REMEMProt: a resource of membrane-enriched proteome profiles, their disease associations, and biomarker status.

The differential expression of plasma membrane proteins is integrally analyzed for their diagnosis, prognosis, and therapeutic applications in diverse clinical manifestations. Necessarily, distinct membrane protein enrichment methods and mass spectrometry platforms are employed for their global and relative quantitation. First of its kind to explore, we compiled membrane-associated proteomes in human and mouse systems into a database named, Resource of Experimental Membrane-Enriched Mass spectrometry-derived Proteome (REMEMProt). It currently hosts 14,626 proteins (9,507 proteins in Homo sapiens; 5,119 proteins in Mus musculus) with information on their membrane-protein enrichment methods, experimental/physiological context of detection in cells or tissues, transmembrane domain analysis, and their current attribution as biomarkers. Based on these annotations and the transmembrane domain analysis in proteins or their binary/complex protein-protein interactors, REMEMProt facilitates the assessment of the plasma membrane localization potential of proteins through batch query. A cross-study enrichment analysis platform is enabled in REMEMProt for comparative analysis of proteomes using novel/modified membrane enrichment methods and evaluation of methods for targeted enrichment of membrane proteins. REMEMProt data are made freely accessible to explore and download at https://rememprot.ciods.in/.

Tyr352 as a Predominant Phosphosite in the Understudied Kinase and Molecular Target, HIPK1: Implications for Cancer Therapy.

Homeodomain-interacting protein kinase 1 (HIPK1) is majorly found in the nucleoplasm. HIPK1 is associated with cell proliferation, tumor necrosis factor-mediated cellular apoptosis, transcription regulation, and DNA damage response, and thought to play significant roles in health and common diseases such as cancer. Despite this, HIPK1 remains an understudied molecular target. In the present study, based on a systematic screening and mapping approach, we assembled 424 qualitative and 44 quantitative phosphoproteome datasets with 15 phosphosites in HIPK1 reported across multiple studies. These HIPK1 phosphosites were not currently attributed to any functions. Among them, Tyr352 within the kinase domain was identified as the predominant phosphosite modulated in 22 differential datasets. To analyze the functional association of HIPK1 Tyr352, we first employed a stringent criterion to derive its positively and negatively correlated protein phosphosites. Subsequently, we categorized the correlated phosphosites in known interactors, known/predicted kinases, and substrates of HIPK1, for their prioritized validation. Bioinformatics analysis identified their significant association with biological processes such as the regulation of RNA splicing, DNA-templated transcription, and cellular metabolic processes. HIPK1 Tyr352 was also identified to be upregulated in Her2+ cell lines and a subset of pancreatic and cholangiocarcinoma tissues. These data and the systems biology approach undertaken in the present study serve as a platform to explore the functional role of other phosphosites in HIPK1, and by extension, inform cancer drug discovery and oncotherapy innovation. In all, this study highlights the comprehensive phosphosite map of HIPK1 kinase and the first of its kind phosphosite-centric analysis of HIPK1 kinase based on global-level phosphoproteomics datasets derived from human cellular differential experiments across distinct experimental conditions.

A global phosphosite-correlated network map of Thousand And One Kinase 1 (TAOK1).

Thousand and one amino acid kinase 1 (TAOK1) is a sterile 20 family Serine/Threonine kinase linked to microtubule dynamics, checkpoint signaling, DNA damage response, and neurological functions. Molecular-level alterations of TAOK1 have been associated with neurodevelopment disorders and cancers. Despite their known involvement in physiological and pathophysiological processes, and as a core member of the hippo signaling pathway, the phosphoregulatory network of TAOK1 has not been visualized. Aimed to explore this network, we first analyzed the predominantly detected and differentially regulated TAOK1 phosphosites in global phosphoproteome datasets across diverse experimental conditions. Based on 709 qualitative and 210 quantitative differential cellular phosphoproteome datasets that were systematically assembled, we identified that phosphorylation at Ser421, Ser9, Ser965, and Ser445 predominantly represented TAOK1 in almost 75% of these datasets. Surprisingly, the functional role of all these phosphosites in TAOK1 remains unexplored. Hence, we employed a robust strategy to extract the phosphosites in proteins that significantly correlated in expression with predominant TAOK1 phosphosites. This led to the first categorization of the phosphosites including those in the currently known and predicted interactors, kinases, and substrates, that positively/negatively correlated with the expression status of each predominant TAOK1 phosphosites. Subsequently, we also analyzed the phosphosites in core proteins of the hippo signaling pathway. Based on the TAOK1 phosphoregulatory network analysis, we inferred the potential role of the predominant TAOK1 phosphosites. Especially, we propose pSer9 as an autophosphorylation and TAOK1 kinase activity-associated phosphosite and pS421, the most frequently detected phosphosite in TAOK1, as a significant regulatory phosphosite involved in the maintenance of genome integrity. Considering that the impact of all phosphosites that predominantly represent each kinase is essential for the efficient interpretation of global phosphoproteome datasets, we believe that the approach undertaken in this study is suitable to be extended to other kinases for accelerated research.

Computational discovery of novel FYN kinase inhibitors: a cheminformatics and machine learning-driven approach to targeted cancer and neurodegenerative therapy.

In this study, we explored the potential of novel inhibitors for FYN kinase, a critical target in cancer and neurodegenerative disorders, by integrating advanced cheminformatics, machine learning, and molecular simulation techniques. Our approach involved analyzing key interactions for FYN inhibition using established multi-kinase inhibitors such as Staurosporine, Dasatinib, and Saracatinib. We utilized ECFP4 circular fingerprints and the t-SNE machine learning algorithm to compare molecular similarities between FDA-approved drugs and known clinical trial inhibitors. This led to the identification of potential inhibitors, including Afatinib, Copanlisib, and Vandetanib. Using the DrugSpaceX platform, we generated a vast library of 72,196 analogues from these leads, which after careful refinement, resulted in 6008 promising candidates. Subsequent clustering identified 48 analogues with significant similarity to known inhibitors. Notably, two candidates derived from Vandetanib, DE27123047 and DE27123035, exhibited strong docking affinities and stable binding in molecular dynamics simulations. These candidates showed high potential as effective FYN kinase inhibitors, as evidenced by MMGBSA calculations and MCE-18 scores exceeding 50. Additionally, our exploration into their molecular architecture revealed potential modification sites on the quinazolin-4-amine scaffold, suggesting opportunities for strategic alterations to enhance activity and optimize ADME properties. Our research is a pioneering effort in drug discovery, unveiling novel candidates for FYN inhibition and demonstrating the efficacy of a multi-layered computational strategy. The molecular insights gained provide a pathway for strategic refinements and future experimental validations, setting a new direction in targeted drug development against diseases involving FYN kinase.

Cisplatin and Procaterol Combination in Gastric Cancer? Targeting Checkpoint Kinase 1 for Cancer Drug Discovery and Repurposing by an Integrated Computational and Experimental Approach.

Checkpoint kinase 1 (CHK1), a serine/threonine kinase, plays a crucial role in cell cycle arrest and is a promising therapeutic target for drug development against cancers. CHK1 coordinates cell cycle checkpoints in response to DNA damage, facilitating repair of single-strand breaks, and maintains the genome integrity in response to replication stress. In this study, we employed an integrated computational and experimental approach to drug discovery and repurposing, aiming to identify a potent CHK1 inhibitor among existing drugs. An e-pharmacophore model was developed based on the three-dimensional crystal structure of the CHK1 protein in complex with CCT245737. This model, characterized by seven key molecular features, guided the screening of a library of drugs through molecular docking. The top 10% of scored ligands were further examined, with procaterol emerging as the leading candidate. Procaterol demonstrated interaction patterns with the CHK1 active site similar to CHK1 inhibitor (CCT245737), as shown by molecular dynamics analysis. Subsequent in vitro assays, including cell proliferation, colony formation, and cell cycle analysis, were conducted on gastric adenocarcinoma cells treated with procaterol, both as a monotherapy and in combination with cisplatin. Procaterol, in synergy with cisplatin, significantly inhibited cell growth, suggesting a potentiated therapeutic effect. Thus, we propose the combined application of cisplatin and procaterol as a novel potential therapeutic strategy against human gastric cancer. The findings also highlight the relevance of CHK1 kinase as a drug target for enhancing the sensitivity of cytotoxic agents in cancer.

Computational tools for exploring peptide-membrane interactions in gram-positive bacteria.

The vital cellular functions in Gram-positive bacteria are controlled by signaling molecules known as quorum sensing peptides (QSPs), considered promising therapeutic interventions for bacterial infections. In the bacterial system QSPs bind to membrane-coupled receptors, which then auto-phosphorylate and activate intracellular response regulators. These response regulators induce target gene expression in bacteria. One of the most reliable trends in drug discovery research for virulence-associated molecular targets is the use of peptide drugs or new functionalities. In this perspective, computational methods act as auxiliary aids for biologists, where methodologies based on machine learning and in silico analysis are developed as suitable tools for target peptide identification. Therefore, the development of quick and reliable computational resources to identify or predict these QSPs along with their receptors and inhibitors is receiving considerable attention. The databases such as Quorumpeps and Quorum Sensing of Human Gut Microbes (QSHGM) provide a detailed overview of the structures and functions of QSPs. The tools and algorithms such as QSPpred, QSPred-FL, iQSP, EnsembleQS and PEPred-Suite have been used for the generic prediction of QSPs and feature representation. The availability of compiled key resources for utilizing peptide features based on amino acid composition, positional preferences, and motifs as well as structural and physicochemical properties, including biofilm inhibitory peptides, can aid in elucidating the QSP and membrane receptor interactions in infectious Gram-positive pathogens. Herein, we present a comprehensive survey of diverse computational approaches that are suitable for detecting QSPs and QS interference molecules. This review highlights the utility of these methods for developing potential biomarkers against infectious Gram-positive pathogens.

A network map of cytoskeleton-associated protein 4 (CKAP4) mediated signaling pathway in cancer.

Cytoskeleton-associated protein 4 (CKAP4) is a non-glycosylated type II transmembrane protein that serves as a cell surface-activated receptor. It is expressed primarily in the plasma membranes of bladder epithelial cells, type II alveolar pneumocytes, and vascular smooth muscle cells. CKAP4 is involved in various biological activities including cell proliferation, cell migration, keratinocyte differentiation, glycogenesis, fibrosis, thymic development, cardiogenesis, neuronal apoptosis, and cancer. CKAP4 has been described as a pro-tumor molecule that regulates the progression of various cancers, including lung cancer, breast cancer, esophageal squamous cell carcinoma, hepatocellular carcinoma, cervical cancer, oral cancer, bladder cancer, cholangiocarcinoma, pancreatic cancer, myeloma, renal cell carcinoma, melanoma, squamous cell carcinoma, colorectal cancer, and osteosarcoma. CKAP4 and its isoform bind to DKK1 or DKK3 (Dickkopf proteins) or antiproliferative factor (APF) and regulates several downstream signaling cascades. The CKAP4 complex plays a crucial role in regulating the signaling pathways including PI3K/AKT and MAPK1/3. Recently, CKAP4 has been recognized as a potential target for cancer therapy. Due to its biomedical importance, we integrated a network map of CKAP4. The available literature on CKAP4 signaling was manually curated according to the NetPath annotation criteria. The consolidated pathway map comprises 41 activation/inhibition events, 21 catalysis events, 35 molecular associations, 134 gene regulation events, 83 types of protein expression, and six protein translocation events. CKAP4 signaling pathway map data is freely accessible through the WikiPathways Database ( https://www.wikipathways.org/index.php/Pathway:WP5322 ). Generation of CKAP4 signaling pathway map.

A network map of GDNF/RET signaling pathway in physiological and pathological conditions.

Glial cell line-derived neurotrophic factor (GDNF) signals through a multi-component receptor system predominantly consisting of glycosyl-phosphatidylinositol-anchored GDNF family receptor alpha-1 (GFRα1) and the Rearranged during transfection (RET) receptor tyrosine kinase. GDNF/RET signaling is vital to the central and peripheral nervous system, kidney morphogenesis, and spermatogenesis. In addition, the dysregulation of the GDNF/RET signaling has been implicated in the pathogenesis of cancers. Despite the extensive research on GDNF/RET signaling, a molecular network of reactions induced by GDNF reported across the published literature. However, a comprehensive GDNF/RET pathway resource is currently unavailable. We describe an integrated signaling pathway reaction map of GDNF/RET consisting of 1151 molecular reactions. These include information pertaining to 52 molecular association events, 70 enzyme catalysis events, 36 activation/inhibition events, 22 translocation events, 856 gene regulation events, and 115 protein-level expression events induced by GDNF in diverse cell types. We developed a comprehensive GDNF/RET signaling network map based on these molecular reactions. The pathway map was made accessible through WikiPathways database ( https://www.wikipathways.org/index.php/Pathway:WP5143 ). Biocuration and development of gene regulatory network map of GDNF/RET signaling pathway.