Biology:Protein aggregation predictors

From HandWiki

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

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
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[32] 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[33] 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[34] 2015 Web Server - Comprec (currently offline) Consensus method sequence - Overall generic and amyloidogenic regions
SolubiS[35][36] 2016 Web Server - SolubiS 3D structure pdb file chain, threshold, gatekeeper Aggregation propensity and stability vs mutations
CamSol Structurally Corrected[37][38] 2017 Web Server - Chemistry of Health 3D structure pdb file pH, patch radius Exposed aggregation-prone patches and mutated variants design
CamSol intrinsic[39][40] 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[41] 2017 Web Server - AmyloGram Phenomenological

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

sequence - Overall aggregation and amyloidogenic regions
BetaSerpentine[42] 2017 Web Server - BetaSerpentine-1.0 Sequence-related

Reconstruction of amyloid structures containing adjacent β-arches.

sequence - Amyloidogenic regions
AggScore[43] 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[44] 2018 Web Server - AggreRAE-Pred Secondary structure-related

Predict changes in aggregation rate upon point mutations

sequence pdb mutations
AGGRESCAN 3D 2.0[45][46][47][48][49] 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[50] 2021 Web Server - Budapest amyloid predictor Hexapeptide sequence Amyloidgenecity of hexapeptide
ANuPP[51] 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

PhasAGE toolbox

Amyloid

Protein aggregation

References

  1. ↑ Paz, Manuela López de la; Serrano, Luis (2004-01-06). "Sequence determinants of amyloid fibril formation" (in en). Proceedings of the National Academy of Sciences 101 (1): 87–92. doi:10.1073/pnas.2634884100. ISSN 0027-8424. PMID 14691246. Bibcode: 2004PNAS..101...87L. 
  2. ↑ Rousseau, F; Schymkowitz, J; Serrano, L (February 2006). "Protein aggregation and amyloidosis: confusion of the kinds?" (in en). Current Opinion in Structural Biology 16 (1): 118–126. doi:10.1016/j.sbi.2006.01.011. PMID 16434184. https://linkinghub.elsevier.com/retrieve/pii/S0959440X06000121. 
  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" (in en). Nature Biotechnology 22 (10): 1302–1306. doi:10.1038/nbt1012. ISSN 1087-0156. PMID 15361882. http://www.nature.com/articles/nbt1012. 
  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" (in en). Journal of Molecular Biology 342 (1): 345–353. doi:10.1016/j.jmb.2004.06.088. PMID 15313629. https://linkinghub.elsevier.com/retrieve/pii/S0022283604007715. 
  5. ↑ Galzitskaya, Oxana V.; Garbuzynskiy, Sergiy O.; Lobanov, Michail Yurievich (2006-12-29). "Prediction of Amyloidogenic and Disordered Regions in Protein Chains" (in en). PLOS Computational Biology 2 (12): e177. doi:10.1371/journal.pcbi.0020177. ISSN 1553-7358. PMID 17196033. Bibcode: 2006PLSCB...2..177G. 
  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" (in en). Protein Science 16 (5): 906–918. doi:10.1110/ps.062624507. 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. 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" (in en). Protein Science 16 (5): 906–918. doi:10.1110/ps.062624507. ISSN 1469-896X. 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. 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. 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" (in en). Nucleic Acids Research 37 (Web Server): W469–W473. doi:10.1093/nar/gkp351. ISSN 0305-1048. 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" (in en). Computational Biology and Chemistry 31 (5–6): 373–377. doi:10.1016/j.compbiolchem.2007.06.002. PMID 17644485. https://linkinghub.elsevier.com/retrieve/pii/S1476927107000837. 
  14. ↑ Yoon, Sukjoon; Welsh, William J. (2005-04-22). "Rapid assessment of contact-dependent secondary structure propensity: Relevance to amyloidogenic sequences" (in en). Proteins: Structure, Function, and Bioinformatics 60 (1): 110–117. doi:10.1002/prot.20477. PMID 15849755. https://onlinelibrary.wiley.com/doi/10.1002/prot.20477. 
  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. PMID 15273309. PMC 2279810. http://dx.doi.org/10.1110/ps.04790604. 
  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" (in en). PLOS Computational Biology 5 (3): e1000333. doi:10.1371/journal.pcbi.1000333. ISSN 1553-7358. PMID 19325876. Bibcode: 2009PLSCB...5E0333B. 
  17. ↑ Garbuzynskiy, S. O.; Lobanov, M. Yu.; Galzitskaya, O. V. (2010-02-01). "FoldAmyloid: a method of prediction of amyloidogenic regions from protein sequence" (in en). Bioinformatics 26 (3): 326–332. doi:10.1093/bioinformatics/btp691. ISSN 1367-4803. PMID 20019059. https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btp691. 
  18. ↑ Oliveberg, Mikael (March 2010). "Waltz, an exciting new move in amyloid prediction" (in en). Nature Methods 7 (3): 187–188. doi:10.1038/nmeth0310-187. ISSN 1548-7091. PMID 20195250. http://www.nature.com/articles/nmeth0310-187. 
  19. ↑ Maurer-Stroh, Sebastian; Debulpaep, Maja; Kuemmerer, Nico; de la Paz, Manuela Lopez; Martins, Ivo Cristiano; Reumers, Joke; Morris, Kyle L.; Copland, Alastair et al. (March 2010). "Exploring the sequence determinants of amyloid structure using position-specific scoring matrices" (in en). Nature Methods 7 (3): 237–242. doi:10.1038/nmeth.1432. ISSN 1548-7105. PMID 20154676. https://www.nature.com/articles/nmeth.1432. 
  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" (in en). Proceedings of the National Academy of Sciences 103 (11): 4074–4078. doi:10.1073/pnas.0511295103. ISSN 0027-8424. PMID 16537487. Bibcode: 2006PNAS..103.4074T. 
  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" (in en). Nature 435 (7043): 773–778. doi:10.1038/nature03680. ISSN 1476-4687. PMID 15944695. Bibcode: 2005Natur.435..773N. 
  22. ↑ Kuhlman, Brian; Baker, David (2000-09-12). "Native protein sequences are close to optimal for their structures" (in en). Proceedings of the National Academy of Sciences 97 (19): 10383–10388. doi:10.1073/pnas.97.19.10383. ISSN 0027-8424. PMID 10984534. Bibcode: 2000PNAS...9710383K. 
