometry of the model of the macromolecule-along with any bound ligand(s)-within the context of the experimental, X-ray density. These methods generally lack explicit terms for electrostatics, polarization, dispersion, hydrogen bonds, and other key interactions, and instead they use pre-determined parameters (e.g. bond lengths, angles, and torsions) to drive structural refinement. In order to address this deficiency and obtain a more complete and ultimately more accurate structure, we have developed an automated approach for macromolecular refinement based on a two layer, QM/MM (ONIOM) scheme as implemented within our DivCon Discovery Suite and "plugged in" to two mainstream crystallographic packages: PHENIX and BUSTER. This implementation is able to use one or more region layer(s), which is(are) characterized using linear-scaling, semi-empirical quantum mechanics, followed by a system layer which includes the balance of the model and which is described using a molecular mechanics functional. In this work, we applied our Phenix/DivCon refinement method-coupled with our XModeScore method for experimental tautomer/protomer state determination-to the characterization of structure sets relevant to structure-based drug design (SBDD). We then use these newly refined structures to show the impact of QM/MM X-ray refined structure on our understanding of function by exploring the influence of these improved structures on protein:ligand binding affinity prediction (and we likewise show how we use post-refinement scoring outliers to inform subsequent X-ray crystallographic efforts). Through this endeavor, we demonstrate a computational chemistry ↔ structural biology (X-ray crystallography) "feedback loop" which has utility in industrial and academic pharmaceutical research as well as other allied fields.

Structural Basis of Inhibition of DCLK1 by Ruxolitinib.

Given the functional attributes of Doublecortin-like kinase 1 (DCLK1) in tumor growth, invasion, metastasis, cell motility, and tumor stemness, it is emerging as a therapeutic target in gastrointestinal cancers. Although a series of specific or nonspecific ATP-competitive inhibitors were identified against DCLK1, different types of scaffolds that can be utilized for the development of highly selective inhibitors or structural understanding of binding specificities of the compounds remain limited. Here, we present our work to repurpose a Janus kinase 1 inhibitor, ruxolitinib as a DCLK1 inhibitor, showing micromolar binding affinity and inhibitory activity. Furthermore, to gain an insight into its interaction mode with DCLK1, a crystal structure of the ruxolitinib-complexed DCLK1 has been determined and analyzed. Ruxolitinib as a nonspecific DCLK1 inhibitor characterized in this work is anticipated to provide a starting point for the structure-guided discovery of selective DCLK1 inhibitors.

An allosteric binding site of the α7 nicotinic acetylcholine receptor revealed in a humanized acetylcholine-binding protein.

Nicotinic acetylcholine receptors (nAChRs) belong to the family of pentameric ligand-gated ion channels and mediate fast excitatory transmission in the central and peripheral nervous systems. Among the different existing receptor subtypes, the homomeric α7 nAChR has attracted considerable attention because of its possible implication in several neurological and psychiatric disorders, including cognitive decline associated with Alzheimer's disease or schizophrenia. Allosteric modulators of ligand-gated ion channels are of particular interest as therapeutic agents, as they modulate receptor activity without affecting normal fluctuations of synaptic neurotransmitter release. Here, we used X-ray crystallography and surface plasmon resonance spectroscopy of α7-acetylcholine-binding protein (AChBP), a humanized chimera of a snail AChBP, which has 71% sequence similarity with the extracellular ligand-binding domain of the human α7 nAChR, to investigate the structural determinants of allosteric modulation. We extended previous observations that an allosteric site located in the vestibule of the receptor offers an attractive target for receptor modulation. We introduced seven additional humanizing mutations in the vestibule-located binding site of AChBP to improve its suitability as a model for studying allosteric binding. Using a fragment-based screening approach, we uncovered an allosteric binding site located near the ß8-ß9 loop, which critically contributes to coupling ligand binding to channel opening in human α7 nAChR. This work expands our understanding of the topology of allosteric binding sites in AChBP and, by extrapolation, in the human α7 nAChR as determined by electrophysiology measurements. Our insights pave the way for drug design strategies targeting nAChRs involved in ion channel-mediated disorders.

Pyridine derivatives as anticancer lead compounds with Fatty Acid Synthase as the target: An in silico-guided in vitro study.

