Computational biology and chemistry最新文献

The viruses has spread globally and have been impacted lives of people socially and economically, which causes immense suffering throughout the world. Thousands of people died and millions of illnesses were brought, by the outbreak worldwide. In order to control the coronavirus pandemic, mathematical modeling proved to be an invaluable tool for analyzing and determining the potential and severity of the illness. This work proposed and assessed a deterministic six-compartment model with a novel stochastic neural network. The significance of the proposed model was demonstrated by numerical simulation in which the results are agreed with sensitivity analysis. Furthermore, the efficacy of stochastic neural network has been proven with the help of numerical simulations. Some investigations have been conducted through graphs and tables that how the vaccination process is helpful to minimize stress in society. The numerical simulations also focused on preventing the community-wide spread of the disease. The lowest residual errors have been achieved by our proposed stochastic neural network and compared with numerical solvers to assess the accuracy and robustness of the proposed approach.

Influenza A virus is a leading cause of acute respiratory tract infections, posing a significant global health threat. Current treatment options are limited and increasingly ineffective due to viral mutations. This study aimed to identify potential drug candidates targeting the nucleoprotein of the H3N2 subtype of Influenza A virus. We focused on epicatechin derivatives and employed a series of computational approaches, including ADMET profiling, drug-likeness evaluation, PASS predictions, molecular docking, molecular dynamics simulations, Principal Component Analysis (PCA), dynamic cross-correlation matrix (DCCM) analyses, and free energy landscape assessments. Molecular docking and dynamics simulations revealed strong and stable binding interactions between the derivatives and the target protein, with complexes 01 and 81 exhibiting the highest binding affinities. Additionally, ADMET profiling indicated favorable pharmacokinetic properties for these compounds, supporting their potential as effective antiviral agents. Compound 81 demonstrated exceptional quantum chemical descriptors, including a small HOMO-LUMO energy gap, high electronegativity, and significant softness, suggesting high chemical reactivity and strong electron-accepting capabilities. These properties enhance Compound 81's potential to interact effectively with the H3N2 nucleoprotein. Experimental validation is strongly recommended to advance these compounds toward the development of novel antiviral therapies to address the global threat of influenza.

LIMK2 is crucial in regulating actin cytoskeleton dynamics, significantly contributing to cancer cell proliferation, invasion, and metastasis. Inhibitors like LIMKi3 effectively suppress LIMK2 kinase activity by directly affecting actin polymerization and preventing the formation of structures like filopodia and lamellipodia, which are typical of motile cancer cells. By modulating these actin dynamics, LIMKi3 inhibits cancer cell migration and invasion, reducing the potential for metastasis. Thus, this study aims to explore potential anti-cancer therapeutic LIMK2 inhibitors with properties resembling LIMKi3. Henceforth, molecular docking was utilized in this study to comprehend the ATP mimetic binding mode of LIMKi3, followed by Pharmacophore-based virtual screening to identify small molecules resembling LIMKi3. In addition, molecular dynamics simulations were performed to explore the dynamic behavior of LIMK2 and potential inhibitors. Further, network analysis and binding free energy calculations were implemented to comprehensively assess the interactions between the compounds and LIMK2. In molecular docking, LIMKi3 demonstrated an ATP mimetic hinge binding mode with hydrogen bonds at Ile408. Among the screened compounds (NCI300395, ChemDiv-8020-2508, and ChemDiv-7997-0024), three displayed "ADRH" pharmacophoric features like LIMKi3, with favorable ADMET properties, higher binding affinity, and significant hydrogen bond interactions at Ile408. LIMK2-inhibitor complexes showed lower RMSD than LIMK2-LIMKi3, indicating higher equilibrium by identified compounds. Protein-drug Complexes exhibited significant inter-domain correlation in N-lobe residues of LIMK2, including conserved β3, αC, and Hinge residues. Binding free energy analysis ranked LIMK2-NCI300395 highest, followed by LIMK2-ChemDiv-7997-0024 and LIMK2-ChemDiv-8020-2508, highlighting their potential as effective LIMK2-targeting compounds. Hence, this study emphasizes LIMKi3's significance and identifies potential candidates (NCI300395, ChemDiv-7997-0024, and ChemDiv-8020-2508) for developing cancer therapeutics targeting LIMK2. These findings open avenues for further investigations into the complex interplay between cytoskeletal dynamics and cancer progression.

Background: Pediatric septic arthritis, driven by Staphylococcus aureus, leads to substantial morbidity due to the host's complex inflammatory response. This study integrates bioinformatics analyses to map the genomic and immune profiles of pediatric septic arthritis, aiming to identify key biomarkers and therapeutic targets.

Methods: An integrative bioinformatics approach was adopted to analyze gene expression datasets from the GEO database, focusing on pediatric septic arthritis. DEGs were identified using GEO2R, and gene co-expression networks were generated via GeneMANIA. STRING database and Cytoscape software facilitated PPI network construction. DAVID enabled functional enrichment analysis to elucidate biological processes and pathways, while iRegulon predicted transcription factor regulation. CIBERSORT provided a detailed profile of immune cell alterations in the condition.

Results: From the datasets analyzed, 576 DEGs were extracted, with 35 shared between the two datasets, revealing an innate immunity signature with notable hub genes such as MPO and ELANE, indicative of a pronounced neutrophilic response. Functional enrichment analysis highlighted pathways pertinent to antimicrobial defense and NET formation. Key transcription factors, including PBX1, POLR2A, and STAT3, were identified as potential modulators of these pathways. Immune profiling demonstrated significant shifts in cell populations, with increased plasma cells and reduced CD4+ naïve T cells.

Conclusions: This study elucidates the complex genomic and immunological milieu of pediatric septic arthritis, uncovering potential biomarkers and signaling pathways for targeted therapeutic intervention. These findings underscore the preeminence of innate immune mechanisms in the disease's pathology and offer a foundation for future research to explore diagnostic and treatment innovations. Translation of these bioinformatics discoveries into clinical applications requires further validation and consideration of the limitations inherent to gene expression data and its interpretation.

The rapid growth of abnormal cells in the brain presents a serious risk to the health of humans as it can result in death. Since these tumors have a varied range of shapes, sizes, and positions, identifying Brain Tumors (BTs) is challenging. Magnetic Resonance Images (MRI) are most utilized for identifying malignant tumors. This paper develops a new approach, named EfficientNet-Deep batch normalized eLUAlexnet (EfficientNet-DbneAlexnet) for detecting BTs. Firstly, the input MRI image is transmitted for image enhancement. Here, the image is enhanced by the Piecewise Linear Transformation (PLT). After this, skull stripping is carried out, which is performed by the Fuzzy Local Information C Means (FLICM). Following this, the tumor area in the image is segmented with the help of a Projective Adversarial Network (PAN). The segmented image is later applied to the feature extraction module, wherein features like textural and statistical features are extracted. Finally, the BT detection is accomplished using the developed EfficientNet-DbneAlexnet, which is created by assimilating EfficientNet and Deep batch normalized eLUAlexnet (DbneAlexnet). The results demonstrate that EfficientNet-DbneAlexnet obtained a sensitivity of 90.36 %, accuracy of 92.77 %, and specificity of 91.82 %.