KinasePhos 3.0:Redesign and Expansion of the Prediction on Kinase-specific Phosphorylation Sites

The purpose of this work is to enhance KinasePhos,a machine learning-based kinasespecific phosphorylation site prediction tool.Experimentally verified kinase-specific phosphorylation data were collected from PhosphoSitePlus,UniProtKB,the GPS 5.0,and Phospho.ELM.In total,41,421 experimentally verified kinase-specific phosphorylation sites were identified.A total of 1380 unique kinases were identified,including 753 with existing classification information from KinBase and the remaining 627 annotated by building a phylogenetic tree.Based on this kinase classification,a total of 771 predictive models were built at the individual,family,and group levels,using at least 15 experimentally verified substrate sites in positive training datasets.The improved models demonstrated their effectiveness compared with other prediction tools.For example,the prediction of sites phosphorylated by the protein kinase B,casein kinase 2,and protein kinase A families had accuracies of 94.5%,92.5%,and 90.0%,respectively.The average prediction accuracy for all 771 models was 87.2%.For enhancing interpretability,the SHapley Additive exPlanations(SHAP)method was employed to assess feature importance.The web interface of KinasePhos 3.0 has been redesigned to provide comprehensive annotations of kinase-specific phosphorylation sites on multiple proteins.Additionally,considering the large scale of phosphoproteomic data,a downloadable prediction tool is available at https://awi.cuhk.edu.cn/KinasePhos/download.html or https://github.com/tom-209/KinasePhos-3.0-executable-file.

Co-culture of tumor spheroids and monocytes in a collagen matrix-embedded microfluidic device to study the migration of breast cancer cells

Recapitulating the tumor microenvironment is a major challenge in the development of in vitro tumor model for the study of cancer biology and therapeutic treatments. 3D multicellular tumor spheroids (MCTS) have been used as reliable models of mimicking in vivo solid tumors. Macrophages and extracellular matrix (ECM), regarded as two key factors of the tumor microenvironment, play significant roles in tumor progression and drug resistance. In order to investigate their effects on tumor cell migration, a microfluidic chip-based 3D breast cancer model was developed by co-culturing monodisperse MCTS with monocytes in 3 D collagen matrix. A reversible bonding technique was employed for the fabrication of the microfluidic chip, which made it easier for MCTS formation and tailoring the MCTS co-culture conditions. When co-culturing monocytes with low invasive T47D spheroids or high invasive MD-MBA-231 spheroids, we found that T47 D cells with the stimulation of macrophage colony-stimulating factor (M-CSF) and MD-MBA-231 cells could polarize monocytes into tumor-associated macrophages (TAMs). The increased stiffness via increasing collagen concentration decreased tumor cell migration, whereas the presence of TAMs enhanced the migration ability of cells.Moreover, M-CSF-activated TAMs promoted the migration of T47 D tumor cells via the regulation of TGFβ1. Overall, this 3D co-culture microfluidic model may be useful for studying tumor progress and may offer a reliable and low-cost method for evaluation of drug efficiency.