For the past few decades, structure-based drug discovery (SBDD) has become an inevitable technique in the drug development process for screening hit compounds against therapeutic targets. Here, we have successfully used the SBDD approach viz. virtual high-throughput screening to identify potential inhibitors against the Ketoacyl synthase (KS) domain of Fatty acid synthase (FASN). Overexpression of FASN, and subsequent enhancement of de novo lipogenesis is a key survival strategy of cancer cells. Hence, targeting lipid metabolism using FASN inhibitors has been considered as a promising method to induce metabolic stress, thereby posing a survival disadvantage to cancer cells. In the present study, we have successfully identified eight FASN inhibitors from Asinex Elite database by implementing in silico tools. Five of the hit compounds share a common ring structure, which enables characteristic binding interactions with FASN-KS. Among them, in vitro validation showed that SFA 22637550 possesses significant FASN inhibitory activity and antiproliferative effect in human cancer cells of various origins. The maximum sensitivity was exhibited towards HepG2 hepatocellular carcinoma cells (IC50 = 28 µM). The mode of cell death was found to be apoptosis with a significant increase in SubG0 population without affecting any other phases of the cell cycle. The current study puts forward an excellent core structure for the development of potent FASN inhibitors for successfully targeting cancer cell metabolism, thereby causing selective cell death.

Alkynamide phthalazinones as a new class of TbrPDEB1 inhibitors.

Several 3',5'-cyclic nucleotide phosphodiesterases (PDEs) have been validated as good drug targets for a large variety of diseases. Trypanosoma brucei PDEB1 (TbrPDEB1) has been designated as a promising drug target for the treatment of human African trypanosomiasis. Recently, the first class of selective nanomolar TbrPDEB1 inhibitors was obtained by targeting the parasite specific P-pocket. However, these biphenyl-substituted tetrahydrophthalazinone-based inhibitors did not show potent cellular activity against Trypanosoma brucei (T. brucei) parasites, leaving room for further optimization. Herein, we report the discovery of a new class of potent TbrPDEB1 inhibitors that display improved activities against T. brucei parasites. Exploring different linkers between the reported tetrahydrophthalazinone core scaffold and the amide tail group resulted in the discovery of alkynamide phthalazinones as new TbrPDEB1 inhibitors, which exhibit submicromolar activities versus T. brucei parasites and no cytotoxicity to human MRC-5 cells. Elucidation of the crystal structure of alkynamide 8b (NPD-048) bound to the catalytic domain of TbrPDEB1 shows a bidentate interaction with the key-residue Gln874 and good directionality towards the P-pocket. Incubation of trypanosomes with alkynamide 8b results in an increase of intracellular cAMP, validating a PDE-mediated effect in vitro and providing a new interesting compound series for further studies towards selective TbrPDEB1 inhibitors with potent phenotypic activity.

Alkynamide phthalazinones as a new class of TbrPDEB1 inhibitors (Part 2).

Inhibitors against Trypanosoma brucei phosphodiesterase B1 (TbrPDEB1) and B2 (TbrPDEB2) have gained interest as new treatments for human African trypanosomiasis. The recently reported alkynamide tetrahydrophthalazinones, which show submicromolar activities against TbrPDEB1 and anti-T. brucei activity, have been used as starting point for the discovery of new TbrPDEB1 inhibitors. Structure-based design indicated that the alkynamide-nitrogen atom can be readily decorated, leading to the discovery of 37, a potent TbrPDEB1 inhibitor with submicromolar activities against T. brucei parasites. Furthermore, 37 is more potent against TbrPDEB1 than hPDE4 and shows no cytotoxicity on human MRC-5 cells. The crystal structures of the catalytic domain of TbrPDEB1 co-crystalized with several different alkynamides show a bidentate interaction with key-residue Gln874, but no interaction with the parasite-specific P-pocket, despite being (uniquely) a more potent inhibitor for the parasite PDE. Incubation of blood stream form trypanosomes by 37 increases intracellular cAMP levels and results in the distortion of the cell cycle and cell death, validating phosphodiesterase inhibition as mode of action.

Protein Allostery in Rational Drug Design.

