Very fast empirical prediction and rationalization of protein pKa values

By Hui Li, A. D. Robertson, Jan H. Jensen. 2005

2,011 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, 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 Computational Theory and Mathematics

1. Walter Thiel
2. Hans Senn
3. Adrian H. Elcock
4. Sean R. McGuffee

In Materials Chemistry

1. Sean R. McGuffee
2. Adrian H. Elcock
3. Walter Thiel
4. Hans Senn

In Molecular Biology

1. Sean R. McGuffee
2. Adrian H. Elcock
3. Hans Senn
4. Walter Thiel
5. Xin Li
6. Keiji Morokuma
7. Lung Wa Chung
8. Hajime Hirao

Citations in the corpus

• Graphical analysis of pH-dependent properties of proteins predicted using PROPKA (2011, background): “PROPKA [7-9] is one of the popular protein pKa prediction software packages mainly because of its speed and accuracy compared to other methods [4,6], but also because it offers a structural rationalization of the predicted pKa values.”
• Redox chemistry of biological thiols (2010, neutral): “, [93]).”
• Diffusion, Crowding & Protein Stability in a Dynamic Molecular Model of the Bacterial Cytoplasm (2010, methodology): “; partial charges for the modified bases of tRNAs, such as pseudouridine, were assigned based on similarity to functional groups already represented in the parameter sets. The protonation states of all protein ionizable residues were assigned using the fast empirical algorithm PropKa”
• QM/MM Methods for Biological Systems (2006, methodology): “An alternative is the calculation of local pKa values with a fast increment method, such as propka [412, 722].”