Journal of Biomolecular Structure & Dynamics最新文献

Breast cancer (BC) is the most prevalent malignancy among women around the world. The epidermal growth factor receptor (EGFR) is a tyrosine kinase receptor (RTK) of the ErbB/HER family. It is essential for triggering the cellular signaling cascades that control cell growth and survival. However, perturbations in EGFR signaling lead to cancer development and progression. Hence, EGFR is regarded as a prominent therapeutic target for breast cancer. Therefore, in the current investigation, EGFR was targeted with phytochemicals from Clerodendrum inerme (L.) Gaertn (C. inerme). A total of 121 phytochemicals identified by gas chromatography-mass spectrometry (GC-MS) analysis were screened against EGFR through molecular docking, ADMET analyses (Absorption, Distribution, Metabolism, Excretion, and Toxicity), PASS predictions, and molecular dynamics simulation, which revealed three potential hit compounds with CIDs 10586 [i.e. alpha-bisabolol (-6.4 kcal/mol)], 550281 [i.e. 2,(4,4-Trimethyl-3-hydroxymethyl-5a-(3-methyl-but-2-enyl)-cyclohexene) (-6.5 kcal/mol)], and 161271 [i.e. salvigenin (-7.4 kcal/mol)]. The FDA-approved drug gefitinib was used to compare the inhibitory effects of the phytochemicals. The top selected compounds exhibited good ADMET properties and obeyed Lipinski's rule of five (ROF). The molecular docking analysis showed that salvigenin was the best among the three compounds and formed bonds with the key residue Met 793. Furthermore, the molecular mechanics generalized born surface area (MMGBSA) calculations, molecular dynamics simulation, and normal mode analysis validated the binding affinity of the compounds and also revealed the strong stability and compactness of phytochemicals at the docked site. Additionally, DFT and DOS analyses were done to study the reactivity of the compounds and to further validate the selected phytochemicals. These results suggest that the identified phytochemicals possess high inhibitory potential against the target EGFR and can treat breast cancer. However, further in vitro and in vivo investigations are warranted towards the development of these constituents into novel anti-cancer drugs.

Indiscriminate use of anti-microbial agents has resulted in the inception, frequency, and spread of antibiotic resistance among targeted bacterial pathogens and the commensal flora. Mur enzymes, playing a crucial role in cell-wall synthesis, are one of the most appropriate targets for developing novel inhibitors against antibiotic-resistant bacterial pathogens. In the present study, in-silico high-throughput virtual (HTVS) and Standard-Precision (SP) screening was carried out with 0.3 million compounds from several small-molecule libraries against the E. coli Mur D enzyme (PDB ID 2UUP). The docked complexes were further subjected to extra-precision (XP) docking calculations, and highest Glide-score compound was further subjected to molecular simulation studies. The top six virtual hits (S1-S6) displayed a glide score (G-score) within the range of -9.013 to -7.126 kcal/mol and compound S1 was found to have the highest stable interactions with the Mur D enzyme (2UUP) of E. coli. The stability of compound S1 with the Mur D (2UUP) complex was validated by a 100-ns molecular dynamics simulation. Binding free energy calculation by the MM-GBSA strategy of the S1-2UUP (Mur D) complex established van der Waals, hydrogen bonding, lipophilic, and Coulomb energy terms as significant favorable contributors for ligand binding. The final lead molecules were subjected to ADMET predictions to study their pharmacokinetic properties and displayed promising results, except for certain modifications required to improve QPlogHERG values. So, the compounds screened against the Mur D enzyme can be further studied as preparatory points for in-vivo studies to develop potential drugs. HIGHLIGHTSE.coli is a common cause of urinary tract infections.E.coli MurD enzyme is a suitable target for drug development.Novel inhibitors against E.coli MurD enzyme were identified.Molecular dynamics studies identified in-silico potential of identified compound.ADMET predictions and Lipinski's rule of five studies showed promising results.

