Identification of mitogen-activated protein kinase 7 inhibitors from natural products: Combined virtual screening and dynamic simulation studies.

Mitogen-activated protein kinase 7 (MAPK7) is a serine/threonine protein kinase that belongs to the MAPK family and plays a vital role in various cellular processes such as cell proliferation, differentiation, gene transcription, apoptosis, metabolism, and cell survival. The elevated expression of MAPK7 has been associated with the onset and progression of multiple aggressive tumors in humans, underscoring the potential of targeting MAPK7 pathways in therapeutic research. This pursuit holds promise for the advancement of anticancer drug development by developing potential MAPK7 inhibitors. To look for potential MAPK7 inhibitors, we exploited structure-based virtual screening of natural products from the ZINC database. First, the Lipinski rule of five criteria was used to filter a large library of ~90,000 natural compounds, followed by ADMET and pan-assay interference compounds (PAINS) filters. Then, top hits were chosen based on their strong binding affinity as determined by molecular docking. Further, interaction analysis was performed to find effective and specific compounds that can precisely bind to the binding pocket of MAPK7. Consequently, two compounds, ZINC12296700 and ZINC02123081, exhibited significant binding affinity and demonstrated excellent drug-like properties. All-atom molecular dynamics simulations for 200 ns confirmed the stability of MAPK7-ZINC12296700 and MAPK7-ZINC02123081 docked complexes. According to the molecular mechanics Poisson-Boltzmann surface area investigation, the binding affinities of both complexes were considerable. Overall, the result suggests that ZINC12296700 and ZINC02123081 might be used as promising leads to develop novel MAPK7 inhibitors. Since these compounds would interfere with the kinase activity of MAPK7, therefore, may be implemented to control cell growth and proliferation in cancer after required validations.

Deciphering the molecular choreography of Janus kinase 2 inhibition via Gaussian accelerated molecular dynamics simulations: a dynamic odyssey.

The Janus kinases (JAK) are crucial targets in drug development for several diseases. However, accounting for the impact of possible structural rearrangements on the binding of different kinase inhibitors is complicated by the extensive conformational variability of their catalytic kinase domain (KD). The dynamic KD contains mainly four prominent mobile structural motifs: the phosphate-binding loop (P-loop), the αC-helix within the N-lobe, the Asp-Phe-Gly (DFG) motif, and the activation loop (A-loop) within the C-lobe. These distinct structural orientations imply a complex signal transmission path for regulating the A-loop's flexibility and conformational preference for optimal JAK function. Nevertheless, the precise dynamical features of the JAK induced by different types of inhibitors still remain elusive. We performed comparative, microsecond-long, Gaussian accelerated molecular dynamics simulations in triplicate of three phosphorylated JAK2 systems: the KD alone, type-I ATP-competitive inhibitor (CI) bound KD in the catalytically active DFG-in conformation, and the type-II inhibitor (AI) bound KD in the catalytically inactive DFG-out conformation. Our results indicate significant conformational variations observed in the A-loop and αC helix motions upon inhibitor binding. Our studies also reveal that the DFG-out inactive conformation is characterized by the closed A-loop rearrangement, open catalytic cleft of N and C-lobe, the outward movement of the αC helix, and open P-loop states. Moreover, the outward positioning of the αC helix impacts the hallmark salt bridge formation between Lys882 and Glu898 in an inactive conformation. Finally, we compared their ligand binding poses and free energy by the MM/PBSA approach. The free energy calculations suggested that the AI's binding affinity is higher than CI against JAK2 due to an increased favorable contribution from the total non-polar interactions and the involvement of the αC helix. Overall, our study provides the structural and energetic insights crucial for developing more promising type I/II JAK2 inhibitors for treating JAK-related diseases.

Integrated machine learning-based virtual screening and biological evaluation for identification of potential inhibitors against cathepsin K.

