KinScan: AI-based rapid profiling of activity across the kinome.

Kinases play a vital role in regulating essential cellular processes, including cell cycle progression, growth, apoptosis, and metabolism, by catalyzing the transfer of phosphate groups from adenosing triphosphate to substrates. Their dysregulation has been closely associated with numerous diseases, including cancer development, making them attractive targets for drug discovery. However, accurately predicting the binding affinity between chemical compounds and kinase targets remains challenging due to the highly conserved structural similarities across the kinome. To address this limitation, we present KinScan, a novel computational approach that leverages large-scale bioactivity data and integrates the Multi-Scale Context Aware Transformer framework to construct a virtual profiling model encompassing 391 protein kinases. The developed model demonstrates exceptional prediction capability, distinguishing between kinases by utilizing structurally aligned kinase binding site features derived from multiple sequence alignment for fast and accurate predictions. Through extensive validation and benchmarking, KinScan demonstrated its robust predictive power and generalizability for large-scale kinome-wide profiling and selectivity, uncovering associations with specific diseases and providing valuable insights into kinase activity profiles of compounds. Furthermore, we deployed a web platform for end-to-end profiling and selectivity analysis, accessible at https://kinscan.drugonix.com/softwares/kinscan.

AiKPro: deep learning model for kinome-wide bioactivity profiling using structure-based sequence alignments and molecular 3D conformer ensemble descriptors.

The discovery of selective and potent kinase inhibitors is crucial for the treatment of various diseases, but the process is challenging due to the high structural similarity among kinases. Efficient kinome-wide bioactivity profiling is essential for understanding kinase function and identifying selective inhibitors. In this study, we propose AiKPro, a deep learning model that combines structure-validated multiple sequence alignments and molecular 3D conformer ensemble descriptors to predict kinase-ligand binding affinities. Our deep learning model uses an attention-based mechanism to capture complex patterns in the interactions between the kinase and the ligand. To assess the performance of AiKPro, we evaluated the impact of descriptors, the predictability for untrained kinases and compounds, and kinase activity profiling based on odd ratios. Our model, AiKPro, shows good Pearson's correlation coefficients of 0.88 and 0.87 for the test set and for the untrained sets of compounds, respectively, which also shows the robustness of the model. AiKPro shows good kinase-activity profiles across the kinome, potentially facilitating the discovery of novel interactions and selective inhibitors. Our approach holds potential implications for the discovery of novel, selective kinase inhibitors and guiding rational drug design.

Triton X-114 Fractionated Subcellular Proteome of Leptospira interrogans Shows Selective Enrichment of Pathogenic and Outer Membrane Proteins in the Detergent Fraction.

The Triton X-114-based solubilization and temperature-dependent phase separation of proteins is used for subcellular fractionation where, aqueous, detergent, and pellet fractions represents cytoplasmic, outer membrane (OM), and inner membrane proteins, respectively. Mass spectrometry-based proteomic analysis of Triton X-114 fractions of proteomic analysis of Leptospira interrogans identified 2957 unique proteins distributed across the fractions. The results are compared with bioinformatics predictions on their subcellular localization and pathogenic nature. Analysis of the distribution of proteins across the Triton X-114 fractions with the predicted characteristics is performed based on "number" of unique type of proteins, and "quantity" which represents the amount of unique protein. The highest number of predicted outer membrane proteins (OMPs) and pathogenic proteins are found in aqueous and pellet fractions, whereas detergent fraction representing the OM has the highest quantity of OMPs and pathogenic proteins though lower in number than the aqueous and pellet fractions. This leaves the possibility of an upsurge in pathogenic proteins and OMPs on the OM under pathogenic conditions suggesting their potential use to combat leptospirosis. Further, the Triton X-114 subcellular fractions are more correlated to enrichment of pathogenic proteins predicted by MP3 software than predicted localization.

Identification of Hub Genes and Pathways in Zika Virus Infection Using RNA-Seq Data: A Network-Based Computational Approach.

