Identification of amyloidogenic peptide sequences using a coarse‐grained physicochemical model

Abstract

Cross-beta amyloid is implicated in over 20 human diseases. Experiments suggest that specific sequence elements within amyloidogenic proteins play a major role in seeding amyloid formation. Identifying these seeding sequences is important for rationalizing the molecular mechanisms of amyloid formation and for elaborating therapeutic strategies that target amyloid. Theoretical techniques play an important role in facilitating the identification and structural characterization of putative seeding sequences; most amyloid species are not amenable to high resolution experimental structure techniques. In this study we have combined a coarse-grained physicochemical protein model with a highly efficient Monte Carlo sampling technique to identify amyloidogenic sequences in four proteins for which respective experimental peptide fragmentation data exist. Peptide sequences were defined as amyloidogenic if the ensemble structure predicted for three interacting peptides described a stable and regular three-stranded beta-sheet. For such peptides, free energies were calculated to provide a measure of amyloid propensity. The overall agreement between the experimental and predicted data is good, and we correctly identify several self-recognition motifs proposed to define the cross-beta amyloid fibril architectures of two of the proteins. Our results compare very favorably with those obtained using atomistic molecular dynamics methods, though our simulations are 30-40 times faster.

Aggregation in Protein-Based Biotherapeutics: Computational Studies and Tools to Identify Aggregation-Prone Regions

“…In some cases, the aggregation mechanism has been studied using coarse-grained lattice models. [14][15][16][17] These studies are summarized in the section CoarseGrained Models. In other studies (presented in section Atomistic Simulations of Aggregation in Peptides and Proteins), it has been studied via atomistic simulations using small peptides that have been experimentally shown to be amyloidogenic.…”

“…The authors conclude that strengthening hydrophobic interactions within the molecules could suppress aggregation by stabilizing their native states. 16 Cellmer et al 14 have studied a 64-mer threedimensional (3D) lattice protein with a specific amino acid sequence. The protein molecules were depicted as on-lattice single and multiple chains.…”

“…Using the same system, Bratko et al 32 have shown that a single amino acid substitution may significantly alter aggregation behavior of a protein. 32 Clarke and Parker 16 have performed MC sampling studies using coarse-grained models of four experimentally characterized amyloidogenic proteins with available peptide fragmentation data. The protein models were constructed using reduced Ramachandran angle and force field for tertiary structure prediction methodology.…”

“…[45][46][47] For example, Tjernberg et al 46 have experimentally studied aggregation in four peptides with the following sequences KFFE, KVVE, KLLE, and KAAE. 46 These peptide sequences are based on A$ [16][17][18][19][20][21][22] . Their experiments indicate that Phe and Val containing peptides (KFFE and KVVE) form amyloid-like fibrils but not the ones containing Ala and Leu (KAAE and KLLE).…”

“…Other methods based on pattern recognition, 3D profiles, and molecular simulations are also emerging. 11,16,21,34,35,41,[58][59][60] To be able to determine which prediction tool performs the best, these tools need to be evaluated on standardized dataset(s) in a statistically rigorous manner. These testing datasets should contain different protein sequences and structures than those used in the training datasets used to develop the tools.…”