Cathepsin K is a type of cysteine proteinase that is primarily expressed in osteoclasts and has a key role in the breakdown of bone matrix protein during bone resorption. Many studies suggest that the deficiency of cathepsin K is concomitant with a suppression of osteoclast functioning, therefore rendering the resorptive properties of cathepsin K the most prominent target for osteoporosis. This innovative work has identified a novel anti-osteoporotic agent against Cathepsin K by using a comparison of machine learning and deep learning-based virtual screening followed by their biological evaluation. Out of ten shortlisted compounds, five of the compounds (JFD02945, JFD02944, RJC01981, KM08968 and SB01934) exhibit more than 50% inhibition of the Cathepsin K activity at 0.1 µM concentration and are considered to have a promising inhibitory effect against Cathepsin K. The comprehensive docking, MD simulation, and MM/PBSA investigations affirm the stable and effective interaction of these compounds with Cathepsin K to inhibit its function. Furthermore, the compounds RJC01981, KM08968 and SB01934 are represented to have promising anti-osteoporotic properties for the management of osteoporosis owing to their significantly well predicted ADMET properties.

Design and characterization of neutral linker-based bis-intercalator via computer simulations: Balancing DNA binding and cellular uptake.

Bis-intercalators play a significant role in altering the DNA structure, affecting its stability, and potentially influencing various cellular processes. These compounds have gained considerable attention in medicinal chemistry and biochemistry due to their potential applications in cancer therapy, where they may interfere with DNA replication and transcription, leading to anticancer effects. Traditionally, these molecules often possess a high positive charge to enhance their affinity for the negatively charged DNA. However, due to a high positive charge, their cellular uptake is compromised, along with their enhanced potential off-target effects. In this study, we utilized bis-intercalator TOTO and replaced the charged linker segment (propane-1,3-diammonium) with a neutral peroxodisulphuric acid linker. Using molecular modeling and computer simulations (500 ns, 3 replicas), we investigated the potential of the designed molecule as a bis-intercalator and compared the properties with the control bis-intercalator bound to DNA. We observed that the designed bis-intercalator exhibited improved DNA binding (as assessed through MM-PBSA and Delphi methods) and membrane translocation permeability. With an overall reduced charge, significantly less off-target binding of the designed molecule is also anticipated. Consequently, bis-intercalators based on peroxodisulphuric linkers can potentially target DNA effectively, and their role in the future design of bis-intercalators is foreseen.

A Machine Learning Approach to Identify Key Residues Involved in Protein-Protein Interactions Exemplified with SARS-CoV-2 Variants.

Human infection with the coronavirus disease 2019 (COVID-19) is mediated by the binding of the spike protein of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to the human angiotensin-converting enzyme 2 (ACE2). The frequent mutations in the receptor-binding domain (RBD) of the spike protein induced the emergence of variants with increased contagion and can hinder vaccine efficiency. Hence, it is crucial to better understand the binding mechanisms of variant RBDs to human ACE2 and develop efficient methods to characterize this interaction. In this work, we present an approach that uses machine learning to analyze the molecular dynamics simulations of RBD variant trajectories bound to ACE2. Along with the binding free energy calculation, this method was used to characterize the major differences in ACE2-binding capacity of three SARS-CoV-2 RBD variants-namely the original Wuhan strain, Omicron BA.1, and the more recent Omicron BA.5 sublineages. Our analyses assessed the differences in binding free energy and shed light on how it affects the infectious rates of different variants. Furthermore, this approach successfully characterized key binding interactions and could be deployed as an efficient tool to predict different binding inhibitors to pave the way for new preventive and therapeutic strategies.

Study on the Mechanism of Interaction between Dipeptidyl Peptidase 4 and Inhibitory Peptides Based on Gaussian Accelerated Molecular Dynamic Simulation.