Zika virus (ZIKV), a single-strand RNA flavivirus, is transmitted primarily through Aedes aegypti. The recent outbreaks in America and unexpected association between ZIKV infection and birth defects have triggered the global attention. This vouches to understand the molecular mechanisms of ZIKV infection to develop effective drug therapy. A systems-level understanding of biological process affected by ZIKV infection in fetal brain sample led us to identify the candidate genes for pharmaceutical intervention and potential biomarkers for diagnosis. To identify the key genes, transcriptomics data (RNA-Seq) with GSE93385 of ZIKV (Strain: MR766) infected human fetal neural stem cell are analyzed. In total, 1,084 differentially expressed genes (DEGs) are identified, that is, 471 upregulated and 613 downregulated genes. Further analysis such as the gene ontology term suggested that the downregulated genes are mostly enriched in defense response to virus, receptor binding, laminin binding, extracellular matrix, endoplasmic reticulum, and for upregulated DEGs: translation initiation, RNA binding, cytosol, and nucleosome are enriched. And through pathway analysis, systemic lupus erythematosus (SLE) is found to be the most enriched pathway. Protein-protein interaction (PPI) network is constructed to find the hub genes using STRING database. The seven key genes namely cyclin-dependent kinase 1 (CDK1), cyclin B1 (CCNB1), histone cluster 1 H2B family member K, (HIST1H2BK) histone cluster 1 H2B family member O (HIST1H2BO), and histone cluster 1 H2B family member B (HIST1H2BB), polo-like kinase 1 (PLK1), and cell division cycle 20 (CDC20) with highest degree are found to be hub genes using Centiscape, a Cytoscape plugin. The modules of PPI network using Molecular Complex Detection plugin are found significant in structural constituent of ribosome, defense response to virus, nucleosome, SLE, extracellular region, and regulation of gene silencing. Thus, identified key hub genes and pathways shed light on molecular mechanism that may contribute to the discovery of novel therapeutic targets and development of new strategies for the intervention of ZIKV disease.

Comparative proteome analysis reveals pathogen specific outer membrane proteins of Leptospira.

Proteomes of pathogenic Leptospira interrogans and L. borgpetersenii and the saprophytic L. biflexa were filtered through computational tools to identify Outer Membrane Proteins (OMPs) that satisfy the required biophysical parameters for their presence on the outer membrane. A total of 133, 130, and 144 OMPs were identified in L. interrogans, L. borgpetersenii, and L. biflexa, respectively, which forms approximately 4% of proteomes. A holistic analysis of transporting and pathogenic characteristics of OMPs together with Clusters of Orthologous Groups (COGs) among the OMPs and their distribution across 3 species was made and put forward a set of 21 candidate OMPs specific to pathogenic leptospires. It is also found that proteins homologous to the candidate OMPs were also present in other pathogenic species of leptospires. Six OMPs from L. interrogans and 2 from L. borgpetersenii observed to have similar COGs while those were not found in any intermediate or saprophytic forms. These OMPs appears to have role in infection and pathogenesis and useful for anti-leptospiral strategies.

ZikaBase: An integrated ZIKV- Human Interactome Map database.

Re-emergence of ZIKV has caused infections in more than 1.5 million people. The molecular mechanism and pathogenesis of ZIKV is not well explored due to unavailability of adequate model and lack of publically accessible resources to provide information of ZIKV-Human protein interactome map till today. This study made an attempt to curate the ZIKV-Human interaction proteins from published literatures and RNA-Seq data. 11 direct interaction, 12 associated genes are retrieved from literatures and 3742 Differentially Expressed Genes (DEGs) are obtained from RNA-Seq analysis. The genes have been analyzed to construct the ZIKV-Human Interactome Map. The importance of the study has been illustrated by the enrichment analysis and observed that direct interaction and associated genes are enriched in viral entry into host cell. Also, ZIKV infection modulates 32% signal and 27% immune system pathways. The integrated database, ZikaBase has been developed to help the virology research community and accessible at https://test5.bicpu.edu.in.