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Protein structure prediction pipelines based on artificial intelligence, such as AlphaFold2, have achieved near-experimental accuracy. These advanced pipelines mainly rely on multiple sequence alignments (MSAs) as inputs to learn the co-evolution information from the homologous sequences. Nonetheless, searching MSAs from protein databases is time consuming, usually taking tens of minutes. Consequently, we attempt to explore the limits of fast protein structure prediction by using only primary structures of proteins. Our proposed method, HelixFold-Single, combines a large-scale protein language model with the superior geometric learning capability of AlphaFold2. HelixFold-Single first pre-trains a large-scale protein language model with thousands of millions of primary structures utilizing the self-supervised learning paradigm, which will be used as an alternative to MSAs for learning the co-evolution …