Methicillin-resistant Staphylococcus aureus (MRSA) is a notorious pathogen that has emerged as a serious global health concern over the past few decades. Staphylococcal accessory regulator A (SarA) and 4,4'-diapophytoene synthase (CrtM) play a crucial role in biofilm formation and staphyloxanthin biosynthesis. Thus, the present study used a machine learning-based QSAR model to screen 1261 plant-derived natural organic compounds in order to identify a medication candidate with both biofilm and virulence inhibitory potential. Additionally, the in-silico molecular docking analysis has demonstrated significant binding efficacy of the identified hit compound, that is 85137543, with SarA and CrtM when compared to the control compound, hesperidin. Post-MD simulation analysis of the complexes depicted strong binding of 85137543 to both SarA and CrtM. Moreover, 85137543 showed hydrogen bonding with the key residues of both proteins during docking (ALA138 of SarA and ALA134 of CrtM) and post-MD simulation (LYS273 of CrtM and ASN212 of SarA). The RMSD of 85137543 was stable and consistent when bound to both CrtM and SarA with RMSDs of 1.3 and 1 nm, respectively. In addition, principal component analysis and the free energy landscape showed stable complex formation with both proteins. Low binding free energy (ΔGTotal) was observed by 85137543 for SarA (-47.92 kcal/mol) and CrtM (-36.43 kcal/mol), which showed strong binding. Overall, this study identified 85137543 as a potential inhibitor of both SarA and CrtM in MRSA.

Meyerozyma guilliermondii is a rare yeast pathogen contributing to the deadly invasive candidiasis. M. guilliermondii strain SO, as a promising protein expression host, showed 99% proteome similarity with the clinically isolated ATCC 6260 (type strain) in a recent comparative genomic analysis. However, their in vitro virulence features and in vivo pathogenicity were uncharacterized. This study aimed to characterize the in vitro and in vivo pathogenicity of M. guilliermondii strain SO and analyze its Als proteins (MgAls) via comprehensive bioinformatics approaches. M. guilliermondii strain SO showed lower and higher sensitivity towards β-mercaptoethanol and lithium, respectively than the avirulent S. cerevisiae but exhibited the same tolerance towards cell wall-perturbing Congo Red with C. albicans. With 7.5× higher biofilm mass, M. guilliermondii strain SO also demonstrated 75% higher mortality rate in the zebrafish embryos with a thicker biofilm layer on the chorion compared to the avirulent S. cerevisiae. Being one of the most important Candida adhesins, sequence and structural analyses of four statistically identified MgAls showed that MgAls1056 was predicted to exhibit the most conserved amyloid-forming regions, tandem repeat domain and peptide binding cavity (PBC) compared to C. albicans Als3. Favoured from the predicted largest ligand binding site and druggable pockets, it showed the highest affinity towards hepta-threonine. Non-PBC druggable pockets in the most potent virulence contributing MgAls1056 provide new insights into developing antifungal drugs targeting non-albicans Candida spp. Virtual screening of available synthetic or natural bioactive compounds and MgAls1056 deletion from the fungal genome should be further performed and validated experimentally.

Point mutation D54K in the human N-terminal caspase recruitment domain (CARD) of nucleotide-binding oligomerization domain -1 (NOD1) abrogates an imperative downstream interaction with receptor-interacting protein kinase (RIPK2) that entails combating bacterial infections and inflammatory dysfunction. Here, we addressed the molecular details concerning conformational changes and interaction patterns (monomeric-dimeric states) of D54K by signature-based molecular dynamics simulation. Initially, the sequence analysis prioritized D54K as a pathogenic mutation, among other variants, based on a sequence signature. Since the mutation is highly conserved, we derived the distant ortholog to predict the sequence and structural similarity between native and mutant. This analysis showed the utility of 33 communal core residues associated with structural-functional preservation and variations, concurrently served to infer the cryptic hotspots Cys39, Glu53, Asp54, Glu56, Ile57, Leu74, and Lys78 determining the inter helical fold forming homodimers for putative receptor interaction. Subsequently, the atomistic simulations with free energy (MM/PB(GB)SA) calculations predicted structural alteration that takes place in the N-terminal mutant CARD where coils changed to helices (45 _{α3- L4-α4-L6- α6}83) in contrast to native (45_{T2-L4-α4-L6-T4}83). Likewise, the C-terminal helices 93_T1-α7105 connected to the loops distorted compared to native 93_α6-L7105 may result in conformational misfolding that promotes functional regulation and activation. These structural perturbations of D54K possibly destabilize the flexible adaptation of critical homotypic _NOD1CARD-CARD_RIPK2 interactions (_α4Asp42-Arg488_α5 and _α6Phe86-Lys471_α4) is consistent with earlier experimental reports. Altogether, our findings unveil the conformational plasticity of mutation-dependent immunomodulatory response and may aid in functional validation exploring clinical investigation on CARD-regulated immunotherapies to prevent systemic infection and inflammation.

A series of S-adenosyl-L-homosysteine (SAH) analogs, with modification in the base and sugar moiety, have been designed, synthesized and screened as nsp14 and PLpro inhibitors of severe acute respiratory syndrome corona virus (SARS-CoV-2). The outcomes of ADMET (Adsorption, Distribution, Metabolism, Excretion, and Toxicity) studies demonstrated that the physicochemical properties of all analogs were permissible for development of these SAH analogs as antiviral agents. All molecules were screened against different SARS-CoV-2 targets using molecular docking. The docking results revealed that the SAH analogs interacted well in the active site of nsp14 protein having H-bond interactions with the amino acid residues Arg289, Val290, Asn388, Arg400, Phe401 and π-alkyl interactions with Arg289, Val290 and Phe426 of Nsp14-MTase site. These analogs also formed stable H-bonds with Leu163, Asp165, Arg167, Ser246, Gln270, Tyr274 and Asp303 residues of PLpro proteins and found to be quite stable complexes therefore behaved as probable nsp14 and PLpro inhibitors. Interestingly, analog 3 showed significant in silico activity against the nsp14 N7 methyltransferase of SARS-CoV-2. The molecular dynamics (MD) and post-MD results of analog 3 unambiguously established the higher stability of the nsp14 (N7 MTase):3 complex and also indicated its behavior as probable nsp14 inhibitor like the reference sinefungin. The docking and MD simulations studies also suggested that sinefungin did act as SARS-CoV-2 PLpro inhibitor as well. This study's findings not only underscore the efficacy of the designed SAH analogs as potent inhibitors against crucial SARS-CoV-2 proteins but also pinpoint analog 3 as a particularly promising candidate. All the study provides valuable insights, paving the way for potential advancements in antiviral drug development against SARS-CoV-2.

Compounds with a chalcone scaffold-based structure have demonstrated promising anticancer biological activity. However, the molecular interactions between chalcone scaffold-based compounds and breast cancer-associated proteins remain unclear. Through network pharmacology, molecular docking, and molecular dynamics (MD) simulation analyses, compounds with a chalcone scaffold-based structure were evaluated for their interaction with potential breast cancer targets. The compounds were retrieved from the ASINEX database, resulting in 575,302 compounds. A total of 342 compounds with chalcone scaffold-based structures were discovered. From the 342 compounds that was analysed, ten were chosen due to their adherence to Lipinski's rule, having an appropriate range of lipophilicity (LOGP), and topological polar surface area (TPSA), and absence of any toxicity. Based on target intersection, 50 target genes were found and subjected to protein-protein interaction (PPI), gene ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Four target genes were found to be involved in the breast cancer pathway. Consequently, molecular docking was utilised to analyse the molecular interactions between the compounds and four target protein receptors. Compound 211 exhibited the highest binding affinities for the epidermal growth factor receptor (EGFR), fibroblast growth factor receptor 1 (FGFR1), oestrogen receptor (ESR1), and cyclin dependent kinase 6 (CDK6) with values of -8.95 kcal/mol, -8.60 kcal/mol, -10.33 kcal/mol, and -9.90 kcal/mol, respectively. During MD simulation, compound 211 and its respective proteins were stable, compact, and had minimal flexibility. The findings provide foundations for future studies into the interaction underlying the anti-breast cancer potential of compounds with chalcone-based scaffold structures.

