Peroxisome proliferator-activated receptor-γ (PPAR-γ) has emerged as one of the most extensively studied transcription factors since its discovery in 1990, highlighting its importance in the etiology and treatment of numerous diseases involving various types of cancer, type 2 diabetes mellitus, autoimmune, dermatological and cardiovascular disorders. Ligands are regarded as the key determinant for the tissue-specific activation of PPAR-γ. However, the mechanism governing this process is merely a contradictory debate which is yet to be systematically researched. Either these receptors get weakly activated by endogenous or natural ligands or leads to a direct over-activation process by synthetic ligands, serving as complete full agonists. Therefore, fine-tuning on the action of PPAR-γ and more subtle modulation can be a rewarding approach which might open new avenues for the treatment of several diseases. In the recent era, researchers have sought to develop safer partial PPAR-γ agonists in order to dodge the toxicity induced by full agonists, akin to a balanced activation. With a particular reference to cancer, this review concentrates on the therapeutic role of partial agonists, especially in cancer treatment. Additionally, a timely examination of their efficacy on various other disease-fate decisions has been also discussed.

Tetraspanins superfamily proteins have been shown to play an important role in several physiological processes and diseases such as cancer. Transmembrane polar residues of tetraspanins have an implication in regulating the process of cancer metastasis. Tetraspanin CD82 has been demonstrated to exert an anti-metastatic role while mutating polar residues in its transmembrane domains (TMDs) abrogates its metastasis inhibitory role. However, CD151, being pro-metastatic tetraspanin, the role of polar residues in TMDs of CD151 in cholesterol binding and its stability has not been investigated. In this study, we employed bioinformatics analysis of CD151 sequences to determine polar residue in TMDs, molecular docking, dynamics, and normal mode analysis studies to investigate the contribution of polar residues on cholesterol binding by modelling and comparing wild type with polar residue mutant of CD151. The study finds three polar residues viz Asn24, Glu104 and Gln234, in the TMD1, TMD2, and TMD4, respectively, in CD151 that are conserved in most vertebrates indicating their functional significance. Furthermore, we found that these polar residues are required for CD151 stability and facilitate stable interactions with cholesterol. Further cholesterol binding influenced the differences in the distance between large and small extracellular domains of CD151 indicating its important role in the open and closed conformation of the protein. In summary, CD151 exists in open and closed conformation by regulating its interactions with cholesterol. This interaction may modulate the cell membrane dynamics suggesting its potential implication in cancer metastasis.

The role of artificial intelligence (AI) has advanced from an analysis and prediction tool to extending human capabilities. Currently, AI is more of a reliable assistant fueled by human experience and need of the hour in the healthcare along with simplifying daily life. AI and Internet of Things (IoT) have opened new avenues in intelligent diagnostics, drug discovery, clinical decision support, enhancing physician-patient communication, transcribing medical documents, and remote treatment. With the advent of enhanced computational power, AI has revolutionized discovery of optimal and efficient healthcare solutions and has accelerated the development of smart solutions involving IoT-based technologies. Starting from telemedicine to predict possible health disorders, AI is gaining focus to facilitate and advance healthcare solutions in developed and underdeveloped countries. This chapter deals with the scope of AI in the present scenario to future developments as AI will soon surpass human and poses threat pertaining to misuse of cognitive sciences development.

MicroRNAs (miRNAs), a distinct category of non-coding RNAs, exert multifaceted regulatory functions in a variety of organisms, including humans, animals, and plants. The inventory of identified miRNAs stands at approximately 60,000 among all species and 1,926 in Homo sapiens manifests miRNA expression. Their theranostic role has been explored by researchers over the last few decades, positioning them as prominent therapeutic targets as our understanding of RNA targeting advances. However, the limited availability of experimentally determined miRNA structures has constrained drug discovery efforts relying on virtual screening or computational methods, including machine learning. To address this limitation, miRVim has been developed, providing a repository of human miRNA structures derived from both two-dimensional (MXFold2, CentroidFold, and RNAFold) and three-dimensional (RNAComposer and 3dRNA) structure prediction algorithms, in addition to experimentally available structures from the RCSB PDB repository. This data server aims to facilitate computational data analysis for drug discovery, opening new avenues for advancing technologies such as machine learning-based predictions in the field. The publicly accessible structures provided by miRVim, available at https://mirna.in/miRVim , offer a valuable resource for the research community, advancing the field of miRNA-related computational analysis and drug discovery.

Introduction: MicroRNAs (miRNAs), a distinct category of non-coding RNAs, exert multifaceted regulatory functions in a variety of organisms, including humans, animals, and plants. The inventory of identified miRNAs stands at approximately 60,000 among all species and 1,926 in Homo sapiens manifests miRNA expression. Their theranostic role has been explored by researchers over the last few decades, positioning them as prominent therapeutic targets as our understanding of RNA targeting advances. However, limited availability of experimentally determined miRNA structures has constrained drug discovery efforts relying on virtual screening or computational methods, including machine learning and artificial intelligence. Methods: To address this lacuna, miRVim has been developed, providing a repository of human miRNA structures derived from both two-dimensional (MXFold2, CentroidFold, and RNAFold) and three-dimensional (RNAComposer and 3dRNA) structure prediction algorithms, in addition to experimentally available structures from the RCSB PDB repository. Results: miRVim contains 13,971 predicted secondary structures and 17,045 predicted three-dimensional structures filling the gap of unavailability of miRNA structure data bank. This database aims to facilitate computational data analysis for drug discovery, opening new avenues for advancing technologies such as machine learning-based predictions in the field of RNA biology. Conclusion: The publicly accessible structures provided by miRVim, available at https://mirna.in/miRVim, offer a valuable resource for the research community, advancing the field of miRNA-related computational analysis and drug discovery.