Prediction of aggregation rate and aggregation-prone segments in polypeptide sequences (original) (raw)
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Prediction of sequence-dependent and mutational effects on the aggregation of peptides and proteins
Nature Biotechnology, 2004
We have developed a statistical mechanics algorithm, TANGO, to predict protein aggregation. TANGO is based on the physico-chemical principles of b-sheet formation, extended by the assumption that the core regions of an aggregate are fully buried. Our algorithm accurately predicts the aggregation of a data set of 179 peptides compiled from the literature as well as of a new set of 71 peptides derived from human diseaserelated proteins, including prion protein, lysozyme and b2-microglobulin. TANGO also correctly predicts pathogenic as well as protective mutations of the Alzheimer b-peptide, human lysozyme and transthyretin, and discriminates between b-sheet propensity and aggregation. Our results confirm the model of intermolecular b-sheet formation as a widespread underlying mechanism of protein aggregation. Furthermore, the algorithm opens the door to a fully automated, sequence-based design strategy to improve the aggregation properties of proteins of scientific or industrial interest.
Prediction of Aggregation of Biologically-Active Peptides with the UNRES Coarse-Grained Model
Biomolecules
The UNited RESidue (UNRES) model of polypeptide chains was applied to study the association of 20 peptides with sizes ranging from 6 to 32 amino-acid residues. Twelve of those were potentially aggregating hexa- or heptapeptides excised from larger proteins, while the remaining eight contained potentially aggregating sequences, functionalized by attaching larger ends rich in charged residues. For 13 peptides, the experimental data of aggregation were used. The remaining seven were synthesized, and their properties were measured in this work. Multiplexed replica-exchange simulations of eight-chain systems were conducted at 12 temperatures from 260 to 370 K at concentrations from 0.421 to 5.78 mM, corresponding to the experimental conditions. The temperature profiles of the fractions of monomers and octamers showed a clear transition corresponding to aggregate dissociation. Low simulated transition temperatures were obtained for the peptides, which did not precipitate after incubation,...
Aggregation kinetics of short peptides: All-atom and coarse-grained molecular dynamics study
Biophysical Chemistry, 2019
Peptides can aggregate into ordered structures with different morphologies. The aggregation mechanism and evolving structures are the subject of intense research. In this paper we have used molecular dynamics to examine the sequence-dependence of aggregation kinetics for three short peptides: octaalanine (Ala8), octaasparagine (Asn8), and the heptapeptide GNNQQNY (abbreviated as GNN). First, we compared the aggregation of 20 randomly distributed peptides using the coarse-grained MARTINI force field and the atomistic OPLS-AA force field. We found that the MARTINI and OPLS-AA aggregation kinetics are similar for Ala8, Asn8, and GNN. Second, we used the MARTINI force field to study the early stages of aggregation kinetics for a larger system with 72 peptides. In the initial stage of aggregation small clusters grow by monomer addition. In the second stage, when the free monomers are depleted, the dominant cluster growth path is cluster-cluster coalescence. We quantified the aggregation kinetics in terms of rate equations. Our study shows that the initial aggregation kinetics are similar for Ala8, Asn8, and GNN but the molecular details can be different, especially for MARTINI Ala8. We hypothesize that peptide aggregation proceed in two steps. In the first step amorphous aggregates are formed, and then, in the second step, they reorganize into ordered structures. We conclude that sequence-specific differences show up in the second step of aggregation.
A Kinetic Approach to the Sequence–Aggregation Relationship in Disease-Related Protein Assembly
The Journal of Physical Chemistry B, 2014
It is generally accepted that oligomers of aggregating proteins play an important role in the onset of neurodegenerative diseases. While in silico aggregation studies of full length amyloidogenic proteins are computationally expensive, the assembly of short protein fragments derived from these proteins with similar aggregating properties has been extensively studied. In the present work molecular dynamics simulations are performed to follow peptide aggregation on the microsecond time scale. By defining aggregation states we identify transition networks, disconnectivity graphs and first passage time distributions to describe the kinetics of the assembly process. This approach unravels differences in the aggregation into hexamers of two peptides with different primary structures. The first is GNNQQNY, a hydrophilic fragment from the prion protein Sup35, and the second is KLVFFAE, a fragment from amyloid β-protein, with a hydrophobic core delimited by two charged amino acids. The assembly of GNNQQNY suggests a mechanism of monomer addition, with a bias towards parallel peptide pairs and a gradual increase in the amount of β-strand content. For KLVFFAE a mechanism involving dimers rather than monomers is revealed, involving a generally higher β-strand content and a transition towards a larger number of antiparallel peptide pairs during the rearrangement of the hexamer. The differences observed for the aggregation of the two peptides suggests the existence of a sequenceaggregation relationship.
Can Peptide Folding Simulations Provide Predictive Information for Aggregation Propensity?
The Journal of Physical Chemistry B, 2010
Nonnative peptide aggregation underlies many diseases and is a major problem in the development of peptidebased therapeutics. Efforts in the past decade have revealed remarkable correlations between aggregation rates or propensities and very simple sequence metrics like hydrophobicity and charge. Here, we investigate the extent to which a molecular picture of peptide folding bears out similar relationships. Using replica exchange molecular dynamics folding simulations, we compute equilibrium conformational ensembles of 142 hexaand decapeptide systems, of which about half readily form amyloid fibrils and half do not. The simulations are used to compute a variety of ensemble-based properties, and we investigate the extent to which these metrics provide molecular clues about fibril formation. To assess whether multiple metrics together are useful in understanding aggregation, we also develop a number of logistic regression models, some of which predict fibril formers with 70-80% accuracy and identify aggregation-prone regions in larger proteins. Importantly, these models quantify the importance of different molecular properties in aggregation driving forces; notably, they suggest that hydrophobic interactions play a dominant role.
Sequence dependent aggregation of peptides and fibril formation
The Journal of chemical physics, 2017
Deciphering the links between amino acid sequence and amyloid fibril formation is key for understanding protein misfolding diseases. Here we use Monte Carlo simulations to study the aggregation of short peptides in a coarse-grained model with hydrophobic-polar (HP) amino acid sequences and correlated side chain orientations for hydrophobic contacts. A significant heterogeneity is observed in the aggregate structures and in the thermodynamics of aggregation for systems of different HP sequences and different numbers of peptides. Fibril-like ordered aggregates are found for several sequences that contain the common HPH pattern, while other sequences may form helix bundles or disordered aggregates. A wide variation of the aggregation transition temperatures among sequences, even among those of the same hydrophobic fraction, indicates that not all sequences undergo aggregation at a presumable physiological temperature. The transition is found to be the most cooperative for sequences for...
2021
Protein aggregation is a common and complex phenomenon in biological processes, yet a robust analysis of this aggregation process remains elusive. The commonly used methods such as center-of-mass to center-of-mass (COM-COM) distance, the radius of gyration (Rg), hydrogen bonding (HB) and solvent accessible surface area (SASA) do not quantify the aggregation accurately. Herein, a new and robust method that uses an aggregation matrix (AM) approach to investigate peptide aggregation in a MD simulation trajectory is presented. A nxn two-dimensional aggregation matrix (AM) is created by using the inter-peptide Cα-Cα cut-off distances which are binarily encoded (0 or 1). These aggregation matrices are analyzed to enumerate, hierarchically order and structurally classify the aggregates. Comparison of the present AM method suggests that it is superior to the HB method since it can incorporate non-specific interactions and Rg, COM-COM methods since the cut-off distance is independent of the ...
A consensus method for the prediction of 'aggregation-prone' peptides in globular proteins
PloS one, 2013
The purpose of this work was to construct a consensus prediction algorithm of 'aggregation-prone' peptides in globular proteins, combining existing tools. This allows comparison of the different algorithms and the production of more objective and accurate results. Eleven (11) individual methods are combined and produce AMYLPRED2, a publicly, freely available web tool to academic users (http://biophysics.biol.uoa.gr/AMYLPRED2), for the consensus prediction of amyloidogenic determinants/'aggregation-prone' peptides in proteins, from sequence alone. The performance of AMYLPRED2 indicates that it functions better than individual aggregation-prediction algorithms, as perhaps expected. AMYLPRED2 is a useful tool for identifying amyloid-forming regions in proteins that are associated with several conformational diseases, called amyloidoses, such as Altzheimer's, Parkinson's, prion diseases and type II diabetes. It may also be useful for understanding the properties of protein folding and misfolding and for helping to the control of protein aggregation/solubility in biotechnology (recombinant proteins forming bacterial inclusion bodies) and biotherapeutics (monoclonal antibodies and biopharmaceutical proteins). Citation: Tsolis AC, Papandreou NC, Iconomidou VA, Hamodrakas SJ (2013) A Consensus Method for the Prediction of 'Aggregation-Prone' Peptides in Globular Proteins. PLoS ONE 8(1): e54175.