Conformational biases of tau protein’s microtubule binding repeat regions

O. Cehlar^1,2, R. Skrabana^1,2, M. Novak¹

¹ Institute of Neuroimmunology, Slovak Academy of Sciences, Dubravska cesta 9, 845 10 Bratislava, Slovakia

² Axon Neuroscience R&D Services SE, Dvorakovo Nabrezie 10, 811 02 Bratislava, Slovakia

ondrej.cehlar@savba.sk

Accumulation of intrinsically disordered protein tau in the form of insoluble aggregates is a common feature of neurodegenerative tauopathies. Monoclonal antibody DC8E8 is able to inhibit tau-tau interaction and therefore it holds promise for the immunotherapy of Alzheimer’s disease. The active vaccine based on the DC8E8 epitope peptide has successfully passed the phase 1 clinical trial [1]. Minimal epitope of DC8E8 represents amino acid motif HXPGGG that is present in each of the four microtubule binding repeats (MTBRs) of tau. However, the affinity of DC8E8 for its MTBR epitopes differs and descends as follows: MTBR2 > MTBR1 > MTBR3 > MTBR4. These differences in the antibody affinity for highly homologous epitopes can be attributed to different conformational biases of epitope peptides for the bound conformation. The crystal structure was solved so far only for the complex of MTBR2 peptide with DC8E8 Fab. We have performed 300 ns long molecular dynamics simulations of 18 amino acids containing peptides from all four MTBRs in NAMD program with CHARMM36m force field suitable for simulation of intrinsically disordered proteins [2].  The percentage of sampled bound-like conformation was compared with the antibody affinity to different MTBRs. Unravelling the unique mode of recognition of DC8E8 antibody and conformational biases of tau protein repeat regions can aid to reveal the hindered structural features of tau protein biology.

1. Novak P, Schmidt R, Kontsekova E, Zilka N, Kovacech B, Skrabana R, Vince-Kazmerova Z, Katina S, Fialova L, Prcina M, et al., Lancet Neurology, 16, (2017), 123-134.

2. Huang, J., Rauscher, S., Nawrocki, G., Ran, T., Feig, M., de Groot, B. L., Grubmuller, H. & MacKerell, A. D., Nat Methods, 14, (2017), 71.

Acknowledgement: This work was supported by the VEGA No. 2/0177/15. Calculations were performed in the Computing Centre of the Slovak Academy of Sciences using the supercomputing infrastructure acquired in project ITMS 26230120002 and 26210120002 (Slovak infrastructure for high-performance computing) supported by the Research & Development Operational Programme funded by the ERDF.