Molecular Skin Surface-Based Transformation Visualization between Biological Macromolecules

Abstract

Molecular skin surface (MSS), proposed by Edelsbrunner, is a C2 continuous smooth surface modeling approach of biological macromolecules. Compared to the traditional methods of molecular surface representations (e.g., the solvent exclusive surface), MSS has distinctive advantages including having no self-intersection and being decomposable and transformable. For further promoting MSS to the field of bioinformatics, transformation between different MSS representations mimicking the macromolecular dynamics is demanded. The transformation process helps biologists understand the macromolecular dynamics processes visually in the atomic level, which is important in studying the protein structures and binding sites for optimizing drug design. However, modeling the transformation between different MSSs suffers from high computational cost while the traditional approaches reconstruct every intermediate MSS from respective intermediate union of balls. In this study, we propose a novel computational framework named general MSS transformation framework (GMSSTF) between two MSSs without the assistance of union of balls. To evaluate the effectiveness of GMSSTF, we applied it on a popular public database PDB (Protein Data Bank) and compared the existing MSS algorithms with and without GMSSTF. The simulation results show that the proposed GMSSTF effectively improves the computational efficiency and is potentially useful for macromolecular dynamic simulations.

Knowledge Graph Construction and Application of Power Grid Equipment

“…e usage of the proposed algorithm in other knowledge/data management areas is not justified. As one of the future works, the proposed knowledge graph construction algorithm will be extended to the research field, such as molecular modeling [34,35], healthcare engineering [36,37], and business applications [38]. In addition, the topology analysis function development of the power grid subtasks is another future task for power grid appliances flow calculation, state estimation, line loss calculation, etc., targeting more efficient analysis tools for the operating states and faults of the power grid.…”