Electrostatic potential, energies, and forces affect virtually any process in molecular biology, however, computing these quantities is a difficult task due to irregularly shaped macromolecules and the presence of water. Here, we report a new edition of the popular software package DelPhi along with describing its functionalities. The new DelPhi is a C++ object‐oriented package supporting various levels of multiprocessing and memory distribution. It is demonstrated that multiprocessing results in significant improvement of computational time. Furthermore, for computations requiring large grid size (large macromolecular assemblages), the approach of memory distribution is shown to reduce the requirement of RAM and thus permitting large‐scale modeling to be done on Linux clusters with moderate architecture. The new release comes with new features, whose functionalities and applications are described as well. © 2019 The Authors. Journal of Computational Chemistry published by Wiley Periodicals, Inc.

Electrostatics play indispensable role in practically any process in molecular biology. Indeed, at distances larger than several Angstroms all other forces are negligibly small and electrostatic force dominates. However, modeling electrostatics in molecular biology is a complicated task due to presence of water phase, mobile ions and irregularly shaped inhomogeneous biological macromolecules. A particular approach to calculating electrostatics in such systems is to apply Poisson-Boltzmann equation (PBE). Here we provide a tutorial for the popular DelPhi package that solves PBE using a finite difference method and returns the electrostatic potential distribution throughout the modeling box. The tutorial comes with detailed description of different tasks that DelPhi can handle, an assessment of the accuracy against cases with analytical solution and recommendations about DelPhi usage. Furthermore, since electrostatics is a key component of virtually any modeling in molecular biology, we have created many additional resources utilizing DelPhi to model various biology relevant quantities. Tutorials for these resources are also provided along with examples of their usage.

The matched interface and boundary method (MIB) and ghost fluid method (GFM) are two well-known methods for solving elliptic interface problems. Moreover, they can be coupled with efficient time advancing methods, such as the alternating direction implicit (ADI) methods, for solving time-dependent partial differential equations (PDEs) with interfaces. However, to our best knowledge, all existing interface ADI methods for solving parabolic interface problems concern only constant coefficient PDEs, and no efficient and accurate ADI method has been developed for variable coefficient PDEs. In this work, we propose to incorporate the MIB and GFM in the framework of the ADI methods for generalized methods to solve two-dimensional parabolic interface problems with variable coefficients. Various numerical tests are conducted to investigate the accuracy, efficiency, and stability of the proposed methods. Both the semi-implicit MIB-ADI and fully-implicit GFM-ADI methods can recover the accuracy reduction near interfaces while maintaining the ADI efficiency. In summary, the GFM-ADI is found to be more stable as a fully-implicit time integration method, while the MIB-ADI is found to be more accurate with higher spatial and temporal convergence rates.