In-silico assessment of novel peptidomimetics inhibitor targeting STAT3 and STAT4 N-terminal domain dimerization: A comprehensive study using molecular docking, molecular dynamics simulation, and binding free energy analysis.

Dysregulation in Janus kinase-Signal Transducer and Activation of Transcription (JAK-STAT) pathway is closely linked to various cancer types. The N-terminal domain (NTD) of STAT proteins, upon dimerization, assumes a multifaceted role with remarkable adaptability in mediating interactions between proteins. Consequently, the strategic targeting of the N-terminal domain of STATs has emerged as a promising tactic for disrupting dimerization and impeding the translocation of STAT proteins. In this study, we have deployed an integrated in-silico methodology to rationally design Peptidomimetic foldamers as inhibitors of the N-terminal domains of STAT3 and STAT4, with the objective of disrupting protein dimerization. Consequently, we have judiciously designed a series of peptidomimetics that encompass ß3-amino acids, bearing side chains that mimic the residues within interface II of the dimeric structures of the NTDs. Employing molecular docking techniques; we have assessed the binding affinity of these designed peptidomimetics toward both the NTDs. Furthermore, we have conducted an evaluation of the stability and conformational alterations within the docked complexes over an extensive Molecular Dynamics, subsequently computing the binding free energy utilizing MM/PBSA calculations. Our findings unequivocally demonstrate that the peptidomimetic foldamers we have devised (Peptide-A, Peptide-B, and Peptide-C) exhibit a propensity to bind to and impede the dimerization process of the NTDs of both STAT3 and STAT4. These outcomes serve to underscore the potential of these meticulously designed peptidomimetics as potential candidates meriting further exploration in the realm of cancer prevention and management.

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.

AI-Assisted Generation and In-depth In-silico Evaluation of Potential Inhibitor Targeting Aurora Kinase A (AURKA): An Anticancer Discovery Exploiting Synthetic Lethality Approach.

Genetic alterations are lead causative agents behind the complex pathologies of cancers which render all treatments unarmed. Such alterations in oncogenes can be treated by direct inhibition by specific drugs while alteration in tumor suppressor genes mediating loss of function is challenging to treat. Identification of synthetic lethal partners to specific tumor suppressor genes and mediating their inhibition can be a potential approach to deal with loss of function mutations. Aurora kinase A (AURKA) has been established as an effective synthetic lethal partner of several tumor suppressor genes and is overexpressed in cancerous conditions, mediating adverse pathologies. The present AI-assisted study deals with the generation of novel inhibitor compounds against AURKA and the exhaustive evaluation of the best compound using molecular docking, molecular dynamic simulation, MM/PBSA, and QM/MMGBSA-based analysis. Out of the 200 novel compounds generated using features of ATP binding pocket of AURKA and previously reported inhibitor, compound 1 (4-{5-fluoro-6-[(1Z)-3-hydrazinyl-3-oxo-2-phenylprop-1-en-1-yl]pyridin-2-yl}benzoic acid) was identified as the most potent candidate with high negative binding energy of -10.4 kcal/mol in molecular docking analysis. The molecular dynamic simulation analysis of the AURKA-compound 1 complex and the apo-AURKA protein resulted in major conformational changes in the conserved DFG motif and loop 277-291 of AURKA in the AURKA-compound 1 complex thus inhibiting the entry of ATP to its binding site. The free energy landscape displayed a persistence of folded states of the enzyme in complex form. The MM/PBSA revealed effective Gibb's free energy of binding of -11 kcal/mol for compound 1 inhibiting AURKA. The QM/MMGBSA analysis resulted in a significantly high negative binding energy of -13.98 kcal/mol proving significant inhibition potential of compound 1 against AURKA. Therefore, further in-vitro investigation can provide a novel effective, and safe treatment against a wide range of cancers by targeting a well-established cancer target AURKA.

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.

Repurposing of FDA-approved drugs against oligomerization domain of dengue virus NS1 protein: a computational approach.