Dipeptidyl peptidase 4 (DPP4) inhibitors can effectively inhibit the activity of DPP4, increasing the concentrations of glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP), which allows for them to effectively contribute to the reduction of blood sugar levels. Leu-Pro-Ala-Val-Thr-Ile-Arg (LPAVTIR) and Leu-Pro-Pro-Glu-His-Asp-Trp-Arg (LPPEHDWR) were the two peptides with the strongest inhibitory activity against DPP4 selected from silkworm pupa proteins. In this study, four systems were established: Apo (ligand-free DPP4), IPI (IPI-bound DPP4), LPAVTIR (LPAVTIR-bound DPP4), LPPEHDWR (LPPEHDWR-bound DPP4), and Gaussian accelerated molecular dynamic (GaMD) simulation was conducted to investigate the mechanism of action of two inhibitory peptides binding to DPP4. Our study revealed that the LPAVTIR peptide possessed a more stable structure and exhibited a tighter binding to the Ser630 active site in DPP4, thus exhibiting a favorable competitive inhibition effect. In contrast, the LPPEHDWR peptide caused the horizontal α-helix (residues 201-215) composed of Glu205 and Glu206 residues in DPP4 to disappear. The spatial arrangement of active sites Ser630 relative to Glu205 and Glu206 was disrupted, resulting in enzyme inactivation. Moreover, the size of the substrate channel and cavity volume was significantly reduced after the binding of the inhibitory peptide to the protein, which was an important factor in the inhibition of the enzyme activity. A similar effect was also found from IPI (our positive control). By stabilizing the active site of DPP4, the IPI peptide induced the disappearance of the horizontal α-helix and a notable reduction in the active cavity volume. In conclusion, our study provided a solid theoretical foundation for the inhibitory mechanisms of IPI, LPAVTIR, and LPPEHDWR on DPP4, offering valuable insights for advancing the development of drug targets for type 2 diabetes.

QSAR Study, Molecular Docking and Molecular Dynamic Simulation of Aurora Kinase Inhibitors Derived from Imidazo[4,5-b]pyridine Derivatives.

Cancer is a serious threat to human life and social development and the use of scientific methods for cancer prevention and control is necessary. In this study, HQSAR, CoMFA, CoMSIA and TopomerCoMFA methods are used to establish models of 65 imidazo[4,5-b]pyridine derivatives to explore the quantitative structure-activity relationship between their anticancer activities and molecular conformations. The results show that the cross-validation coefficients q2 of HQSAR, CoMFA, CoMSIA and TopomerCoMFA are 0.892, 0.866, 0.877 and 0.905, respectively. The non-cross-validation coefficients r2 are 0.948, 0.983, 0.995 and 0.971, respectively. The externally validated complex correlation coefficients r2pred of external validation are 0.814, 0.829, 0.758 and 0.855, respectively. The PLS analysis verifies that the QSAR models have the highest prediction ability and stability. Based on these statistics, virtual screening based on R group is performed using the ZINC database by the Topomer search technology. Finally, 10 new compounds with higher activity are designed with the screened new fragments. In order to explore the binding modes and targets between ligands and protein receptors, these newly designed compounds are conjugated with macromolecular protein (PDB ID: 1MQ4) by molecular docking technology. Furthermore, to study the nature of the newly designed compound in dynamic states and the stability of the protein-ligand complex, molecular dynamics simulation is carried out for N3, N4, N5 and N7 docked with 1MQ4 protease structure for 50 ns. A free energy landscape is computed to search for the most stable conformation. These results prove the efficient and stability of the newly designed compounds. Finally, ADMET is used to predict the pharmacology and toxicity of the 10 designed drug molecules.

Conformational and binding mode assessment of the human IL-3 recognition by its alpha receptor.

Protein-protein interactions (PPIs) are attractive targets as they are critical in a variety of biological processes and pathologies. As an illustration, the interleukin 3 (IL-3) and its α subunit receptor (IL-3Rα) are two proteins belonging to the cytokine or receptor ßc family and are involved in several disorders like inflammatory diseases or hematological malignancies. This PPI exhibits a low binding affinity and a complex formed by a mutant form of IL-3 (superkine) and IL-3Rα have emerged from the literature, with an increase of the affinity. Therefore, in this study, we performed molecular dynamics simulations and binding energy calculation in order to evaluate protein dynamics and to characterize the main interactions between IL-3 and IL-3Rα, considering both wild-type and mutant. First, in the case of IL-3Rα/IL-3 wild-type complex, IL-3Rα can adopt three different conformations essentially driven by NTD domain, including the open and closed conformations, previously observed in crystal structures. Additionally, our results reveal a third conformation that has a distinct interaction profile that the others. Interestingly, these conformational changes are attenuated in IL-3Rα/IL-3 mutant complex. Then, we highlighted the contribution of different residues which interact principally with IL-3 or IL-3Rα conserved region. As for the mutated residue at position 135 of IL-3, other residues such as IL-3 E138, IL-3 D40, IL-3Rα Y279, IL-3Rα K235, or IL-3Rα R277 seem important for a low or a high binding affinity. Altogether these findings yield new information that could be exploited in a drug discovery process.

