Background:CXCL12 is a critical chemokine involved in immune cell trafficking and tumor metastasis through its interaction with CXCR4 and CXCR7. The C55Y mutation in CXCL12 is hypothesized to disrupt its structure and function. Unlike previously reported CXCL12 mutations that affect transcriptional regulation or single-receptor binding, C55Y targets a conserved cysteine residue essential for a structural disulfide bridge, potentially inducing unique global destabilization. This study investigates the molecular consequences of the C55Y mutation using computational tools. Objectives:This study aimed to evaluate the structural and functional impact of the C55Y mutation in CXCL12, particularly its interactions with CXCR4 and CXCR7, and its relevance to prostate cancer pathogenesis. Methods:Molecular dynamics simulations (MDS), evolutionary conservation analysis, and molecular docking with CXCR4 and CXCR7 receptors were performed to assess the impact of the C55Y mutation. Tools such as I-TASSER, Meta-SNP, and PolyPhen-2 were used to analyze structural stability, while HDOCK and Schrödinger simulations assessed receptor binding. RMSD and RMSF calculations were used to evaluate protein dynamics. Results:The C55Y mutation induces significant structural instability in CXCL12, with increased RMSD and altered secondary structure. Molecular dynamics simulation further demonstrated the stability of the complexes through RMSD, RMSF, hydrogen bond, and Radius of Gyration (RoG) analyses, where the normal complexes exhibited comparatively stable structural compactness during the simulation period. Docking studies showed reduced binding affinity to CXCR4 and CXCR7, indicating disrupted receptor interactions. Notably, this dual-receptor binding loss distinguishes C55Y from other mutations that typically impair only CXCR4 signaling. These findings suggest that the mutation impairs CXCL12's function in prostate cancer, metastasis of prostate cancer, and immune response. Conclusion:The C55Y mutation in CXCL12 disrupts its structural integrity and receptor binding, uniquely compromising both the CXCR4 and CXCR7 pathways. This highlights its potential role in prostate cancer progression through mechanisms distinct from previously characterized CXCL12 variants. This study provides insights into the molecular mechanisms of CXCL12-mediated signaling and its implications for future therapeutic strategies targeting chemokine signaling to inhibit prostate cancer.
Background: Prime mass amino acids residues, assigned based upon the nominal mass of their repeating structure in peptides and proteins, have been found to occur more often than by chance. These 9 such residues are predominantly hydrophobic in character and thus play an important role in protein stability and folding. This study investigates their prevalence in human proteins and the function of such proteins. Objectives: To identify human proteins rich in the prime mass residues and assess their functions. Results: Prime-rich proteins are found to proliferate among those important to transcriptional events, cell adhesion, and the formation of cell surface membranes. Eighteen prime-rich proteins are identified that are abundant in 3 or 4 prime residues, the latter notably contain alanine, cysteine, histidine or threonine, and proline. The percentage of prime residues in these prime-rich proteins exceeds one-half (>50%) of all residues in many cases, and these residues are found to be concentrated in particular regions of the protein. Conclusion: Proteins rich in prime residues are found to play an important role in biological events important to cellular life. Such prime residues represent those associated with the highest hydrophobicity, and those essential to protein folding and function. Many prime residues were recruited early into the genetic code according to the composition of the last universal common ancestor (LUCA) proteins. Consideration is given to whether the formation of evolution of these proteins is predicated by the primality of the residues or their physicochemical and structural properties. This remains an open question and awaits further inquiry and debate.
Background: Severe dengue (SD) represents a life-threatening progression of dengue virus infection. Early identification of patients at risk of transitioning from dengue fever (DF) to SD remains a major clinical challenge. Unraveling the transcriptomic changes underlying this progression may aid in developing timely therapeutic interventions. Methods: RNA-seq datasets comprising 103 samples (62 SD and 41 DF) were retrieved from the GEO repository. Following normalization using DESeq2, differentially expressed genes (DEGs) were identified between the 2 disease stages. Functional enrichment analysis was performed to uncover dysregulated biological processes. A hybrid computational framework combining classical machine learning (Logistic Regression, Support Vector Machine, and Random Forest) and deep learning models (Artificial Neural Network, Convolutional Neural Network, and Transformer-based architectures) were applied to classify SD and DF samples. Model performance was evaluated using ROC-AUC and balanced accuracy metrics. Results: Differential expression analysis identified 55 significantly dysregulated genes distinguishing severe dengue from dengue fever. These genes were enriched in pathways related to metal ion homeostasis, platelet signaling, ferroptosis, and oxidative stress. Among multiple machine learning and deep learning models, the Transformer-CNN achieved the best performance (test AUC = 0.85; balanced accuracy = 0.89). SHAP-based interpretation highlighted ILDR2, TCP1, HNRNPUL1, SEC14L5, ATP2C2, LOXL3, ACVRL1, STEAP3, and ST8SSIA5 as key discriminative features. Integrated network analyses further implicated coordinated regulation of iron metabolism, calcium signaling, and platelet dysfunction in severe dengue. Conclusion: This study integrates RNA-seq and hybrid Artificial Intelligence modeling to identify transcriptomic signatures associated with dengue severity. The study highlights candidate genes and pathways that provide a hypothesis-generating foundation; further increasing the sample size and experimental validation will support early risk stratification in severe dengue.
Objective: Mycobacterium leprae causes leprosy, an infectious disease that has persisted over centuries and is still an issue in public health in many nations. Diverse bioinformatics techniques have been effectively employed to annotate the functions of hypothetical proteins (HPs) originating from different pathogenic bacteria. The objective of the current research was to elucidate the functions of an HP obtained from M. leprae.Methods: A variety of in silico tools were utilized to make predictions regarding the structure and function of this protein. To identify homologous proteins, the BLASTp program was used to search for sequence similarity across the available biological databases. Additionally, using the proper bioinformatics methods, a number of properties were determined, including physicochemical characteristics, subcellular localization, phylogenetic analysis, functional annotation, pathway analysis, protein-protein interaction, secondary and tertiary structure determination, active site detection, quality assessment analysis, molecular docking, pharmacokinetic and toxicity profiling, and further molecular dynamics simulations.Results: The HP exhibited putative biological activity associated with a conserved functional domain, the CT_C_D superfamily domain. The allophanate hydrolase activity of the chosen HP was predicted by the functional annotation. Pathway analysis demonstrated the protein's involvement in cellular and metabolic processes. Numerous functional partners that play a crucial role in bacterial survival were identified through the chosen HP's protein-protein interactions. Furthermore, active site prediction and molecular docking analysis of the HP with ligands indicated that it could be a therapeutic target for M. leprae. ADMET analysis indicated that the selected compound has favorable bioavailability, drug-likeness, and safety. The stability of these complexes was verified by molecular dynamics simulations, which suggests they have therapeutic potential.Conclusion: This study emphasizes the effectiveness of in silico methods in predicting the biological functions of HP and generating hypotheses for potential therapeutic targets.
