In Silico Identification and Molecular Dynamics Analysis of PfLysRS Inhibitors for Malaria Treatment

Integrated virtual screening, machine learning and molecular dynamics identify novel phytochemical FabI inhibitors against MRSA.

The novel SARS‐CoV‐2 outbreak has swept the world since 2019. Because of the lack of direct‐acting antivirals (DAAs) targeting SARS‐CoV‐2 as well as the side effects of synthetic drugs, naturally sourced DAAs targeting SARS‐CoV‐2 should be urgently developed. The SARS‐CoV‐2 main protease (M pro ) can be reasonably used as a drug target because it is crucially involved in viral replication and transcription. To design potent DAAs targeting M pro , the M pro ‐inhibitor binding mechanism, and the conformational changes of M pro induced by inhibitor binding must be essentially clarified. Here, conventional molecular dynamics (cMD) simulation, principal component analysis (PCA), and free energy landscape (FEL) analysis were combined to probe 5 known phytochemical inhibitors‐mediated conformational changes of M pro . The results revealed that inhibitor binding significantly affected the kinetic behavior of M pro , and induced conformational rearrangement. The calculations of binding free energies using molecular mechanics generalized Born surface area (MM‐GBSA) and solvation interaction energy (SIE) methods and residue‐based free energy decomposition values showed that there are 23 key residues involved in the M pro ‐inhibitor binding. We found that one of the tested phytochemicals (PubChem CID 637112) could be used as a potent SARS‐CoV‐2 M pro inhibitor, which can block viral replication and translation.

Molecular dynamics simulation, free energy landscape and binding free energy computations in exploration the anti-invasive activity of amygdalin against metastasis

cAMP-specific 3′,5′-cyclic phosphodiesterase 4 A (PDE4A) holds a pivotal role in modulating intracellular levels of cyclic adenosine monophosphate (cAMP). Targeting PDE4A with novel therapeutic agents shows promise in addressing neurological disorders (e.g. Alzheimer’s and Parkinson’s diseases), mood disorders (depression, anxiety), inflammatory conditions (asthma, chronic obstructive pulmonary disease), and even cancer. In this study, we present a comprehensive approach that integrates virtual screening and molecular dynamics (MD) simulations to identify potential inhibitors of PDE4A from the existing pool of FDA-approved drugs. The initial compound selection was conducted focusing on binding affinity scores, which led to the identification of several high-affinity compounds with potential PDE4A binding properties. From the refined selection process, two promising compounds, Fluspirilene and Dihydroergocristine, emerged as strong candidates, displaying substantial affinity and specificity for the PDE4A binding site. Interaction analysis provided robust evidence of their binding capabilities. To gain deeper insights into the dynamic behavior of Fluspirilene and Dihydroergocristine in complex with PDE4A, we conducted 300 ns MD simulations, principal components analysis (PCA), and free energy landscape (FEL) analysis. These analyses revealed that Fluspirilene and Dihydroergocristine binding stabilized the PDE4A structure and induced minimal conformational changes, highlighting their potential as potent binders. In conclusion, our study systematically explores repurposing existing FDA-approved drugs as PDE4A inhibitors through a comprehensive virtual screening pipeline. The identified compounds, Fluspirilene and Dihydroergocristine, exhibit a strong affinity for PDE4A, displaying characteristics that support their suitability for further development as potential therapeutic agents for conditions associated with PDE4A dysfunction.

Unveiling key hub genes in E. coli biofilm formation: An in silico approach integrating differential gene expression, biosurfactant targeting, MD simulation and MM-PBSA free energy calculations.

