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PIP-on-a-chip: A Label-free Study of Protein-phosphoinositide Interactions
Published on: July 27, 2017
Protonation and pK changes in protein-ligand binding.
Alexey V Onufriev1, Emil Alexov
1Department of Computer Science and Physics, 2050 Torgersen Hall, Virginia Tech, Blacksburg, VA 24061, USA. alexey@cs.vt.edu
Protein-ligand binding alters receptor and ligand ionization states, impacting binding affinity. These changes are pH-dependent and can occur distant from the binding site, influencing drug design strategies.
Area of Science:
- Biochemistry
- Computational Biology
- Structural Biology
Background:
- Protein-ligand complex formation induces significant changes in the ionization states of amino acid residues.
- Understanding these alterations in pK values and protonation states is crucial for comprehending molecular interactions.
Purpose of the Study:
- To review the physical origins, computational prediction methods, and impact of pK and protonation state changes during protein-ligand binding.
- To analyze the prevalence, magnitude, and distribution of these ionization changes.
- To explore the pH-dependence of binding and its implications for drug design.
Main Methods:
- Review of experimental and theoretical studies on protein-ligand binding.
- Discussion of computational approaches for predicting pK and protonation state shifts.
- Analysis of statistical data on the prevalence and location of ionization changes.
Main Results:
- Ionization state changes upon binding are common but not universal, varying by complex and methodology.
- These changes can extend beyond the immediate binding interface.
- Binding affinity is often pH-dependent, with a tendency for minimal ionization changes at physiologically relevant pH.
Conclusions:
- Accounting for protonation state changes is essential for accurate modeling of protein-ligand interactions and binding affinities.
- Native interactions may have evolved to minimize ionization energy costs at physiological pH.
- Improved prediction of these changes can enhance structure-based drug design outcomes.
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