Dengue fever is a serious health hazard on a global scale and its primary causative agent is the dengue virus (DENV). The non-structural protein 1 (NS1) of DENV plays a pivotal role in pathogenesis. It is associated with several autoimmune events, endothelial cell apoptosis, and vascular leakage, which increase mainly during the critical phase of infection. In this study, important residues of the oligomerization domain of NS1 protein were identified by literature searches. Virtual screening has been conducted using the entire dataset of the DrugBank database and the potential small-molecule inhibitors against the NS1 protein have been chosen on the basis of binding energy values. This is succeeded by molecular dynamics (MD) simulations of the shortlisted compounds, ultimately giving rise to five compounds. These five compounds were further subjected to RAMD simulations by applying a random direction force of specific magnitude on the ligand center of mass in order to push the ligand out of the protein-binding pocket, for the quantitative estimation of their binding energy values to determine the interaction strength between protein and ligand which prevents ligand unbinding from its binding site, ultimately leading to the selection of three major compounds, DB00826 (Natamycin), DB11274 (Dihydro-alphaergocryptine), and DB11275 (Epicriptine), with the DB11274 having a role against idiopathic Parkinson's disease, and thus may have possible important roles in the prevention of dengue-associated Parkinsonism. These compounds may act as prospective drugs against dengue, by preventing the oligomerization of the NS1 protein, thereby preventing disease progression and pathogenesis.

Novel α-glucosidase Inhibitors Designed as Type 2 Diabetes Drugs by QSAR, Molecular Docking and Molecular Dynamics Simulation Methods.

Diabetes mellitus is a globally prevalent disease of significant concern. Alpha-glucosidase has emerged as a prominent target for the treatment of type 2 diabetes. In this study, 39 α-glucosidase inhibitors (AGIs) of tetrahydrobenzo[b]thiophene-2-ylurea derivatives to establish a stable and valid Topomer CoMFA model, with a cross-validation coefficient (q2) of 0.766 and a non-cross-validation coefficient (r2) of 0.960. Subsequently, the ZINC15 database was used to screen the fragments, based on which 13 novel inhibitor molecules with theoretically potentially high activity were designed. Molecular docking and molecular dynamics simulations to understand the binding status of the inhibitor molecules to the target proteins showed that amino acids ASP215, GLN279 and ARG442 may form hydrogen bonds with the ligands and therefore enhance the inhibitory effect of the small molecules. Additionally, MM/PBSA calculations indicate that the newly designed molecules exhibit more stable binding modes. These molecules also demonstrate favorable ADMET properties with potential as AGIs. The findings would provide valuable guidance and a theoretical foundation for the design and development of novel AGIs.

Design and Characterization of Neutral Linker-Based Bis-Intercalator via Computer Simulations: Balancing DNA Binding and Cellular Uptake.

Bis-intercalators refer to a class of chemical compounds known for their unique ability to simultaneously intercalate, or insert, into DNA at two distinct sites. These molecules typically feature two intercalating moieties connected by a linker, allowing them to engage with DNA base pairs at multiple locations. The bis-intercalation phenomenon plays 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.

Deciphering the 4',7-Dihydroxy-3'-Methylflavone from Boerhavia, Agonist/Antagonist Interactions Against 5-HT2 Receptors using Homology Modeling, Molecular Docking, and Dynamic Simulation Studies.

Seizures, depression, and anxiety are neurological disorders that affected innumerable people worldwide. Recent research has revealed that targeting 5-hydroxytryptamine 2 (5-HT2) receptors can help suppress these conditions. An in-depth literature study has identified that phytocompounds from the Boerhavia genus could reduce seizures. Therefore, homology models of 5-HT2 receptors were generated and validated using techniques such as the alignment of amino acid sequences and the Ramachandran plot. Later, a comparison of modeled structures was made with a non-redundant set of PDB structures. The pharmacokinetics, drug-likeness, blood-brain barrier (BBB) permeability, and Lipinski's rule of five shed light on 22 phytocompounds, which are the potential candidates for molecular docking among 127 Boerhavia's bioactive. Notably, molecular docking analysis revealed 4',7-dihydroxy-3'-methylflavone as the most potent lead compound, which has a strong binding affinity to all modeled receptors. Additionally, with a remarkably high docking score of -9.1 kcal/mol, 4',7-dihydroxy-3'-methylflavone showed promising interactions, particularly with 5-HT2A receptor, as seen from the RMSD, SASA, Rg, and number of hydrogen bonds during 100 ns molecular dynamic (MD) simulation. Principal component analysis (PCA) and Molecular Mechanics-Poisson-Boltzmann Surface Area (MM-PBSA) further confirmed that 4',7-dihydroxy-3'-methylflavone is the best novel phytocompound in Boerhavia genus for 5-HT2 receptor as agonist/antagonist activity against seizures.

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.

