Replica exchange molecular dynamics simulations of amyloid peptide aggregation

Cecchini, Marco; Rao, Francesco; Seeber, Michele; Caflisch, Amedeo

doi:10.1063/1.1809588

Cited by 199 publications

(274 citation statements)

References 62 publications

Supporting

Mentioning

256

Contrasting

Order By: Relevance

“…Similar acceptances have been reported in recent replica simulations of peptide aggregation in a continuum representation 50 . In our reduced units, temperature is expressed in units T 0 , energy in units k B T 0…”

Section: Ii2 Simulationsupporting

confidence: 89%

Effect of Single-Point Sequence Alterations on the Aggregation Propensity of a Model Protein

et al. 2006

View full text Add to dashboard Cite

Sequences of contemporary proteins are believed to have evolved through process that optimized their overall fitness including their resistance to deleterious aggregation.Biotechnological processing may expose therapeutic proteins to conditions that are much more conducive to aggregation than those encountered in a cellular environment. An important task of protein engineering is to identify alternative sequences that would protect proteins when processed at high concentrations without altering their native structure associated with specific biological function. Our computational studies exploit parallel tempering simulations of coarse-grained model proteins to demonstrate that isolated amino-acid residue substitutions can result in significant changes in the aggregation resistance of the protein in a crowded environment while retaining protein structure in isolation. A thermodynamic analysis of protein clusters subject to competing processes of folding and association shows that moderate mutations can produce effects similar to those caused by changes in system conditions, including temperature, concentration, and solvent composition that affect the aggregation propensity. The range of conditions where a protein can resist aggregation can therefore be tuned by sequence alterations although the protein generally may retain its generic ability for aggregation. *dnb@berkeley.edu 2

show abstract

Section: Ii2 Simulationsupporting

confidence: 89%

Effect of Single-Point Sequence Alterations on the Aggregation Propensity of a Model Protein

et al. 2006

View full text Add to dashboard Cite

show abstract

“…To characterize the fibril state of short peptides we used the nematic order parameter P 2 (41). In terms of the unit vector u ជ i linking N and C termini for the ith peptide, P 2 is…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

Monomer adds to preformed structured oligomers of Aβ-peptides by a two-stage dock–lock mechanism

Nguyen

Stock

et al. 2007

Proc. Natl. Acad. Sci. U.S.A.

348

421

View full text Add to dashboard Cite

Nonfibrillar soluble oligomers, which are intermediates in the transition from monomers to amyloid fibrils, may be the toxic species in Alzheimer's disease. To monitor the early events that direct assembly of amyloidogenic peptides we probe the dynamics of formation of (A␤ 16 -22)n by adding a monomer to a preformed (A␤ 16 -22)n؊1 (n ‫؍‬ 4 -6) oligomer in which the peptides are arranged in an antiparallel ␤-sheet conformation. All atom molecular dynamics simulations in water and multiple long trajectories, for a cumulative time of 6.9 s, show that the oligomer grows by a two-stage dock-lock mechanism. The largest conformational change in the added disordered monomer occurs during the rapid (Ϸ50 ns) first dock stage in which the ␤-strand content of the monomer increases substantially from a low initial value. In the second slow-lock phase, the monomer rearranges to form in register antiparallel structures. Surprisingly, the mobile structured oligomers undergo large conformational changes in order to accommodate the added monomer. The time needed to incorporate the monomer into the fluid-like oligomer grows even when n ‫؍‬ 6, which suggests that the critical nucleus size must exceed six. Stable antiparallel structure formation exceeds hundreds of nanoseconds even though frequent interpeptide collisions occur at elevated monomer concentrations used in the simulations. The dock-lock mechanism should be a generic mechanism for growth of oligomers of amyloidogenic peptides.T here is intense interest in determining the structures, kinetics, and growth mechanisms of amyloid fibrils (1-8) because they are associated with a number of diseases such as Alzheimer's (9) and Parkinson's (6) disease as well as prion pathology (10). Recently, significant progress has been made in determining the structures of amyloid fibrils (1,(11)(12)(13). The structures of fibrils of a number of peptides including A␤ 1-40 and A␤ 1-42 that have been proposed using constraints obtained from solid state NMR (13) are also consistent with molecular dynamics simulations (14). In addition, a high resolution crystal structure of peptides extracted from N-terminal segments of Sup35 has been recently reported (15). These studies have confirmed that many peptides, which are unrelated by sequence, adopt the characteristic cross ␤-pattern in the fibril state.It is also important to understand the mechanisms of their formation starting from monomers because it is becoming increasingly clear that the nonfibrillar intermediates may be the toxic species in at least the Alzheimer's disease (9). Experimental characterization of the mechanism of formation of oligomers and their structures is difficult because of their diverse morphologies and rapid conformational fluctuations (16-19). Molecular dynamics (MD) simulations (14,16,20) can not only identify the interactions that drive the oligomer formation, but also can provide a molecular picture of the dynamics of the early events in the route to amyloid fibrils (16).In a previous study, we investigated the factor...

