Protein aggregation predictors

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Computational methods that use protein sequence and/ or protein structure to predict protein aggregation. The table below, shows the main features of software for prediction of protein aggregation

Table[edit]

Table 1
Method Last Update Access (Web server/downloadable) Principle Input Output
Sequence / 3D Structure Additional parameters
Amyloidogenic Patten[1] 2004 Web Server- AMYLPRED2 Secondary structure-related

Amyloidogenic pattern

Submissions are scanned for the existence of this pattern {P}-{PKRHW}-[VLSCWFNQE]-[ILTYWFNE]-[FIY]-{PKRH} at identity level, with the use of a simple custom script.

sequence - Amyloidogenic regions
Tango [2][3][4] 2004 Web Server-TANGO Phenomenological

Based on physico-chemical principles of secondary structure formation extended by the assumption that the core regions of an aggregate are fully buried.

sequence pH/ionic strength Overall aggregation and amyloidoidogenic regions
Average Packing Density[5] 2006 Web Server-AMYLPRED2 Secondary structure-related

Relates average packing density of residues to the formation of amyloid fibrils.

sequence - Amyloidogenic regions
Beta-strand contiguity[6] 2007 Web Server- AMYLPRED2 Phenomenological

Prediction of B-strand propensity score to locate in the amyloid fibril.

sequence - beta-strand formation
Hexapeptide Conformational Energy /Pre-amyl[7] 2007 Web Server- AMYLPRED2 Secondary structure-related

Hexapeptides of a submitted protein are threaded onto over 2500 templates of microcrystallic structure of NNQQNY, energy values below -27.00 are considered as hits.

sequence - Amyloidogenic regions and energy
AGGRESCAN[8] 2007 Web Servers -AMLYPRED2 & AGGRESCAN Phenomenological

Prediction of 'aggregation-prone' in protein sequences, based on an aggregation propensity scale for natural amino acids derived from in vivo experiments.

sequence - Overall aggregation and amyloidogenic regions
Salsa[9] 2007 Web server - AMYPdb[10] Phenomenological

Prediction of the aggregation propensities single or multiple sequences based on physicochemical properties.

sequence hot spot length Amyloidogenic regions
Pafig[11] 2009 Web server- AMYLPRED2

Download

Phenomenological

Identification of Hexapeptides associated to amyloid fibrillar aggregates.

sequence - Amyloidogenic regions
Net-CSSP[12][13][14][15] 2020 Web Server - Net-CSSP

AMYLPRED2

Secondary structure-related

Quantification of the influence of the tertiary interation on secondary structural preference.

sequence/pdb single/dual network-threshold Amyloidogenic propensity regions
Betascan[16] 2009 Web Server - Betascan

Download - Betascan

Secondary structure-related

Predict the probability that particular portions of a protein will form amyloid.

sequence length Amyloidogenic regions
FoldAmyloid[17] 2010 Web Server - FoldAmyloid Secondary structure-related

Prediction of amyloid regions using expected probability of hydrogen bonds formation and packing densitites of residues.

sequence scale, threshold, averaging frame Amyloidogenic regions
Waltz[18][19] 2010 Web Server - Waltz &

AMYLPRED2

Secondary structure-related

Application of position-specific substitution matrices (PSSM) obtained from amyloidogenic peptides.

sequence pH, specificity, sensitivity Amyloidogenic regions
Zipper DB [20][21][22][23] 2010 Web Server- Zipper DB Secondary structure-related

Structure based prediction of fribrillation propoensities, using crystal strucutrue of the fibril forming peptide NNQQNY from the sup 35 prion protein of Saccharomyces cerevisiae.

sequence - Amyloidogenic regions and, energy and beta-sheet conformation
STITCHER[24] 2012 Web Server - Stitcher (currently offline) Secondary structure-related sequence - Amyloidogenic regions
MetAmyl[25][26][27][28] 2013 Web Server - MetAmyl Consensus method

Amyloidogenic patterns, average packing density, beta-strand contiguity, pafig, Net-CSSP, STITCHER

sequence threshold Overall generic and amyloidogenic regions based on the consensus
AmylPred2[29] 2013 Web Server - AMYLPRED2 Consensus method

