MM/GBSA prediction of relative binding affinities of carbonic anhydrase inhibitors: effect of atomic charges and comparison with Autodock4(Zn)

Discovery of natural MCL1 inhibitors using pharmacophore modelling, QSAR, docking, ADMET, molecular dynamics, and DFT analysis

Mcl-1, a member of the Bcl-2 family, is a crucial regulator of apoptosis, frequently overexpressed in various cancers, including lung, breast, pancreatic, cervical, ovarian cancers, leukemia, and lymphoma. Its anti-apoptotic function allows tumor cells to evade cell death and contributes to drug resistance, making it an essential target for anticancer drug development. This study aimed to discover potent antileukemic compounds targeting Mcl-1. We selected diverse molecules from the BindingDB database to construct a structure-based pharmacophore model, which facilitated the virtual screening of 407,270 compounds from the COCONUT database. An e-pharmacophore model was developed using the co-crystallized inhibitor, followed by QSAR modeling to estimate IC50 values and filter compounds with predicted values below the median. The top hits underwent molecular docking and MMGBSA binding energy calculations against Mcl-1, resulting in the selection of two promising candidates for further ADMET analysis. DFT calculations assessed their electronic properties, confirming favorable reactivity profiles of the screened compounds. Predictions for physicochemical and ADMET properties aligned with expected bioactivity and safety. Molecular dynamics simulations further validated their strong binding affinity and stability, positioning them as potential Mcl-1 inhibitors. Our comprehensive computational approach highlights these compounds as promising antileukemic agents, with future in vivo and in vitro validation recommended for further confirmation.

Multiscale insights into cornuside's effects on NAFLD: A cross-disciplinary integrating bioinformatics, computational chemistry, and machine learning

BACKGROUND

Non-alcoholic fatty liver disease (NAFLD) is a complex metabolic disorder involving intertwined signaling pathways, posing challenges for targeted therapeutic interventions. Cornus Fructus (CF), a traditional medicinal herb, holds potential for NAFLD treatment, with cornuside (COR) identified as its primary active component.

METHODS

This study employed a cross-disciplinary approach, integrating bioinformatics, computational chemistry, and machine learning to uncover COR's therapeutic mechanisms with precision and depth.

RESULTS

Using bioinformatics-driven analysis, 27 core targets were identified, revealing that COR modulated critical metabolic and inflammatory pathways. COR mitigated insulin resistance by regulating the AKT/GSK3β axis, enhanced cholesterol metabolism through LXR signaling, promoted fatty acid oxidation via PPARα activation, and suppressed inflammation by inhibiting NF-κB signaling. These results highlighted COR's ability to orchestrate multi-pathway regulation essential for restoring metabolic homeostasis in NAFLD. Molecular docking and molecular dynamics (MD) simulations provided atomistic insights, demonstrating COR's stable and high-affinity interactions with key targets. Additionally, machine learning algorithms enhanced target identification and pathway prediction, improving the precision and efficiency of the discovery process.

CONCLUSION

This study offered multi-scale mechanistic insights into COR's therapeutic effects on NAFLD, bridging experimental pharmacology and computational methods. The integration of bioinformatics, molecular simulation, and machine learning established a comprehensive framework for drug discovery, positioning COR as a promising candidate for NAFLD therapy and guiding future development of precision interventions.

Computational discovery of AKT serine/threonine kinase 1 inhibitors through shape screening for rheumatoid arthritis intervention

