Prediction of “Aggregation-prone” and “Aggregation-susceptible” Regions in Proteins Associated with Neurodegenerative Diseases

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Increasing evidence indicates that many peptides and proteins can be converted in vitro into highly organised amyloid structures, provided that the appropriate experimental conditions can be found. In this work, we define intrinsic propensities for the aggregation of individual amino acids and develop a method for identifying the regions of the sequence of an unfolded peptide or protein that are most important for promoting amyloid formation. This method is applied to the study of three polypeptides associated with neurodegenerative diseases, Aβ42, α-synuclein and tau. In order to validate the approach, we compare the regions of proteins that are predicted to be most important in driving aggregation, either intrinsically or as the result of mutations, with those determined experimentally. The knowledge of the location and the type of the “sensitive regions” for aggregation is important both for rationalising the effects of sequence changes on the aggregation of polypeptide chains and for the development of targeted strategies to combat diseases associated with amyloid formation.

Introduction

Biological macromolecules such as proteins, lipids and nucleic acids have the ability to assemble into functional complexes in a highly regulated manner within densely crowded environments.1, 2 Moreover, the balance between normal and pathological self-association has been carefully tuned by molecular evolution.3, 4 Failures of the regulatory mechanisms do, however, occur and may result in conditions such as Alzheimer's and Creutzfeldt–Jakob diseases, and type II diabetes. Such diseases are associated with the deposition in tissue of pathogenic aggregates that are composed largely of misfolded proteins in the form of amyloid fibrils or plaques.5, 6, 7, 8, 9, 10 Recent work suggests that not only are amyloid aggregates formed in vivo from a group of otherwise unrelated proteins, but they can be induced in vitro from proteins not associated with known deposition diseases.3, 10, 11, 12, 13 Such observations have led to the suggestion that the ability to form amyloid fibrils may be a common characteristic of polypeptide chains,3, 5 although individual propensities vary greatly with the sequence and the environmental conditions.

A range of diverse factors has been reported to influence the rate of amyloid formation. Extrinsic factors that can affect the formation of protein aggregates in vivo include the interaction with cellular or extracellular components such as molecular chaperones that inhibit misfolding,14 proteases that frequently generate or process the amyloidogenic precursors,15 and the effectiveness of quality control mechanisms such as the ubiquitin–proteasome system.16, 17 They also include physicochemical properties that describe the environment of the polypeptide chains, such as pH, temperature, ionic strength, protein concentration, denaturant concentration and pressure.18, 19, 20, 21, 22, 23, 24, 25 Intrinsic factors associated with amyloid formation include a range of fundamental characteristics of polypeptide chains, such as charge,26, 27, 28, 29 hydrophobicity,30, 31, 32 patterns of polar and non-polar residues,33 and the propensities to adopt diverse secondary structure elements.27, 32, 34, 35 In the case of globular proteins, the propensities to form amyloid structures are generally inversely related to the stability of their native states.24, 36, 37, 38, 39, 40, 41, 42 Many of the proteins associated with amyloid diseases are, however, at least partially unfolded under physiological conditions. In addition, it is thought that many globular proteins unfold, at least partially, before aggregating. The present study is therefore focused on the conversion of unfolded or partially unfolded states into amyloid aggregates.

One of the most intriguing recent observations in studies of the kinetics of amyloid formation is that polypeptide sequences appear to contain local regions that are “sensitive” for aggregation.32 Single amino acid mutations in these regions can change the aggregation rates dramatically, while similar changes in other regions may have relatively little effect.32, 43 In addition, it has been shown that it is possible to describe with considerable accuracy the in vitro amyloid aggregation propensities of polypeptides using algorithms that take into account the physico-chemical properties of their sequences and of their environment.44, 45, 46, 47, 48 Here, our purpose is to apply this type of analysis to the rationalisation and the prediction of the sensitive regions of polypeptide sequences in general and of proteins associated with neurodegenerative diseases in particular.

Section snippets

Definition of intrinsic aggregation propensities

We define the intrinsic propensity of an unfolded polypeptide chain to form amyloid aggregates, Pagg, by considering just the intrinsic factors in the formula that we have recently introduced to define the absolute aggregation rates of unstructured polypeptide chains:45Pagg=αhydrIhydr+ααIα+αβIβ+αpatIpat+αchIchwhere Ihydr represents the hydrophobicity of the sequence,49, 50 Iα is the α-helical propensity,51 Iβ is the β-sheet propensity,51 Ipat is the hydrophobic patterning,52 and Ich is the

Discussion and Conclusions

Here, we describe a method for calculating the intrinsic amyloid aggregation propensities of polypeptide sequences, and we have used this approach to calculate the aggregation propensity profiles for three natively unfolded polypeptide chains associated with neurodegenerative diseases, Aβ42, α-synuclein and tau. These profiles have allowed the regions of these sequences that influence their aggregation behaviour to be identified and compared to the results of experimental studies.

We can

Fitting of the coefficients using known aggregation rates

The intrinsic factors included in the algorithm developed45 were used in this work to calculate Pagg, the intrinsic aggregation propensity of a sequence. The weights of the various intrinsic factors were determined simultaneously by using regression techniques using an extended database of 203 sequences that included the 83 sequences used by DuBay et al.45 We used normalised β-sheet and α-helix propensity scales,51 with the following modifications: for β-sheet propensity calculations, we set

Acknowledgements

We are grateful for support from the Gates Cambridge Trust (A.P.P., K.F.D.), the Leverhulme Trust (to J.Z., M.V. and C.M.D.), the Italian MIUR and CNR (to F.C.), the Royal Society (to M.V.) and the Wellcome Trust (to C.M.D.).

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    A.P.P. & K.F.D. contributed equally to this work.

    Present address: K. F. DuBay, Department of Chemistry, UC Berkeley, 419 Latimer Hall, Berkeley, CA 94720-1460, USA.

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