MD*: A novel Molecular Dynamics approach to reveal the Target/small-molecule interaction secrets

Abstract In 1957, John Kendrew determined successfully the atomic structure of the myoglobin. Since, the scientific community increased the interest towards the knowledge of how the macromolecular structures are able to fold, move and interact inside the biological environment. To know the molecular basis of how a biological system works is a key step to reveal the secrets of the life. Molecular dynamics (MD) simulation, first developed in the late 60s, has advanced from simulating gases as elastic collisions between hard spheres to complex biological systems formed by thousands of atoms. However, several limits occur, such as: computational time, resource usage, non-feasibility etc. Automation, algorithm research and standardizations are crucial in order to curb cost of resources and achievement of results. In particular, accelerated search methods in the phase-space are increasingly studied to overcome the present barriers of IT capabilities. In this thesis, we expose a novel and innovative approach of MD, named, Molecular dynamics-star (MD*), MD* is an accelerated binding/unbinding path finding MD algorithm based on the semantics from Artificial Intelligence (AI) Astar (A*) informed-search algorithm. MD* is implemented in GROMACS with control and evaluation cycles written in python for the accelerated simulation. The viability of MD* was evaluated simulating the binding/unbinding process of the LUSH protein/Ethanol co-crystal. The MD* simulation showed an accurate overlapping of the ethanol binding pose compared with the crystal, revealing its reliability. Our work, could be open novel frontiers in computational biochemistry field, providing the molecular basis of biological system interactions