Molecular dynamics (MD) simulations are a powerful tool that enable the atomic-level study of biological systems. Over many years of development and with significant improvements in computer hardware, MD simulations have been used to study a variety of systems of increasing size and complexity. Despite considerable advances, computing electrostatic interactions has consistently remained the ratelimiting step in the efficiency of MD engines. Electrostatic interactions are costly to compute as a result of the slow 1/r decay and poor O N 2 scaling of the Coulomb potential. Algorithms such as the Fast Multipole Method (FMM) 2-4 and Particle-Mesh Ewald method (PME) accelerate the calculation by splitting the Coulomb potential into short-range and long-range components. Both FMM and PME provide significant performance improvements, as the algorithms achieve O(N) and O(N log N) scaling, respectively. However, in spite of the performance gains over the traditional Coulomb potential, both FMM and PME have limitations. While PME is the standard method used in most MD simulations, the algorithm becomes extremely inefficient when implemented on large, multi-node machines as a consequence of the large amount of communication required by the 3-dimensional (3D) Fast Fourier Transform. While FMM is able to achieve linear scaling, it comes at the cost of a sophisticated computational prefactor calculation which prevents it from being used over PME for most systems. In a previous paper, we have developed the Spherical Grid a) Electronic mail: gonzalesje@nih.gov and Treecode (SGT) algorithm which utilizes a 3D cubature to evaluate the long-range component of the Ewald factorization of the Coulomb potential. Based on this, we further developed the Gauss-Legendre-Spherical-t method (GLST) as a method that can be more practically implemented . Here we give a brief overview of the GLST algorithm, outline the computational details of our implementation, and present the performance and accuracy of the method.