Furthermore, HelixFold-Single consumes much less time than the mainstream pipelines for protein structure prediction, demonstrating its potential in tasks requiring many predictions. ![]() HelixFold-Single is validated in datasets CASP14 and CAMEO, achieving competitive accuracy with the MSA-based methods on the targets with large homologous families. 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. 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. HelixFold-Single is proposed to combine a large-scale protein language model with the superior geometric learning capability of AlphaFold2. Consequently, we attempt to explore the limits of fast protein structure prediction by using only primary sequences of proteins. ![]() Nonetheless, searching MSAs from protein databases is time-consuming, usually taking dozens of minutes. These advanced pipelines mainly rely on Multiple Sequence Alignments (MSAs) as inputs to learn the co-evolution information from the homologous sequences. Download a PDF of the paper titled HelixFold-Single: MSA-free Protein Structure Prediction by Using Protein Language Model as an Alternative, by Xiaomin Fang and 9 other authors Download PDF Abstract:AI-based protein structure prediction pipelines, such as AlphaFold2, have achieved near-experimental accuracy.
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