Protein structure prediction has reached the single-structure frontier

By T. J. Lane. 2023

99 citations recorded by the source corpus.

Dramatic advances in protein structure prediction have sparked debate as to whether the problem of predicting structure from sequence is solved or not. Here, I argue that AlphaFold2 and its peers are currently limited by the fact that they predict only a single structure, instead of a structural distribution, and that this realization is crucial for the next generation of structure prediction algorithms.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

State-specific protein–ligand complex structure prediction with a multiscale deep generative model (2022, positive): “Protein structures are dynamically modulated by small molecules such as ligands and posttranslational modifications 13–16 ; such dynamical conformational changes can trigger diverse downstream responses that are crucial to the regulation of biological functions and with far-reaching implications…”
Protein structure generation via folding diffusion (2022, background): “Despite their lauded success, models like AlphaFold2 have been shown to perform poorly on proteins with highly dynamic folds, such as intrinsically disordered proteins 53,54 .”
SE(3) diffusion model with application to protein backbone generation (2023, background): “One important research direction is to extend FrameDiff to conditional generative modeling tasks, such as probabilistic sequence-to-structure prediction which is key to capture functional motion (Lane, 2023), but also probabilistic scaffolding design given a functional motif (Trippe et al.”