Ligands-induced open-close conformational change during DapE catalysis: Insights from molecular dynamics simulations.

The microbial enzyme DapE plays a critical role in the lysine biosynthetic pathway and is considered as a potentially safe antibiotic target. In this study, atomistic simulations are employed to identify the modes of essential dynamics that define the conformational response of the enzyme to ligand binding and unbinding. The binding modes and the binding affinities of the products to the DapE enzyme are estimated from the MM-PBSA method, and the residues contributing to the ligand binding are identified. Various structural analyses and the principal component analysis of the molecular dynamics trajectories reveal that the removal of products from the active site causes a significant change in the overall enzyme structure. Both Cartesian and dihedral principal component analyses are used to characterize the structural changes in terms of domain unfolding and domain twisting motions. In the most dominant mode, that is, the domain unfolding motion, the two catalytic domains move away from the two dimerization domains of the dimeric enzyme, representing a closed-to-open conformational change. The conformational changes are initiated by the coordinated movement of three loops (Asp75-Pro82, Gly240-Asn244, and Thr347-Glu353) that trigger a domain-level movement. From multiple short trajectories, the time constant associated with the domain opening motion is estimated as 43.6 ns. Physiologically, this close-to-open conformational change is essential for the regeneration of the initial state of the enzyme for the subsequent cycle of catalytic action and provides the apo enzyme enough flexibility for efficient substrate binding.

Novel Phthalic-Based Anticancer Tyrosine Kinase Inhibitors: Design, Synthesis and Biological Activity.

In this work, fragments of isophthalic and terephthalic acids are proposed as a structural scaffold to develop potential inhibitors of protein kinases. Novel isophthalic and terephthalic acid derivatives were designed as type-2 protein kinase inhibitors, synthesized and subjected to physicochemical characterization. The screening of their cytotoxic actions against a panel of cell lines derived from different types of tumors (liver, renal, breast and lung carcinomas, as well as chronic myelogenous and promyelocytic leukemia) and normal human B lymphocyte, for the sake of comparison, was performed. Compound 5 showed the highest inhibitory activity against four cancer cell lines, K562, HL-60, MCF-7 and HepG2 (IC50 = 3.42, 7.04, 4.91 and 8.84 µM, respectively). Isophthalic derivative 9 revealed a high potency against EGFR and HER2, at the levels of 90% and 64%, respectively, being comparable to lapatinib at 10 µM. In general, tumor cell cultures were more sensitive to isophthalic acid derivatives than to terephthalic acid ones. In cell cycle studies, isophthalic analogue 5 showed a pronounced dose-dependent effect, and with the increase in its concentration up to 10.0 µM, the number of living cells decreased to 38.66%, while necrosis reached 16.38%. The considered isophthalic compounds had a similar docking performance to that of sorafenib against the VEGFR-2 (PDB id: 4asd, 3wze). The correct binding of compounds 11 and 14 with VEGFR-2 was validated using MD simulations and MM-GPSA calculations.

Large scale peptide screening against main protease of SARS CoV-2.

