A comprehensive assessment of long intrinsic protein disorder from the DisProt database

By Marco Necci, Damiano Piovesan, Zsuzsanna Dosztányi, Peter Tompa, Silvio C. E. Tosatto. 2018

60 citations recorded by the source corpus.

Subfields: Materials Chemistry, 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 Materials Chemistry

1. Zsuzsanna Dosztányi
2. Gábor Erdös
3. Kuldip K. Paliwal
4. Jack Hanson
5. Bálint Mészáros
6. Yaoqi Zhou
7. Thomas Litfin
8. Lukasz Kurgan

In Molecular Biology

1. Yaoqi Zhou
2. Kuldip K. Paliwal
3. Jack Hanson
4. Gábor Erdös
5. Zsuzsanna Dosztányi
6. Thomas Litfin
7. Bálint Mészáros
8. Vladimir N Uversky

Citations in the corpus

• SPOT-Disorder2: Improved Protein Intrinsic Disorder Prediction by Ensembled Deep Learning (2019, methodology): “These datasets were the subsets from the established SL477 [16], MobiDB [43], and DisProt Complement [44] sets, respectively, after removing long proteins (>700 residues) and homologous proteins in our training dataset (25% sequence identity cutoff with BLASTClust).”
• IUPred2A: context-dependent prediction of protein disorder as a function of redox state and protein binding (2018, positive): “The efficiencies of IUPred2 and the original IUPred are consistent with earlier independent testing results (22,24), and are virtually the same.”