Targeting Olokizumab-Interleukin 6 interaction interface to discover novel IL-6 inhibitors

The IL-6/IL-6R or IL-6/GP130 protein-protein interactions play a significant role in controlling the development of chronic inflammatory diseases, such as rheumatoid arthritis, Castleman disease, psoriasis, and, most recently, COVID-19. Modulating or antagonizing protein-protein interactions of IL6 binding to its receptors by oral drugs promises similar efficacy to biological therapy in patients, namely monoclonal antibodies. In this study, we used a crystal structure of the Fab part of olokizumab in a complex with IL-6 (PDB ID: 4CNI) to uncover starting points for small molecule IL-6 antagonist discovery. Firstly, a structure‑based pharmacophore model of the protein active site cavity was generated to identify possible candidates, followed by virtual screening with a significant database Drugbank. After the docking protocol validation, a virtual screening by molecular docking was carried out and a total of 11 top hits were reported. Detailed analysis of the best scoring molecules was performed with ADME/T analysis and molecular dynamics simulation. Furthermore, the Molecular Mechanics-Generalized Born Surface Area (MM/GBSA) technique has been utilized to evaluate the free binding energy. Based on the finding, one newly obtained compound in this study, namely DB15187, may serve as a lead compound for the discovery of IL-6 inhibitors.Communicated by Ramaswamy H. Sarma.

Biological functions and structural biology of Plasmodium falciparum autophagy-related proteins: The under-explored options for novel antimalarial drug design

Malaria remains a threat to global public health and the available antimalarial drugs are undermined by side effects and parasite resistance, suggesting an emphasis on new potential targets. Among the novel targets, Plasmodium falciparum autophagy-related proteins (PfAtg) remain a priority. In this paper, we reviewed the existing knowledge on the functions and structural biology of PfAtg including the compounds with inhibitory activity toward P. falciparum Atg8-Atg3 protein-protein interaction (PfAtg8-PfAtg3 PPI). A total of five PfAtg (PfAtg5, PfAtg8, PfAtg12, PfAtg18, and Rab7) were observed to have autophagic and/or non-autophagic roles. Available data showed that PfAtg8 has conserved hydrophobic pockets, which allows it to interact with PfAtg3 to form PfAtg8-PfAtg3 PPI. Additionally, 2-bromo-N-(4-pyridin-2-yl-1,3-thiazol-2-yl) benzamide was identified as the most powerful inhibitor of PfAtg8-PfAtg3 PPI. Due to the dearth of knowledge in this field, we hope that the article would open an avenue to further research on the remaining PfAtg as possible drug candidates.

A Hybrid Docking and Machine Learning Approach to Enhance the Performance of Virtual Screening Carried out on Protein-Protein Interfaces

The modulation of protein-protein interactions (PPIs) by small chemical compounds is challenging. PPIs play a critical role in most cellular processes and are involved in numerous disease pathways. As such, novel strategies that assist the design of PPI inhibitors are of major importance. We previously reported that the knowledge-based DLIGAND2 scoring tool was the best-rescoring function for improving receptor-based virtual screening (VS) performed with the Surflex docking engine applied to several PPI targets with experimentally known active and inactive compounds. Here, we extend our investigation by assessing the vs. potential of other types of scoring functions with an emphasis on docking-pose derived solvent accessible surface area (SASA) descriptors, with or without the use of machine learning (ML) classifiers. First, we explored rescoring strategies of Surflex-generated docking poses with five GOLD scoring functions (GoldScore, ChemScore, ASP, ChemPLP, ChemScore with Receptor Depth Scaling) and with consensus scoring. The top-ranked poses were post-processed to derive a set of protein and ligand SASA descriptors in the bound and unbound states, which were combined to derive descriptors of the docked protein-ligand complexes. Further, eight ML models (tree, bagged forest, random forest, Bayesian, support vector machine, logistic regression, neural network, and neural network with bagging) were trained using the derivatized SASA descriptors and validated on test sets. The results show that many SASA descriptors are better than Surflex and GOLD scoring functions in terms of overall performance and early recovery success on the used dataset. The ML models were superior to all scoring functions and rescoring approaches for most targets yielding up to a seven-fold increase in enrichment factors at 1% of the screened collections. In particular, the neural networks and random forest-based ML emerged as the best techniques for this PPI dataset, making them robust and attractive vs. tools for hit-finding efforts. The presented results suggest that exploring further docking-pose derived SASA descriptors could be valuable for structure-based virtual screening projects, and in the present case, to assist the rational design of small-molecule PPI inhibitors.

