The Protein-Folding Problem, 50 Years On

By Ken A. Dill, Justin L. MacCallum. 2012

1,526 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

AlphaFold2 and its applications in the fields of biology and medicine (2023, background): “The high appraisal to AF2 is not excessive because understanding the three-dimensional (3D) structures of proteins is one of the most challenging issues in the field of biology, which has puzzled scientists for 50 years.(4) Although multiple technologies including nuclear magnetic resonance (NMR),(5”
Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields (2015, methodology): “Our method differs from Zhou’s deep learning method (denoted as ICML2014)36 in the following aspects: (1) our method places only input features in a visible layer and treats the SS labels as hidden states while Zhou’s method places both the input features and SS labels in a visible layer; (2) our me”
End-to-end differentiable learning of protein structure (2018, background): “Errors in these critical non-overlapping regions can be masked by large overlapping regions, inflating overall accuracy (Contreras-Moreira et al., 2005; Dill and MacCallum, 2012; Liu et al., 2018a; Perez et al., 2016).”
MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins (2014, background): “Determining protein structure from its amino acid sequence remains an unsolved problem within bioinformatics (Dill and MacCallum, 2012).”
WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020, neutral): “This is known as the protein folding problem, and has sometimes been referred to as the Holy Grail of structural biology (Dill and MacCallum, 2012).”
An introduction to deep learning on biological sequence data: examples and solutions (2017, background): “Therefore de novo prediction of protein structure from sequence is a problem of great biological interest (Dill and MacCallum, 2012).”
AlphaFold2: A Role for Disordered Protein/Region Prediction? (2022, background): “Predicting the three-dimensional structure of a protein from its primary amino acid sequence is a grand challenge in molecular structural biology dating back to the late 1950’s [1,2].”
Inverse statistical problems: from the inverse Ising problem to data science (2017, background): “Given the experimental effort required, the determination of a protein’s structure from its sequence alone has been a key challenge to computational biology for several decades [60, 65].”