Molecular Docking on Azepine Derivatives as Potential Inhibitors for H1N1-A Computational Approach

Azepine are an important class of organic compounds. They are effective in a wide range of biological activity such as antifeedants, antidepressants, CNS stimulants, calcium channel blocker, antimicrobial and antifungal properties. In our continue efforts to search for a potent inhibitor for H1N1 virus using molecular docking. In this study, 15 azepine (ligands) derivatives were docked to the neuraminidase of A/Breving Mission/1/1918 H1N1 strain in complex with zanamivir (protein). The Cdocker energy was then calculated for these complexes (protein-ligand). Based on the calculation, the lowest Cdocker interaction energy was selected and potential inhibitors can be identified. Compounds MA4, MA7, MA8, MA10, MA11 and MA12 with promising Cdocker energy was expected to be very effective against the neuraminidase H1N1

Molecular Docking Sianidin dan Peonidin sebagai Antiinflamasi pada Aterosklerosis secara In Silico

Atherosclerosis is a chronic inflammatory disease that begins with endothelial dysfunction resulting in plaque growth in the inner walls of the arteries. Endothelial dysfunction causes endothelial activates NF-?B resulting in a transcription of proinflammatory gene supporting the growth of atherosclerotic plaque. The purple sweet potato anthocyanin is a compound known to have activity inhibiting the inflammatory process. The major anthocyanins contained in purple sweetpotato are cyanidine and peonidine. The cyanidine and peonidin activity test was performed as antiinflammatory at atherosclerosis based on their interaction on NF-?B protein using molecular docking method in silico. The stages of this research are preparation of protein structure database of NF-?B, protein preparation using Chimera1.10.1 application, preparation and optimization of cyanidin and peonidin 3D structure using HyperChem8 application, and validation of molecular docking and docking method of cyanidin and peonidin on NF-?B protein using application Autodock4.2. The results showed that cyanidine and peonidine had affinity and formed a hydrogen bond with the NF-?B protein. The bond energy between cyanidine and peonidine with the NF-?B protein is -7.92 kcal/mol and -7.86 kcal/mol which together form the hydrogen bond with the LEU472 amino acid on the binding site equal to the native ligand. Cyanidin and peonidine have the potential of activity as antiatherosklerosis because it has an affinity with the NF-?B protein so that it prevents the inflammatory process in the formation of atherosclerotic plaque

A Study of Archiving Strategies in Multi-Objective PSO for Molecular Docking

Molecular docking is a complex optimization problem aimed at predicting the position of a ligand molecule in the active site of a receptor with the lowest binding energy. This problem can be formulated as a bi-objective optimization problem by minimizing the binding energy and the Root Mean Square Deviation (RMSD) difference in the coordinates of ligands. In this context, the SMPSO multi-objective swarm-intelligence algorithm has shown a remarkable performance. SMPSO is characterized by having an external archive used to store the non-dominated solutions and also as the basis of the leader selection strategy. In this paper, we analyze several SMPSO variants based on different archiving strategies in the scope of a benchmark of molecular docking instances. Our study reveals that the SMPSOhv, which uses an hypervolume contribution based archive, shows the overall best performance.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

A New Multi-Objective Approach for Molecular Docking Based on RMSD and Binding Energy

Ligand-protein docking is an optimization problem based on predicting the position of a ligand with the lowest binding energy in the active site of the receptor. Molecular docking problems are traditionally tackled with single-objective, as well as with multi-objective approaches, to minimize the binding energy. In this paper, we propose a novel multi-objective formulation that considers: the Root Mean Square Deviation (RMSD) difference in the coordinates of ligands and the binding (intermolecular) energy, as two objectives to evaluate the quality of the ligand-protein interactions. To determine the kind of Pareto front approximations that can be obtained, we have selected a set of representative multi-objective algorithms such as NSGA-II, SMPSO, GDE3, and MOEA/D. Their performances have been assessed by applying two main quality indicators intended to measure convergence and diversity of the fronts. In addition, a comparison with LGA, a reference single-objective evolutionary algorithm for molecular docking (AutoDock) is carried out. In general, SMPSO shows the best overall results in terms of energy and RMSD (value lower than 2A for successful docking results). This new multi-objective approach shows an improvement over the ligand-protein docking predictions that could be promising in in silico docking studies to select new anticancer compounds for therapeutic targets that are multidrug resistant.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Whey-derived peptides interactions with ACE by molecular docking as a potential predictive tool of natural ACE inhibitors

