Molecular Docking of Mangostin and Sinensetin Derivatives on SUR1-Pancreatic KATP Channel Target as Antidiabetic

Background: Diabetes Mellitus (DM) is a complex chronic disease characterized by increased blood glucose. The incidence of this disease is rising, especially type 2 diabetes which is caused by insulin resistance in the body. SUR1-Pancreatic KATP Channel is a receptor as an antidiabetic target because its inhibition process can increase insulin production so that it can reduce blood glucose in people with type 2 diabetes. Objective: This study aims to identify the in-silico activity of the SUR1-Pancreatic KATP Channel macromolecules. Methods: Identification of macromolecular binding sites using Protein Plus software, then carried out molecular docking using AutoDock software, where the formed molecular interactions are further identified using the BIOVIA Discovery Studio software. Results: After determining the macromolecular binding site, the RMSD value was 1.253, allowing for further molecular docking. Molecular docking showed that the Ligands of mangostin (α, β, γ-mangostin) and sinensetin derivatives had a good affinity, namely α-mangostin -6,31 kcal/mol; β-mangostin -5.78 kcal/mol; γ-mangostin -6.17 kcal/mol and sinensetin -4.75 kcal/mol. Conclusion: The affinity sequence in the docking process for the SUR1 KATP channel macromolecules is α-mangostin > γ-mangostin > β-mangostin > sinensetin. The highest affinity for the docking process on the macromolecule SUR1 KATP channel was α-mangostin with a value of ΔG -6.31 kcal/mol Ki 23.65 μM.

Evolution of abiotic cubane chemistries in a nucleic acid aptamer allows selective recognition of a malaria biomarker

Nucleic acid aptamers selected through systematic evolution of ligands by exponential enrichment (SELEX) fold into exquisite globular structures in complex with protein targets with diverse translational applications. Varying the chemistry of nucleotides allows evolution of nonnatural nucleic acids, but the extent to which exotic chemistries can be integrated into a SELEX selection to evolve nonnatural macromolecular binding interfaces is unclear. Here, we report the identification of a cubane-modified aptamer (cubamer) against the malaria biomarkerPlasmodium vivaxlactate dehydrogenase (PvLDH). The crystal structure of the complex reveals an unprecedented binding mechanism involving a multicubane cluster within a hydrophobic pocket. The binding interaction is further stabilized through hydrogen bonding via cubyl hydrogens, previously unobserved in macromolecular binding interfaces. This binding mechanism allows discriminatory recognition ofP. vivaxoverPlasmodium falciparumlactate dehydrogenase, thereby distinguishing these highly conserved malaria biomarkers for diagnostic applications. Together, our data demonstrate that SELEX can be used to evolve exotic nucleic acids bearing chemical functional groups which enable remarkable binding mechanisms which have never been observed in biology. Extending to other exotic chemistries will open a myriad of possibilities for functional nucleic acids.

Studying macromolecular interactions on the large scale : a bioinformatics approach

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Characterization of macromolecular interactions is not only critical for understanding how macromolecules perform their biological functions, such as promoting chemical reactions and acting as antibodies, but is also important for finding out molecular mechanisms behind the human diseases. Furthermore, the information of macromolecular binding is pivotal for elucidating metabolic, signal transduction, and other networks. Finally, our knowledge about macromolecular interactions may be critical in studying how complex genetic variations and alternative splicing affect the development and course of diseases such as cancer. Researchers are trying to understand the evolution and physics of macromolecular interactions by collecting and analyzing the interaction data, developing predictive models for characterization of macromolecular structure and function, and, applying the developed techniques to study specific biological systems or particular diseases. Some research methods like machine learning, statistical modeling and data mining based of the macromolecular interaction data derived from experimentally determined structures of macromolecule complexes are frequently used to discover the principle of interactions. Our work incorporates data mining, machine learning and statistical modeling methodology together into the location of macromolecular binding and establishes a comprehensive relational macromolecular database. Additionally, a sequence-based protein binding site prediction method was built using machine learning method and statistic model. This predictor intelligently integrates the information derived from the protein?s sequence and its homology model so that it can offer accurate predictions irrespective of the varying quality of comparative models. Our methods to analyze the mutations have been applied to studying the role of these interactions in diseases, like cancer.