Wikipedia

Protein aggregation predictors

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

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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

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  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.
  22. ^ Kuhlman, Brian; Baker, David (2000-09-12). "Native protein sequences are close to optimal for their structures". Proceedings of the National Academy of Sciences. 97 (19): 10383–10388. Bibcode:2000PNAS...9710383K. doi:10.1073/pnas.97.19.10383. ISSN 0027-8424. PMC 27033. PMID 10984534.
  23. ^ Sawaya, Michael R.; Sambashivan, Shilpa; Nelson, Rebecca; Ivanova, Magdalena I.; Sievers, Stuart A.; Apostol, Marcin I.; Thompson, Michael J.; Balbirnie, Melinda; Wiltzius, Jed J. W.; McFarlane, Heather T.; Madsen, Anders Ø. (May 2007). "Atomic structures of amyloid cross-β spines reveal varied steric zippers". Nature. 447 (7143): 453–457. Bibcode:2007Natur.447..453S. doi:10.1038/nature05695. ISSN 0028-0836. PMID 17468747. S2CID 4400866.
  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.
  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". BMC Bioinformatics. 15 (1): 54. doi:10.1186/1471-2105-15-54. ISSN 1471-2105. PMC 3941796. PMID 24564523.
  32. ^ Thangakani, A. Mary; Kumar, Sandeep; Nagarajan, R.; Velmurugan, D.; Gromiha, M. Michael (2014-03-28). "GAP: towards almost 100 percent prediction for β-strand-mediated aggregating peptides with distinct morphologies". Bioinformatics. 30 (14): 1983–1990. doi:10.1093/bioinformatics/btu167. ISSN 1460-2059. PMID 24681906.
  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, vol. 304, Berlin, Heidelberg: Springer Berlin Heidelberg, 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.
  38. ^ 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.
  39. ^ Van Durme, Joost; De Baets, Greet; Van Der Kant, Rob; Ramakers, Meine; Ganesan, Ashok; Wilkinson, Hannah; Gallardo, Rodrigo; Rousseau, Frederic; Schymkowitz, Joost (August 2016). "Solubis: a webserver to reduce protein aggregation through mutation". Protein Engineering Design and Selection. 29 (8): 285–289. doi:10.1093/protein/gzw019. ISSN 1741-0126. PMID 27284085.
  40. ^ De Baets, Greet; Van Durme, Joost; van der Kant, Rob; Schymkowitz, Joost; Rousseau, Frederic (2015-08-01). "Solubis: optimize your protein: Fig. 1". Bioinformatics. 31 (15): 2580–2582. doi:10.1093/bioinformatics/btv162. ISSN 1367-4803. PMID 25792555.
  41. ^ Sormanni, Pietro; Aprile, Francesco A.; Vendruscolo, Michele (January 2015). "The CamSol Method of Rational Design of Protein Mutants with Enhanced Solubility". Journal of Molecular Biology. 427 (2): 478–490. doi:10.1016/j.jmb.2014.09.026. PMID 25451785.
  42. ^ Sormanni, Pietro; Amery, Leanne; Ekizoglou, Sofia; Vendruscolo, Michele; Popovic, Bojana (December 2017). "Rapid and accurate in silico solubility screening of a monoclonal antibody library". Scientific Reports. 7 (1): 8200. Bibcode:2017NatSR...7.8200S. doi:10.1038/s41598-017-07800-w. ISSN 2045-2322. PMC 5558012. PMID 28811609.
  43. ^ Sormanni, Pietro; Aprile, Francesco A.; Vendruscolo, Michele (January 2015). "The CamSol Method of Rational Design of Protein Mutants with Enhanced Solubility". Journal of Molecular Biology. 427 (2): 478–490. doi:10.1016/j.jmb.2014.09.026. PMID 25451785.
  44. ^ Sormanni, Pietro; Amery, Leanne; Ekizoglou, Sofia; Vendruscolo, Michele; Popovic, Bojana (December 2017). "Rapid and accurate in silico solubility screening of a monoclonal antibody library". Scientific Reports. 7 (1): 8200. Bibcode:2017NatSR...7.8200S. doi:10.1038/s41598-017-07800-w. ISSN 2045-2322. PMC 5558012. PMID 28811609.
  45. ^ Burdukiewicz, Michał; Sobczyk, Piotr; Rödiger, Stefan; Duda-Madej, Anna; Mackiewicz, Paweł; Kotulska, Małgorzata (2017-10-11). "Amyloidogenic motifs revealed by n-gram analysis". Scientific Reports. 7 (1): 12961. Bibcode:2017NatSR...712961B. doi:10.1038/s41598-017-13210-9. ISSN 2045-2322. PMC 5636826. PMID 29021608.
  46. ^ 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.
  47. ^ 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". Proteins: Structure, Function, and Bioinformatics. 86 (11): 1147–1156. doi:10.1002/prot.25594. PMID 30168197. S2CID 52131048.
  48. ^ 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.
  49. ^ 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. PMC 6602499. PMID 31049593.
  50. ^ 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.
  51. ^ 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. PMC 4489226. PMID 25883144.
  52. ^ Gil-Garcia, Marcos; Bañó-Polo, Manuel; Varejão, Nathalia; Jamroz, Michal; Kuriata, Aleksander; Díaz-Caballero, Marta; Lascorz, Jara; Morel, Bertrand; Navarro, Susanna; Reverter, David; Kmiecik, Sebastian (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. S2CID 206688348.
  53. ^ Pujols, Jordi; Iglesias, Valentín; Santos, Jaime; Kuriata, Aleksander; Kmiecik, Sebastian; Ventura, Salvador (2021-04-14). "A3D 2.0 update for the prediction and optimization of protein solubility". doi:10.1101/2021.04.13.439600. S2CID 233329012. {{cite journal}}: Cite journal requires |journal= (help)
  54. ^ Keresztes, László; Szögi, Evelin; Varga, Bálint; Farkas, Viktor; Perczel, András; Grolmusz, Vince (April 2021). "The Budapest Amyloid Predictor and Its Applications". Biomolecules. 11 (4): 500. doi:10.3390/biom11040500. PMC 8067080. PMID 33810341.
  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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