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

SUBMITTER: Tarafder S 

PROVIDER: S-EPMC11600500 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature