# LightRoseTTA: High‐Efficient and Accurate Protein Structure Prediction Using a Light‐Weight Deep Graph Model

## TL;DR

LightRoseTTA is a lightweight deep learning model that predicts protein structures accurately and efficiently, using less computational power than existing methods.

## Contribution

LightRoseTTA introduces a lightweight deep graph model for protein structure prediction with high accuracy and efficiency.

## Key findings

- LightRoseTTA achieves competitive accuracy with RoseTTAFold on CASP14 and CAMEO datasets.
- It requires only 1.4M parameters and trains in one week on a single GPU.
- It performs best on MSA-insufficient datasets like Orphan and De novo.

## Abstract

Accurately predicting protein structure, from sequences to 3D structures, is of great significance in biological research. To tackle this issue, a representative deep big model, RoseTTAFold, is proposed with promising success. Here, “a light‐weight deep graph network, named LightRoseTTA,” is reported to achieve accurate and highly efficient prediction for proteins. Notably, three highlights are possessed by LightRoseTTA: i) high‐accurate structure prediction for proteins, being “competitive with RoseTTAFold” on multiple popular datasets including CASP14 and CAMEO; ii) high‐efficient training and inference with a light‐weight model, costing “only 1 week on one single NVIDIA 3090 GPU for model‐training” (vs 30 days on 8 NVIDIA V100 GPUs for RoseTTAFold) and containing “only 1.4M parameters” (vs 130M in RoseTTAFold); iii) low dependency on multi‐sequence alignment (MSA), achieving the best performance on three MSA‐insufficient datasets: Orphan, De novo, and Orphan25. Besides, LightRoseTTA is “transferable” from general proteins to antibody data, as verified in the experiments. The time and resource costs of LightRoseTTA and RoseTTAFold are further discussed to demonstrate the feasibility of light‐weight models for protein structure prediction, which may be crucial in resource‐limited research for universities and academic institutions. The code and model are released to speed biological research (https://github.com/psp3dcg/LightRoseTTA).

Accurately predicting protein structure is of great significance in biological research. LightRoseTTA, a light‐weight deep graph network, to achieve prediction for proteins is presented. Notably, three highlights are possessed by LightRoseTTA: i) high‐accurate structure prediction for proteins; ii) high‐efficient training and inference; and iii) low dependency on multi‐sequence alignment (MSA). Finally, LightRoseTTA is evaluated on several benchmarks and outperforms alternative methods.