Fully differentiable full-atom protein backbone generation

By Namrata Anand, Raphael R. Eguchi, Po-Ssu Huang. 2019

55 citations recorded by the source corpus.

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Citations in the corpus

De novo protein design by deep network hallucination (2020, background): “The first component to the score in Eq.(6) -- DKL(P||Q) -- is the same as in Eq.”
De novo protein design by inversion of the AlphaFold structure prediction network (2022, methodology): “Deep learning methods can also be used for sequence generation given a target backbone; through the interpolation between sequence and structural data, sequences that fit a target topology can be found (Anand et al., 2022; Dauparas et al., 2022; Ingraham et al., 2019).”
Recent advances in de novo protein design: Principles, methods, and applications (2021, background): “A generative adversarial network (81) model builds protein structures represented as pairwise distances between all backbone atoms.”
Deep learning methods for designing proteins scaffolding functional sites (2021, methodology): “Generative adversarial networks (GANs) and variational autoencoders (VAEs) trained on specific fold families have been used to design biophysically plausible protein backbones (45, 46), but not ones containing functional sites.”
Deep Learning in Protein Structural Modeling and Design (2020, background): “sequence design and folding Anand et al.(68)”
Protein structure generation via folding diffusion (2022, background): “However, due to the incredibly complex structure of proteins, these commonly do not directly generate protein structures, but rather constraints (such as pairwise distance between residues) that are heavily post-processed to obtain structures (Anand et al., 2019; Lee & Kim, 2022).”
Generating Novel, Designable, and Diverse Protein Structures by Equivariantly Diffusing Oriented Residue Clouds (2023, background): “Anand et al. (2019) later introduced an additional refinement network to improve coordinate reconstruction.”
AlphaDesign: A de novo protein design framework based on AlphaFold (2021, background): “[60, 63] have trained generative adversarial networks to generate realistic backbone distance maps and coordinates.”