Several milk/whey derived peptides possess high in vitro angiotensin I-converting enzyme (ACE) inhibitory activity. However, in some cases, poor correlation between the in vitro ACE inhibitory activity and the in vivo antihypertensive activity has been observed. The aim of this study is to gain insight into the structure-activity relationship of peptide sequences present in whey/milk protein hydrolysates with high ACE inhibitory activity, which could lead to a better understanding and prediction of their in vivo antihypertensive activity. The potential interactions between peptides produced from whey proteins, previously reported as high ACE inhibitors such as IPP, LIVTQ, IIAE, LVYPFP, and human ACE were assessed using a molecular docking approach. The results show that peptides IIAE, LIVTQ, and LVYPFP formed strong H bonds with the amino acids Gln 259, His 331, and Thr 358 in the active site of the human ACE. Interestingly, the same residues were found to form strong hydrogen bonds with the ACE inhibitory drug Sampatrilat. Furthermore, peptides IIAE and LVYPFP interacted with the amino acid residues Gln 259 and His 331, respectively, also in common with other ACE-inhibitory drugs such as Captopril, Lisinopril and Elanapril. Additionally, IIAE interacted with the amino acid residue Asp 140 in common with Lisinopril, and LIVTQ interacted with Ala 332 in common with both Lisinopril and Elanapril. The peptides produced naturally from whey by enzymatic hydrolysis interacted with residues of the human ACE in common with potent ACE-inhibitory drugs which suggests that these natural peptides may be potent ACE inhibitors

Structure based de novo design of IspD inhibitors as anti-tubercular agents

Tuberculosis is one of the leading contagious diseases, caused by Mycobacterium tuberculosis. Despite improvements in anti-tubercular agents, it remains one of the most prevalent infectious diseases worldwide, responsible for a total of 1.6 million deaths annually. The emergence of multidrug resistant strains highlighted the need of discovering novel drug targets for the development of anti-tubercular agents. 2-C-methyl-D-erythritol-4-phosphate cytidyltransferase (IspD) is an enzyme involved in MEP pathway for isoprenoid biosynthesis, which is considered an attractive target for the discovery of novel antibiotics for its essentiality in bacteria and absence in mammals. In the present study, we have employed structure based drug design approach to develop novel and potent inhibitors for IspD receptor. To explore binding affinity and hydrogen bond interaction between the ligand and active site of IspD receptor, docking studies were performed. ADMET and synthetic accessibility filters were used to screen designed molecules. Finally, ten compounds were selected and subsequently submitted for the synthesis and in vitro studies as IspD inhibitors

Molecular Docking With Haptic Guidance and Path Planning

Molecular docking drives many important biological processes including immune system recognition and cellular signalling. Molecular docking occurs when molecules interact and form complexes. Predicting how specific molecules dock with each other using computational methods has several applications including understanding diseases and virtual drug design. The goal of molecular docking prediction is to find the lowest energy ligand states. The lower the energy state, the more probable the state is docked and biologically feasible. Existing automated computational methods can be time intensive, especially when using direct molecular dynamic simulation. One way to reduce this computational cost is to use more coarse-grained models that approximate molecular docking. Coarse-grained molecular docking prediction is generally performed first by sampling ligand states using a rigid body model or a partial flexibility model to reduce computation, then by screening the states. The ligand states are screened using a scoring function, usually a potential energy function for interactions between the atoms in each molecule. Ligand state search algorithms still have a significant computational cost if a large portion of the state space is to be explored. Instead of an automated ligand state search method, a human operator can explore the state space instead. Haptic force feedback devices providing guidance based off the energy function can aid the human operator. Haptic-guidance has been used for immersive semi-automatic and manual molecular docking on a single operator scale. A large amount of ligand state space can be explored with many human operators in a crowdsourced effort. Players in an interactive crowdsourced protein folding puzzle game have aided in finding protein folding prediction solutions, but without haptic feedback. Interactive crowdsourced methods for molecular docking prediction is not well-explored, although non-interactive crowdsourced systems such as Folding@home can be adapted for molecular docking. This thesis presents a molecular docking game that produces low potential energy ligand states and motion paths with crowdsource scale potential. In an exploratory user study, participants were assigned four different types of devices with varying levels of haptic guidance to search for a potentially docked ligand state. The results demonstrate some effect on the type of device and haptic guidance seen in the study. However, differences are minimal thus potentially enabling the use of commonly available input devices in a crowdsourced setting