This chapter focuses on protein kinases that transfer the phosphate group of ATP to the hydroxyl group of a substrate protein. Five hundred eighteen human protein kinases are classified into serine/threonine kinases and tyrosine kinases and individually or synergistically transduce physiologic stimuli into cell to promote cell proliferation or apoptosis, etc. Protein kinases are identified as drug targets because dysfunction of kinases leads to severe diseases such as cancers and autoimmune diseases. A large number of the crystal structures of the protein kinase inhibitor complex are available in Protein Data Bank and facilitated the drug discovery targeting protein kinases. The protein kinase inhibitors are classified into categories, Type-I, Type-II, Type-III, Type-IV, and Type-V, and as a separate class, covalent-type inhibitors. In any type, a protein kinase inhibitor bound to the allosteric region is advantageous in terms of selectivity compared to the traditional ATP-competitive one. In the following sections, the successful and promising examples of the partially or fully allosteric protein kinase inhibitors are illustrated in the following pages.

A Structure-Based Drug Discovery Paradigm.

Structure-based drug design is becoming an essential tool for faster and more cost-efficient lead discovery relative to the traditional method. Genomic, proteomic, and structural studies have provided hundreds of new targets and opportunities for future drug discovery. This situation poses a major problem: the necessity to handle the "big data" generated by combinatorial chemistry. Artificial intelligence (AI) and deep learning play a pivotal role in the analysis and systemization of larger data sets by statistical machine learning methods. Advanced AI-based sophisticated machine learning tools have a significant impact on the drug discovery process including medicinal chemistry. In this review, we focus on the currently available methods and algorithms for structure-based drug design including virtual screening and de novo drug design, with a special emphasis on AI- and deep-learning-based methods used for drug discovery.

A Practical Perspective: The Effect of Ligand Conformers on the Negative Image-Based Screening.

Negative image-based (NIB) screening is a rigid molecular docking methodology that can also be employed in docking rescoring. During the NIB screening, a negative image is generated based on the target protein's ligand-binding cavity by inverting its shape and electrostatics. The resulting NIB model is a drug-like entity or pseudo-ligand that is compared directly against ligand 3D conformers, as is done with a template compound in the ligand-based screening. This cavity-based rigid docking has been demonstrated to work with genuine drug targets in both benchmark testing and drug candidate/lead discovery. Firstly, the study explores in-depth the applicability of different ligand 3D conformer generation software for acquiring the best NIB screening results using cyclooxygenase-2 (COX-2) as the example system. Secondly, the entire NIB workflow from the protein structure preparation, model build-up, and ligand conformer generation to the similarity comparison is performed for COX-2. Accordingly, hands-on instructions are provided on how to employ the NIB methodology from start to finish, both with the rigid docking and docking rescoring using noncommercial software. The practical aspects of the NIB methodology, especially the effect of ligand conformers, are discussed thoroughly, thus, making the methodology accessible for new users.

Predictive Power of Different Types of Experimental Restraints in Small Molecule Docking: A Review.

Incorporating experimental restraints is a powerful method of increasing accuracy in computational protein small molecule docking simulations. Different algorithms integrate distinct forms of biochemical data during the docking and/or scoring stages. These so-called hybrid methods make use of receptor-based information such as nuclear magnetic resonance (NMR) restraints or small molecule-based information such as structure-activity relationships (SARs). A third class of methods directly interrogates contacts between the protein receptor and the small molecule. This work reviews the current state of using such restraints in docking simulations, evaluates their feasibility across broad systems, and identifies potential areas of algorithm development.

Enabling the hypothesis-driven prioritization of ligand candidates in big databases: Screenlamp and its application to GPCR inhibitor discovery for invasive species control.

While the advantage of screening vast databases of molecules to cover greater molecular diversity is often mentioned, in reality, only a few studies have been published demonstrating inhibitor discovery by screening more than a million compounds for features that mimic a known three-dimensional (3D) ligand. Two factors contribute: the general difficulty of discovering potent inhibitors, and the lack of free, user-friendly software to incorporate project-specific knowledge and user hypotheses into 3D ligand-based screening. The Screenlamp modular toolkit presented here was developed with these needs in mind. We show Screenlamp's ability to screen more than 12 million commercially available molecules and identify potent in vivo inhibitors of a G protein-coupled bile acid receptor within the first year of a discovery project. This pheromone receptor governs sea lamprey reproductive behavior, and to our knowledge, this project is the first to establish the efficacy of computational screening in discovering lead compounds for aquatic invasive species control. Significant enhancement in activity came from selecting compounds based on one of the hypotheses: that matching two distal oxygen groups in the 3D structure of the pheromone is crucial for activity. Six of the 15 most active compounds met these criteria. A second hypothesis-that presence of an alkyl sulfate side chain results in high activity-identified another 6 compounds in the top 10, demonstrating the significant benefits of hypothesis-driven screening.