Ultra-fast protein structure prediction to capture effects of sequence variation in mutation movies

Abstract

Top protein three-dimensional (3D) structure predictions require evolutionary information from multiple-sequence alignments (MSAs) and deep, convolutional neural networks and appear insensitive to small sequence changes. Here, we describe EMBER3D using embeddings from the pre-trained protein language model (pLM) ProtT5 to predict 3D structure directly from single sequences. Orders of magnitude faster than others, EMBER3D predicts average-length structures in milliseconds on consumer-grade machines. Although not nearly as accurate as AlphaFold2, the speed of EMBER3D allows a glimpse at future applications such as the almost real-time rendering of deep mutational scanning (DMS) movies that visualize the effect of all point mutants on predicted structures. This also enables live-editing of sequence/structure pairs. EMBER3D is accurate enough for highly sensitive rapid remote homology detection by Foldseek identifying structural similarities. Overall, our use cases suggest that speed can complement accuracy, in particular when accessible through consumer-grade machines. EMBER3D is free and publicly available: https://github.com/kWeissenow/EMBER3D.