The COVID-19 pandemic has been a public health emergency, with deadly forms constantly emerging around the world, highlighting the dire need for highly effective antiviral therapeutics. Peptide therapeutics show significant potential for this viral disease due to their efficiency, safety, and specificity. Here, two thousand seven hundred eight antibacterial peptides were screened computationally targeting the Main protease (Mpro) of SARS CoV-2. Six top-ranked peptides according to their binding scores, binding pose were investigated by molecular dynamics to explore the interaction and binding behavior of peptide-Mpro complexes. The structural and energetic characteristics of Mpro-DRAMP01760 and Mpro-DRAMP01808 complexes fluctuated less during a 250 ns MD simulation. In addition, three peptides (DRAMP01760, DRAMP01808, and DRAMP01342) bind strongly to Mpro protein, according to the free energy landscape and principal component analysis. Peptide helicity and secondary structure analysis are in agreement with our findings. Interaction analysis of protein-peptide complexes demonstrated that Mpro's residue CYS145, HIS41, PRO168, GLU166, GLN189, ASN142, MET49, and THR26 play significant contributions in peptide-protein attachment. Binding free energy analysis (MM-PBSA) demonstrated the energy profile of interacting residues of Mpro in peptide-Mpro complexes. To summarize, the peptides DRAMP01808 and DRAMP01760 may be highly Mpro specific, resulting disruption in a viral replication and transcription. The results of this research are expected to assist future research toward the development of antiviral peptide-based therapeutics for Covid-19 treatment.

Development and test of highly accurate endpoint free energy methods. 1: Evaluation of ABCG2 charge model on solvation free energy prediction and optimization of atom radii suitable for more accurate solvation free energy prediction by the PBSA method.

Accurate estimation of solvation free energy (SFE) lays the foundation for accurate prediction of binding free energy. The Poisson-Boltzmann (PB) or generalized Born (GB) combined with surface area (SA) continuum solvation method (PBSA and GBSA) have been widely used in SFE calculations because they can achieve good balance between accuracy and efficiency. However, the accuracy of these methods can be affected by several factors such as the charge models, polar and nonpolar SFE calculation methods and the atom radii used in the calculation. In this work, the performance of the ABCG2 (AM1-BCC-GAFF2) charge model as well as other two charge models, that is, RESP (Restrained Electrostatic Potential) and AM1-BCC (Austin Model 1-bond charge corrections), on the SFE prediction of 544 small molecules in water by PBSA/GBSA was evaluated. In order to improve the performance of the PBSA prediction based on the ABCG2 charge, we further explored the influence of atom radii on the prediction accuracy and yielded a set of atom radius parameters for more accurate SFE prediction using PBSA based on the ABCG2/GAFF2 by reproducing the thermodynamic integration (TI) calculation results. The PB radius parameters of carbon, oxygen, sulfur, phosphorus, chloride, bromide and iodine, were adjusted. New atom types, on, oi, hn1, hn2, hn3, were introduced to further improve the fitting performance. Then, we tuned the parameters in the nonpolar SFE model using the experimental SFE data and the PB calculation results. By adopting the new radius parameters and new nonpolar SFE model, the root mean square error (RMSE) of the SFE calculation for the 544 molecules decreased from 2.38 to 1.05 kcal/mol. Finally, the new radius parameters were applied in the prediction of protein-ligand binding free energies using the MM-PBSA method. For the eight systems tested, we could observe higher correlation between the experiment data and calculation results and smaller prediction errors for the absolute binding free energies, demonstrating that our new radius parameters can improve the free energy calculation using the MM-PBSA method.

Development and test of highly accurate endpoint free energy methods. 2: Prediction of logarithm of n-octanol-water partition coefficient (logP) for druglike molecules using MM-PBSA method.

The logarithm of n-octanol-water partition coefficient (logP) is frequently used as an indicator of lipophilicity in drug discovery, which has substantial impacts on the absorption, distribution, metabolism, excretion, and toxicity of a drug candidate. Considering that the experimental measurement of the property is costly and time-consuming, it is of great importance to develop reliable prediction models for logP. In this study, we developed a transfer free energy-based logP prediction model-FElogP. FElogP is based on the simple principle that logP is determined by the free energy change of transferring a molecule from water to n-octanol. The underlying physical method to calculate transfer free energy is the molecular mechanics-Poisson Boltzmann surface area (MM-PBSA), thus this method is named as free energy-based logP (FElogP). The superiority of FElogP model was validated by a large set of 707 structurally diverse molecules in the ZINC database for which the measurement was of high quality. Encouragingly, FElogP outperformed several commonly-used QSPR or machine learning-based logP models, as well as some continuum solvation model-based methods. The root-mean-square error (RMSE) and Pearson correlation coefficient (R) between the predicted and measured values are 0.91 log units and 0.71, respectively, while the runner-up, the logP model implemented in OpenBabel had an RMSE of 1.13 log units and R of 0.67. Given the fact that FElogP was not parameterized against experimental logP directly, its excellent performance is likely to be expanded to arbitrary organic molecules covered by the general AMBER force fields.

