Protein structure prediction has reached the single-structure frontier

Lane, Thomas J

doi:10.1038/s41592-022-01760-4

Cited by 91 publications

(62 citation statements)

References 26 publications

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“…As a further indirect validation, a recent experimental work 40 showed that mutation of E 3.39 dramatically enhances the in vitro expression of olfactory receptors, further supporting the structural and functional importance of this residue. As pointed out by a recent commentary, 41 de novo structure determination is dramatically limited by the “single answer problem”: the algorithms return a single structure that is, following the training, the most probable candidate. From a general point of view, this can be correct only in the realm of single-state proteins, while here (and in the majority of the biologically-relevant cases) our target GPCR has a set of different conformational states.…”

Section: Models With Sodium In Its Binding Sitementioning

confidence: 99%

Machine Learning-based Modeling of Olfactory Receptors in their Inactive State: Human OR51E2 as a Case Study

Alfonso‐Prieto

Capelli

2023

Preprint

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Atomistic-level investigation of olfactory receptors (ORs) is a challenging task due to the experimental/computational difficulties in the structural determination/prediction for members of this family of G-protein coupled receptors. Here we have developed a protocol that performs a series of molecular dynamics simulations from a set of structures predicted de novo by recent machine learning algorithms and apply it to a well-studied receptor, the human OR51E2. Our study demonstrates the need for extensive simulations to refine and validate such models. Furthermore, we demonstrate the need for the sodium ion at a binding site near D2.50 and E3.39 to stabilize the inactive state of the receptor. Considering the conservation of these two acidic residues across human ORs, we surmise this requirement also applies to the other ~400 members of this family.

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Section: Models With Sodium In Its Binding Sitementioning

confidence: 99%

Machine Learning-based Modeling of Olfactory Receptors in their Inactive State: Human OR51E2 as a Case Study

Alfonso‐Prieto

Capelli

2023

Preprint

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“…Furthermore, the initial predictions of the native structures of proteins have been recently extended to protein complexes 4,5 . These advances have prompted the question of whether it is possible to use this type of approach for the prediction of the conformational fluctuations of the native states of folded proteins [6][7][8][9][10][11][12] , and more generally for the characterisation of the structural properties of the native states of disordered proteins [13][14][15] . Support for this idea comes from the observation that AlphaFold performs as well as current state-of-the-art predictors of protein disorder 16,17 .…”

Section: Introductionmentioning

confidence: 99%

AlphaFold Prediction of Structural Ensembles of Disordered Proteins

Brotzakis

Zhang

Vendruscolo

2023

Preprint

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Deep learning methods of predicting protein structures have reached an accuracy comparable to that of high-resolution experimental methods. It is thus possible to generate accurate models of the native states of hundreds of millions of proteins. An open question, however, concerns whether these advances can be translated to disordered proteins, which should be represented as structural ensembles because of their heterogeneous and dynamical nature. Here we show that the inter-residue distances predicted by AlphaFold for disordered proteins can be used to construct accurate structural ensembles. These results illustrate the application to disordered proteins of deep learning methods originally trained for predicting the structures of folded proteins.

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“…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, 2022) In terms of methodology, we stress the importance of understanding and questioning the benefits and necessity of pretraining with protein structure prediction which heavily relies on evolutionary couplings that are unused in de novo protein design. Given our preliminary results, we hypothesize scaling up FrameDiff to train with more data and improvements in sampling designable backbones could overcome the need for pretraining with protein structure prediction.…”

Section: Discussionmentioning

confidence: 99%

SE(3) diffusion model with application to protein backbone generation

Yim¹,

Trippe²,

Bortoli³

et al. 2023

Preprint

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The design of novel protein structures remains a challenge in protein engineering for applications across biomedicine and chemistry. In this line of work, a diffusion model over rigid bodies in 3D (referred to as frames) has shown success in generating novel, functional protein backbones that have not been observed in nature. However, there exists no principled methodological framework for diffusion on SE(3), the space of orientation preserving rigid motions in R 3 , that operates on frames and confers the group invariance. We address these shortcomings by developing theoretical foundations of SE(3) invariant diffusion models on multiple frames followed by a novel framework, FrameDiff, for learning the SE(3) equivariant score over multiple frames. We apply FrameDiff on monomer backbone generation and find it can generate designable monomers up to 500 amino acids without relying on a pretrained protein structure prediction network that has been integral to previous methods. We find our samples are capable of generalizing beyond any known protein structure. 1

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Protein structure prediction has reached the single-structure frontier

Cited by 91 publications

References 26 publications

Machine Learning-based Modeling of Olfactory Receptors in their Inactive State: Human OR51E2 as a Case Study

Machine Learning-based Modeling of Olfactory Receptors in their Inactive State: Human OR51E2 as a Case Study

AlphaFold Prediction of Structural Ensembles of Disordered Proteins

SE(3) diffusion model with application to protein backbone generation

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