Predictive QM/MM Modeling of Modulations in Protein–Protein Binding by Lysine Methylation. Issue 3 (5th February 2021)

• Record Type: Journal Article
• Authors: Rahman, Sanim; Wineman-Fisher, Vered; Al-Hamdani, Yasmine; Tkatchenko, Alexandre; Varma, Sameer
• Is Part Of: Journal of molecular biology. Volume 433:Issue 3(2021)
• Publication Date: 2021-02-05
• Subjects: Protein–protein interactions -- Methylation -- Polarization -- Electrostatics -- Quantum mechanics; Molecular biology -- Periodicals; Biology -- Periodicals; Biochemistry -- Periodicals; Bacteriology -- Periodicals; Molecular Biology -- Periodicals; Biochemistry -- Periodicals; Biologie moléculaire -- Périodiques; Biologie -- Périodiques; Biochimie -- Périodiques; Moleculaire biologie; Biochemistry; Biology; Molecular biology; Periodicals; 572.805
• DOI: 10.1016/j.jmb.2020.166745
• Languages: English
• ISSNs: 0022-2836

Abstract

Graphical abstract: Highlights: Method to predict effect of methylation on protein–protein binding free energy. Strength of the method lies in description of local interactions at the QM level. Predicts fourteen out of sixteen free energy changes in agreement with experiment. Critical assessment of cases yields overarching principles that drive selectivity. Abstract: Lysine methylation is a key regulator of protein–protein binding. The amine group of lysine can accept up to three methyl groups, and experiments show that protein–protein binding free energies are sensitive to the extent of methylation. These sensitivities have been rationalized in terms of chemical and structural features present in the binding pockets of methyllysine binding domains. However, understanding their specific roles requires an energetic analysis. Here we propose a theoretical framework to combine quantum and molecular mechanics methods, and compute the effect of methylation on protein–protein binding free energies. The advantages of this approach are that it derives contributions from all local non-trivial effects of methylation on induction, polarizability and dispersion directly from self-consistent electron densities, and at the same time determines contributions from well-characterized hydration effects using a computationally efficient classical mean field method. Limitations of the approach are discussed, and we note that predicted free energies of fourteen out of the sixteen cases agree with experiment. Critical assessment of these cases leads to the following overarching principles that drive methylation-state recognition by protein domains. Methylation typically reduces the pairwise interaction between proteins. This biases binding toward lower methylated states. Simultaneously, however, methylation also makes it easier to partially dehydrate proteins and place them in protein–protein complexes. This latter effect biases binding in favor of higher methylated states. The overall effect of methylation on protein–protein binding depends ultimately on the balance between these two effects, which is observed to be tuned via several combinations of local features.