GARN3: A coarse-grained helix centered technique for RNA 3D structures prediction

The study of predicting three-dimensional structures of RNA (ribonucleic acids) has increased over the last few decades, especially with advances in artificial intelligence. Despite these advances, there are still many gaps. Among the known techniques, the GARN (Game Algorithms for RNa 3D sampling) framework has demonstrated good performance on large RNA molecules. Nevertheless, the GARN technique also left room for improvement in the final 3D structures of predicted molecules, which can be further refined by including additional elements, also known as pseudoatoms. We present GARN3, an extension of GARN2 in which additional pseudoatoms are placed along helices to improve the granularity of 3D models, and a machine learning component is incorporated into the scoring function to estimate interaction distances. In our experiments, GARN3 achieved RMSD values comparable to or better than those of several existing RNA 3D structure prediction methods. TM-score evaluations indicate that GARN3 achieves a consistent global structural accuracy across multiple molecules, comparable to that of several existing methods. Relative to previous versions of GARN, GARN3 lowers the RMSD on most molecules in Test Set A and remains competitive on Test Set B (CASP targets), while providing a finer coarse-grained representation; performance is particularly consistent on large RNA structures. The implementation of the GARN3 technique is publicly available at https://github.com/jhonatans01/garn3, written and executable in Java.