Insights into RNA-dependent RNA Polymerase Inhibitors as Antiinfluenza Virus Agents

BACKGROUND

Influenza is a seasonal disease that affects millions of people every year and has a significant economic impact. Vaccines are the best strategy to fight this viral pathology, but they are not always available or administrable, prompting the search for antiviral drugs. RNA-dependent RNA polymerase (RdRp) recently emerged as a promising target because of its key role in viral replication and its high conservation among viral strains.

DISCUSSION

This review presents an overview of the most interesting RdRp inhibitors that have been discussed in the literature since 2000. Compounds already approved or in clinical trials and a selection of inhibitors endowed with different scaffolds are described, along with the main features responsible for their activity.

RESULTS

RdRp inhibitors are emerging as a new strategy to fight viral infections and the importance of this class of drugs has been confirmed by the FDA approval of baloxavir marboxil in 2018. Despite the complexity of the RdRp machine makes the identification of new compounds a challenging research topic, it is likely that in the coming years, this field will attract the interest of a number of academic and industrial scientists because of the potential strength of this therapeutic approach.

Development of an Improved Guanidine-Based Rac1 Inhibitor with in vivo Activity against Non-Small Cell Lung Cancer

The Rho GTPase Rac1 is involved in the control of cytoskeleton reorganization and other fundamental cellular functions. Aberrant activity of Rac1 and its regulators is common in human cancer. In particular, deregulated expression/activity of Rac GEFs, responsible for Rac1 activation, has been associated to a metastatic phenotype and drug resistance. Thus, the development of novel Rac1-GEF interaction inhibitors is a promising strategy for finding new preclinical candidates. Here, we studied structure-activity relationships within a new family of N,N'-disubstituted guanidine as Rac1 inhibitors. We found that compound 1D-142, presents superior antiproliferative activity in human cancer cell lines and higher potency as Rac1-GEF interaction inhibitor in vitro than parental compounds. In addition, 1D-142 reduces Rac1-mediated TNFα-induced NF-κB nuclear translocation during cell proliferation and migration in NSCLC. Notably, 1D-142 allowed us to show for the first time the application of a Rac1 inhibitor in a lung cancer animal model.

Structure-based discovery of interleukin-33 inhibitors: a pharmacophore modelling, molecular docking, and molecular dynamics simulation approach

Interleukin (IL)-33 is a new cytokine of the IL-1 family that is related to several inflammatory and autoimmune diseases. IL-33 binds to its ST2 receptor and leads to biological responses thereof. Currently, no drugs have been approved for the treatment of IL-33 related diseases. The aim of this study was to search for small molecules that inhibit the protein-protein interaction between IL-33 and ST2. A virtual screening was first performed to identify potential molecules that can bind IL-33. By analysing the interactions between key residues in the complex of IL-33/ST2, two pharmacophore hypotheses were then generated based on the 'mimicry' and 'pair-rule' principles. From a database of 62,074 compounds, 60 molecules satisfying the pharmacophore models were identified and docked to IL-33. Among 35 compounds successfully docked into the protein, 9 potential ligands in complex with IL-33 were selected for further analysis by molecular dynamics simulations. Based on the stability of the complexes and the interactions of each ligand with the key residues of IL-33, two compounds DB00158 and DB00642 were identified as the most potential inhibitors that can be further investigated as promising novel IL-33 inhibitory drugs.

Protein-Templated Hit Identification through an Ugi Four-Component Reaction*

Kinetic target-guided synthesis represents an efficient hit-identification strategy, in which the protein assembles its own inhibitors from a pool of complementary building blocks via an irreversible reaction. Herein, we pioneered an in situ Ugi reaction for the identification of novel inhibitors of a model enzyme and binders for an important drug target, namely, the aspartic protease endothiapepsin and the bacterial β-sliding clamp DnaN, respectively. Highly sensitive mass-spectrometry methods enabled monitoring of the protein-templated reaction of four complementary reaction partners, which occurred in a background-free manner for endothiapepsin or with a clear amplification of two binders in the presence of DnaN. The Ugi products we identified show low micromolar activity on endothiapepsin or moderate affinity for the β-sliding clamp. We succeeded in expanding the portfolio of chemical reactions and biological targets and demonstrated the efficiency and sensitivity of this approach, which can find application on any drug target.

