Protein Structure and Sequence Generation with Equivariant Denoising Diffusion Probabilistic Models

By Namrata Anand, Tudor Achim. 2022

210 citations recorded by the source corpus.

Proteins are macromolecules that mediate a significant fraction of the cellular processes that underlie life. An important task in bioengineering is designing proteins with specific 3D structures and chemical properties which enable targeted functions. To this end, we introduce a generative model of both protein structure and sequence that can operate at significantly larger scales than previous molecular generative modeling approaches. The model is learned entirely from experimental data and conditions its generation on a compact specification of protein topology to produce a full-atom backbone configuration as well as sequence and side-chain predictions. We demonstrate the quality of the model via qualitative and quantitative analysis of its samples. Videos of sampling trajectories are available at https://nanand2.github.io/proteins .

Subfields: Molecular Biology, Generative Models, Computational Biology & Bioinformatics

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Citations in the corpus

End-to-end protein–ligand complex structure generation with diffusion-based generative models (2022, neutral): “Our model may be extended to the atomic level with little increase in computational cost by incorporating backbone orientations and side-chain dihedral angles into the diffusion model [32].”
State-specific protein–ligand complex structure prediction with a multiscale deep generative model (2022, positive): “2e and Supplementary Information Section E) is inspired by previous works on 3D graph and attentional neural networks for point clouds 61,62 , rigid-body simulations 63 and biopolymer representation learning 1,64–66 .”
Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem (2022, neutral): “Anand & Achim (2022) extended diffusion models for generation of protein backbone frames and sequences conditioned on secondary structure adjacency matrices.”
Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models (2022, result): “This definition of success is significantly more stringent than those described elsewhere (refs [5,8,16,25], Fig.”
Retrieval-Augmented Generation for AI-Generated Content: A Survey (2024, unknown): “In this section, we introduce RAG foundations and outline enhancement methods that further improve the effectiveness.”
Illuminating protein space with a programmable generative model (2022, background): “9,23), in the number of residues N .”
SE(3) diffusion model with application to protein backbone generation (2023, methodology): “Past works have developed diffusion models over different representations of protein structures without pretraining (Wu et al., 2022; Trippe et al., 2022; Anand & Achim, 2022; Qiao et al., 2022; Ingraham et al., 2022).”
Dynamic-Backbone Protein-Ligand Structure Prediction with Multiscale Generative Diffusion Models (2022, methodology): “The architecture of ESDM (Figure1e) is inspired by prior works on 3D graph and attentional neural networks for point clouds [37, 38], rigid-body simulations [39] and biopolymer representation learning [9, 40–42].”
Protein structure generation via folding diffusion (2022, methodology): “Most importantly, since each residue forms its own independent reference frame, there is no need for an equivariant neural network, as required by diffusion models that work on Cartesian coordinates of protein structure 14,15 .”
Diffusion Models: A Comprehensive Survey of Methods and Applications (2022, background): “Interdisciplinary Applications Molecular Graph Modeling [107],[94],[3],[259],[233], [247],[210],[152] Material Design [256],[153] Medical Image Reconstruction [224],[36],[37],[38],[178],[257]”
A Survey on Generative Diffusion Models (2022, background): “One approach is to generate the positions of molecules in three-dimensional space, capturing the conformation of molecules in space [23], [135], [198].”
Structure-based drug design with equivariant diffusion models (2022, unknown): “Similarly, 3D diffusion models have been applied to generative design of larger biomolecular structures, such as antibodies [48] and other proteins [58, 74].”
Diffusion models in medical imaging: A comprehensive survey (2022, background): “the platforms presented in [75, 170] can be used for designing drugs and vaccines.”