Rheumatoid Arthritis (RA) is a chronic, symmetrical inflammatory autoimmune disorder characterized by painful, swollen synovitis and joint erosions, which can cause damage to bone and cartilage and be associated with progressive disability. Despite expanded treatment options, some patients still experience inadequate response or intolerable adverse effects. Consequently, the treatment options for RA remain quite limited. The enzyme AKT1 is crucial in designing drugs for various human diseases, supporting cellular functions like proliferation, survival, metabolism, and angiogenesis in both normal and malignant cells. Therefore, AKT serine/threonine kinase 1 is considered crucial for targeting therapeutic strategies aimed at mitigating RA mechanisms. In this context, directing efforts toward AKT1 represents an innovative approach to developing new anti-arthritis medications. The primary objective of this research is to prioritize AKT1 inhibitors using computational techniques such as molecular modeling and dynamics simulation (MDS) and shape-based virtual screening (SBVS). A combined SBVS approach was employed to predict potent inhibitors against AKT1 by screening a pool of compounds sourced from the ChemDiv and IMPPAT databases. From the SBVS results, only the top three compounds, ChemDiv_7266, ChemDiv_2796, and ChemDiv_9468, were subjected to stability analysis based on their high binding affinity and favorable ADME/Tox properties. The SBVS findings have revealed that critical residues, including Glu17, Gly37, Glu85, and Arg273, significantly contribute to the successful binding of the highest-ranked lead compounds at the active site of AKT1. This insight helps to understand the specific binding mechanism of these leads in inhibiting RA, facilitating the rational design of more effective therapeutic agents.

Potential Benefits of In Silico Methods: A Promising Alternative in Natural Compound's Drug Discovery and Repurposing for HBV Therapy

Hepatitis B virus (HBV) is an important global public health issue. The World Health Organization (WHO) 2024 Global Hepatitis Report estimated that the global prevalence of people living with HBV infection is 254 million, with an estimated prevalence incidence of 1.2 million new HBV infections yearly. Previous studies have shown that natural compounds have antiviral inhibition potentials. In silico methods such as molecular docking, virtual screening, pharmacophore modeling, quantitative structure-activity relationship (QSAR), and molecular dynamic simulations have been successfully applied in identifying bioactive compounds with strong binding energies in HBV treatment targets. The COVID-19 pandemic necessitated the importance of repurposing already approved drugs using in silico methods. This study is aimed at unveiling the benefits of in silico techniques as a potential alternative in natural compounds' drug discovery and repurposing for HBV therapy. Relevant articles from PubMed, Google Scholar, and Web of Science were retrieved and analyzed. Furthermore, this study comprehensively reviewed the literature containing identified bioactive compounds with strong inhibition of essential HBV proteins. Notably, hesperidin, quercetin, kaempferol, myricetin, and flavonoids have shown strong binding energies for hepatitis B surface antigen (HBsAg). The investigation reveals that in silico drug discovery methods offer an understanding of the mechanisms of action, reveal previously overlooked viral targets (including PreS1 Domain of HBsAg and cccDNA (Covalently Closed Circular DNA) regulators, and facilitate the creation of specific inhibitors. The integration of in silico, in vitro, and in vivo techniques is essential for the discovery of new drugs for HBV therapy. The insights further highlight the importance of natural compounds and in silico methods as targets in drug discovery for HBV therapy. Moreover, the combination of natural compounds, an in silico approach, and drug repurposing improves the chances of personalized and precision medicine in HBV treatment. Therefore, we recommend drug repurposing strategies that combine in vitro, in vivo, and in silico approaches to facilitate the discovery of effective HBV drugs.

Predicting Carbonic Anhydrase Binding Affinity: Insights from QM Cluster Models

A systematic series of QM cluster models has been developed to predict the trend in the carbonic anhydrase binding affinity of a structurally diverse dataset of ligands. Reference DLPNO-CCSD(T)/CBS binding energies were generated for a cluster model and used to evaluate the performance of contemporary density functional theory methods, including Grimme's "3c" DFT composite methods (r2SCAN-3c and ωB97X-3c). It is demonstrated that when validated QM methods are used, the predictive power of the cluster models improves systematically with the size of the cluster models. This provided valuable insights into the key interactions that need to be modeled quantum mechanically and could inform how the QM region should be defined in hybrid quantum mechanics/molecular mechanics (QM/MM) models. The use of r2SCAN-3c on the largest cluster model composed of 16 residues appears to be an economical approach to predicting binding trends compared with using more robust DFT methods such as ωB97M-V and provides a significant improvement compared with docking.

Exploring Phytochemicals as Potential Inhibitors of Cancer Cell Metabolic Pathways: A Computational Study

OBJECTIVE

The objective of this study is to explore the therapeutic potential of phytochemicals in cancer cell metabolism by investigating their ability to inhibit key molecular targets involved in tumor growth and drug resistance.

