SPIN2: Predicting sequence profiles from protein structures using deep neural networks

By James C. O’Connell, Zhixiu Li, Jack Hanson, Rhys Heffernan, James G. Lyons, Kuldip K. Paliwal, Abdollah Dehzangi, Yuedong Yang, Yaoqi Zhou. 2018

101 citations recorded by the source corpus.

Subfields: Molecular Biology, Representation & Self-Supervised Learning, Computational Biology & Bioinformatics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

In Representation & Self-Supervised Learning

In Computational Biology & Bioinformatics

Citations in the corpus

Learning inverse folding from millions of predicted structures (2022, methodology): “Early machine learning approaches in structure-based protein sequence design used fragment-based and energy-based global features derived from structures (Li et al., 2014; O’Connell et al., 2018).”
Protein sequence design with a learned potential (2022, methodology): “For each test case backbone, we selected 4 out of 50 designs for further characterization based on ranking by the model PLL and other metrics (Supplementary Tables 22 – 26).”
Learning from Protein Structure with Geometric Vector Perceptrons (2020, methodology): “…residue contacts (Olechnovi ˇ c & Venclovas, 2017), orientations or positions collectively projected to local coordinates (Karasikov et al., 2019), physics-inspired energy terms (O’Connell et al., 2018; Uziela et al., 2017), or context-free grammars of protein topology (Greener et al., 2018).”
Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design (2021, background): “For proteins, O’Connell et al. (2018); Ingraham et al. (2019); Strokach et al. (2020); Karimi et al. (2020); Cao et al. (2021) further developed models conditioned on a backbone structure or protein fold.”
Using AlphaFold for Rapid and Accurate Fixed Backbone Protein Design (2021, background): “Design tasks tackled with deep learning include fixed backbone design (O’Connell et al., 2018; Ingraham et al., 2019; Qi and Zhang, 2020; Norn et al., 2021), antibody design (Wang et al.”
Protein sequence design with a learned potential (2020, unknown): “Other deep learning approaches have been developed for sequence design or rotamer packing [ 90, 91, 92, 93, 94, 95], but most of these methods’ designs have not been comprehensively validated by a range of biochemical metrics or by folding in silico or in vitro .”
A structure-based deep learning framework for protein engineering (2019, result): “Taken together, these data confirm our model classifies wild-type amino acids with unprecedented accuracy compared to previously reported deep learning approaches 9, 12-14 .”
AlphaDesign: A graph protein design method and benchmark on AlphaFoldDB (2022, methodology): “The MLP methods have a high inference speed, but their accuracy is relatively low due to the partial considering of structural information.”
Generative Models for Graph-Based Protein Design (2019, background): “Recently [8, 9, 14] proposed neural network-based models for sequences given 3D structure, where the amino acids are modeled independently of one another.”