LociPARSE: a locality-aware invariant point attention model for scoring RNA 3D structures.

ABSTRACT: A scoring function that can reliably assess the accuracy of a 3D RNA structural model in the absence of experimental structure is not only important for model evaluation and selection but also useful for scoring-guided conformational sampling. However, high-fidelity RNA scoring has proven to be difficult using conventional knowledge-based statistical potentials and currently-available machine learning-based approaches. Here we present lociPARSE, a locality-aware invariant point attention architecture for scoring RNA 3D structures. Unlike existing machine learning methods that estimate superposition-based root mean square deviation (RMSD), lociPARSE estimates Local Distance Difference Test (lDDT) scores capturing the accuracy of each nucleotide and its surrounding local atomic environment i

SUBMITTER: Tarafder S 

PROVIDER: S-EPMC10635153 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature