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1.P. Ewald, “Die Berechnung optischer und elektrostatischer Gitterpotentiale,” Ann. Phys. 369, 253-287 (1921).
2.T. A. Darden, D. M. York, and L. G. Pedersen, “Particle mesh Ewald: An N⋅log(N) method for Ewald sums in large systems,” J. Chem. Phys. 98, 10089-10092 (1993).
3.M. P. Allen and D. J. Tildesley, Computer Simulation of Liquids (Clarendon Press, New York, NY, USA, 1989).
4.J. S. Hub, B. L. de Groot, H. Grubmuller, and G. Groenhof, “Quantifying artifacts in Ewald simulations of inhomogeneous systems with a net charge,” J. Chem. Theory Comput. 10(1), 381-390 (2014).
5.P. E. Smith and B. M. Pettit, “Ewald artifacts in liquid state molecular dynamics simulations,” J. Chem. Phys. 105, 4289 (1996).
6.M. A. Villarreal and G. G. Montich, “On the Ewald artifacts in computer simulations. the test-case of the octaalanine peptide with charged termini,” J. Biomol. Struct Dyn. 23(2), 135-142 (2005).
7.W. Weber, P. H. Hunenberger, and J. A. McCammon, “Molecular dynamics simulations of a polyalanine octapeptide under Ewald boundary conditions: Influence of artificial periodicity on peptide conformation,” J. Phys. Chem. B104, 3668-3675 (2000).
8.H. X. Zhou, “Influence of crowded cellular environments on protein folding, binding, and oligomerization: biological consequences and potentials of atomistic modeling,” FEBS Lett. 587(8), 1053-1061 (2013).
9.Y. C. Kim, R. B. Best, and J. Mittal, “Macromolecular crowding effects on protein-protein binding affinity and specificity,” J. Chem. Phys. 133(20), 205101 (2010).
10.Y. C. Kim and J. Mittal, “Crowding induced entropy-enthalpy compensation in protein association equilibria,” Phys. Rev. Lett. 110, 208102 (2013).
11.P. E. Smith, “The alanine dipeptide free energy surface in solution,” J. Chem. Phys. 111, 5568-5579 (1999).
12.D. J. Tobias and C. L. Brooks, “Conformational equilibrium in the alanine dipeptide in the gas phase and aqueous solution: A comparison of theoretical results,” J. Phys. Chem. 96, 3864-3870 (1992).
13.C. Chipot and A. Pohorille, “Conformational equilibria of terminally blocked single amino acids at the water-hexane interface. A molecular dynamics study,” J. Phys. Chem. B 102(1), 281-290 (1998).
14.H. Jang and T. B. Woolf, “Multiple pathways in conformational transitions of the alanine dipeptide: an application of dynamic importance sampling,” J. Comput. Chem. 27(11), 1136-1141 (2006).
15.C. E. Felder, J. Prilusky, I. Silman, and J. L. Sussman, “A server and database for dipole moments of proteins,” Nucleic Acids Res. 35(Web Server issue), W512-W521 (2007).
16.AMBER 12 (University of California, San Francisco, 2012).
17.W. L. Jorgensen, J. Chandrasekhar, J. D. Madura, R. W. Impey, and M. L. Klein, “Comparison of simple potential functions for simulating liquid water,” J. Chem. Phys. 79(2), 926-935 (1983).
18.J.-P. Ryckaert, G. Ciccotti, and H. J. C. Berendsen, “Numerical integration of the cartesian equations of motion of a system with constraints: Molecular dynamics of n-alkanes,” J. Comput. Phys. 23(3), 327-341 (1977).
19.WHAM: an implementation of the weighted histogram analysis method, version 2.0.6 (2012).

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Standard Molecular Dynamics simulations (MD) are usually performed under periodic boundary conditions using the well-established “Ewald summation”. This implies that the distance among each element in a given lattice cell and its corresponding element in another cell, as well as their relative orientations, are constant. Consequently, protein-protein interactions between proteins in different cells—important in many biological activities, such as protein cooperativity and physiological/pathological aggregation—are severely restricted, and features driven by protein-protein interactions are lost. The consequences of these restrictions, although conceptually understood and mentioned in the literature, have not been quantitatively studied before. The effect of protein-protein interactions on the free energy landscape of a model system, dialanine, is presented. This simple system features a free energy diagram with well-separated minima. It is found that, in the case of absence of peptide-peptide (p-p) interactions, the ψ = 150° dihedral angle determines the most energetically favored conformation (global free-energy minimum). When strong p-p interactions are induced, the global minimum switches to the ψ = 0° conformation. This shows that the free-energy landscape of an individual molecule is dramatically affected by the presence of other freely interacting molecules of its same type. Results of the study suggest how taking into account p-p interactions in MD allows having a more realistic picture of system activity and functional conformations.


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