An exploration of the binding prediction of anatoxin-a and atropine to acetylcholinesterase enzyme using multi-level computer simulations.

Acetylcholinesterase (AChE) is crucial for the breakdown of acetylcholine to acetate and choline, while the inhibition of AChE by anatoxin-a (ATX-a) results in severe health complications. This study explores the structural characteristics of ATX-a and its interactions with AChE, comparing to the reference molecule atropine for binding mechanisms. Molecular docking simulations reveal strong binding affinity of both ATX-a and atropine to AChE, interacting effectively with specific amino acids in the binding site as potential inhibitors. Quantitative assessment using the MM-PBSA method demonstrates a significantly negative binding free energy of -81.659 kJ mol-1for ATX-a, indicating robust binding, while atropine exhibits a stronger binding affinity with a free energy of -127.565 kJ mol-1. Umbrella sampling calculates the ΔGbindvalues to evaluate binding free energies, showing a favorable ΔGbindof -36.432 kJ mol-1for ATX-a and a slightly lower value of -30.12 kJ mol-1for atropine. This study reveals the dual functionality of ATX-a, acting as both a nicotinic acetylcholine receptor agonist and an AChE inhibitor. Remarkably, stable complexes form between ATX-a and atropine with AChE at its active site, exhibiting remarkable binding free energies. These findings provide valuable insights into the potential use of ATX-a and atropine as promising candidates for modulating AChE activity.

In the quest for histone deacetylase inhibitors: current trends in the application of multilayered computational methods.

Histone deacetylase (HDAC) inhibitors have gained attention over the past three decades because of their potential in the treatment of different diseases including various forms of cancers, neurodegenerative disorders, autoimmune, inflammatory diseases, and other metabolic disorders. To date, 5 HDAC inhibitor drugs are marketed for the treatment of hematological malignancies and several drug-candidate HDAC inhibitors are at different stages of clinical trials. However, due to the toxic side effects of these drugs resulting from the lack of target selectivity, active studies are ongoing to design and develop either class-selective or isoform-selective inhibitors. Computational methods have aided the discovery of HDAC inhibitors with the desired potency and/or selectivity. These methods include ligand-based approaches such as scaffold hopping, pharmacophore modeling, three-dimensional quantitative structure-activity relationships (3D-QSAR); and structure-based virtual screening (molecular docking). The current trends involve the application of the combination of these methods and incorporating molecular dynamics simulations coupled with Poisson-Boltzmann/molecular mechanics generalized Born surface area (MM-PBSA/MM-GBSA) to improve the prediction of ligand binding affinity. This review aimed at understanding the current trends in applying these multilayered strategies and their contribution to the design/identification of HDAC inhibitors.

Investigating the role of glycans in Omicron sub-lineages XBB.1.5 and XBB.1.16 binding to host receptor using molecular dynamics and binding free energy calculations.

