Introduction

Motor proteins such as kinesin, mysosin and dynein are interesting because they occur in all living organisms, and are critical to life, but perhaps more directly they could be used to design nanoscale biomimetic devices if they are understood sufficiently. However the small size of motor proteins makes them difficult to study; the structure and function cannot be fully elucidated through experimental techniques. Even something as simple as how a kinesin protein walks is still in debate [2]. Direct observation of protein function cannot be performed using current methods of observation, such as coating the specimen with metals for the electron microscopes, as this prevents the protein from functioning [5]. Because of this problem, little is known about the conformational changes¹ of motor proteins function. Specifically we are working on modeling the electrostatic interactions of the thousands or even hundreds of thousands of atoms in a molecular motor protein and the surrounding aqueous solution. The electrostatic charges derive from the polarity of the water molecules, the ions in solution, and the charged portions of the molecular motor. The interactions between these charges are important because they are the main long-range forces in molecular simulations. Our work primarily is concerned with helping make calculation of the interactions between individual pairs of atoms as efficient as possible. As each atom interacts with each other atom, the time required to perform the calculation goes up as the square of the number of atoms. In order to more efficiently calculate these interactions, we have used a method known as Particle Mesh Ewald (PME) to model the electrostatic particle interactions. Specifically, our goal has been to develop a way of modeling biological molecules² in a realistic environment, that is to say, one in which the molecule is surrounded by water molecules. PME, an improvement over the Ewald method [7], makes it possible to compute these interactions more quickly and thus permits the solution of large problems that would otherwise be inaccessible to modern machines in a reasonable timescale.