ABSTRACTFlavonoids have been showing diversified bioactivities. Primary amoebic meningoencephalitis is a brain inflammation caused by Naegleria fowleri brain eating amoeba. In this manuscript, we selected 93 flavonoids by extensive literature survey and 83 flavonoids passed drug likeness parameter. Selected flavonoids were molecular docked against primary amoebic meningoencephalitis N. fowleri CYP51 receptor considering voriconazole as standard. Beta naphthoflavone, abyssinone I, and abyssinone III showed maximum docking scores of −10.9 kcal/mol, −10.7 kcal/mol, and −10.6 kcal/mol, respectively, whereas voriconazole showed docking scores of −7.6 kcal/mol. Molecular dynamic simulation data showed that RMSD values attained almost a static value during the simulation, and all nearest interacting amino acid residues were fluctuated within limit. Molecular Mechanics Poisson‐Boltzmann Surface Area (MMPBSA) data of beta naphthoflavone abyssinone I, abyssinone III, and voriconazole showed free binding energies of −82.755 kJ/mol, −1924.193 kJ/mol, −1890.335 kJ/mol, and −540.141 kJ/mol, respectively. Frontier molecular orbital (FMO) analysis showed that abyssinone III was the chemically reactive molecule and beta naphthoflavone showed maximum electrophilicity. Molecular electrostatic potential (MEP) analysis portrayed possible nucleophilic‐electrophilic attack areas of the structures. PCA and free energy landscape (FEL) analysis data confirmed the stable conformations between flavonoids and receptor. Abyssinone I and III showed nontoxic behavior. These data confirmed that if we repurpose these flavonoids against primary amoebic meningoencephalitis, it will be beneficial for mankind.

Spike glycoprotein has a significant role in the entry of SARS-CoV-2 to host cells, which makes it a potential drug target. Continued accumulation of non-synonymous mutations in the receptor binding domain of spike protein poses great challenges in identifying antiviral drugs targeting this protein. This study aims to identify potential entry inhibitors of SARS-CoV-2 using virtual screening and molecular dynamics (MD) simulations from three distinct chemical libraries including Pandemic Response Box, Drugbank and DrugCentral, comprising 6971 small molecules. The molecules were screened against a binding pocket identified in the receptor-binding domain (RBD) region of the spike protein which is known as the linoleic acid binding pocket, a highly conserved motif among several SARS-CoV-2 variants. Through virtual screening and binding free energy calculations, we identified four top-scoring compounds, MMV1579787 ([2-Oxo-2-[2-(3-phenoxyphenyl)ethylamino]ethyl]phosphonic acid), Tretinoin, MMV1633963 ((2E,4E)-5-[3-(3,5-dichlorophenoxy)phenyl]penta-2,4-dienoic acid) and Polydatin, which were previously reported to have antibacterial, antifungal or antiviral properties. These molecules showed stable binding on MD simulations over 100 ns and maintained stable interactions with TYR365, PHE338, PHE342, PHE377, TYR369, PHE374 and LEU368 of the spike protein RBD that are found to be conserved among SARS-CoV-2 variants. Our findings were further validated with free energy landscape, principal component analysis and dynamic cross-correlation analysis. Our in silico analysis of binding mode and MD simulation analyses suggest that the identified compounds may impede viral entrance by interacting with the linoleic acid binding site of the spike protein of SARS-CoV-2 regardless of its variants, and they thus demand for further in vitro and in vivo research.

