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What stabilizes close arginine pairing in proteins?

Cited 39 time in Web of Science Cited 41 time in Scopus

Lee, Dong Seon; Lee, Ju Yong; Seok, Chaok

Issue Date
Royal Society of Chemistry
Physical Chemistry Chemical Physics, Vol.15 No.16, pp.5844-5853
Close stacking of arginine residues are often observed in protein structures despite the highly repulsive nature of the close like-charged groups. Physical factors stabilizing the close guanidinium ions of arginine side-chains have been previously studied in water and in protein-like environments, and the hydration free energy has been emphasized to be an important factor. However, how close arginine pairs are stabilized in real proteins has not been fully understood yet. In this paper, we show that arginine pairs are more frequently found in the protein interior than expected from the frequency of unpaired arginines buried inside protein through a statistical analysis of the protein structure database. We then confirm that 4 selected arginine pairs buried in the protein are indeed positively charged rather than neutralized, by molecular dynamics simulations and pK(a) estimation with molecular mechanics-Poisson-Boltzmann calculations. Further energy decomposition analysis shows that the hydration free energy may not be strong enough to overcome the repulsive Coulomb interaction between the positively charged arginine residues buried inside the protein. Instead, a highly polar interaction network is identified around each buried arginine pair, and the electrostatic interactions within such network are strong enough to stabilize the repulsive interaction of the buried arginine pair for the 4 selected cases. The polar interaction network is highly conserved evolutionarily in some proteins, implicating their roles in protein stabilization or biochemical function.
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  • Graduate School of Convergence Science & Technology
  • Dept. of Molecular and Biopharmaceutical Sciences
Research Area AI models for drug discovery, Free energy calculation, Molecular dynamics, 분자동역학, 신약개발을 위한 AI 모델, 자유에너지 계산


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