Journal of Biomolecular Structure & Dynamics最新文献

Gwt1, an essential acyltransferase in the glycosylphosphatidylinositol (GPI) biosynthesis pathway, is a promising target for the development of high-selectivity antifungal agents. In this study, we combined molecular dynamics (MD) simulations and free energy calculations to characterize the binding mechanism of Gwt1 with its native substrate, palmitoyl-CoA. Our simulations identified key hydrogen-bonding and ionic interactions critical for substrate recognition, particularly involving residues Lys123, Arg181, and Asn432. Potential of mean force (PMF) calculations revealed multiple conformational states of palmitoyl-CoA, including an I-shaped conformation that sterically occludes the GlcN-PI binding site, thereby hindering the acyl transfer step. Leveraging these structural insights, we performed virtual screening targeting the hydrophobic pocket formed by Tyr129, Tyr400, Phe404, and Tyr408, which identified two approved drugs, tivozanib and rosiglitazone, as potential Gwt1 inhibitors. Experimental validation confirmed their antifungal activities against pathogenic fungi, including Cryptococcus neoformans, Candida albicans, and Aspergillus fumigatus. This work provides dynamic mechanistic insights into Gwt1 function and offers a rational strategy for repurposing existing drugs as antifungals targeting the GPI pathway.

The rise of drug-resistant Mycobacterium tuberculosis (Mtb) strains has driven the search for novel therapeutic targets beyond conventional anti-tubercular agents. One such promising target is the ClpP protease complex, composed of ClpP1 and ClpP2 subunits, which is essential for proteostasis and bacterial survival under stress. This study explores the molecular dynamics (MD) and activation mechanism of Mtb ClpP subunits by N-[(benzyloxy)carbonyl]-L-isoleucyl-L-leucine (ZIL), an N-blocked dipeptide activator. MD simulations (200-1000 ns) were used to analyze structural stability, ligand interactions, and domain dynamics of both subunits in active and inactive states. ZIL-bound simulations showed that ClpP1 and ClpP2 maintained structural integrity, with conserved ligand-proximal residues forming stable interactions, although ClpP2 exhibited more variable polar contacts. In contrast, ligand-free simulations (500 ns) revealed significant instability, particularly in the handle domain and S1 binding pocket, underscoring the stabilizing role of ZIL. A 1000 ns simulation, with ZIL placed away from its known binding site on inactive ClpP1, showed that the ligand approached its target site and triggered a conformational shift in the handle domain, an early allosteric response, even though it did not fully dock as observed in the crystal structure. Notably, the residues in proximity to ZIL were associated with the observed structural changes in the simulations. The resulting MD trajectories provide a continuous, atomic-level view of ligand-induced dynamics and early activation events. Conducted without prior mechanistic assumptions, this unbiased simulation highlights the potential of targeting allosteric activation mechanisms and offers valuable insight into the rational design of ClpP-based therapeutics against drug-resistant Mtb.

Subcellular localization of mRNA plays a crucial regulatory role in eukaryotic cells, directly affecting protein synthesis, functional localization and cellular activities. Its abnormal regulation is closely associated with various pathological conditions. Therefore, accurate elucidation of the mechanisms underlying mRNA subcellular localization is of great significance for biomedical research. However, existing multi-label prediction methods mainly rely on traditional feature encoding techniques and still face considerable limitations. To address these challenges, this study proposes a novel resampling technique that combines Manhattan Mean-Direction Oversampling with Manhattan Density-Preserved Undersampling. Moreover, in light of the advantages of large language models, this study explores the use of several popular models to extract key information from sequences. Based on the experimental results, ESM2 was ultimately selected for feature extraction. Building upon these methods, we developed a novel prediction tool named EMMPREDMLsub. Results demonstrate that EMMPREDMLsub outperforms current state-of-the-art models in multi-label prediction tasks. Furthermore, SHAP-based interpretability analysis reveals that traditional models tend to focus on single key features, while deep learning models rely on synergistic interactions among multiple features. Notably, the A and T nucleotides at the 5' end and the C and G nucleotides at the 3' end of mRNA sequences contribute significantly to the predictions, suggesting that nucleotide composition and feature combinations in different regions play critical biological roles in subcellular localization. To facilitate broader use, we have developed a free and open-access online tool: http://www.emmpredmlsub.com.

The conformational dynamics of the switch domain 1 (SWI) of KRAS plays an important role in binding of KRAS to effectors. Clarifying molecular mechanism of the effect of mutations in SWI on conformational dynamics of KRAS is of significance for understanding the function of KRAS. Gaussian accelerated molecular dynamics (GaMD) simulations were performed on GDP/GTP-wild type (WT) and mutated KRAS to investigate the influences of two mutations P34R and T35S in SWI on conformational dynamics of KRAS. The analyses of free energy landscapes (FELs) reveal that P34R and T35S induce looser switch regions than WT KRAS, moreover the switch regions in GTP-P34R and T35S KRAS are wider than those in GDP-P34R and T35S one. Meanwhile, P34R and T35S highly affect structural flexibility of SWI and the loop L3, which disturbs binding of KRAS to effectors or regulators and the allosteric regulation of KRAS activity. In addition, the analyses of interaction networks suggest that P34R and T35S weaken hydrogen bonding interactions (HBIs) of SWI with GDP/GTP and influence electrostatic interactions (EIs) of SWI with magnesium ion (Mg²⁺), which also implies the effects of P34R and T35S on binding of KRAS to effectors or regulators and KRAS activity. This work is expected to contribute theoretical help and dynamics information for further understanding the function of KRAS and drug design toward the RAS proteins.

Neurons in the brain communicate through interactions between neurotransmitters and their receptors. Structure-based rational design of opioid drugs remains a major challenge, largely due to a lack of mechanistic insight into opioid-receptor selectivity and receptor activation. To address this gap, we present an enhanced Triangular Spatial Relationship (TSR)-based method to define and quantitatively characterize ligand-induced conformational changes in both receptors and ligands. To accurately model the geometries of neurotransmitters and opioids, we developed a novel algorithm for extracting their three-dimensional structural features. The key contributions of this work are summarized as follows: (i) Synergistic improvements in elucidating structure-function relationships were achieved by simultaneously applying two feature-engineering strategies. (ii) The influence of local receptor environments on the structural variations of glutamate and aspartate was quantitatively analyzed to elucidate conformational changes. (iii) Complementary structural features between fentanyl and its biosensor were identified, providing insights into binding specificity. (iv) Tyrosine residues within neurotransmitter binding sites were shown to be structurally distinct from those located outside these sites. (v) For the first time, the TSR-based method was integrated with Density Functional Theory and Quantum Mechanics/Molecular Mechanics optimization, revealing a clear relationship between structure and energy. (vi) The TSR-based method demonstrated superior performance compared with RMSD, USR, ROSHAMBO, and Phase approaches. In conclusion, this study establishes an advanced computational framework for representing and quantifying neurotransmitter structures. The TSR-based approach provides a powerful tool for dissecting structural specificity in ligand-receptor interactions and lays a solid foundation for deeper mechanistic insight and more effective rational drug design.

Drug repurposing for cancer treatment is a valuable strategy to identify existing drugs with known safety profiles that could combat the neoplasm, by reducing costs. Oral squamous cell carcinoma, an ulcer-proliferative lesion on the mucosal epithelium, is the most common oral malignancy. About 10% of cancer patients within the Indian subcontinent suffer from OSCC, primarily due to chewing of betel plant derivatives. Concomitant administration of the chemotherapeutic agent (Cisplatin/Paclitaxel) is the treatment of choice. Analysis of the oral mycobiome of OSCC patients has projected the role of Candida albicans in potentiating OSCC. Hence, repurposing antifungal drugs emerges as a promising approach, as these drugs could target both the cancer cells and the infection. Cancer cells often have heightened energy requirements, and targeting mitochondrial proteins to disrupt mitochondrial division and induce dysfunction contributing to cell death, offers a method for treating OSCC. We identified 18 mitochondrial targets playing a crucial role in the maintenance of mitochondrial homeostasis. They were docked against 125 antifungal ligand molecules sourced from PUBCHEM. Ligand profiling was performed using Lipinski's rule of 5, SwissADME and ProTox. Also, molecular dynamics and MM-PBSA were performed to validate our results. Among all protein ligand interactions, we observed that targeting DRP1 with itraconazole yielded superior binding and stability. Overall, lower toxicity and thumping ADME properties solidified the choice of ligand. We hope this experimental approach will enable us to provide a basis for selecting a lead molecule for a possible novel nano-formulation and validate our finding through in-vitro cell line-based testing.

Superoxide dismutase 1 (SOD1) is a vital enzyme responsible for attenuating oxidative stress through its ability to facilitate the dismutation of the superoxide radical into oxygen and hydrogen peroxide. The progressive loss of motor neurons characterize amyotrophic lateral sclerosis (ALS), a crippling neurodegenerative disease that is caused by mutations in the SOD1 gene. In this study, in silico mutational analysis was performed to study the various mutations, the pathogenicity and stability ΔΔG (binding free energy) of the variant of SOD1. x in the protein variant analysis showed a considerable destabilizing effect with a ΔΔG value of -4.2 kcal/mol, signifying a notable impact on protein stability. Molecular dynamics simulations were conducted on both wild-type and C146R mutant SOD1. RMSD profiles indicated that both maintained consistent structural conformation over time. Additionally, virtual screening of 3067 FDA-approved drugs against the mutant SOD1 identified two potential binders, Tucatinib (51039094) and Regorafenib (11167602), which interacted with Leu106, similar to the control drug, Ebselen. Further simulations assessed the dynamic properties of SOD1 in monomeric and dimeric forms while bound to these compounds. 11167602 maintained stable interaction with the monomeric SOD1 mutant, whereas 51039094 and Ebselen dissociated from the monomeric protein's binding site. However, all three compounds were stably bound to the dimeric SOD1. MM/GBSA analysis revealed similar negative binding free energies for 11167602 and 51039094, identifying them as strong binders due to their interaction with Cys111. Experimental validation, including in vitro, cell-based, and in vivo assays are essential to confirm these candidates before advancing to clinical trials.

The main aim of this study is to address the global health crisis posed by tuberculosis (TB) through the exploration of novel therapeutic strategies targeting Mycobacterial phosphoribosyl pyrophosphate synthetase (MtPrsA), an untried enzyme involved in essential metabolic pathways of Mycobacterium tuberculosis. This enzyme plays a crucial role in cell wall synthesis, nucleotide biosynthesis and amino acid synthesis in M tb. Any hindrance to these may affect the growth and survival of the organism. Phytochemicals were systematically screened for potential inhibitors to MtPrsA. Subsequently, based on molecular docking studies, three compounds, namely, hesperidin, rebaudiosideA and rutin were selected. The binding stabilities of these compounds were analyzed using molecular dynamics simulation. Based on the RMSD score obtained, the binding stability of the compounds was confirmed. To validate the findings, an enzyme inhibition assay was done using recombinant MtPrsA. Ligation Independent Cloning (LIC cloning) method was used to produce recombinant His-tagged MtPrsA, followed by purification using Histrap columns. Enzyme kinetic studies unveiled the distinct modes of inhibition exhibited by each compound towards MtPrsA. RebaudiosideA and rutin emerged as competitive inhibitors, while hesperidin showcased a mixed inhibition profile. In conclusion, the study contributes valuable insights into potential therapeutic strategies for TB, through the exploration of alternative enzyme targets and the identification of phytochemical inhibitors. Notably, todate, no effective plant compounds have been reported as inhibitors to MtPrsA.