The urokinase receptor (uPAR) serves as a docking site to the serine protease urokinase-type plasminogen activator (uPA) to promote extracellular matrix (ECM) degradation and tumor invasion and metastasis. Previously, we had reported a small molecule inhibitor of the uPAR·uPA interaction that emerged from structure-based virtual screening. Here, we measure the affinity of a large number of derivatives from commercial sources. Synthesis of additional compounds was carried out to probe the role of various groups on the parent compound. Extensive structure-based computational studies suggested a binding mode for these compounds that led to a structure-activity relationship study. Cellular studies in non-small cell lung cancer (NSCLC) cell lines that include A549, H460 and H1299 showed that compounds blocked invasion, migration and adhesion. The effects on invasion of active compounds were consistent with their inhibition of uPA and MMP proteolytic activity. These compounds showed weak cytotoxicity consistent with the confined role of uPAR to metastasis.
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http://dx.doi.org/10.1016/j.bmc.2012.06.002 | DOI Listing |
Sci Rep
January 2025
State Key Laboratory of Chemical Resources Engineering, Beijing University of Chemical Technology, Beijing, 100029, People's Republic of China.
Tyrosine-protein kinase Src plays a key role in cell proliferation and growth under favorable conditions, but its overexpression and genetic mutations can lead to the progression of various inflammatory diseases. Due to the specificity and selectivity problems of previously discovered inhibitors like dasatinib and bosutinib, we employed an integrated machine learning and structure-based drug repurposing strategy to find novel, targeted, and non-toxic Src kinase inhibitors. Different machine learning models including random forest (RF), k-nearest neighbors (K-NN), decision tree, and support vector machine (SVM), were trained using already available bioactivity data of Src kinase targeting compounds.
View Article and Find Full Text PDFBrief Bioinform
November 2024
Department of Computer Science, Yonsei University, Yonsei-ro 50, Seodaemun-gu, 03722, Seoul, Republic of Korea.
Identifying new compounds that interact with a target is a crucial time-limiting step in the initial phases of drug discovery. Compound-protein complex structure-based affinity prediction models can expedite this process; however, their dependence on high-quality three-dimensional (3D) complex structures limits their practical application. Prediction models that do not require 3D complex structures for binding-affinity estimation offer a theoretically attractive alternative; however, accurately predicting affinity without interaction information presents significant challenges.
View Article and Find Full Text PDFBiophys Physicobiol
September 2024
Department of Biological Sciences, Faculty of Science and Engineering, Chuo University, Bunkyo-ku, Tokyo 112-8551, Japan.
Computerized molecular docking methodologies are pivotal in screening, a crucial facet of modern drug design. ChooseLD, a docking simulation software, combines structure- and ligand-based drug design methods with empirical scoring. Despite advancements in computerized molecular docking methodologies, there remains a gap in optimizing the predictive capabilities of docking simulation software.
View Article and Find Full Text PDFNat Commun
January 2025
Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, PR China.
The pursuit of obtaining enzymes with high activity and stability remains a grail in enzyme evolution due to the stability-activity trade-off. Here, we develop an isothermal compressibility-assisted dynamic squeezing index perturbation engineering (iCASE) strategy to construct hierarchical modular networks for enzymes of varying complexity. Molecular mechanism analysis elucidates that the peak of adaptive evolution is reached through a structural response mechanism among variants.
View Article and Find Full Text PDFMaterials (Basel)
January 2025
School of Civil Engineering, Central South University, Changsha 410075, China.
Small-section steel-shell concrete immersed tube tunnels are intended for minibuses and have a low fire heat release rate. Standard fire rise curves do not apply to such tunnels. In this study, a coupled method of computational fluid dynamics (CFD) and the finite element method (FEM) was used to simulate the structural temperature distribution in tunnels.
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