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Keyword: orphan proteins ×Clear all

3 claims from 2 papers

1. Biochemistry, Genetics and Molecular Biology › Protein Structure and Dynamics

   Single-sequence protein structure prediction using a language model and deep learning

   Chowdhury, Bouatta, Biswas et al. · Nature Biotechnology · 2022

   The authors built RGN2, a deep-learning system with a protein language model that predicts structure from a single sequence, aimed at cases where alignment-based tools such as AlphaFold2 struggle.

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   1. UncheckedOn average, RGN2 predicted structures of orphan proteins and some designed proteins better than AlphaFold2 and RoseTTAFold, using up to a millionfold less compute time.“On average, RGN2 outperforms AlphaFold2 and RoseTTAFold on orphan proteins and classes of designed proteins while achieving up to a 10 6 -fold reduction in compute time.”

2. Biochemistry, Genetics and Molecular Biology › Protein Structure and Dynamics

   High-resolution de novo structure prediction from primary sequence

   Wu, Ding, Wang et al. · bioRxiv (Cold Spring Harbor Laboratory) · 2022

   The paper introduces OmegaFold, a method that predicts high-resolution protein structures from a single amino acid sequence, without needing multiple sequence alignments.

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   1. UncheckedOmegaFold, which predicts protein structure from a single sequence, is reported to beat RoseTTAFold and match AlphaFold2 on recently released structures.“Using a new combination of a protein language model that allows us to make predictions from single sequences and a geometry-inspired transformer model trained on protein structures, OmegaFold outperforms RoseTTAFold and achieves similar prediction accuracy to AlphaFold2 on recently released structu…”
   2. UncheckedOmegaFold is reported to predict structures accurately for orphan proteins and for antibodies, whose sequence alignments tend to be noisy.“OmegaFold enables accurate predictions on orphan proteins that do not belong to any functionally characterized protein family and antibodies that tend to have noisy MSAs due to fast evolution.”