Cholera, a life-threatening diarrheal disease caused by Vibrio cholerae, remains a global health concern. Traditional medicinal plants such as Berberis vulgaris (barberry) and Hydrastis canadensis (goldenseal) have long been used for their antimicrobial properties. This study employed integrated computational approaches to identify potential anti-cholera compounds from these plants by targeting sodium-pumping NADH-ubiquinone oxidoreductase (Na⁺-NQR). Virtual screening identified compounds 122623, 197835, and 638024, along with the control Korormicin, exhibiting favorable binding affinities and hydrogen bonding interactions. Machine learning-based prediction models were further applied to assess functional activity and binding affinity. The study incorporated ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profiling and density functional theory (DFT) calculations to evaluate physicochemical properties and electronic characteristics of the compounds. Molecular dynamics (MD) simulations demonstrated stable RMSD values for the ligands, with 122623 exhibiting the lowest RMSD (0.3-0.5nm), closely resembling the control (0.3-0.4nm), suggesting stable binding. Principal component analysis (PCA) showed tight conformational clusters for complexes with 122623 and 197835, while free energy landscape (FEL) analysis revealed deep energy minima, supporting complex stability. MM/GBSA calculations showed that 122623 had the lowest binding free energy (-38.71kcal/mol), followed by 638024 (-35.14kcal/mol) and 197835 (-30.68kcal/mol). Per-residue energy decomposition identified key residues (Phe137, Val155, Phe159, Phe160) involved in ligand binding. Network pharmacology analysis predicted additional gene targets for the selected compounds, providing insights into their broader therapeutic relevance. Collectively, compounds 122623, 197835, and 638024 derived from Hydrastis canadensis and Berberis vulgaris demonstrated promising inhibitory interactions with the Na⁺-NQR enzyme, suggesting their potential as anti-cholera agents.

Inhibiting MDM2-p53 interaction is considered an efficient mode of cancer treatment. In our current study, Gaussian-accelerated molecular dynamics (GaMD), deep learning (DL), and binding free energy calculations were combined together to probe the binding mechanism of non-peptide inhibitors K23 and 0Y7 and peptide ones PDI6W and PDI to MDM2. The GaMD trajectory-based DL approach successfully identified significant functional domains, predominantly located at the helixes α2 and α2', as well as the β-strands and loops between α2 and α2'. The post-processing analysis of the GaMD simulations indicated that inhibitor binding highly influences the structural flexibility and collective motions of MDM2. Calculations of molecular mechanics-generalized Born surface area (MM-GBSA) and solvated interaction energy (SIE) not only suggest that the ranking of the calculated binding free energies is in agreement with that of the experimental results, but also verify that van der Walls interactions are the primary forces responsible for inhibitor-MDM2 binding. Our findings also indicate that peptide inhibitors yield more interaction contacts with MDM2 compared to non-peptide inhibitors. Principal component analysis (PCA) and free energy landscape (FEL) analysis indicated that the piperidinone inhibitor 0Y7 shows the most pronounced impact on the free energy profiles of MDM2, with the piperidinone inhibitor demonstrating higher fluctuation amplitudes along primary eigenvectors. The hot spots of MDM2 revealed by residue-based free energy estimation provide target sites for drug design toward MDM2. This study is expected to provide useful theoretical aid for the development of selective inhibitors of MDM2 family members.

Insights into the substrate binding specificity of quorum-quenching acylase PvdQ

Here, we present a comprehensive computational evaluation of three FDA (Food and Drug Administration) approved drug leads, ZINC150338755, ZINC6716957, and ZINC203686879, as potential inhibitors targeting the Cripto/FRL‐1/Cryptic (CFC) domain of the CRIPTO (Teratocarcinoma‐derived growth factor1) protein. CRIPTO is a key oncogenic protein implicated in tumor progression, metastasis, and therapy resistance across multiple cancer types. Its overexpression promotes cancer stemness and survival in various tumors, such as liver cancer and glioblastoma, while its restricted expression in normal tissues makes it an attractive therapeutic target. Through an integrated approach merging molecular docking (with results −8.6 to −9.1 kcal/mol affinities), molecular dynamics (MD) simulations, molecular mechanics–generalized Born surface area (MM‐GBSA) binding free energy calculations, and free energy landscape (FEL) analysis, we delineate distinct binding modes and thermodynamic fingerprints of the inhibitor complexes. Virtual screening determined the lead compounds, which were subjected to 250 ns MD simulations to check the stability and dynamics of interactions. Binding free energy calculation revealed striking disparities in binding energies (ΔG bind from −26.83 to −57.37 kcal/mol), where Complex1 exhibited increased stability through hydrophobic superiority, Complex2 showed similar polar/nonpolar interactions, and Complex3 exhibited unique electrostatic‐driven recognition. These findings shed atomic‐level insight into CRIPTO‐inhibitor interactions and offer a solid foundation for structure‐based optimization of CRIPTO inhibitors.

