LY3041658/ interleukin-8 complex structure as targets for IL-8 small molecule inhibitors discovery using a combination of in silico methods.

Since interleukin-8 (IL-8/CXCL8) and its receptor, CXCR1 and CXCR2, were known in the early 1990s, biological pathways related to these proteins were proven to have high clinical value in cancer and inflammatory/autoimmune conditions treatment. Recently, IL-8 has been identified as biomarker for severe COVID-19 patients and COVID-19 prognosis. Boyles et al. (mAbs 12 (2020), pp. 1831880) have published a high-resolution X-ray crystal structure of the LY3041658 Fab in a complex human CXCL8. They described the ability to bind to IL-8 and the blocking of IL-8/its receptors interaction by the LY3041658 monoclonal antibody. Therefore, the study has been designed to identify potential small molecules inhibiting interleukin-8 by targeting LY3041658/IL-8 complex structure using an in silico approach. A structure­based pharmacophore and molecular docking models of the protein active site cavity were generated to identify possible candidates, followed by virtual screening with the ZINC database. ADME analysis of hit compounds was also conducted. Molecular dynamics simulations were then performed to survey the behaviour and stability of the ligand-protein complexes. Furthermore, the MM/PBSA technique has been utilized to evaluate the free binding energy. The final data confirmed that one newly obtained compound, ZINC21882765, may serve as the best potential inhibitor for IL-8.

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.

Peramivir binding affinity with influenza A neuraminidase and research on its mutations using an induced-fit docking approach.

Influenza A virus (IAV) has caused epidemic infections worldwide, with many strains resistant to inhibitors of a surface protein, neuraminidase (NA), due to point mutations on its structure. A novel NA inhibitor named peramivir was recently approved, but no exhaustive computational research regarding its binding affinity with wild-type and mutant NA has been conducted. In this study, a thorough investigation of IAV-NA PDB entries of 9 subtypes is described, providing a list of residues constituting the protein-ligand binding sites. The results of induced-fit docking approach point out key residues of wild-type NA participating in hydrogen bonds and/or ionic interactions with peramivir, among which Arg 368 is responsible for a peramivir-NA ionic interaction. Mutations on this residue greatly reduced the binding affinity of peramivir with NA, with 3 mutations R378Q, R378K and R378L (NA6) capable of deteriorating the docking performance of peramivir by over 50%. 200 compounds from 6-scaffolds were docked into these 3 mutant versions, revealing 18 compounds giving the most promising results. Among them, CMC-2012-7-1527-56 (benzoic acid scaffold, IC50 = 32 nM in inhibitory assays with IAV) is deemed the most potential inhibitor of mutant NA resisting both peramivir and zanamivir, and should be further investigated.

Machine learning-, rule- and pharmacophore-based classification on the inhibition of P-glycoprotein and NorA.

The efflux pumps P-glycoprotein (P-gp) in humans and NorA in Staphylococcus aureus are of great interest for medicinal chemists because of their important roles in multidrug resistance (MDR). The high polyspecificity as well as the unavailability of high-resolution X-ray crystal structures of these transmembrane proteins lead us to combining ligand-based approaches, which in the case of this study were machine learning, perceptual mapping and pharmacophore modelling. For P-gp inhibitory activity, individual models were developed using different machine learning algorithms and subsequently combined into an ensemble model which showed a good discrimination between inhibitors and noninhibitors (acctrain-diverse = 84%; accinternal-test = 92% and accexternal-test = 100%). For ligand promiscuity between P-gp and NorA, perceptual maps and pharmacophore models were generated for the detection of rules and features. Based on these in silico tools, hit compounds for reversing MDR were discovered from the in-house and DrugBank databases through virtual screening in an attempt to restore drug sensitivity in cancer cells and bacteria.

Three- and four-class classification models for P-glycoprotein inhibitors using counter-propagation neural networks.

P-glycoprotein (P-gp) is an ATP binding cassette (ABC) transporter that helps to protect several certain human organs from xenobiotic exposure. This efflux pump is also responsible for multi-drug resistance (MDR), an issue of the chemotherapy approach in the fight against cancer. Therefore, the discovery of P-gp inhibitors is considered one of the most popular strategies to reverse MDR in tumour cells and to improve therapeutic efficacy of commonly used cytotoxic drugs. Until now, several generations of P-gp inhibitors have been developed but they have largely failed in preclinical and clinical studies due to lack of selectivity, poor solubility and severe pharmacokinetic interactions. In this study, three models (SION, SIO, SIN) to classify specific 'true' P-gp inhibitors as well as three other models (CPBN, CPB1, CPN) to distinguish between P-gp inhibitors, CYP 3A inhibitors and co-inhibitors of these proteins with rather high accuracy values for the test set and the external set were generated based on counter-propagation neural networks (CPG-NN). Such three and four-class classification models helped provide more information about the bioactivities of compounds not only on one target (P-gp), but also on a combination of multiple targets (P-gp, CYP 3A).