Omicron derived lineages viz. BA.2, BA.3, BA.4 BA.5, BF.7 and XBBs show prominence with improved immune escape, transmissibility, infectivity, and pathogenicity in general. Sub-variants, XBB.1.5 and XBB.1.16 have shown rapid spread, with mutations embedded throughout the viral genome, including the spike protein. Changing atomic landscapes in spike contributes significantly to modulate host pathogen interactions and infections thereof. In the present work, we computationally analyzed the binding affinities of spike receptor binding domains (RBDs) of XBB.1.5 and XBB.1.16 towards human angiotensin-converting enzyme 2 (hACE2) compared to Omicron. We have employed simulations and binding energy estimation of molecular complexes of spike-hACE2 to assess the interplay of interaction pattern and effect of mutations if any in the binding mode of the RBDs of these novel mutants. We calculated the binding free energy (BFE) of the RBD of the Omicron, XBB.1.5 and XBB.1.16 spike protein to hACE2. We showed that XBB.1.5 and XBB.1.16 can bind to human cells more strongly than Omicron due to the increased charge of the RBD, which enhances the electrostatic interactions with negatively charged hACE2. The per-residue decompositions further show that the Asp339His, Asp405Asn and Asn460Lys mutations in the XBBs RBD play a crucial role in enhancing the electrostatic interactions, by acquiring positively charged residues, thereby influencing the formation/loss of interfacial bonds and thus strongly affecting the spike RBD-hACE2 binding affinity. Simulation results also indicate less interference of heterogeneous glycans of XBB.1.5 spike RBD towards binding to hACE2. Moreover, despite having less interaction at the three interfacial contacts between XBB S RBD and hACE2 compared to Omicron, variants XBB.1.5 and XBB.1.16 had higher total binding free energies (ΔGbind) than Omicron due to the contribution of non-interfacial residues to the free energy, providing insight into the increased binding affinity of XBB1.5 and XBB.1.16. Furthermore, the presence of large positively charged surface patches in the XBBs act as drivers of electrostatic interactions, thus support the possibility of a higher binding affinity to hACE2.

tLyp-1: A peptide suitable to target NRP-1 receptor.

Targeting vascular endothelial growth factor receptor (VEFGR) and its co-receptor neuropilin-1 (NRP-1) is an interesting vascular strategy. tLyp-1 is a tumor-homing and penetrating peptide of 7 amino acids (CGNKRTR). It is a truncated form of Lyp-1 (CGNKRTRGC), which is known to target NRP-1 receptor, with high affinity and specificity. It is mediated by endocytosis via C-end rule (CendR) internalization pathway. The aim of this study is to evaluate the importance of each amino acid in the tLyp-1 sequence through alanine-scanning (Ala-scan) technique, during which each of the amino acid in the sequence was systematically replaced by alanine to produce 7 different analogues. In silico approach through molecular docking and molecular dynamics are employed to understand the interaction between the peptide and its analogues with the NRP-1 receptor, followed by in vitro ligand binding assay study. The C-terminal Arg is crucial in the interaction of tLyp-1 with NRP-1 receptor. Substituting this residue dramatically reduces the affinity of this peptide which is clearly seen in this study. Lys-4 is also important in the interaction, which is confirmed via the in vitro study and the MM-PBSA analysis. The finding in this study supports the CendR, in which the presence of R/K-XX-R/K motif is essential in the binding of a ligand with NRP-1 receptor. This presented work will serve as a guide in the future work pertaining the development of active targeting agent towards NRP-1 receptor.

Exploration of potential inhibitors for SARS-CoV-2 Mpro considering its mutants via structure-based drug design, molecular docking, MD simulations, MM/PBSA, and DFT calculations.

The main protease (Mpro) of SARS-COV-2 plays a vital role in the viral life cycle and pathogenicity. Due to its specific attributes, this 3-chymotrypsin like protease can be a reliable target for the drug design to combat COVID-19. Since the advent of COVID-19, Mpro has undergone many mutations. Here, the impact of 10 mutations based on their frequency and five more based on their proximity to the active site was investigated. For comparison purposes, the docking process was also performed against the Mpros of SARS-COV and MERS-COV. Four inhibitors with the highest docking score (11b, α-ketoamide 13b, Nelfinavir, and PF-07321332) were selected for the structure-based ligand design via fragment replacement, and around 2000 new compounds were thus obtained. After the screening of these new compounds, the pharmacokinetic properties of the best ones were predicted. In the last step, comparative molecular dynamics (MD) simulations, molecular mechanics Poisson-Boltzmann surface area calculations (MM/PBSA), and density functional theory calculations were performed. Among the 2000 newly designed compounds, three of them (NE1, NE2, and NE3), which were obtained by modifications of Nelfinavir, showed the highest affinity against all the Mpro targets. Together, NE1 compound is the best candidate for follow-up Mpro inhibition and drug development studies.

