Identifying amyloidogenic regions in protein sequences is useful in understanding the underlying cause of several human diseases and finding potential therapeutic targets. Given the laborious nature of experimental validation of segments most prone to form fibrils, it was essential that computational approaches be developed that could produce reliable, affordable and testable in silico predictions. In this paper, we present and assess some of the recently developed computational tools for predicting amyloid fibril forming motifs that remain as one of the key means used to decipher the role of such regions in disease diagnosis, prognosis and drug discovery.

1. Sergiy O. Garbuzynskiy, Michail Yu. Lobanov and Oxana V. Galzitskaya, " FoldAmyloid: a method of prediction of amyloidogenic regions from protein sequence," Struct Bioinformatics 2010, Vol. 26, No. 3, pp. 326-332.
2. Zhuqing Zhang, Hao Chen and Luhua La,”Identification of amyloid fibril-forming segments based on structure and residue-based statistical potential,” Structural Bioinformatics 2007, Vol. 23 no. 17, pp. 2218–2225.
3. Kimon K Frousios, Vassiliki A Iconomidou, Carolina-Maria Karletidi, Stavros J Hamodrakas, “Amyloidogenic deteminants are usually not buried,” BMC Structural Biology 2009, 9:44.
4. http://www.rosettacommons.org
5. Michael J. Thompson, Stuart A. Sievers, John Karanicolas, Magdalena I. Ivanova, David Baker, “The 3D profile method for identifying fibril-forming segments of proteins,” PNAS 2006, Vol. 103, No. 11, pp. 4074–4078.
6. Oscar Conchillo-Sole, Natalia S de Groot, Francesc X Avilés,Josep Vendrell, Xavier Daura, and Salvador Ventura, “AGGRESCAN: a server for the prediction of “hot spots” of aggregation in polypeptides,” BMC Bioinformatics 2007, 8:65.
7. Kimon K Frousios, Vassiliki A Iconomidou, Carolina-Maria Karletidi, Stavros J Hamodrakas, “Amyloidogenic deteminants are usually not buried,” BMC Structural Biology 2009, 9:44.
8. Hamodrakas SJ: A protein secondary structure prediction scheme for the IBM PC and compatibles. Comput Appl Biosci 1988, 4:473-477.
9. López de la Paz M, Serrano L: Sequence determinants of amyloid fibril formation. Proc Natl Acad Sci 2004, 101:87-92.
10. Fernandez-Escamilla AM, Rousseaux F, Schymkowitz J, Serrano L: Prediction of sequence-dependent and mutational effects on the aggregation of peptides and proteins. Nature Biotechnology 2004, 22:1302-1306.
11. Jian Tian, Ningfeng Wu, Jun Guo and Yunliu Fan, “Prediction of amyloid fibril-forming segments based on a support vector machine,” BMC Bioinformatics, January 2009, 10(Suppl 1):S45.
12. Kawashima S, Kanehisa M, “AAindex: amino acid index database,“ Nucleic Acids Res 2000, 28(1):374
13. Natalia Sánchez de Groot, Irantzu Pallarés, Francesc X Avilés, Josep Vendrell, and Salvador Ventura, “Prediction of "hot spots" of aggregation in disease-linked polypeptides,” BMC Structural Biology 2005, 5:18, doi:10.1186/1472-6807-5-18.
14. Susan Idicula-Thomas and Petety V Balaji, “Understanding the relationship between the primary structure of proteins and their amyloidogenic propensity: clues from inclusion body formation.,”Journal of Protein Engineering, Design & Selection 2005, Vol. 18, No. 4, pp. 175-180.
15. Sukjoon Yoon, William J. Welsh, “Detecting hidden sequence propensity for amyloid fibril formation, “Protein Science 2004, 13: 2149-2160.

Amyloid fibrils Computational tools Prediction accuracy