(570e) Computational Modeling of Amyloid Aggregation Kinetics to Gain Insights into the Effect of Ions on Amyloid Aggregation

Sharma, A., Georgia Institute of Technology
Behrens, S. H., Georgia Institute of Technology
Chernoff, Y. O., Georgia Institute of Technology
Bommarius, A. S., Georgia Institute of Technology
Rose, H. B., Georgia Institute of Technology
The aggregation of amyloidogenic proteins through cross beta-sheet interactions depends upon two main factors: primary sequence of the protein and environmental conditions. The ordered fibrous aggregates formed as a result of this process are involved in a number of neurodegenerative diseases in mammals. The process of amyloid formation involves a two-step pattern of initial nucleation corresponding to a lag phase followed by a fiber elongation phase. This behavior has been modeled empirically using mathematical functions such as the logistic function, and other kinetic models, such as the Finke-Watzky (F-W) 2-step mechanism, which capture the near sigmoidal shape of the aggregation curve. However, these empirical models do not provide any information about the underlying mechanism of aggregation.

Previous work from our group on amyloid formation by Sup35NM protein containing the prion domain of yeast prion protein Sup35p has shown that strongly hydrated anions (kosmotropes) result in fast aggregation, whereas poorly hydrated anions (chaotropes) result in slower aggregation1,2. We have also previously shown that the ionic composition of the solution influences the formation of amyloid variants, or “strains”2,3. In this work, we have compared different population balance models for amyloid aggregation kinetics based on mechanisms comprising of nucleation, elongation, breakage, and secondary nucleation. Fitting kinetic data to population balance models has previously been used to arrive at plausible mechanisms for aggregation of amyloid beta peptides4,5. Here global fitting of aggregation data is performed to arrive at the most plausible model which is used to fit aggregation data obtained in the presence of different ions. Comparison of the fit parameters is then performed to understand how the presence of ions affects some of the above mentioned underlying processes.

[1] Yeh, V., Broering, J.M., Romanyuk, A., Chen, B., Chernoff, Y.O. and Bommarius, A.S., 2010. The Hofmeister effect on amyloid formation using yeast prion protein. Protein Science, 19(1), pp.47-56. doi: 10.1002/pro.281

[2] Rubin, J., Khosravi, H., Bruce, K. L., Lydon, M. E., Behrens, S. H., Chernoff, Y. O. and Bommarius, A. S., 2013. Ion-specific effects on prion nucleation and strain formation. Journal of Biological Chemistry, 288(42), pp.30300-30308. doi: 10.1074/jbc.M113.467829

[3] Sharma, A., Bruce, K. L., Chen, B., Gyoneva, S., Behrens, S. H., Bommarius, A. S. and Chernoff, Y. O., 2016. Contributions of the Prion Protein Sequence, Strain, and Environment to the Species Barrier. Journal of Biological Chemistry, 291(3), pp.1277-1288. doi: 10.1074/jbc.M115.684100

[4] Cohen, S.I., Linse, S., Luheshi, L.M., Hellstrand, E., White, D.A., Rajah, L., Otzen, D.E., Vendruscolo, M., Dobson, C.M. and Knowles, T.P., 2013. Proliferation of amyloid-β42 aggregates occurs through a secondary nucleation mechanism. Proceedings of the National Academy of Sciences, 110(24), pp.9758-9763. doi: 10.1073/pnas.1218402110

[5] Meisl, G., Yang, X., Hellstrand, E., Frohm, B., Kirkegaard, J.B., Cohen, S.I., Dobson, C.M., Linse, S. and Knowles, T.P., 2014. Differences in nucleation behavior underlie the contrasting aggregation kinetics of the Aβ40 and Aβ42 peptides. Proceedings of the National Academy of Sciences, 111(26), pp.9384-9389. doi: 10.1073/pnas.1401564111