We use the very efficient docking method PIPER [1] to systematically sample the rotational/translational space in protein-protein association using a physics based energy function. The global sampling enables us to analyze the distribution of low energy states rather than focusing only on a limited number of minima [2]. First we use comparison to experimental intermolecular paramagnetic relaxation enhancement (PRE) data to shows that the non-native states generated by the docking are real since they represent encounter complexes occurring in the process of protein-protein association [3]. Second, we cluster the low energy states and show that under very mild assumptions the relative population of clusters is proportional to their probability [2]. Third, we discuss how similar statistics can be used to determine the most likely interaction sites, even without the knowledge of partner proteins. Fourth, we expand the analysis to series of closely related proteins, and show that accounting for the populations of near-native clusters also allows for determining the relative binding free energies. Fifth, as a very simple application of the statistical approach, we discuss how the method can be used to differentiate between interacting and non-interacting protein pairs. Finally we further explore the energy landscape in the vicinity of the native state, and show that the energy surface is canyon-like rather than funnel-shaped, with the low energy regions lying in a lower dimensional subspace [3]. Thus, proteins associate along preferred pathways rather than sampling the available space, similar to sliding of a protein along DNA in the process of protein-DNA recognition. 1. Kozakov D, Brenke R, Comeau SR, Vajda S: PIPER: An FFT-based protein docking program with pairwise potentials. Proteins (2006) 65(2):392-406. 2. Vajda S, Hall DR, Kozakov D: Sampling and scoring: A marriage made in heaven. Proteins (2013) 81(11):1874-1884. 3. Kozakov D, Li K, Hall DR, Beglov D, Zheng J, Vakili P, Schueler-Furman O, Paschalidis I, Clore GM, Vajda S: Encounter complexes and dimensionality reduction in protein-protein association. eLife (2014) 3(e01370.

Statistical mechanics of protein-protein association

251st National Meeting (2016)

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