show abstract

“…Periodically, coordinates are exchanged by using a Metropolis criterion (3) that ensures that at any given temperature a canonical distribution is realized. RE methods, particularly REMD (4), have become very popular for the study of protein biophysics, including peptide and protein folding (5,6), aggregation (7)(8)(9), and protein-ligand interactions (10,11). Previous studies of protein folding appear to show a significant increase in the number of reversible folding events in REMD simulations versus conventional MD (12,13).…”

mentioning

confidence: 99%

Simulating replica exchange simulations of protein folding with a kinetic network model

Zheng

Andrec

Gallicchio

et al. 2007

Proc. Natl. Acad. Sci. U.S.A.

100

149

View full text Add to dashboard Cite

Replica exchange (RE) is a generalized ensemble simulation method for accelerating the exploration of free-energy landscapes, which define many challenging problems in computational biophysics, including protein folding and binding. Although temperature RE (T-RE) is a parallel simulation technique whose implementation is relatively straightforward, kinetics and the approach to equilibrium in the T-RE ensemble are very complicated; there is much to learn about how to best employ T-RE to protein folding and binding problems. We have constructed a kinetic network model for RE studies of protein folding and used this reduced model to carry out ''simulations of simulations'' to analyze how the underlying temperature dependence of the conformational kinetics and the basic parameters of RE (e.g., the number of replicas, the RE rate, and the temperature spacing) all interact to affect the number of folding transitions observed. When protein folding follows anti-Arrhenius kinetics, we observe a speed limit for the number of folding transitions observed at the low temperature of interest, which depends on the maximum of the harmonic mean of the folding and unfolding transition rates at high temperature. The results shown here for the network RE model suggest ways to improve atomic-level RE simulations such as the use of ''training'' simulations to explore some aspects of the temperature dependence for folding of the atomic-level models before performing RE studies.anti-Arrhenius ͉ Markov process ͉ parallel tempering O ne of the key challenges in the computer simulation of proteins at the atomic level is the sampling of conformational space. The efficiency of many common sampling protocols, such as Monte Carlo (MC) and molecular dynamics (MD), is limited by the need to cross high free-energy barriers between conformational states and rugged energy landscapes. One class of methods for studying equilibrium properties of quasi-ergodic systems that has received a great deal of recent attention is based on the replica exchange (RE) algorithm (1, 2) (also known as parallel tempering). To accomplish barrier crossings, RE methods simulate a series of replicas over a range of temperatures. Periodically, coordinates are exchanged by using a Metropolis criterion (3) that ensures that at any given temperature a canonical distribution is realized. RE methods, particularly REMD (4), have become very popular for the study of protein biophysics, including peptide and protein folding (5, 6), aggregation (7-9), and protein-ligand interactions (10, 11). Previous studies of protein folding appear to show a significant increase in the number of reversible folding events in REMD simulations versus conventional MD (12,13). Given the wide use of REMD, a better understanding of the RE algorithm and how it can be used most effectively for the study of protein folding and binding is of considerable interest.The effectiveness of RE methods is determined by the number of temperatures (replicas) that are simulated, their range and spacing, the rate at ...

show abstract

Replica exchange molecular dynamics simulations of amyloid peptide aggregation

Cited by 199 publications

References 62 publications

Effect of Single-Point Sequence Alterations on the Aggregation Propensity of a Model Protein

Effect of Single-Point Sequence Alterations on the Aggregation Propensity of a Model Protein

Monomer adds to preformed structured oligomers of Aβ-peptides by a two-stage dock–lock mechanism

Simulating replica exchange simulations of protein folding with a kinetic network model

Contact Info

Product

Resources

About