METHODS

We evaluated specific phytochemicals against critical cancer-related targets such as GLS1, CKα, MGLL, IDH1, PDHK1, and PHGDH. Molecular docking methods were used to understand the binding interactions between phytochemicals and their selected targets. ADME (absorption, distribution, metabolism, and excretion) analysis and molecular dynamics (MD) simulations were conducted to assess pharmacokinetic properties and ligand-protein interaction dynamics, respectively. MM-PBSA (molecular mechanics Poisson-Boltzmann surface area) calculations were utilized to estimate binding free energies.

RESULTS

Molecular dynamics simulations demonstrate that phytochemicals like EGCG, Diosgenin, Withaferin A, and Celastrol exhibit stable binding to their respective targets, suggesting potential therapeutic benefits. Specifically, EGCG shows strong and non-toxic binding affinity with GLS1, making it a promising candidate for cancer treatment.

CONCLUSION

Our study underscores the potential of phytochemicals as effective inhibitors of cancer cell metabolism. The stable binding interactions highlight promising avenues for developing innovative cancer therapies. Further experimental investigations are warranted to validate these findings and advance the development of hybrid phytochemical-based treatments for combating chemoresistance.

Computational Insights for Interactions between nsP2 and nsP3 of CHIKV and Hormones through DFT Computations and Molecular Dynamics Simulations

The non-structural protein (nsP2 & nsP3) of the Chikungunya virus (CHIKV) is responsible for the transmission of viral infection. The main role of non-structural proteins are involved in the transcription process at an early stage of the infection. In this work, authors have studied the impact of nsP2 and nsP3 of CHIKV on hormones present in the human body using a computational approach. The ten hormones of chemical properties such as 4-Androsterone-2,17-dione, aldosterone, androsterone, corticosterone, cortisol, cortisone, estradiol, estrone, progesterone and testosterone were taken as a potency. From the molecular docking, the binding energy of the complexes is estimated, and cortisone was found to be the highest negative binding energy (-6.57 kcal/mol) with the nsP2 and corticosterone with the nsP3 (-6.47 kcal/mol). This is based on the interactions between hormones and nsP2/nsP3, which are types of noncovalent intermolecular interactions categorized into three types: electrostatic interactions, van der Waals (vdW) interactions, and hydrogen-bonding (H-bonding) interactions. To validate the docking results, additional molecular dynamics simulations and MM-GBSA methods were performed. The change in enthalpy, entropy, and free energy were calculated using MM-GBSA methods. The nsP2 and nsP3 of CHIKV interact strongly with the cortisone and corticosterone with free energy changes of -20.55 & -36.08 kcal/mol, respectively. Methods: The crystal structures of 3TKR and 3GPO proteins of nsP2 and nsP3 were extracted from the RCSB Protein Data Bank. Initially, unnecessary atoms like extra cations or anions and missing explicit hydrogen atoms were removed and added from the native domain of nsP2 and nsP3. The alignment of coordinated in the native domain was performed using Chimera and Notepad++ tools. The molecular docking of protein and ligand was performed usingAutoDock tool; it is essential for the prediction of the orientation of the ligand into the cavity of the target protein based on binding affinity. Based on thermodynamic parameters, MD Simulations were employed to calculate the change in binding free energies of various complexes followed by a change in enthalpy and entropy with time. According to MD production, the CPPTAJ and PTRAJ programs were used to analyse the trajectories, such as dynamic stability (RMSD), residual fluctuation (RMSF), compatibility, and hydrogen bonds of the newly formed complexes. After that, the Density Functional Theory (DFT) were used to calculate the electronic properties of selected molecules by Gaussian 16 on applying the B3LYP method with the 6-311G (d, p) basis set.

