The future of biomolecular simulation in the pharmaceutical industry: what we can learn from aerodynamics modelling and weather prediction. Part 1. understanding the physical and computational complexity of in silico drug design

Current Trends and Changes in Use of Membrane Molecular Dynamics Simulations within Academia and the Pharmaceutical Industry

There has been an almost exponential increase in the use of molecular dynamics simulations in basic research and industry over the last 5 years, with almost a doubling in the number of publications each year. Many of these are focused on neurological membranes, and biological membranes in general, applied to the medical industry. A smaller portion have utilized membrane simulations to answer more basic questions related to the function of specific proteins, chemicals or biological processes. This review covers some newer studies, alongside studies from the last two decades, to determine changes in the field. Some of these are basic, while others are more profound, such as multi-component embedded membrane machinery. It is clear that many facets of the discipline remain the same, while the focus on and uses of the technology are broadening in scope and utilization as a general research tool. Analysis of recent literature provides an overview of the current methodologies, covers some of the recent trends or advances and tries to make predictions of the overall path membrane molecular dynamics will follow in the coming years. In general, the overview presented is geared towards the general scientific community, who may wish to introduce the use of these methodologies in light of these changes, making molecular dynamic simulations more feasible for general scientific or medical research.

Pharmacophore modelling, docking and molecular dynamic simulation studies in the discovery of potential human renin inhibitors

Hypertension is the main cause of human death and the Renin- Angiotensin- Aldosterone System (RAAS) has a key role in the control of human blood pressure. In this research a multi-step virtual screening strategy was applied in order to find new potential renin inhibitors. The crystal structure of renin-aliskiren complex was explored and receptor-ligand pharmacophore model was developed and validated using pharmit. ZINC database was screened by pharmacophore model and Lipinski's rule of five. Thereafter, the retrieved hits were docked in the active site of renin by using Vina. ADME parameters and toxicity of the filtered compounds were approximated using in silico methods and the selected compounds were subjected to high performance docking in order to improve the accuracy of screening. The non-bond interactions of the hit molecules with renin were explored and the compounds with the highest affinity and appropriate interactions were selected. In the last step, molecular dynamic simulation studies were performed on the complex of renin with aliskiren and three top-ranked structures including ZINC6085004, ZINC426421106, and ZINC5481346. RMSD, RMSF, Rg and number of hydrogen bonds were calculated. The binding free energy was calculated using the MM/PBSA method. The result of MD simulation indicated the stable binding of ZINC426421106 and ZINC5481346 with the active site of renin. According to this in-silico study these two compounds are drug-like, nontoxic, and have a high potential for inhibiting renin and could serve as appropriate lead molecules for the development of renin inhibitors as antihypertensive agents.

Towards rational computational peptide design

Peptides are prevalent in biology, mediating as many as 40% of protein-protein interactions, and involved in other cellular functions such as transport and signaling. Their ability to bind with high specificity make them promising therapeutical agents with intermediate properties between small molecules and large biologics. Beyond their biological role, peptides can be programmed to self-assembly, and they are already being used for functions as diverse as oligonuclotide delivery, tissue regeneration or as drugs. However, the transient nature of their interactions has limited the number of structures and knowledge of binding affinities available-and their flexible nature has limited the success of computational pipelines that predict the structures and affinities of these molecules. Fortunately, recent advances in experimental and computational pipelines are creating new opportunities for this field. We are starting to see promising predictions of complex structures, thermodynamic and kinetic properties. We believe in the following years this will lead to robust rational peptide design pipelines with success similar to those applied for small molecule drug discovery.