Structural Basis of Signal Transduction within Environmental Sensing PAS Regulated Ser/Thr Kinases

To sense environmental cues, nature has evolved molecular switches that are widely used across life. This process often relies on environmentally controlled PPIs (Protein-Protein Interactions) within a sensory domain that senses various stimuli and alters the functional activities of downstream effectors. This study focused on the superfamily of PAS (Per-ARNT-Sim) environmental sensory domains and their roles in regulating two Ser/Thr Kinases (STKs). PAS domains achieve their regulatory control by internally binding small molecule ligands/cofactors (e.g., O2 binding to heme in FixL; flavins within the Light-Oxygen-Voltage (LOV)-type PAS domains). While aspects of PAS sensing are understood, the generality of signaling from the sensory to the effector domain and PPIs within full-length proteins or complexes remain elusive. To address these issues, we have investigated how the PAS domains of a human (hPASK) and plant phototropin (Avena sativa phototropin 1, AsPhot1) control effector STK function. Both proteins contain tandem sensory PAS domains (PAS-A & PAS-B in hPASK; LOV1 and LOV2 in AsPhot1) N-terminal to the STK domain. In hPASK, the PAS-A domain interacts and regulates the kinase activity, whereas in AsPhot1 LOV2 domain is the regulator of kinase function. hPASK is an important regulator of cellular energy states and metabolic pathways and has shown to be dysregulated in metabolic diseases (e.g., diabetes, obesity), while phototropins are important for plant physiology (e.g., phototropism, chloroplast relocation) in response to blue light. Studying the mechanism of signal transduction from the sensory PAS/LOV domains to the kinase domains of these proteins is significant for understanding potential commonalities among PAS domain signaling in addition to the regulatory mechanism of hPASK and AsPhot1 cellular pathways. To this end, I am using complementary biochemical and biophysical tools, including HDX-MS, cryo-EM, NMR, and X-ray crystallography to elucidate the structural dynamics of these proteins both at the domain and multi-domain levels. In hPASK, our high-pressure NMR and HDX-MS data with artificial ligands identified ligand binding site around the cavity of the PAS-A domain consistent with our previous observations. Additionally, we elucidate the mechanistic basis for the signal-regulated nuclear translocation of PASK. In our current study, we unexpectedly identified a novel intramolecular interaction involving the hPASK PAS-A domain and a short-linear PAS interacting motif (PIM) in the C-terminal region to the PAS-A and STK linker and important for nuclear localization of hPASK. Additionally, our NMR and modeling data supports PAS-A-PIM interaction occurs adjacent to the site where we have previously demonstrated small molecule binding to a cavity within the PAS domain, suggesting a novel allosteric control of intramolecular association in PASK. Using both PIM peptide and artificial ligands we were able to study the structure and dynamics of hPASK PAS-A PLIs and PPIs by highlighting important segment and residues that are crucial for these interactions. Taken together, our data provide avenues for precise metabolic control of hPASK subcellular distribution and catalytic activity. For AsPhot1, we used complementary approaches to unravel the dynamic interplay between the LOV2 and the STK domain, shedding light on the molecular basis of light-induced kinase activation. Limited proteolysis and Hydrogen Deuterium Exchange Mass Spectrometry (HDX-MS) unveiled differential conformational changes induced by light activation, particularly in the linker regions connecting the LOV2 and kinase domains. These changes propagated through Ja-LH1-LH2 linkers, connecting LOV2 to the STK domain and highlighting the role of the n-lobe of the STK in mediating these interactions with the LOV2 domain. Additionally, our Cryo-Electron Microscopy (cryo-EM) data revealed multiple conformational states of the dark-state LOV2-STK assembly, supporting the notion of light-induced interactions between the domains and their dynamic nature. Mutational studies corroborate the importance of these LOV2-STK interactions, with mutations disrupting the LOV2-STK interface resulting in constitutive activation or decreased kinase activity. Our study provides a comprehensive view of LOV2-STK dynamics and a mechanistic framework for understanding how light signals are transduced into kinase activation with implications for unraveling the broader role of LOV2-STK signaling in plant physiology, novel optogenetic tools development, and enhancing our understanding of other PAS-regulated kinases (e.g., hPASK in human). Overall, for our studies with hPASK opens up avenues for understanding the intricate regulatory mechanisms that govern cellular processes through ligand-regulated PAS domains, shedding light on the complex interplay between environmental cues, cellular signaling, and metabolic regulation. Additionally, our studies have illuminated the mechanism by which the LOV2-STK domain of AsPhot1 translates light signals into kinase activity, providing a foundation for further research that could have implications for understanding light-responsive signaling in a variety of biological systems including PAS-regulated STK in human (hPASK). The insights gained from our study offer potential applications in biotechnology and agriculture. Manipulating the LOV2-STK signaling pathway could provide a means to develop novel engineer light-responsive systems for controlling cellular processes in various contexts

Multiclass blood cancer classification using deep CNN with optimized features

Breast cancer, lung cancer, skin cancer, and blood malignancies such as leukemia and lymphoma are just a few instances of cancer, which is a collection of cells that proliferate uncontrollably within the body. Acute lymphoblastic leukemia is of one the significant form of malignancy. The hematologists frequently makes an oversight while determining a blood cancer diagnosis, which requires an excessive amount of time. Thus, this research reflects on a novel method for the grouping of the leukemia with the aid of the modern technologies like Machine Learning and Deep Learning. The proposed research pipeline is occupied into some interconnected parts like dataset building, feature extraction with pre-trained Convolutional Neural Network (CNN) architectures from each individual images of blood cells, and classification with the conventional classifiers. The dataset for this study is divided into two identical categories, Benign and Malignant, and then reshaped into four significant classes, each with three subtypes of malignant, namely, Benign, Early Pre-B, Pre-B, and Pro-B. The research first extracts the features from the individual images with CNN models and then transfers the extracted features to the features selections such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and SVC Feature Selectors along with two nature inspired algorithms like Particle Swarm Optimization (PSO) and Cat Swarm Optimization (CSO). After that, research has applied the seven Machine Learning classifiers to accomplish the multi-class malignant classification. To assess the efficacy of the proposed architecture a set of experimental data have been enumerated and interpreted accordingly. The study discovered a maximum accuracy of 98.43% when solely using pre-trained CNN and classifiers. Nevertheless, after incorporating PSO and CSO, the proposed model achieved the highest accuracy of 99.84% by integrating the ResNet50 CNN architecture, SVC feature selector, and LR classifiers. Although the model has a higher accuracy rate, it does have some drawbacks. However, the proposed model may also be helpful for real-world blood cancer classification