3D-QSAR, Scaffold Hopping, Virtual Screening, and Molecular Dynamics Simulations of Pyridin-2-one as mIDH1 Inhibitors

Isocitrate dehydrogenase 1 (IDH1) is a necessary enzyme for cellular respiration in the tricarboxylic acid cycle. Mutant isocitrate dehydrogenase 1 (mIDH1) has been detected overexpressed in a variety of cancers. mIDH1 inhibitor ivosidenib (AG-120) was only approved by the Food and Drug Administration (FDA) for marketing, nevertheless, a range of resistance has been frequently reported. In this study, several mIDH1 inhibitors with the common backbone pyridin-2-one were explored using the three-dimensional structure-activity relationship (3D-QSAR), scaffold hopping, absorption, distribution, metabolism, excretion (ADME) prediction, and molecular dynamics (MD) simulations. Comparative molecular field analysis (CoMFA, R2 = 0.980, Q2 = 0.765) and comparative molecular similarity index analysis (CoMSIA, R2 = 0.997, Q2 = 0.770) were used to build 3D-QSAR models, which yielded notably decent predictive ability. A series of novel structures was designed through scaffold hopping. The predicted pIC50 values of C3, C6, and C9 were higher in the model of 3D-QSAR. Additionally, MD simulations culminated in the identification of potent mIDH1 inhibitors, exhibiting strong binding interactions, while the analyzed parameters were free energy landscape (FEL), radius of gyration (Rg), solvent accessible surface area (SASA), and polar surface area (PSA). Binding free energy demonstrated that C2 exhibited the highest binding free energy with IDH1, which was -93.25 ± 5.20 kcal/mol. This research offers theoretical guidance for the rational design of novel mIDH1 inhibitors.

Identification and Validation of Compounds Targeting Leishmania major Leucyl-Aminopeptidase M17

Leishmaniasis is a neglected tropical disease; there is currently no vaccine and treatment is reliant upon a handful of drugs suffering from multiple issues including toxicity and resistance. There is a critical need for development of new fit-for-purpose therapeutics, with reduced toxicity and targeting new mechanisms to overcome resistance. One enzyme meriting investigation as a potential drug target in Leishmania is M17 leucyl-aminopeptidase (LAP). Here, we aimed to chemically validate LAP as a drug target in L. major through identification of potent and selective inhibitors. Using RapidFire mass spectrometry, the compounds DDD00057570 and DDD00097924 were identified as selective inhibitors of recombinant Leishmania major LAP activity. Both compounds inhibited in vitro growth of L. major and L. donovani intracellular amastigotes, and overexpression of LmLAP in L. major led to reduced susceptibility to DDD00057570 and DDD00097924, suggesting that these compounds specifically target LmLAP. Thermal proteome profiling revealed that these inhibitors thermally stabilized two M17 LAPs, indicating that these compounds selectively bind to enzymes of this class. Additionally, the selectivity of the inhibitors to act on LmLAP and not against the human ortholog was demonstrated, despite the high sequence similarities LAPs of this family share. Collectively, these data confirm LmLAP as a promising therapeutic target for Leishmania spp. that can be selectively inhibited by drug-like small molecules.

In silico evaluation of a new compound incorporating 4(3H)-quinazolinone and sulfonamide as a potential inhibitor of a human carbonic anhydrase

The present study investigates the potential of a new compound containing sulfonamide and 4(3H)-quinazolinone to inhibit the hCA-IIX enzyme using in silico methods. Density functional theory-based calculations of electronic properties have been addressed through the analysis of frontier molecular orbitals, molecule electrostatic potential, and IR and UV-vis spectroscopy data. A molecular electrostatic potential analysis predicts that the target protein will be most inhibited by the sulfonamide groups since it has the highest potential spots for electrophile and nucleophile attack. The investigated compound exhibited good ADMET properties and satisfied the Lipinski rule of drug likeness. The hCA-IIX protein binding affinity with the proposed compound was determined by molecular docking analysis, which revealed a stable conformation with more negative binding energy (-12.19 kcal/mol) than the standard AZA drug (-7.36 kcal/mol). Moreover, a molecular dynamics study confirmed the docking results through trajectory analysis. The RMSD and RMSF both showed convergence and no significant fluctuations during the simulation time, which revealed a stable interaction within the active domain of the target protein. According to these findings, the proposed compound has a good pharmacological nature and could potentially be an efficient drug against hCAIX enzymes.