Amyloidogenic patterns, average packing density, beta-strand contiguity, pafig, Net-CSSP, STITCHER

sequence - Overall generic and amyloidogenic regions based on the consensus
PASTA 2.0[30] 2014 Web Server - PASTA 2.0 Secondary structure-related

Predicts the most aggregation-prone portions and the corresponding β-strand inter-molecular pairing for multiple input sequences.

sequence top pairings and energies, mutations and protein-protein Amyloidogenic regions, energy, and beta-sheet orientation in aggregates
FISH Amyloid[31] 2014 Web Server - Comprec (currently offline) Secondary structure-related sequence threshold Amyloidogenic regions
GAP[32][33][34][35] 2014 Web Server - GAP Secondary structure-related

Identification of amyloid forming peptides and amorphous peptides using a dataset of 139 amyloids and 168 amorphous peptides.

sequence - Overall aggregation and amyloidogenic regions
APPNN[36] 2015 Download - CRAN Phenomenological

Amyloidogenicity propensity predictor based on a machine learning approach through recursive feature selection and feed-forward neural networks, taking advantage of newly published sequences with experimental, in vitro, evidence of amyloid formation.

sequence - Amyloidogenic regions
ArchCandy[37] 2015 Download- BiSMM Secondary structure-related

Based on an assumption that protein sequences that are able to form β-arcades are amyloidogenic.

sequence - Amyloidogenic regions
Amyload[38] 2015 Web Server - Comprec (currently offline) Consensus method sequence - Overall generic and amyloidogenic regions
SolubiS[39][40] 2016 Web Server - SolubiS 3D structure pdb file chain, threshold, gatekeeper Aggregation propensity and stability vs mutations
CamSol Structurally Corrected[41][42] 2017 Web Server - Chemistry of Health 3D structure pdb file pH, patch radius Exposed aggregation-prone patches and mutated variants design
CamSol intrinsic[43][44] 2017 Web Server- Chemistry of Health Phenomenological

Sequence-based method of predicting protein solubility and generic aggregation propensity.

sequence pH Calculation of the overall intrinsic solubility score and solubility profile
AmyloGram[45] 2017 Web Server - AmyloGram Phenomenological

AmyloGram predicts amyloid proteins using n-gram encoding and random forests.

sequence - Overall aggregation and amyloidogenic regions
BetaSerpentine[46] 2017 Web Server - BetaSerpentine-1.0 Sequence-related

Reconstruction of amyloid structures containing adjacent β-arches.

sequence - Amyloidogenic regions
AggScore[47] 2018 AggScore is available through Schrödinger's BioLuminate Suite as of software release 2018-1. Secondary structure-related

Method that uses the distribution of hydrophobic and electrostatic patches on the surface of the protein, factoring in the intensity and relative orientation of the respective surface patches into an aggregation propensity function that has been trained on a benchmark set of 31 adnectin proteins.

sequence - Amyloidogenic regions
AggreRATE-Pred[48] 2018 Web Server - AggreRAE-Pred Secondary structure-related

Predict changes in aggregation rate upon point mutations

sequence pdb mutations
AGGRESCAN 3D 2.0[49][50][51][52][53] 2019 Web Server - Aggrescan3D 3D structure pdb file dynamic mode, mutations, patch radius, stability, enhance solubility Dynamic exposed aggregation-prone patches and mutated variants design
Budapest amyloid predictor[54] 2021 Web Server - Budapest amyloid predictor Hexapeptide sequence Amyloidgenecity of hexapeptide
ANuPP[55] 2021 Web Server - ANuPP Hexapeptide and Sequence

Identification amyloid-fibril forming peptides and regions in protein sequences

sequence Amyloidogenic hexapeptides and aggregation prone regions

See also[edit]

PhasAGE toolbox

Amyloid

Protein aggregation

References[edit]

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  55. ^ Prabakaran, R.; Rawat, Puneet; Kumar, Sandeep; Michael Gromiha, M. (May 2021). "ANuPP: A Versatile Tool to Predict Aggregation Nucleating Regions in Peptides and Proteins". Journal of Molecular Biology. 433 (11): 166707. doi:10.1016/j.jmb.2020.11.006. PMID 33972019. S2CID 228867153.
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