E2Efold-3D: End-to-End Deep Learning Method for accurate de novo RNA 3D Structure Prediction

by   Tao Shen, et al.

RNA structure determination and prediction can promote RNA-targeted drug development and engineerable synthetic elements design. But due to the intrinsic structural flexibility of RNAs, all the three mainstream structure determination methods (X-ray crystallography, NMR, and Cryo-EM) encounter challenges when resolving the RNA structures, which leads to the scarcity of the resolved RNA structures. Computational prediction approaches emerge as complementary to the experimental techniques. However, none of the de novo approaches is based on deep learning since too few structures are available. Instead, most of them apply the time-consuming sampling-based strategies, and their performance seems to hit the plateau. In this work, we develop the first end-to-end deep learning approach, E2Efold-3D, to accurately perform the de novo RNA structure prediction. Several novel components are proposed to overcome the data scarcity, such as a fully-differentiable end-to-end pipeline, secondary structure-assisted self-distillation, and parameter-efficient backbone formulation. Such designs are validated on the independent, non-overlapping RNA puzzle testing dataset and reach an average sub-4 Å root-mean-square deviation, demonstrating its superior performance compared to state-of-the-art approaches. Interestingly, it also achieves promising results when predicting RNA complex structures, a feat that none of the previous systems could accomplish. When E2Efold-3D is coupled with the experimental techniques, the RNA structure prediction field can be greatly advanced.


RNA Secondary Structure Prediction By Learning Unrolled Algorithms

In this paper, we propose an end-to-end deep learning model, called E2Ef...

Simple End-to-end Deep Learning Model for CDR-H3 Loop Structure Prediction

Predicting a structure of an antibody from its sequence is important sin...

Voxel2Hemodynamics: An End-to-end Deep Learning Method for Predicting Coronary Artery Hemodynamics

Local hemodynamic forces play an important role in determining the funct...

E3Bind: An End-to-End Equivariant Network for Protein-Ligand Docking

In silico prediction of the ligand binding pose to a given protein targe...

Acoustic Structure Inverse Design and Optimization Using Deep Learning

From ancient to modern times, acoustic structures have been used to cont...

DeepSurf: A surface-based deep learning approach for the prediction of ligand binding sites on proteins

The knowledge of potentially druggable binding sites on proteins is an i...

Please sign up or login with your details

Forgot password? Click here to reset