Under physiological conditions, spectroscopic techniques as well as molecular docking simulation have been used to investigate the binding interaction mechanism between Cu(II) complex containing Pyridine-2,6-dicarboxylic acid (PDCA) and Diethanolamine (DEA) ligands, [Cu(DEA)(PDCA)] and human serum albumin (HSA). UV spectral changes of protein in the presence of the Cu(II) complex suggested the formation of a Protein-Cu(II) complex conjugate with specific new structure. The Cu(II) complex quenches the intrinsic fluorescence of the HSA via a static mechanism in which van der Waals interactions along with hydrogen bonds are fundamental binding forces. Displacement experiments performed by warfarin and ibuprofen site probes predict that the Cu(II) complex is located in subdomain IIA, Sudlow site 1 of HSA. Molecular docking results showed close resemblance with experimental data.

Alzheimer's disease (AD) is a degenerative neurologic condition that results in the deterioration of several brain processes (e.g. memory loss). The most notable physical alteration in AD is the impairment of brain cells. An accurate examination of brain pictures may help to find the disease earlier because early diagnosis is crucial to enhancing patient care and treatment outcomes. Therefore, an easy and error-free system for AD diagnosis has recently received much research attention. Conventional image processing techniques sometimes cannot observe the significant features. As a result, the objective of this research is to develop an accurate and efficient method for identifying AD using magnetic resonance imaging (MRI). To begin with, the brain regions in the MRI images are segmented using a powerful Deep ResUnet-based approach. Then, the global and local features from the segmented images are recovered using a Multi-Scale Attention Siamese Network (MASNet)-based network. After extracting the features, the Slime Mould Algorithm-based feature selection process is conducted. Finally, the stages of AD are categorized using the EfficientNetB7 model. The efficacy of the presented method has been tested using brain MRI scans from the Kaggle dataset and the AD Neuroimaging Initiative (ADNI) dataset, and it achieves 99.31% and 99.38% accuracy, respectively. Finally, the study results show that the suggested method is helpful for accurate AD categorization.

Cancer remains one of the most pressing challenges to global healthcare, exerting a significant impact on patient life expectancy. Cancer metastasis is a critical determinant of the lethality and treatment resistance of cancer. The urokinase-type plasminogen activator receptor (uPAR) shows great potential as a target for anticancer and antimetastatic therapies. In this work, we aimed to identify potential uPAR inhibitors by structural dynamics-based virtual screenings against a natural product library on four representative apo-uPAR structural models recently derived from long-timescale molecular dynamics (MD) simulations. Fifteen potential inhibitors (NP1-NP15) were initially identified through molecular docking, consensus scoring, and visual inspection. Subsequently, we employed MD-based molecular mechanics-generalized Born surface area (MM-GBSA) calculations to evaluate their binding affinities to uPAR. Structural dynamics analyses further indicated that all of the top 6 compounds exhibited stable binding to uPAR and interacted with the critical residues in the binding interface between uPAR and its endogenous ligand uPA, suggesting their potential as uPAR inhibitors by interrupting the uPAR-uPA interaction. We finally predicted the ADMET properties of these compounds. The natural products NP5, NP12, and NP14 with better binding affinities to uPAR than the uPAR inhibitors previously discovered by us were proven to be potentially orally active in humans. This work offers potential uPAR inhibitors that may contribute to the development of novel effective anticancer and antimetastatic therapeutics.