Different force fields give rise to different amyloid aggregation pathways in molecular dynamics simulations

bioRxiv (Cold Spring Harbor Laboratory)

Abstract

Abstract The progress towards understanding the molecular basis of Alzheimers’s disease is strongly connected to elucidating the early aggregation events of the amyloid- β (A β ) peptide. Molecular dynamics (MD) simulations provide a viable technique to study the aggregation of A β into oligomers with high spatial and temporal resolution. However, the results of an MD simulation can only be as good as the underlying force field. A recent study by our group showed that none of the force fields tested can distinguish between aggregation-prone and non-aggregating peptide sequences, producing the same and in most cases too fast aggregation kinetics for all peptides. Since then, new force fields specially designed for intrinsically disordered proteins such as A β were developed. Here, we assess the applicability of these new force fields to studying peptide aggregation using the A β 16 − 22 peptide and mutations of it as test case. We investigate their performance in modeling the monomeric state, the aggregation into oligomers, and the stability of the aggregation end product, i.e., the fibrillar state. A main finding is that changing the force field has a stronger effect on the simulated aggregation pathway than changing the peptide sequence. Also the new force fields are not able to reproduce the experimental aggregation propensity order of the peptides. Dissecting the various energy contributions shows that AMBER99SB- disp overestimates the interactions between the peptides and water, thereby inhibiting peptide aggregation. More promising results are obtained with CHARMM36m and especially its version with increased protein–water interactions. It is thus recommended to use this force field for peptide aggregation simulations and base future reparameterizations on it.

Authors 4

1. Suman Samantray Aachen AICES Graduate School

   Forschungszentrum Jülich · Aachen Institute for Advanced Study in Computational Engineering Science · RWTH Aachen University

   Affiliation as printed

   AICES Graduate School, RWTH Aachen University, SchinkelstraSSe 2, 52062 Aachen, Germany

   Institute of Biological Information Processing: Structural Biochemistry (IBI-7), Forschungszentrum Jülich, 52428 Jülich, Germany

2. Feng Yin

   Forschungszentrum Jülich

   Affiliation as printed

   Institute of Biological Information Processing: Structural Biochemistry (IBI-7), Forschungszentrum Jülich, 52428 Jülich, Germany

3. Batuhan Kav

   Forschungszentrum Jülich

   Affiliation as printed

   Institute of Biological Information Processing: Structural Biochemistry (IBI-7), Forschungszentrum Jülich, 52428 Jülich, Germany

4. Birgit Strodel corresponding

   Forschungszentrum Jülich · Heinrich Heine University Düsseldorf

   Affiliation as printed

   Institute of Biological Information Processing: Structural Biochemistry (IBI-7), Forschungszentrum Jülich, 52428 Jülich, Germany

   Institute of Theoretical and Computational Chemistry, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany