Current Topics in Medicinal Chemistry - Volume 22, Issue 21, 2022

Opuntia ficus-indica (L.) Mill. (OFI) is a plant with numerous beneficial properties known in traditional medicine. It has been a domesticated plant in Latin America, Africa, Mediterranean countries, the Middle East, India and Australia. Nowadays, the research concentrates on natural compounds to lower costs and the possible side effects of synthetic compounds. The use of nutraceuticals, bioactive compounds of vegetable origin with important nutritional values, is encouraged. OFI has shown numerous activities due to its high content of antioxidants, including flavonoids and ascorbate, pigments, carotenoids and betalains, phenolic acids and other phytochemical components, such as biopeptides and soluble fibers. The most important effects of OFI are represented by the activity against acne, arthrosis, dermatosis, diabetes, diarrhea, fever, high blood pressure, prostatitis, rheumatism, stomachache, tumor, wart, allergy, wound, colitis and some viral diseases. Moreover, a promising role has been suggested in inflammatory bowel disease, colitis and metabolic syndrome. The most recent studies addressed the role of OFI in preventing and treating COVID-19 disease. In light of the above, this review summarizes the biological activities and health benefits that this plant may exert.

Compounds isolated from natural sources have been used for medicinal purposes for many centuries. Some metabolites of plants and microorganisms possess properties that would make them effective treatments against bacterial infection, inflammation, cancer, and an array of other medical conditions. In addition, natural compounds offer therapeutic approaches with lower toxicity compared to most synthetic analogues. However, it is challenging to identify and isolate potential drug candidates without specific information about structural specificity and limited knowledge of any specific physiological pathways in which they are involved. To solve this problem and find a way to efficiently utilize natural sources for the screening of compounds candidates, technologies, such as next-generation sequencing, bioinformatics techniques, and molecular analysis systems, should be adapted for screening many chemical compounds. Molecular techniques capable of performing analysis of large datasets, such as whole-genome sequencing and cellular protein expression profile, have become essential tools in drug discovery. OMICs, as genomics, proteomics, and metabolomics, are often used in targeted drug discovery, isolation, and characterization. This review summarizes technologies that are effective in natural source drug discovery and aid in a more precisely targeted pharmaceutical approach, including RNA interference or CRISPR technology. We strongly suggest that a multidisciplinary effort utilizing novel molecular tools to identify and isolate active compounds applicable for future drug discovery and production must be enhanced with all the available computational tools.

Membrane proteins (MPs) play an essential role in a broad range of cellular functions, serving as transporters, enzymes, receptors, and communicators, and about ~60% of membrane proteins are primarily used as drug targets. These proteins adopt either α-helical or β-barrel structures in the lipid bilayer of a cell/organelle membrane. Mutations in membrane proteins alter their structure and function, and may lead to diseases. Data on disease-causing and neutral mutations in membrane proteins are available in MutHTP and TMSNP databases, which provide additional features based on sequence, structure, topology, and diseases. These databases have been effectively utilized for analysing sequence and structure-based features in disease-causing and neutral mutations in membrane proteins, exploring disease-causing mechanisms, elucidating the relationship between sequence/structural parameters and diseases, and developing computational tools. Further, machine learning-based tools have been developed for identifying disease-causing mutations using diverse features, such as evolutionary information, physicochemical properties, atomic contacts, contact potentials, and the contribution of different energetic terms. These membrane protein-specific tools are helpful in characterizing the effect of new variants in the whole human membrane proteome. In this review, we provide a discussion of the available databases for disease-causing mutations in membrane proteins, followed by a statistical analysis of membrane protein mutations using sequence and structural features. In addition, available prediction tools for identifying disease-causing and neutral mutations in membrane proteins will be described with their performances. This comprehensive review provides deep insights into designing mutation-specific strategies for different diseases.

The outbreak of the SARS-CoV-2 virus in late 2019 and the spread of the COVID-19 pandemic have caused severe health and socioeconomic damage worldwide. Despite the significant research effort to develop vaccines, antiviral treatments, and repurposed therapeutics to effectively contain the catastrophe, there are no available effective vaccines or antiviral drugs that can limit the threat of the disease, so the infections continue to expand. To date, the search for effective treatment remains a global challenge. Therefore, it is imperative to develop therapeutic strategies to contain the spread of SARS-CoV-2. Like other coronaviruses, SARS-CoV-2 invades and infects human host cells via the attachment of its spike envelope glycoprotein to the human host cell receptor hACE2. Subsequently, several host cell proteases facilitate viral entry via proteolytic cleavage and activation of the S protein. These host cell proteases include type II transmembrane serine proteases (TTSPs), cysteine cathepsins B and L, furin, trypsin, and Factor Xa, among others. Given the critical role of the host cell proteases in coronavirus pathogenesis, their inhibition by small molecules has successfully targeted SARS-CoV-2 in vitro, suggesting that host cell proteases are attractive therapeutic targets for SARS-CoV-2 infection. In this review, we focus on the biochemical properties of host cell proteases that facilitate the entry of SARS-CoV-2, and we highlight therapeutic small molecule candidates that have been proposed through in silico research.

Breast cancer is the most predominantly occurring cancer in the world. Several genes and proteins have been recently studied to predict biomarkers that enable early disease identification and monitor its recurrence. In the era of high-throughput technology, studies show several applications of big data for identifying potential biomarkers. The review aims to provide a comprehensive overview of big data analysis in breast cancer towards the prediction of biomarkers with emphasis on computational methods like text mining, network analysis, next-generation sequencing technology (NGS), machine learning (ML), deep learning (DL), and precision medicine. Integrating data from various computational approaches enables the stratification of cancer patients and the identification of molecular signatures in cancer and their subtypes. The computational methods and statistical analysis help expedite cancer prognosis and develop precision cancer medicine (PCM). As a part of case study in the present work, we constructed a large gene-drug interaction network to predict new biomarkers genes. The gene-drug network helped us to identify eight genes that could serve as novel potential biomarkers.