Application of docking methodologies to modeled proteins
By Amar Singh, Taras Dauzhenka, Petras J. Kundrotas, Michael J.E. Sternberg, Ilya A. Vakser. 2020
35 citations recorded by the source corpus.
Protein docking is essential for structural characterization of protein interactions. Besides providing the structure of protein complexes, modeling of proteins and their complexes is important for understanding the fundamental principles and specific aspects of protein interactions. The accuracy of protein modeling, in general, is still less than that of the experimental approaches. Thus, it is important to investigate the applicability of docking techniques to modeled proteins. We present new comprehensive benchmark sets of protein models for the development and validation of protein docking, as well as a systematic assessment of free and template-based docking techniques on these sets. As opposed to previous studies, the benchmark sets reflect the real case modeling/docking scenario where the accuracy of the models is assessed by the modeling procedure, without reference to the native structure (which would be unknown in practical applications). We also expanded the analysis to include docking of protein pairs where proteins have different structural accuracy. The results show that, in general, the template-based docking is less sensitive to the structural inaccuracies of the models than the free docking. The near-native docking poses generated by the template-based approach, typically, also have higher ranks than those produces by the free docking (although the free docking is indispensable in modeling the multiplicity of protein interactions in a crowded cellular environment). The results show that docking techniques are applicable to protein models in a broad range of modeling accuracy. The study provides clear guidelines for practical applications of docking to protein models.
Subfields: Biomedical Engineering, Materials Chemistry, Molecular Biology
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In Materials Chemistry
- Patrick Bryant
- Gabriele Pozzati
- Arne Elofsson
- Björn Wallner
- Liam J. McGuffin
- Yang Zhang
- David Ménendez Hurtado
- Daniel B. Roche
In Molecular Biology
- Gabriele Pozzati
- Patrick Bryant
- Arne Elofsson
- Björn Wallner
- Liam J. McGuffin
- David Ménendez Hurtado
- Torsten Schwede
- Yang Zhang
Citations in the corpus
- Improved prediction of protein-protein interactions using AlphaFold2 (2021, methodology): “As the bound form of the proteins is used, this should represent an easy case for GRAMM based docking, and performance drops signi�cantly when unbound structures or models are used 46 .”