Graph identification of proteins in tomograms (GRIP-Tomo).

ABSTRACT: In this study, we present a method of pattern mining based on network theory that enables the identification of protein structures or complexes from synthetic volume densities, without the knowledge of predefined templates or human biases for refinement. We hypothesized that the topological connectivity of protein structures is invariant, and they are distinctive for the purpose of protein identification from distorted data presented in volume densities. Three-dimensional densities of a protein or a complex from simulated tomographic volumes were transformed into mathematical graphs as observables. We systematically introduced data distortion or defects such as missing fullness of data, the tumbling effect, and the missing wedge effect into the simulated volumes, and varied the distance cu

SUBMITTER: George A 

PROVIDER: S-EPMC9798246 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature