Protein aggregation predictors

Last updated

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

Contents

Table

Table 1
MethodLast UpdateAccess (Web server/downloadable)PrincipleInputOutput
Sequence / 3D StructureAdditional parameters
Amyloidogenic Patten [1] 2004Web 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] 2004Web 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.

sequencepH/ionic strengthOverall aggregation and amyloidoidogenic regions
Average Packing Density [5] 2006Web Server-AMYLPRED2 Secondary structure-related

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

sequence-Amyloidogenic regions
Beta-strand contiguity [6] 2007Web 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] 2007Web 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] 2007Web 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.

sequencehot spot lengthAmyloidogenic regions
Pafig [11] 2009Web server- AMYLPRED2

Download

Phenomenological

Identification of Hexapeptides associated to amyloid fibrillar aggregates.

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

AMYLPRED2

Secondary structure-related

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

sequence/pdbsingle/dual network-thresholdAmyloidogenic propensity regions
Betascan [16] 2009Web Server - Betascan

Download - Betascan

Secondary structure-related

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

sequencelengthAmyloidogenic regions
FoldAmyloid [17] 2010Web Server - FoldAmyloid Secondary structure-related

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

sequencescale, threshold, averaging frameAmyloidogenic regions
Waltz [18] [19] 2010Web Server - Waltz &

AMYLPRED2

Secondary structure-related

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

sequencepH, specificity, sensitivityAmyloidogenic regions
Zipper DB [20] [21] [22] [23] 2010Web 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] 2012Web Server - Stitcher (currently offline)Secondary structure-relatedsequence-Amyloidogenic regions
MetAmyl [25] [26] [27] [28] 2013Web Server - MetAmyl Consensus method

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

sequencethresholdOverall generic and amyloidogenic regions based on the consensus
AmylPred2 [29] 2013Web 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] 2014Web 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.

sequencetop pairings and energies, mutations and protein-proteinAmyloidogenic regions, energy, and beta-sheet orientation in aggregates
FISH Amyloid [31] 2014Web Server - Comprec (currently offline)Secondary structure-relatedsequencethresholdAmyloidogenic regions
GAP [32] [33] [34] [35] 2014Web 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] 2015Download - 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] 2015Download- 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] 2015Web Server - Comprec (currently offline)Consensus methodsequence-Overall generic and amyloidogenic regions
SolubiS [39] [40] 2016Web Server - SolubiS 3D structurepdb filechain, threshold, gatekeeperAggregation propensity and stability vs mutations
CamSol Structurally Corrected [41] [42] 2017Web Server - Chemistry of Health 3D structurepdb filepH, patch radiusExposed aggregation-prone patches and mutated variants design
CamSol intrinsic [43] [44] 2017Web Server- Chemistry of Health Phenomenological

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

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

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

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

Reconstruction of amyloid structures containing adjacent β-arches.

sequence-Amyloidogenic regions
AggScore [47] 2018AggScore 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] 2018Web Server - AggreRAE-Pred Secondary structure-related

Predict changes in aggregation rate upon point mutations

sequence pdbmutations
AGGRESCAN 3D 2.0 [49] [50] [51] [52] [53] 2019Web Server - Aggrescan3D 3D structurepdb filedynamic mode, mutations, patch radius, stability, enhance solubilityDynamic exposed aggregation-prone patches and mutated variants design
Budapest amyloid predictor [54] 2021Web Server - Budapest amyloid predictor HexapeptidesequenceAmyloidgenecity of hexapeptide
ANuPP [55] 2021Web Server - ANuPP Hexapeptide and Sequence

Identification amyloid-fibril forming peptides and regions in protein sequences

sequenceAmyloidogenic hexapeptides and aggregation prone regions

See also

PhasAGE toolbox

Amyloid

Protein aggregation

Related Research Articles

<span class="mw-page-title-main">Beta sheet</span> Protein structural motif

The beta sheet, (β-sheet) is a common motif of the regular protein secondary structure. Beta sheets consist of beta strands (β-strands) connected laterally by at least two or three backbone hydrogen bonds, forming a generally twisted, pleated sheet. A β-strand is a stretch of polypeptide chain typically 3 to 10 amino acids long with backbone in an extended conformation. The supramolecular association of β-sheets has been implicated in the formation of the fibrils and protein aggregates observed in amyloidosis, Alzheimer's disease and other proteinopathies.

<span class="mw-page-title-main">Amyloid</span> Insoluble protein aggregate with a fibrillar morphology

Amyloids are aggregates of proteins characterised by a fibrillar morphology of typically 7–13 nm in diameter, a β-sheet secondary structure and ability to be stained by particular dyes, such as Congo red. In the human body, amyloids have been linked to the development of various diseases. Pathogenic amyloids form when previously healthy proteins lose their normal structure and physiological functions (misfolding) and form fibrous deposits within and around cells. These protein misfolding and deposition processes disrupt the healthy function of tissues and organs.

<span class="mw-page-title-main">Amylin</span> Peptide hormone that plays a role in glycemic regulation

Amylin, or islet amyloid polypeptide (IAPP), is a 37-residue peptide hormone. It is co-secreted with insulin from the pancreatic β-cells in the ratio of approximately 100:1 (insulin:amylin). Amylin plays a role in glycemic regulation by slowing gastric emptying and promoting satiety, thereby preventing post-prandial spikes in blood glucose levels.

<span class="mw-page-title-main">Amyloid beta</span> Group of peptides

Amyloid beta denotes peptides of 36–43 amino acids that are the main component of the amyloid plaques found in the brains of people with Alzheimer's disease. The peptides derive from the amyloid-beta precursor protein (APP), which is cleaved by beta secretase and gamma secretase to yield Aβ in a cholesterol-dependent process and substrate presentation. Aβ molecules can aggregate to form flexible soluble oligomers which may exist in several forms. It is now believed that certain misfolded oligomers can induce other Aβ molecules to also take the misfolded oligomeric form, leading to a chain reaction akin to a prion infection. The oligomers are toxic to nerve cells. The other protein implicated in Alzheimer's disease, tau protein, also forms such prion-like misfolded oligomers, and there is some evidence that misfolded Aβ can induce tau to misfold.

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<span class="mw-page-title-main">Alpha sheet</span> Secondary protein structure

Alpha sheet is an atypical secondary structure in proteins, first proposed by Linus Pauling and Robert Corey in 1951. The hydrogen bonding pattern in an alpha sheet is similar to that of a beta sheet, but the orientation of the carbonyl and amino groups in the peptide bond units is distinctive; in a single strand, all the carbonyl groups are oriented in the same direction on one side of the pleat, and all the amino groups are oriented in the same direction on the opposite side of the sheet. Thus the alpha sheet accumulates an inherent separation of electrostatic charge, with one edge of the sheet exposing negatively charged carbonyl groups and the opposite edge exposing positively charged amino groups. Unlike the alpha helix and beta sheet, the alpha sheet configuration does not require all component amino acid residues to lie within a single region of dihedral angles; instead, the alpha sheet contains residues of alternating dihedrals in the traditional right-handed (αR) and left-handed (αL) helical regions of Ramachandran space. Although the alpha sheet is only rarely observed in natural protein structures, it has been speculated to play a role in amyloid disease and it was found to be a stable form for amyloidogenic proteins in molecular dynamics simulations. Alpha sheets have also been observed in X-ray crystallography structures of designed peptides.

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Molecular recognition features (MoRFs) are small intrinsically disordered regions in proteins that undergo a disorder-to-order transition upon binding to their partners. MoRFs are implicated in protein-protein interactions, which serve as the initial step in molecular recognition. MoRFs are disordered prior to binding to their partners, whereas they form a common 3D structure after interacting with their partners. As MoRF regions tend to resemble disordered proteins with some characteristics of ordered proteins, they can be classified as existing in an extended semi-disordered state.

<span class="mw-page-title-main">P3 peptide</span>

p3 peptide also known as amyloid β- peptide (Aβ)17–40/42 is the peptide resulting from the α- and γ-secretase cleavage from the amyloid precursor protein (APP). It is known to be the major constituent of diffuse plaques observed in Alzheimer's disease (AD) brains and pre-amyloid plaques in people affected by Down syndrome. However, p3 peptide's role in these diseases is not truly known yet.

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References

  1. ↑ Paz, Manuela López de la; Serrano, Luis (2004-01-06). "Sequence determinants of amyloid fibril formation". Proceedings of the National Academy of Sciences. 101 (1): 87–92. Bibcode:2004PNAS..101...87L. doi: 10.1073/pnas.2634884100 . ISSN   0027-8424. PMC   314143 . PMID   14691246.
  2. ↑ Rousseau, F; Schymkowitz, J; Serrano, L (February 2006). "Protein aggregation and amyloidosis: confusion of the kinds?". Current Opinion in Structural Biology. 16 (1): 118–126. doi:10.1016/j.sbi.2006.01.011. PMID   16434184.
  3. ↑ Fernandez-Escamilla, Ana-Maria; Rousseau, Frederic; Schymkowitz, Joost; Serrano, Luis (October 2004). "Prediction of sequence-dependent and mutational effects on the aggregation of peptides and proteins". Nature Biotechnology. 22 (10): 1302–1306. doi:10.1038/nbt1012. ISSN   1087-0156. PMID   15361882. S2CID   41481025.
  4. ↑ Linding, Rune; Schymkowitz, Joost; Rousseau, Frederic; Diella, Francesca; Serrano, Luis (September 2004). "A Comparative Study of the Relationship Between Protein Structure and β-Aggregation in Globular and Intrinsically Disordered Proteins". Journal of Molecular Biology. 342 (1): 345–353. doi:10.1016/j.jmb.2004.06.088. PMID   15313629.
  5. ↑ Galzitskaya, Oxana V.; Garbuzynskiy, Sergiy O.; Lobanov, Michail Yurievich (2006-12-29). "Prediction of Amyloidogenic and Disordered Regions in Protein Chains". PLOS Computational Biology. 2 (12): e177. Bibcode:2006PLSCB...2..177G. doi: 10.1371/journal.pcbi.0020177 . ISSN   1553-7358. PMC   1761655 . PMID   17196033.
  6. ↑ Zibaee, Shahin; Makin, O. Sumner; Goedert, Michel; Serpell, Louise C. (May 2007). "A simple algorithm locates β-strands in the amyloid fibril core of α-synuclein, Aβ, and tau using the amino acid sequence alone". Protein Science. 16 (5): 906–918. doi:10.1110/ps.062624507. PMC   2206631 . PMID   17456743.
  7. ↑ Zhang, Zhuqing; Chen, Hao; Lai, Luhua (2007-09-01). "Identification of amyloid fibril-forming segments based on structure and residue-based statistical potential". Bioinformatics. 23 (17): 2218–2225. doi: 10.1093/bioinformatics/btm325 . ISSN   1367-4803. PMID   17599928.
  8. ↑ Conchillo-Solé, Oscar; de Groot, Natalia S.; Avilés, Francesc X.; Vendrell, Josep; Daura, Xavier; Ventura, Salvador (2007-02-27). "AGGRESCAN: a server for the prediction and evaluation of "hot spots" of aggregation in polypeptides". BMC Bioinformatics. 8 (1): 65. doi: 10.1186/1471-2105-8-65 . ISSN   1471-2105. PMC   1828741 . PMID   17324296.
  9. ↑ Zibaee, Shahin; Makin, O. Sumner; Goedert, Michel; Serpell, Louise C. (2007). "A simple algorithm locates β-strands in the amyloid fibril core of α-synuclein, Aβ, and tau using the amino acid sequence alone". Protein Science. 16 (5): 906–918. doi:10.1110/ps.062624507. ISSN   1469-896X. PMC   2206631 . PMID   17456743.
  10. ↑ Pawlicki, Sandrine; Le Béchec, Antony; Delamarche, Christian (2008-06-10). "AMYPdb: A database dedicated to amyloid precursor proteins". BMC Bioinformatics. 9 (1): 273. doi: 10.1186/1471-2105-9-273 . ISSN   1471-2105. PMC   2442844 . PMID   18544157.
  11. ↑ Tian, Jian; Wu, Ningfeng; Guo, Jun; Fan, Yunliu (2009-01-30). "Prediction of amyloid fibril-forming segments based on a support vector machine". BMC Bioinformatics. 10 (1): S45. doi: 10.1186/1471-2105-10-S1-S45 . ISSN   1471-2105. PMC   2648769 . PMID   19208147.
  12. ↑ Kim, C.; Choi, J.; Lee, S. J.; Welsh, W. J.; Yoon, S. (2009-07-01). "NetCSSP: web application for predicting chameleon sequences and amyloid fibril formation". Nucleic Acids Research. 37 (Web Server): W469–W473. doi:10.1093/nar/gkp351. ISSN   0305-1048. PMC   2703942 . PMID   19468045.
  13. ↑ Yoon, Sukjoon; Welsh, William J.; Jung, Heeyoung; Yoo, Young Do (October 2007). "CSSP2: An improved method for predicting contact-dependent secondary structure propensity". Computational Biology and Chemistry. 31 (5–6): 373–377. doi:10.1016/j.compbiolchem.2007.06.002. PMID   17644485.
  14. ↑ Yoon, Sukjoon; Welsh, William J. (2005-04-22). "Rapid assessment of contact-dependent secondary structure propensity: Relevance to amyloidogenic sequences". Proteins: Structure, Function, and Bioinformatics. 60 (1): 110–117. doi:10.1002/prot.20477. PMID   15849755. S2CID   44309651.
  15. ↑ Yoon, Sukjoon; Welsh, William J. (August 2004). "Detecting hidden sequence propensity for amyloid fibril formation". Protein Science. 13 (8): 2149–2160. doi:10.1110/ps.04790604. ISSN   0961-8368. PMC   2279810 . PMID   15273309.
  16. ↑ Bryan, Allen W. Jr.; Menke, Matthew; Cowen, Lenore J.; Lindquist, Susan L.; Berger, Bonnie (2009-03-27). "BETASCAN: Probable β-amyloids Identified by Pairwise Probabilistic Analysis". PLOS Computational Biology. 5 (3): e1000333. Bibcode:2009PLSCB...5E0333B. doi: 10.1371/journal.pcbi.1000333 . ISSN   1553-7358. PMC   2653728 . PMID   19325876.
  17. ↑ Garbuzynskiy, S. O.; Lobanov, M. Yu.; Galzitskaya, O. V. (2010-02-01). "FoldAmyloid: a method of prediction of amyloidogenic regions from protein sequence". Bioinformatics. 26 (3): 326–332. doi:10.1093/bioinformatics/btp691. ISSN   1367-4803. PMID   20019059.
  18. ↑ Oliveberg, Mikael (March 2010). "Waltz, an exciting new move in amyloid prediction". Nature Methods. 7 (3): 187–188. doi:10.1038/nmeth0310-187. ISSN   1548-7091. PMID   20195250. S2CID   205417298.
  19. ↑ Maurer-Stroh, Sebastian; Debulpaep, Maja; Kuemmerer, Nico; de la Paz, Manuela Lopez; Martins, Ivo Cristiano; Reumers, Joke; Morris, Kyle L.; Copland, Alastair; Serpell, Louise; Serrano, Luis; Schymkowitz, Joost W. H. (March 2010). "Exploring the sequence determinants of amyloid structure using position-specific scoring matrices". Nature Methods. 7 (3): 237–242. doi:10.1038/nmeth.1432. ISSN   1548-7105. PMID   20154676. S2CID   52874481.
  20. ↑ Thompson, Michael J.; Sievers, Stuart A.; Karanicolas, John; Ivanova, Magdalena I.; Baker, David; Eisenberg, David (2006-03-14). "The 3D profile method for identifying fibril-forming segments of proteins". Proceedings of the National Academy of Sciences. 103 (11): 4074–4078. Bibcode:2006PNAS..103.4074T. doi: 10.1073/pnas.0511295103 . ISSN   0027-8424. PMC   1449648 . PMID   16537487.
  21. ↑ Nelson, Rebecca; Sawaya, Michael R.; Balbirnie, Melinda; Madsen, Anders Ø; Riekel, Christian; Grothe, Robert; Eisenberg, David (June 2005). "Structure of the cross-β spine of amyloid-like fibrils". Nature. 435 (7043): 773–778. Bibcode:2005Natur.435..773N. doi:10.1038/nature03680. ISSN   1476-4687. PMC   1479801 . PMID   15944695.
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  24. ↑ Bryan, Allen W.; O'Donnell, Charles W.; Menke, Matthew; Cowen, Lenore J.; Lindquist, Susan; Berger, Bonnie (February 2012). "STITCHER: Dynamic assembly of likely amyloid and prion β‐structures from secondary structure predictions". Proteins: Structure, Function, and Bioinformatics. 80 (2): 410–420. doi:10.1002/prot.23203. ISSN   0887-3585. PMC   3298606 . PMID   22095906.
  25. ↑ Tian, Jian; Wu, Ningfeng; Guo, Jun; Fan, Yunliu (January 2009). "Prediction of amyloid fibril-forming segments based on a support vector machine". BMC Bioinformatics. 10 (S1): S45. doi: 10.1186/1471-2105-10-S1-S45 . ISSN   1471-2105. PMC   2648769 . PMID   19208147.
  26. ↑ Zibaee, Shahin; Makin, O. Sumner; Goedert, Michel; Serpell, Louise C. (May 2007). "A simple algorithm locates β-strands in the amyloid fibril core of α-synuclein, Aβ, and tau using the amino acid sequence alone". Protein Science. 16 (5): 906–918. doi:10.1110/ps.062624507. PMC   2206631 . PMID   17456743.
  27. ↑ Maurer-Stroh, Sebastian; Debulpaep, Maja; Kuemmerer, Nico; de la Paz, Manuela Lopez; Martins, Ivo Cristiano; Reumers, Joke; Morris, Kyle L; Copland, Alastair; Serpell, Louise; Serrano, Luis; Schymkowitz, Joost W H (March 2010). "Exploring the sequence determinants of amyloid structure using position-specific scoring matrices". Nature Methods. 7 (3): 237–242. doi:10.1038/nmeth.1432. ISSN   1548-7091. PMID   20154676. S2CID   52874481.
  28. ↑ Garbuzynskiy, S. O.; Lobanov, M. Yu.; Galzitskaya, O. V. (2010-02-01). "FoldAmyloid: a method of prediction of amyloidogenic regions from protein sequence". Bioinformatics. 26 (3): 326–332. doi:10.1093/bioinformatics/btp691. ISSN   1367-4803. PMID   20019059.
  29. ↑ Tsolis, Antonios C.; Papandreou, Nikos C.; Iconomidou, Vassiliki A.; Hamodrakas, Stavros J. (2013-01-10). "A Consensus Method for the Prediction of 'Aggregation-Prone' Peptides in Globular Proteins". PLOS ONE. 8 (1): e54175. Bibcode:2013PLoSO...854175T. doi: 10.1371/journal.pone.0054175 . ISSN   1932-6203. PMC   3542318 . PMID   23326595.
  30. ↑ Walsh, Ian; Seno, Flavio; Tosatto, Silvio C.E.; Trovato, Antonio (2014-05-21). "PASTA 2.0: an improved server for protein aggregation prediction". Nucleic Acids Research. 42 (W1): W301–W307. doi:10.1093/nar/gku399. ISSN   1362-4962. PMC   4086119 . PMID   24848016.
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  33. ↑ Thangakani, Anthony Mary; Kumar, Sandeep; Velmurugan, Devadasan; Gromiha, Maria Siluvay Michael (April 2012). "How do thermophilic proteins resist aggregation?". Proteins: Structure, Function, and Bioinformatics. 80 (4): 1003–1015. doi:10.1002/prot.24002. PMID   22389104. S2CID   21496810.
  34. ↑ Gromiha, M. Michael; Thangakani, A. Mary; Kumar, Sandeep; Velmurugan, D. (2012), Huang, De-Shuang; Gupta, Phalguni; Zhang, Xiang; Premaratne, Prashan (eds.), "Sequence Analysis and Discrimination of Amyloid and Non-amyloid Peptides", Emerging Intelligent Computing Technology and Applications, Communications in Computer and Information Science, Berlin, Heidelberg: Springer Berlin Heidelberg, vol. 304, pp. 447–452, doi:10.1007/978-3-642-31837-5_65, ISBN   978-3-642-31836-8 , retrieved 2021-11-26
  35. ↑ Thangakani, A Mary; Kumar, Sandeep; Velmurugan, D; Gromiha, M Michael (May 2013). "Distinct position-specific sequence features of hexa-peptides that form amyloid-fibrils: application to discriminate between amyloid fibril and amorphous β-aggregate forming peptide sequences". BMC Bioinformatics. 14 (S8): S6. doi: 10.1186/1471-2105-14-S8-S6 . ISSN   1471-2105. PMC   3654898 . PMID   23815227.
  36. ↑ Família, Carlos; Dennison, Sarah R.; Quintas, Alexandre; Phoenix, David A. (2015-08-04). Permyakov, Eugene A. (ed.). "Prediction of Peptide and Protein Propensity for Amyloid Formation". PLOS ONE. 10 (8): e0134679. Bibcode:2015PLoSO..1034679F. doi: 10.1371/journal.pone.0134679 . ISSN   1932-6203. PMC   4524629 . PMID   26241652.
  37. ↑ Ahmed, Abdullah B.; Znassi, Nadia; Château, Marie‐Thérèse; Kajava, Andrey V. (June 2015). "A structure‐based approach to predict predisposition to amyloidosis". Alzheimer's & Dementia. 11 (6): 681–690. doi:10.1016/j.jalz.2014.06.007. ISSN   1552-5260. PMID   25150734. S2CID   3130411.
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