  AI-based protein structure prediction pipelines, 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 dozens of minutes.
Consequently, we attempt to explore the limits of fast protein structure
prediction by using only primary sequences of proteins. HelixFold-Single is
proposed to combine a large-scale protein language model with the superior
geometric learning capability of AlphaFold2. Our proposed method,
HelixFold-Single, first pre-trains a large-scale protein language model (PLM)
with thousands of millions of primary sequences utilizing the self-supervised
learning paradigm, which will be used as an alternative to MSAs for learning
the co-evolution information. Then, by combining the pre-trained PLM and the
essential components of AlphaFold2, we obtain an end-to-end differentiable
model to predict the 3D coordinates of atoms from only the primary sequence.
HelixFold-Single is validated in datasets CASP14 and CAMEO, achieving
competitive accuracy with the MSA-based methods on the targets with large
homologous families. Furthermore, HelixFold-Single consumes much less time than
the mainstream pipelines for protein structure prediction, demonstrating its
potential in tasks requiring many predictions. The code of HelixFold-Single is
available at
https://github.com/PaddlePaddle/PaddleHelix/tree/dev/apps/protein_folding/helixfold-single,
and we also provide stable web services on
https://paddlehelix.baidu.com/app/drug/protein-single/forecast.