  23. ↑ Sawaya, Michael R.; Sambashivan, Shilpa; Nelson, Rebecca; Ivanova, Magdalena I.; Sievers, Stuart A.; Apostol, Marcin I.; Thompson, Michael J.; Balbirnie, Melinda et al. (May 2007). "Atomic structures of amyloid cross-β spines reveal varied steric zippers" (in en). Nature 447 (7143): 453–457. doi:10.1038/nature05695. ISSN 0028-0836. PMID 17468747. Bibcode: 2007Natur.447..453S. http://www.nature.com/articles/nature05695. 
  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" (in en). Proteins: Structure, Function, and Bioinformatics 80 (2): 410–420. doi:10.1002/prot.23203. ISSN 0887-3585. 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" (in en). BMC Bioinformatics 10 (S1): S45. doi:10.1186/1471-2105-10-S1-S45. ISSN 1471-2105. 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" (in en). Protein Science 16 (5): 906–918. doi:10.1110/ps.062624507. 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 et al. (March 2010). "Exploring the sequence determinants of amyloid structure using position-specific scoring matrices" (in en). Nature Methods 7 (3): 237–242. doi:10.1038/nmeth.1432. ISSN 1548-7091. PMID 20154676. http://www.nature.com/articles/nmeth.1432. 
  28. ↑ Garbuzynskiy, S. O.; Lobanov, M. Yu.; Galzitskaya, O. V. (2010-02-01). "FoldAmyloid: a method of prediction of amyloidogenic regions from protein sequence" (in en). Bioinformatics 26 (3): 326–332. doi:10.1093/bioinformatics/btp691. ISSN 1367-4803. PMID 20019059. https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btp691. 
  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" (in en). PLOS ONE 8 (1): e54175. doi:10.1371/journal.pone.0054175. ISSN 1932-6203. PMID 23326595. Bibcode: 2013PLoSO...854175T. 
  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. PMID 24848016. PMC 4086119. https://doi.org/10.1093/nar/gku399. 
  31. ↑ Gasior, Pawel; Kotulska, Malgorzata (December 2014). "FISH Amyloid – a new method for finding amyloidogenic segments in proteins based on site specific co-occurence [sic] of aminoacids" (in en). BMC Bioinformatics 15 (1): 54. doi:10.1186/1471-2105-15-54. ISSN 1471-2105. PMID 24564523. 
  32. ↑ 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" (in en). PLOS ONE 10 (8): e0134679. doi:10.1371/journal.pone.0134679. ISSN 1932-6203. PMID 26241652. Bibcode: 2015PLoSO..1034679F. 
  33. ↑ Ahmed, Abdullah B.; Znassi, Nadia; Château, Marie‐Thérèse; Kajava, Andrey V. (June 2015). "A structure‐based approach to predict predisposition to amyloidosis" (in en). Alzheimer's & Dementia 11 (6): 681–690. doi:10.1016/j.jalz.2014.06.007. ISSN 1552-5260. PMID 25150734. https://onlinelibrary.wiley.com/doi/10.1016/j.jalz.2014.06.007. 
  34. ↑ Wozniak, Pawel P.; Kotulska, Malgorzata (2015-06-17). "AmyLoad: website dedicated to amyloidogenic protein fragments". Bioinformatics 31 (20): 3395–3397. doi:10.1093/bioinformatics/btv375. ISSN 1367-4803. PMID 26088800. 
  35. ↑ Van Durme, Joost; De Baets, Greet; Van Der Kant, Rob; Ramakers, Meine; Ganesan, Ashok; Wilkinson, Hannah; Gallardo, Rodrigo; Rousseau, Frederic et al. (August 2016). "Solubis: a webserver to reduce protein aggregation through mutation" (in en). Protein Engineering Design and Selection 29 (8): 285–289. doi:10.1093/protein/gzw019. ISSN 1741-0126. PMID 27284085. https://academic.oup.com/peds/article-lookup/doi/10.1093/protein/gzw019. 
  36. ↑ De Baets, Greet; Van Durme, Joost; van der Kant, Rob; Schymkowitz, Joost; Rousseau, Frederic (2015-08-01). "Solubis: optimize your protein: Fig. 1." (in en). Bioinformatics 31 (15): 2580–2582. doi:10.1093/bioinformatics/btv162. ISSN 1367-4803. PMID 25792555. 
  37. ↑ Sormanni, Pietro; Aprile, Francesco A.; Vendruscolo, Michele (January 2015). "The CamSol Method of Rational Design of Protein Mutants with Enhanced Solubility" (in en). Journal of Molecular Biology 427 (2): 478–490. doi:10.1016/j.jmb.2014.09.026. PMID 25451785. https://linkinghub.elsevier.com/retrieve/pii/S0022283614005312. 
  38. ↑ Sormanni, Pietro; Amery, Leanne; Ekizoglou, Sofia; Vendruscolo, Michele; Popovic, Bojana (December 2017). "Rapid and accurate in silico solubility screening of a monoclonal antibody library" (in en). Scientific Reports 7 (1): 8200. doi:10.1038/s41598-017-07800-w. ISSN 2045-2322. PMID 28811609. Bibcode: 2017NatSR...7.8200S. 
  39. ↑ Sormanni, Pietro; Aprile, Francesco A.; Vendruscolo, Michele (January 2015). "The CamSol Method of Rational Design of Protein Mutants with Enhanced Solubility" (in en). Journal of Molecular Biology 427 (2): 478–490. doi:10.1016/j.jmb.2014.09.026. PMID 25451785. https://linkinghub.elsevier.com/retrieve/pii/S0022283614005312. 
  40. ↑ Sormanni, Pietro; Amery, Leanne; Ekizoglou, Sofia; Vendruscolo, Michele; Popovic, Bojana (December 2017). "Rapid and accurate in silico solubility screening of a monoclonal antibody library" (in en). Scientific Reports 7 (1): 8200. doi:10.1038/s41598-017-07800-w. ISSN 2045-2322. PMID 28811609. Bibcode: 2017NatSR...7.8200S. 
  41. ↑ Burdukiewicz, Michał; Sobczyk, Piotr; Rödiger, Stefan; Duda-Madej, Anna; Mackiewicz, Paweł; Kotulska, Małgorzata (2017-10-11). "Amyloidogenic motifs revealed by n-gram analysis" (in en). Scientific Reports 7 (1): 12961. doi:10.1038/s41598-017-13210-9. ISSN 2045-2322. PMID 29021608. Bibcode: 2017NatSR...712961B. 
  42. ↑ Bondarev, Stanislav A; Bondareva, Olga V; Zhouravleva, Galina A; Kajava, Andrey V (2017-10-04). "BetaSerpentine: a bioinformatics tool for reconstruction of amyloid structures". Bioinformatics 34 (4): 599–608. doi:10.1093/bioinformatics/btx629. ISSN 1367-4803. PMID 29444233. 
  43. ↑ Sankar, Kannan; Krystek, Stanley R.; Carl, Stephen M.; Day, Tyler; Maier, Johannes K. X. (November 2018). "AggScore: Prediction of aggregation-prone regions in proteins based on the distribution of surface patches" (in en). Proteins: Structure, Function, and Bioinformatics 86 (11): 1147–1156. doi:10.1002/prot.25594. PMID 30168197. https://onlinelibrary.wiley.com/doi/10.1002/prot.25594. 
  44. ↑ Rawat, Puneet; Prabakaran, R; Kumar, Sandeep; Gromiha, M Michael (2019-10-10). "AggreRATE-Pred: a mathematical model for the prediction of change in aggregation rate upon point mutation". Bioinformatics 36 (5): 1439–1444. doi:10.1093/bioinformatics/btz764. ISSN 1367-4803. PMID 31599925. https://doi.org/10.1093/bioinformatics/btz764. 
  45. ↑ Kuriata, Aleksander; Iglesias, Valentin; Pujols, Jordi; Kurcinski, Mateusz; Kmiecik, Sebastian; Ventura, Salvador (2019-05-03). "Aggrescan3D (A3D) 2.0: prediction and engineering of protein solubility". Nucleic Acids Research 47 (W1): W300–W307. doi:10.1093/nar/gkz321. ISSN 0305-1048. PMID 31049593. PMC 6602499. https://doi.org/10.1093/nar/gkz321. 
  46. ↑ Kuriata, Aleksander; Iglesias, Valentin; Kurcinski, Mateusz; Ventura, Salvador; Kmiecik, Sebastian (2019-03-02). "Aggrescan3D standalone package for structure-based prediction of protein aggregation properties". Bioinformatics 35 (19): 3834–3835. doi:10.1093/bioinformatics/btz143. ISSN 1367-4803. PMID 30825368. https://doi.org/10.1093/bioinformatics/btz143. 
  47. ↑ Zambrano, Rafael; Jamroz, Michal; Szczasiuk, Agata; Pujols, Jordi; Kmiecik, Sebastian; Ventura, Salvador (2015-04-16). "AGGRESCAN3D (A3D): server for prediction of aggregation properties of protein structures". Nucleic Acids Research 43 (W1): W306–W313. doi:10.1093/nar/gkv359. ISSN 0305-1048. PMID 25883144. PMC 4489226. https://doi.org/10.1093/nar/gkv359. 
  48. ↑ Gil-Garcia, Marcos; Bañó-Polo, Manuel; Varejão, Nathalia; Jamroz, Michal; Kuriata, Aleksander; Díaz-Caballero, Marta; Lascorz, Jara; Morel, Bertrand et al. (2018-09-04). "Combining Structural Aggregation Propensity and Stability Predictions To Redesign Protein Solubility". Molecular Pharmaceutics 15 (9): 3846–3859. doi:10.1021/acs.molpharmaceut.8b00341. ISSN 1543-8384. PMID 30036481. https://doi.org/10.1021/acs.molpharmaceut.8b00341. 
  49. ↑ Pujols, Jordi; Iglesias, Valentín; Santos, Jaime; Kuriata, Aleksander; Kmiecik, Sebastian; Ventura, Salvador (2021-04-14) (in en). A3D 2.0 update for the prediction and optimization of protein solubility. doi:10.1101/2021.04.13.439600. http://biorxiv.org/lookup/doi/10.1101/2021.04.13.439600. 
  50. ↑ Keresztes, László; Szögi, Evelin; Varga, Bálint; Farkas, Viktor; Perczel, András; Grolmusz, Vince (April 2021). "The Budapest Amyloid Predictor and Its Applications" (in en). Biomolecules 11 (4): 500. doi:10.3390/biom11040500. PMID 33810341. 
  51. ↑ Prabakaran, R.; Rawat, Puneet; Kumar, Sandeep; Michael Gromiha, M. (May 2021). "ANuPP: A Versatile Tool to Predict Aggregation Nucleating Regions in Peptides and Proteins" (in en). Journal of Molecular Biology 433 (11): 166707. doi:10.1016/j.jmb.2020.11.006. PMID 33972019. https://linkinghub.elsevier.com/retrieve/pii/S0022283620306252. 



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