NucleoFind: a deep-learning network for interpreting nucleic acid electron density.

ABSTRACT: Nucleic acid electron density interpretation after phasing by molecular replacement or other methods remains a difficult problem for computer programs to deal with. Programs tend to rely on time-consuming and computationally exhaustive searches to recognise characteristic features. We present NucleoFind, a deep-learning-based approach to interpreting and segmenting electron density. Using an electron density map from X-ray crystallography obtained after molecular replacement, the positions of the phosphate group, sugar ring and nitrogenous base group can be predicted with high accuracy. On average, 78% of phosphate atoms, 85% of sugar atoms and 83% of base atoms are positioned in predicted density after giving NucleoFind maps produced following successful molecular replacement. NucleoFind

SUBMITTER: Dialpuri JS 

PROVIDER: S-EPMC11417358 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature