The protein folding problem.
By Ken A. Dill, S. Ozkan, M. Shell, Thomas R. Weikl. 2008
1,379 citations recorded by the source corpus.
Subfields: Molecular Biology
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
- Andriy Kryshtafovych
- Krzysztof Fidelis
- Damiano Piovesan
- Silvio C. E. Tosatto
- Torsten Schwede
- Sergei Grudinin
- Giovanni Minervini
- Yoshua Bengio
Citations in the corpus
- CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction (2020, background): “Protein tertiary structures can be experimentally determined using nuclear magnetic resonance, X-ray crystallography, and cryogenic electron microscopy [2]; however, these technologies are usually costly and time-consuming and cannot keep pace with the rapid accumulation of protein sequences [3].”
- How RNA binding proteins interact with RNA: molecules and mechanisms (2020, background): “, 2014) and contribute 1–2 kcal/ mol per interaction (Dill et al., 2008).”
- Deep convolutional networks for quality assessment of protein folds (2018, background): “The protein folding problem remains one of the outstanding challenges in structural biology [1].”
- Structural validation and assessment of AlphaFold2 predictions for centrosomal and centriolar proteins and their complexes (2022, background): “As the key information about the protein ’ s three-dimensional shape is deeply encoded into its sequence 12 , it should be possible to computationally predict the protein structure from its sequence.”
- The RING 2.0 web server for high quality residue interaction networks (2016, background): “While the energy contribution of a single interaction is almost negligible, together they determine the three-dimensional protein structure (2).”
- Small angle X-ray scattering and cross-linking for data assisted protein structure prediction in CASP 12 with prospects for improved accuracy (2018, background): “Although SAXS has the potential to provide restraints for computational algorithms, 44 – 46 the SAXS data collected was complicated by a high level of flexibility in the targets and/or multimerization.”