Natural flavonoids effectively block the CD81 receptor of hepatocytes and inhibit HCV infection: a computational drug development approach.

Hepatitis C virus (HCV) infection is a major public health concern, and almost two million people are infected per year globally. This is occurred by the diverse spectrum of viral genotypes, which are directly associated with chronic liver disease (fibrosis, and cirrhosis). Indeed, the viral genome encodes three principal proteins as sequentially core, E1, and E2. Both E1 and E2 proteins play a crucial role in the attachment of the host system, but E2 plays a more fundamental role in attachment. The researchers have found the "E2-CD81 complex" at the entry site, and therefore, CD81 is the key receptor for HCV entrance in both humans, and chimpanzees. So, the researchers are trying to block the host CD81 receptor and halt the virus entry within the cellular system via plant-derived compounds. Perhaps that is why the current research protocol is designed to perform an in silico analysis of the flavonoid compounds for targeting the tetraspanin CD81 receptor of hepatocytes. To find out the best flavonoid compounds from our library, web-based tools (Swiss ADME, pKCSM), as well as computerized tools like the PyRx, PyMOL, BIOVIA Discovery Studio Visualizer, Ligplot+ V2.2, and YASARA were employed. For molecular docking studies, the flavonoid compounds docked with the targeted CD81 protein, and herein, the best-outperformed compounds are Taxifolin, Myricetin, Puerarin, Quercetin, and (-)-Epicatechin, and outstanding binding affinities are sequentially - 7.5, - 7.9, - 8.2, - 8.4, and - 8.5 kcal/mol, respectively. These compounds have possessed more interactions with the targeted protein. To validate the post docking data, we analyzed both 100 ns molecular dynamic simulation, and MM-PBSA via the YASARA simulator, and finally finds the more significant outcomes. It is concluded that in the future, these compounds may become one of the most important alternative antiviral agents in the fight against HCV infection. It is suggested that further in vivo, and in vitro research studies should be done to support the conclusions of this in silico research workflow.

Droperidol as a potential inhibitor of acyl-homoserine lactone synthase from A. baumannii: insights from virtual screening, MD simulations and MM/PBSA calculations.

Acinetobacter baumannii belongs to the ESKAPE family of pathogens and is a multi-drug resistant, gram-negative bacteria which follows the anaerobic form of respiration. A. baumannii is known to be the causative agent of hospital-related infections such as pneumonia, meningitis, endocarditis, septicaemia and a plethora of infections such as urinary tract infections found primarily in immunocompromised patients. These attributes of A. baumannii make it a priority pathogen against which potential therapeutic agents need to be developed. A. baumannii employs the formation of a biofilm to insulate its colonies from the outer environment, which allows it to grow under harsh environmental conditions and develop resistance against various drug molecules. Acyl-homoserine lactone synthase (AHLS) is an enzyme involved in the quorum-sensing pathway in A. baumannii, which is responsible for the synthesis of signal molecules known as acyl-homoserine lactones, which trigger the signalling pathway to regulate the factors involved in biofilm formation and regulation. The present study utilised a homology-modelled structure of AHLS to virtually screen it against the ZINC in trial/FDA-approved drug molecule library to find a subset of potential lead candidates. These molecules were then filtered based on Lipinski's, toxicological and ADME properties, binding affinity, and interaction patterns to delineate lead molecules. Finally, three promising molecules were selected, and their estimated binding affinity values were corroborated using AutoDock 4.2. The identified molecules and a control molecule were subsequently subjected to MD simulations to mimic the physiological conditions of protein ligand-binding interaction under the influence of a GROMOS forcefield. The global and essential dynamics analyses and MM/PBSA based binding free energy computations suggested Droperidol and Cipargamin as potential inhibitors against the binding site of AHLS from A. baumannii. The binding free energy calculations based on the MM/PBSA method showed excellent results for Droperidol (- 50.02 ± 4.67 kcal/mol) and Cipargamin (- 42.29 ± 4.05 kcal/mol).