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).