Heterocyclic-Based Analogues against Sarcine-Ricin Loop RNA from Escherichia coli: In Silico Molecular Docking Study and Machine Learning Classifiers

Abstract

aims: To develop potential antibacterial drugs using molecular docking and machine learning approach background: A significant portion of organic chemistry, or about two-thirds of all organic substances, is devoted to heterocyclic chemistry. Carbocyclic is an organic cyclic compound that has all its carbon atoms arranged in rings. A large variety of heterocyclic compounds are designed and synthesized. The heterocyclic compounds are those cyclic molecules where one or more of the ring carbons are replaced by nitrogen, oxygen, sulfur etc. Heterocycles contain nitrogen atoms such as quinolines, indoles, pyrazine, isoindole, pyrrole, pyridine, imidazole, azocine, thiazoles, etc. (Figure 1). Synthesis is always a desirable field in organic chemistry since it demonstrates a variety of biological activities. Due to their diverse biodynamic properties, quinoline, indole, and their derivatives have a special place in the chemistry of nitrogen-containing heterocyclic molecules. The significance of indole can be documented both by the ever increasing number of publications (more than 80,000 in the 20th century) that target chemistry and by its presence in pharmaceuticals, fragrances, agrochemicals, pigments, material science, organic electronics, and natural products. objective: 1. To find out the potential protein responsible for antibacterial activity. 2. To study the interaction study of heterocyclic compounds with specific protein. 3. To optimize the molecular interaction through machine learning approach. method: Molecular docking study and Machine learning approach result: we examine the molecular coupling of drugs with heterocyclic compounds against the E. coli Sarcin-Ricin Loop RNA with an alteration of C-2667-2'-OCF3 (PDB ID: 6ZYB). These compounds in silico molecular docking analysis showed that they exhibit strong binding affinities, adequate residual interactions, and hydrogen bonding interactions with the protein Sarcin-Ricin Loop RNA from E. coli with a C-2667-2'-OCF3 alteration, indicating potential bioactivity. The binding affinity value for heterocyclic compounds 1-9 is -5.3 to -10.1 Kcal/mol. Many residues exhibit interactions with heterocyclic molecules, according to the findings of this study. Some of the identified amino acids are A:G2648, A:C2649, A:A2670, A:G2671, A:G2669, A:U2650, A:QSK2667, A:G2668, A:C2651, A:C2652, A:U2653, A:C2666, A:A2665 A:QSK2667, A:U2672, A:U2650 & A:A2654 many more. The Machine learning tool is also used to choose the best analysis of molecular descriptors. For these classifiers, molecular descriptor dataset is taken, which shows that training accuracy and testing accuracy is very high and crucial for developing similar antibacterial drugs in near future. conclusion: In the near future, powerful antibacterial treatments could be developed using heterocyclic compounds, which shows how useful it is to do research to identify potential effective antibiotic drugs. In this investigation, many software programmes, including AutoDock vina 4 & discovery studio, were employed to analyse the interaction between ligand and protein. In this paper, we examine the molecular coupling of drugs with heterocyclic compounds against the E. coli Sarcin-Ricin Loop RNA with an alteration of C-2667-2'-OCF3 (PDB ID: 6ZYB). These compounds in silico molecular docking analysis showed that they exhibit strong binding affinities, adequate residual interactions, and hydrogen bonding interactions with the protein Sarcin-Ricin Loop RNA from E. coli with a C-2667-2'-OCF3 alteration, indicating potential bioactivity. The binding affinity value for heterocyclic compounds 1-9 is -5.3 to -10.1 Kcal/mol. Many residues exhibit interactions with heterocyclic molecules, according to the findings of this study. Some of the identified amino acids are A:G2648, A:C2649, A:A2670, A:G2671, A:G2669, A:U2650, A:QSK2667, A:G2668, A:C2651, A:C2652, A:U2653, A:C2666, A:A2665 A:QSK2667, A:U2672, A:U2650 & A:A2654 many more. The Machine learning tool is also used to choose the best analysis of molecular descriptors. For these classifiers, molecular descriptor dataset is taken, which shows that training accuracy and testing accuracy is very high and crucial for developing similar antibacterial drugs in near future. The disclosed core, in general, already has antibacterial properties and can be improved upon to act as antibiotic compounds soon. other: Not Applicable