Exploring Radioiodinated Anastrozole and Epirubicin as AKT1-Targeted Radiopharmaceuticals in Breast Cancer: In Silico Analysis and Potential Therapeutic Effect with Functional Nuclear Imagining Implications.

This study evaluates radio-iodinated anastrozole ([125I]anastrozole) and epirubicin ([125I]epirubicin) for AKT1-targeted breast cancer therapy, utilizing radiopharmaceutical therapy (RPT) for personalized treatment. Through molecular docking and dynamics simulations (200 ns), it investigates these compounds' binding affinities and mechanisms to the AKT1 enzyme, compared to the co-crystallized ligand, a known AKT1 inhibitor. Molecular docking results show that [125I]epirubicin has the highest ΔGbind (-11.84 kcal/mol), indicating a superior binding affinity compared to [125I] anastrozole (-10.68 kcal/mol) and the co-crystallized ligand (-9.53 kcal/mol). Molecular dynamics (MD) simulations confirmed a stable interaction with the AKT1 enzyme, with [125I]anastrozole and [125I]epirubicin reaching stability after approximately 68 ns with an average RMSD of around 2.2 Å, while the co-crystallized ligand stabilized at approximately 2.69 Å after 87 ns. RMSF analysis showed no significant shifts in residues or segments, with consistent patterns and differences of less than 2 Å, maintaining enzyme stability. The [125I]epirubicin complex maintained an average of four H-bonds, indicating strong and stable interactions, while [125I]anastrozole consistently formed three H-bonds. The average Rg values for both complexes were ~16.8 ± 0.1 Å, indicating no significant changes in the enzyme's compactness, thus preserving structural integrity. These analyses reveal stable binding and minimal structural perturbations, suggesting the high potential for AKT1 inhibition. MM-PBSA calculations confirm the potential of these radio-iodinated compounds as AKT1 inhibitors, with [125I]epirubicin exhibiting the most favorable binding energy (-23.57 ± 0.14 kcal/mol) compared to [125I]anastrozole (-20.03 ± 0.15 kcal/mol) and the co-crystallized ligand (-16.38 ± 0.14 kcal/mol), highlighting the significant role of electrostatic interactions in stabilizing the complex. The computational analysis shows [125I]anastrozole and [125I]epirubicin may play promising roles as AKT1 inhibitors, especially [125I]epirubicin for its high binding affinity and dynamic receptor interactions. These findings, supported by molecular docking scores and MM-PBSA binding energies, advocate for their potential superior inhibitory capability against the AKT1 enzyme. Nevertheless, it is crucial to validate these computational predictions through in vitro and in vivo studies to thoroughly evaluate the therapeutic potential and viability of these compounds for AKT1-targeted breast cancer treatment.

Insight into the Binding Interaction between PEDCs and hERRγ Utilizing Molecular Docking and Molecular Dynamics Simulations.

Phenolic environmental endocrine-disrupting chemicals (PEDCs) are persistent EDCs that are widely found in food packaging materials and environmental media and seriously threaten human health and ecological security. Human estrogen-related receptor γ (hERRγ) has been proposed as a mediator for the low-dose effects of many environmental PEDCs; however, the atomic-level descriptions of dynamical structural features and interactions of hERRγ and PEDCs are still unclarified. Herein, how three PEDCs, 4-(1-methylpropyl)phenol (4-sec-butylphenol), 5,6,7,8-tetrahydro-2-naphthol (tetrahydro-2-napthol), and 2,2-bis(4-hydroxy-3,5-dimethoxyphenyl)propane (BP(2,2)(Me)), interact with hERRγ to produce its estrogenic disruption effects was studied. Molecular docking and multiple molecular dynamics (MD) simulations were first conducted to distinguish the detailed interaction pattern of hERRγ with PEDCs. These binding structures revealed that residues around Leu271, Leu309, Leu345, and Phe435 are important when binding with PEDCs. Furthermore, the binding energies of PEDCs with hERRγ were also characterized using the molecular mechanics/Poisson Boltzmann surface area (MM-PBSA) and solvated interaction energy (SIE) methods, and the results showed that the interactions of CH-π, π-π, and hydrogen bonds are the major contributors for hERRγ binding to these three PEDCs. What is striking is that the methoxide groups of BP(2,2)(Me), as hydrophobic groups, can help to reduce the binding energy of PEDCs binding with hERRγ. These results provide important guidance for further understanding the influence of PEDCs on human health problems.

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.