A Fluorescent Assay to Search for Inhibitors of HIV-1 Integrase Interactions with Human Ku70 Protein, and Its Application for Characterization of Oligonucleotide Inhibitors

The search for compounds that can inhibit the interaction of certain viral proteins with their cellular partners is a promising trend in the development of antiviral drugs. We have previously shown that binding of HIV-1 integrase with human Ku70 protein is essential for viral replication. Here, we present a novel, cheap, and fast assay to search for inhibitors of these proteins' binding based on the usage of genetically encoded fluorescent tags linked to both integrase and Ku70. Using this approach, we have elucidated structure-activity relationships for a set of oligonucleotide conjugates with eosin and shown that their inhibitory activity is primarily achieved through interactions between the conjugate nucleic bases and integrase. Molecular modeling of HIV-1 integrase in complex with the conjugates suggests that they can shield E212/L213 residues in integrase, which are crucial for its efficient binding to Ku70, in a length-dependent manner. Using the developed system, we have found the 11-mer phosphorothioate bearing 3'-end eosin-Y to be the most efficient inhibitor among the tested conjugates.

Chemical Perturbation of Oncogenic Protein Folding: from the Prediction of Locally Unstable Structures to the Design of Disruptors of Hsp90-Client Interactions

Protein folding quality control in cells requires the activity of a class of proteins known as molecular chaperones. Heat shock protein-90 (Hsp90), a multidomain ATP driven molecular machine, is a prime representative of this family of proteins. Interactions between Hsp90, its co-chaperones, and client proteins have been shown to be important in facilitating the correct folding and activation of clients. Hsp90 levels and functions are elevated in tumor cells. Here, we computationally predict the regions on the native structures of clients c-Abl, c-Src, Cdk4, B-Raf and Glucocorticoid Receptor, that have the highest probability of undergoing local unfolding, despite being ordered in their native structures. Such regions represent potential ideal interaction points with the Hsp90-system. We synthesize mimics spanning these regions and confirm their interaction with partners of the Hsp90 complex (Hsp90, Cdc37 and Aha1) by Nuclear Magnetic Resonance (NMR). Designed mimics selectively disrupt the association of their respective clients with the Hsp90 machinery, leaving unrelated clients unperturbed and causing apoptosis in cancer cells. Overall, selective targeting of Hsp90 protein-protein interactions is achieved without causing indiscriminate degradation of all clients, setting the stage for the development of therapeutics based on specific chaperone:client perturbation.

Small-Molecule Covalent Modification of Conserved Cysteine Leads to Allosteric Inhibition of the TEAD⋅Yap Protein-Protein Interaction

The Hippo pathway coordinates extracellular signals onto the control of tissue homeostasis and organ size. Hippo signaling primarily regulates the ability of Yap1 to bind and co-activate TEA domain (TEAD) transcription factors. Yap1 tightly binds to TEAD4 via a large flat interface, making the development of small-molecule orthosteric inhibitors highly challenging. Here, we report small-molecule TEAD⋅Yap inhibitors that rapidly and selectively form a covalent bond with a conserved cysteine located within the unique deep hydrophobic palmitate-binding pocket of TEADs. Inhibition of TEAD4 binding to Yap1 by these compounds was irreversible and occurred on a longer time scale. In mammalian cells, the compounds formed a covalent complex with TEAD4, inhibited its binding to Yap1, blocked its transcriptional activity, and suppressed expression of connective tissue growth factor. The compounds inhibited cell viability of patient-derived glioblastoma spheroids, making them suitable as chemical probes to explore Hippo signaling in cancer.

CHIPMUNK: A Virtual Synthesizable Small-Molecule Library for Medicinal Chemistry, Exploitable for Protein-Protein Interaction Modulators

A common issue during drug design and development is the discovery of novel scaffolds for protein targets. On the one hand the chemical space of purchasable compounds is rather limited; on the other hand artificially generated molecules suffer from a grave lack of accessibility in practice. Therefore, we generated a novel virtual library of small molecules which are synthesizable from purchasable educts, called CHIPMUNK (CHemically feasible In silico Public Molecular UNiverse Knowledge base). Altogether, CHIPMUNK covers over 95 million compounds and encompasses regions of the chemical space that are not covered by existing databases. The coverage of CHIPMUNK exceeds the chemical space spanned by the Lipinski rule of five to foster the exploration of novel and difficult target classes. The analysis of the generated property space reveals that CHIPMUNK is well suited for the design of protein-protein interaction inhibitors (PPIIs). Furthermore, a recently developed structural clustering algorithm (StruClus) for big data was used to partition the sub-libraries into meaningful subsets and assist scientists to process the large amount of data. These clustered subsets also contain the target space based on ChEMBL data which was included during clustering.