An effective HIV-1 integrase inhibitor screening platform: Rationality validation of drug screening, conformational mobility and molecular recognition analysis for PFV integrase complex with viral DNA

In this study, the ketoprofen derivatives were evaluated against inflammation and nociception using comprehensive computational methods. These derivatives were synthesized by modifying the structure of ketoprofen, a well-known NSAID (nonsteroidal anti-inflammatory drug) used clinically for the management of pain and inflammation. The virtual screening was performed to predict the binding affinities of the ketoprofen-based acyl hydrazone derivatives with key inflammatory and pain-related targets including cyclooxygenase-2 (COX-2), Transient Receptor Potential menthol-8 (TRPV1), c-Jun N-terminal Kinase-3 (JNK3), Extracellular Receptor Kinase (ERK) and Purinergic Receptor Type Y1 (P2Y1) to obtain the top hits. The virtual screening studies revealed the top hits such as COMP2, COMP10, COMP16 and COMP17 against the target protein. Based on the virtual screening, Molecular Dynamic (MD) simulation was performed on the top hits for 50 ns by using parameters like Root Mean Square Fluctuations (RMSF), Root Mean Square Deviation (RMSD), Radius of Gyration (RoG), Solvent Accessible Surface Area (SASA) and hydrogen bonds. The three complexes showed significantly lowered RMSD value and hence, the complexes remained stable throughout the simulation. For binding free energy, Molecular, Mechanics-Poison Boltzmann Surface Area (MM-PBSA) and Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) were performed after MD simulation for the analysis of stability in the context of energy. The MM-PBSA and MM-GBSA evaluation showed overall energy of the system remains negative and indicates favorable binding interaction and hence, stability of the complexes. Furthermore, the per-residue decomposition was carried out to evaluate each amino acid involved in the ligand-protein interaction, and highest contributing amino acid in terms of energy involved in the ligand-protein interactions. The binding free energy calculation was succeeded by Density Functional Theory (DFT) analysis to evaluate the Highest Occupied Molecular Orbital (HOMO), Lowest Unoccupied Molecular Orbital (LUMO) and the HOMO-LUMO gap. In conclusion, the four compounds showed significant activity against pain and inflammation based on the computational analysis; however, to employ it clinically further analysis will be required.

The papain-like protease of SARS-CoV-2 (PLpro2) is integral to viral polyprotein cleavage and the modulation of host immune responses, positioning it as a critical target for antiviral drug development. Here, we elucidate the molecular mechanisms governing the noncovalent inhibition of PLpro2 through a comprehensive computational approach, including molecular docking, extensive molecular dynamics (MD) simulations, binding free energy calculations (MM/GBSA and SIE), principal component and free energy landscape (PCA/FEL) analyses, and protein-ligand interaction fingerprinting (ProLIF). We assessed a structurally diverse set of noncovalent inhibitors for their capacity to induce conformational rearrangements and stabilize key structural motifs of PLpro2, with particular emphasis on the BL2 loop. Notably, XR3 and A19 exhibited superior experimental and predicted binding affinities, which can be attributed to favorable contacts with essential residues Tyr268 and Gln269, the attenuation of loop dynamics, and the stabilization of energetically favorable conformational states. By contrast, less potent inhibitors were associated with increased conformational heterogeneity, fragmented free energy landscapes, and diminished interactions with critical loop residues. Therefore, our integrative analysis delineates the structural and energetic determinants underpinning noncovalent PLpro2 inhibition, underscoring the central roles of loop immobilization and π-stacking interactions in the rational design of next-generation PLpro2 inhibitors.

G-protein-coupled receptor FZD7 as a therapeutic target in glioblastoma: Multi-cohort validation and identification of Cycloartobiloxanthone as an inhibitor.