Modeling implicit reorganization in continuum descriptions of protein-protein interactions

Olson, Mark A.; Reinke, Luke T.

doi:10.1002/(sici)1097-0134(20000101)38:1<115::aid-prot11>3.0.co;2-p

Cited by 26 publications

(27 citation statements)

References 17 publications

(20 reference statements)

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“…As a result, ε = 2 for the protein interior, 51−54 ε = 80 for the exterior aqueous environment and ion concentration of zero were used for further energy calculations. All PB calculations were performed with the PBEQ module; 48,55,56 the van der Waals interaction energy was calculated with the ENERGY module, and molecular surface area was obtained with the SASA module of the CHARMM program.…”

Section: Methodsmentioning

confidence: 99%

Predicting the Impact of Missense Mutations on Protein–Protein Binding Affinity

Petukh

Alexov

et al. 2014

J. Chem. Theory Comput.

111

132

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The crucial prerequisite for proper biological function is the protein’s ability to establish highly selective interactions with macromolecular partners. A missense mutation that alters the protein binding affinity may cause significant perturbations or complete abolishment of the function, potentially leading to diseases. The availability of computational methods to evaluate the impact of mutations on protein–protein binding is critical for a wide range of biomedical applications. Here, we report an efficient computational approach for predicting the effect of single and multiple missense mutations on protein–protein binding affinity. It is based on a well-tested simulation protocol for structure minimization, modified MM-PBSA and statistical scoring energy functions with parameters optimized on experimental sets of several thousands of mutations. Our simulation protocol yields very good agreement between predicted and experimental values with Pearson correlation coefficients of 0.69 and 0.63 and root-mean-square errors of 1.20 and 1.90 kcal mol–1 for single and multiple mutations, respectively. Compared with other available methods, our approach achieves high speed and prediction accuracy and can be applied to large datasets generated by modern genomics initiatives. In addition, we report a crucial role of water model and the polar solvation energy in estimating the changes in binding affinity. Our analysis also reveals that prediction accuracy and effect of mutations on binding strongly depends on the type of mutation and its location in a protein complex.

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Section: Methodsmentioning

confidence: 99%

Predicting the Impact of Missense Mutations on Protein–Protein Binding Affinity

Petukh

Alexov

et al. 2014

J. Chem. Theory Comput.

111

132

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“…a ¼ 0.158 and b ¼ 0.153 were obtained from the fitting of kinesin-microtubule binding data (39). For the PB calculation (42,43), a dielectric constant of ε i ¼ 4 is used for the protein interior (44)(45)(46)(47). A dielectric constant of ε e ¼ 80 is used for the exterior aqueous environment.…”

Section: Tm-actin Binding Calculationmentioning

confidence: 99%

Probing the Flexibility of Tropomyosin and Its Binding to Filamentous Actin Using Molecular Dynamics Simulations

Zheng

Barua

Hitchcock‐DeGregori

2013

Biophysical Journal

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Tropomyosin (Tm) is a coiled-coil protein that binds to filamentous actin (F-actin) and regulates its interactions with actin-binding proteins like myosin by moving between three positions on F-actin (the blocked, closed, and open positions). To elucidate the molecular details of Tm flexibility in relation to its binding to F-actin, we conducted extensive molecular dynamics simulations for both Tm alone and Tm-F-actin complex in the presence of explicit solvent (total simulation time >400 ns). Based on the simulations, we systematically analyzed the local flexibility of the Tm coiled coil using multiple parameters. We found a good correlation between the regions with high local flexibility and a number of destabilizing regions in Tm, including six clusters of core alanines. Despite the stabilization by F-actin binding, the distribution of local flexibility in Tm is largely unchanged in the absence and presence of F-actin. Our simulations showed variable fluctuations of individual Tm periods from the closed position toward the open position. In addition, we performed Tm-F-actin binding calculations based on the simulation trajectories, which support the importance of Tm flexibility to Tm-F-actin binding. We identified key residues of Tm involved in its dynamic interactions with F-actin, many of which have been found in recent mutational studies to be functionally important, and the rest of which will make promising targets for future mutational experiments.

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“…To emphasize its empirical nature, we can point to the fact that the optimum value to use appears to depend on the particular application: for calculating pK a values of ionizable amino acids in proteins, for example, a value of 20 has proven highly successful, 66 whereas in other applications values of 1-8 seem more appropriate. 67 Attempts have been made to account for the varying behaviors by assigning different dielectric values to different protein regions (see, e.g., ref 68).…”

Section: Electrostatic Interactionsmentioning

confidence: 99%

Computer Simulation of Protein−Protein Interactions

2001

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The use of computer simulations in investigations of protein-protein interactions is discussed. First, crystallographic analyses of known protein-protein complexes are summarized with particular emphasis being placed on the atomic nature of the interactions. Models available for describing macromolecular association energetics are then discussed, with special reference to the treatment of electrostatic and nonpolar interactions. The use of these models in combination with efficient search methods is discussed in the context of the so-called protein docking problem and in the description of weaker (i.e., noncrystallizable) protein-protein interactions. Finally, simulations of the dynamics of protein-protein association events are outlined. In all cases, differences are stressed between the atomically detailed view of protein-protein interactions and the view implicit in the use of simpler colloidal models.

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Modeling implicit reorganization in continuum descriptions of protein-protein interactions

Cited by 26 publications

References 17 publications

Predicting the Impact of Missense Mutations on Protein–Protein Binding Affinity

Predicting the Impact of Missense Mutations on Protein–Protein Binding Affinity

Probing the Flexibility of Tropomyosin and Its Binding to Filamentous Actin Using Molecular Dynamics Simulations

Computer Simulation of Protein−Protein Interactions

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