RaptorX-Single: single-sequence protein structure prediction by integrating protein language models

Abstract

AbstractProtein structure prediction has been greatly improved by deep learning in the past few years. However, the most successful methods rely on multiple sequence alignment (MSA) of the sequence homologs of the protein under prediction. In nature a protein folds in the absence of its sequence homologs and thus, a MSA-free structure prediction method is desired. Here we develop a single sequence-based protein structure prediction method RaptorX-Single by integrating several protein language models and a structure generation module and then study its advantage over MSA-based prediction methods. Our experimental results indicate that in addition to running much faster than MSA-based methods such as AlphaFold2, RaptorX-Single outperforms AlphaFold2 and other MSA-free methods in predicting the structure of antibodies, proteins of very few sequence homologs and single mutation effects. RaptorX-Single also compares favorably to MSA-based AlphaFold2 when the protein under prediction has a large number of sequence homologs.