Effect of sampling on BACE-1 ligands binding free energy predictions via MM-PBSA calculations

Identification of small molecules as potential inhibitors of interleukin 6: a multi-computational investigation

IL(interleukin)-6 is a multifunctional cytokine crucial for immunological, hematopoiesis, inflammation, and bone metabolism. Strikingly, IL-6 has been shown to significantly contribute to the initiation of cytokine storm-an acute systemic inflammatory syndrome in Covid-19 patients. Recent study has showed that blocking the IL-6 signaling pathway with an anti-IL-6 receptor monoclonal antibody (mAb) can reduce the severity of COVID-19 symptoms and enhance patient survival. However, the mAb has several drawbacks, such as high cost, potential immunogenicity, and invasive administration due to the large-molecule protein product. Instead, these issues could be mitigated using small molecule IL-6 inhibitors, but none are currently available. This study aimed to discover IL-6 inhibitors based on the PPI with a novel camelid Fab fragment, namely 68F2, in a crystal protein complex structure (PDB ID: 4ZS7). The pharmacophore models and molecular docking were used to screen compounds from DrugBank databases. The oral bioavailability of the top 24 ligands from the screening was predicted by the SwissAMDE tool. Subsequently, the selected molecules from docking and MD simulation illustrated a promising binding affinity in the formation of stable complexes at the active binding pocket of IL-6. Binding energies using the MM-PBSA technique were applied to the top 4 hit compounds. The result indicated that DB08402 and DB12903 could form strong interactions and build stable protein-ligand complexes with IL-6. These potential compounds may serve as a basis for further developing small molecule IL-6 inhibitors in the future.

"Hit" to lead optimization and chemoinformatic studies for a new series of Autotaxin inhibitors

Robust experimental evidence has highlighted the role of Autotaxin (ATX)/Lysophosphatidic acid (LPA) axis not only in the pathogenesis of chronic inflammatory conditions and especially in fibroproliferative diseases but also in several types of cancer. As a result, different series of substrate-, lipid-based and small-molecule ATX inhibitors have been identified thus far by both academia and pharma. The "crowning achievement" of these drug discovery campaigns was the development and entry of the first-in-class ATX inhibitor (ziritaxestat, GLPG-1690) in advanced clinical trials against idiopathic pulmonary fibrosis. Herein, the potency optimization efforts of a new series of Autotaxin inhibitors, namely 2-substituted-2,6-dihydro-4H-thieno[3,4-c]pyrazol-1-substituted amide, is described using a previously identified novel chemical scaffold as a "hit". The mode of inhibition of the most promising ATX inhibitors was investigated, while their cellular activity, aqueous solubility and cytotoxicity were evaluated. Our pharmacological results were corroborated by chemoinformatic tools (molecular docking and molecular dynamics simulations) deployed, to provide insight into the binding mechanism of the synthesized inhibitors to ATX.

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 structure‑based 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.

Comparative assessment of QM-based and MM-based models for prediction of protein-ligand binding affinity trends

Methods which accurately predict protein-ligand binding strengths are critical for drug discovery. In the last two decades, advances in chemical modelling have enabled steadily accelerating progress in the discovery and optimization of structure-based drug design. Most computational methods currently used in this context are based on molecular mechanics force fields that often have deficiencies in describing the quantum mechanical (QM) aspects of molecular binding. In this study, we show the competitiveness of our QM-based Molecules-in-Molecules (MIM) fragmentation method for characterizing binding energy trends for seven different datasets of protein-ligand complexes. By using molecular fragmentation, the MIM method allows for accelerated QM calculations. We demonstrate that for classes of structurally similar ligands bound to a common receptor, MIM provides excellent correlation to experiment, surpassing the more popular Molecular Mechanics Poisson-Boltzmann Surface Area (MM/PBSA) and Molecular Mechanics Generalized Born Surface Area (MM/GBSA) methods. The MIM method offers a relatively simple, well-defined protocol by which binding trends can be ascertained at the QM level and is suggested as a promising option for lead optimization in structure-based drug design.

Identification of potential interleukin-8 inhibitors acting on the interactive site between chemokine and CXCR2 receptor: A computational approach

Interactions between interleukin (IL)-8 and its receptors, CXCR1, and CXCR2, serve crucial roles in inflammatory conditions and various types of cancers. Inhibition of this signaling pathway has been exploited as a promising strategy in treating these diseases. However, most studies only focused on the design of allosteric antagonists-bound receptors on the intracellular side of IL-8 receptors. Recently, the first cryo-EM structures of IL-8-CXCR2-Gi complexes have been solved, revealing the unique binding and activation modes of the endogenous chemokine IL-8. Hence, we set to identify small molecule inhibitors for IL-8 using critical protein-protein interaction between IL-8 and CXCR2 at the orthosteric binding site. The pharmacophore models and molecular docking screened compounds from DrugBank and NCI databases. The oral bioavailability of the top 23 ligands from the screening was then predicted by the SwissAMDE tool. Molecular dynamics simulation and free binding energy calculation were performed for the best compounds. The result indicated that DB14770, DB12121, and DB03916 could form strong interactions and stable protein-ligand complexes with IL-8. These three candidates are potential IL-8 inhibitors that can be further evaluated by in vitro experiments in the next stage.

Importance of Three-Body Problems and Protein-Protein Interactions in Proteolysis-Targeting Chimera Modeling: Insights from Molecular Dynamics Simulations

Proteolysis-targeting chimeras (PROTACs) are a class of bifunctional molecules that can induce the ubiquitin degradation of its target protein by hijacking the E3 ligase to form a target protein-PROTAC-E3 ligase ternary complex. Its underlying principle has inspired the development of a wide range of protein degraders that are similar to or beyond PROTACs in recent years. The formation of the ternary complexes is the key to the success of PROTAC-induced protein degradation. Nevertheless, the lack of effective ternary complex modeling techniques has limited the application of computer-aided drug discovery tools to this emerging and fast developing new land in drug industry. Thus, in this study, we explored the application of the more physically sound molecular dynamics simulation and the molecular mechanics combined with the generalized Born and surface area continuum solvation (MM/GBSA) method to solve the underlying three-body problem in PROTAC modeling. We first verified the accuracy of our approach using a series of known Brd4 BD2 degraders. The calculated binding energy showed a good correlation with the experimental K_d values. The modeling of a unique property, namely, the α value, for PROTACs was also first and accurately performed to our best knowledge. The results also demonstrated the importance of PROTAC-induced protein-protein interactions in its modeling, either qualitatively or quantitatively. Finally, by standing on the success of earlier docking-based approaches, our protocol was also applied as a rescoring function in pose prediction. The results showed a notable improvement in reranking the initial poses generated from a modified Rosetta method, which was reportedly one of the best among a handful of PROTAC modeling approaches available in this field. We hope this work could provide a practical protocol and more insights to study the binding and the design of PROTACs and other protein degraders.

Development of antibacterial compounds that constrain evolutionary pathways to resistance

Antibiotic resistance is a worldwide challenge. A potential approach to block resistance is to simultaneously inhibit WT and known escape variants of the target bacterial protein. Here, we applied an integrated computational and experimental approach to discover compounds that inhibit both WT and trimethoprim (TMP) resistant mutants of E. coli dihydrofolate reductase (DHFR). We identified a novel compound (CD15-3) that inhibits WT DHFR and its TMP resistant variants L28R, P21L and A26T with IC₅₀ 50–75 µM against WT and TMP-resistant strains. Resistance to CD15-3 was dramatically delayed compared to TMP in in vitro evolution. Whole genome sequencing of CD15-3-resistant strains showed no mutations in the target folA locus. Rather, gene duplication of several efflux pumps gave rise to weak (about twofold increase in IC₅₀) resistance against CD15-3. Altogether, our results demonstrate the promise of strategy to develop evolution drugs - compounds which constrain evolutionary escape routes in pathogens.

Doxorubicin Encapsulation in Carbon Nanotubes Having Haeckelite or Stone-Wales Defects as Drug Carriers: A Molecular Dynamics Approach

Doxorubicin (DOX), a recognized anticancer drug, forms stable associations with carbon nanotubes (CNTs). CNTs when properly functionalized have the ability to anchor directly in cancerous tumors where the release of the drug occurs thanks to the tumor slightly acidic pH. Herein, we study the armchair and zigzag CNTs with Stone-Wales (SW) defects to rank their ability to encapsulate DOX by determining the DOX-CNT binding free energies using the MM/PBSA and MM/GBSA methods implemented in AMBER16. We investigate also the chiral CNTs with haeckelite defects. Each haeckelite defect consists of a pair of square and octagonal rings. The armchair and zigzag CNT with SW defects and chiral nanotubes with haeckelite defects predict DOX-CNT interactions that depend on the length of the nanotube, the number of present defects and nitrogen doping. Chiral nanotubes having two haeckelite defects reveal a clear dependence on the nitrogen content with DOX-CNT interaction forces decreasing in the order 0N > 4N > 8N. These results contribute to a further understanding of drug-nanotube interactions and to the design of new drug delivery systems based on CNTs.

The application of the MM/GBSA method in the binding pose prediction of FGFR inhibitors

The success of a structure-based drug is highly dependent on a known binding pose of the protein-ligand system. However, this is not always available. In this study, we set out to explore the applicability of the popular and easy-to-use MD-based MM/GBSA method to determine the binding poses of known FGFR inhibitors. It was found that MM/GBSA combined with 100 ns of MD simulation significantly improved the success rate of docking methods from 30-40% to 70%. This work demonstrates a way that the MM/GBSA method can be more accurate than it is in ligand ranking, filling a gap in structure-based drug discovery when the binding pose is unknown.

Discovery of Novel Tankyrase Inhibitors through Molecular Docking-Based Virtual Screening and Molecular Dynamics Simulation Studies

Tankyrase enzymes (TNKS), a core part of the canonical Wnt pathway, are a promising target in the search for potential anti-cancer agents. Although several hundreds of the TNKS inhibitors are currently known, identification of their novel chemotypes attracts considerable interest. In this study, the molecular docking and machine learning-based virtual screening techniques combined with the physico-chemical and ADMET (absorption, distribution, metabolism, excretion, toxicity) profile prediction and molecular dynamics simulations were applied to a subset of the ZINC database containing about 1.7 M commercially available compounds. Out of seven candidate compounds biologically evaluated in vitro for their inhibition of the TNKS2 enzyme using immunochemical assay, two compounds have shown a decent level of inhibitory activity with the IC₅₀ values of less than 10 nM and 10 μM. Relatively simple scores based on molecular docking or MM-PBSA (molecular mechanics, Poisson-Boltzmann, surface area) methods proved unsuitable for predicting the effect of structural modification or for accurate ranking of the compounds based on their binding energies. On the other hand, the molecular dynamics simulations and Free Energy Perturbation (FEP) calculations allowed us to further decipher the structure-activity relationships and retrospectively analyze the docking-based virtual screening performance. This approach can be applied at the subsequent lead optimization stages.

Identification of therapeutic drugs against COVID-19 through computational investigation on drug repurposing and structural modification

Global prevalence of coronavirus disease 2019 (COVID-19) calls for an urgent development of anti-viral regime. Compared with the development of new drugs, drug repurposing can significantly reduce the cost, time, and safety risks. Given the fact that coronavirus harnesses spike protein to invade host cells through angiotensin-converting enzyme 2 (ACE2), hence we see if any previous anti-virtual compounds can block spike-ACE2 interaction and inhibit the virus entry. The results of molecular docking and molecular dynamic simulations revealed that remdesivir exhibits better than expected anti-viral invasion potential against COVID-19 among the three types of compounds including remdesivir, tenofovir and lopinavir. In addition, a positive correlation between the surface area occupied by remdesivir and anti-viral invasion potential was also found. As such, the structure of remdesivir was modified by linking an N-benzyl substituted diamidine derivative to its hydroxyl group through an ester bond. It was found that this compound has a higher anti-viral invasion potential and greater specificity.

Evaluating the performance of MM/PBSA for binding affinity prediction using class A GPCR crystal structures

The recent expansion of GPCR crystal structures provides the opportunity to assess the performance of structure-based drug design methods for the GPCR superfamily. Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA)-based methods are commonly used for binding affinity prediction, as they provide an intermediate compromise of speed and accuracy between the empirical scoring functions used in docking and more robust free energy perturbation methods. In this study, we systematically assessed the performance of MM/PBSA in predicting experimental binding free energies using twenty Class A GPCR crystal structures and 934 known ligands. Correlations between predicted and experimental binding free energies varied significantly between individual targets, ranging from r = - 0.334 in the inactive-state CB₁ cannabinoid receptor to r = 0.781 in the active-state CB₁ cannabinoid receptor, while average correlation across all twenty targets was relatively poor (r = 0.183). MM/PBSA provided better predictions of binding free energies compared to docking scores in eight out of the twenty GPCR targets while performing worse for four targets. MM/PBSA binding affinity predictions calculated using a single, energy minimized structure provided comparable predictions to sampling from molecular dynamics simulations and may be more efficient when computational cost becomes restrictive. Additionally, we observed that restricting MM/PBSA calculations to ligands with a high degree of structural similarity to the crystal structure ligands improved performance in several cases. In conclusion, while MM/PBSA remains a valuable tool for GPCR structure-based drug design, its performance in predicting the binding free energies of GPCR ligands remains highly system-specific as demonstrated in a subset of twenty Class A GPCRs, and validation of MM/PBSA-based methods for each individual case is recommended before prospective use.

Bennett acceptance ratio method to calculate the binding free energy of BACE1 inhibitors: Theoretical model and design of new ligands of the enzyme

In recent years, the design, development, and evaluation of several inhibitors of the BACE1 enzyme, as part of Alzheimer's treatment, have gathered the scientific community's interest. Here, a linear regression model was built using binding free energy calculations through the Bennett acceptance ratio method for 20 known inhibitors of the BACE1 enzyme, with a Pearson coefficient of R = 0.88 and R²  = 0.78. The validation of this model was verified employing eight additional random inhibitors, which also gave a linear correlation with R = 0.97 and R²  = 0.93. Furthermore, this linear regression model was also used for proposing the structure of four potential BACE1 inhibitors, and the most active of them gave a theoretical K_d  = 10 nM. However, these molecules have not been synthesized yet. Our team used a total time of more than 800 ns for the Molecular Dynamics to carry out this study, and all the software used were freely available.