Prabakaran R, Puneet Rawat, Sandeep Kumar and M. Michael Gromiha (2021) ANuPP: A versatile tool to predict aggregation nucleating regions in proteins. Journal of Molecular Biology, 433(1), 166707. link
Rawat,P. et al. (2020) CPAD 2.0: a repository of curated experimental data on aggregating proteins and peptides. Amyloid, 16.
Prabakaran,R. et al. (2017a) Aggregation prone regions in human proteome: In-sights from large-scale data analyses. Proteins Struct. Funct. Bioinforma., 85, 10991118.
Prabakaran,R. et al. (2017b) Influence of Amino Acid Properties for Characterizing Amyloid Peptides in Human Proteome. Lecture Notes in Computer Sci-ence, 10362. Springer, Cham.
Thangakani,A.M. et al. (2016) CPAD, Curated Protein Aggregation Database: A Repository of Manually Curated Experimental Data on Protein and Peptide Aggregation. PLoS One, 11, e0152949.
Thangakani,A.M. et al. (2014) GAP: towards almost 100 percent prediction for β-strand-mediated aggregating peptides with distinct morphologies. Bioinformatics, 30, 19831990.
References:
Agostini,F. et al. (2014) ccSOL omics: a webserver for solubility prediction of endogenous and heterologous expression in Escherichia coli. Bioinformatics, 30, 29752977.
Ahmed,A.B. et al. (2015) A structure-based approach to predict predisposition to amyloidosis. Alzheimer’s Dement., 11, 681690.
Beerten,J. et al. (2014) WALTZ-DB: A benchmark database of amyloidogenic hexapeptides. Bioinformatics, 31, 16981700.
Bryan,A.W. et al. (2009) BETASCAN: Probable beta-amyloids Identified by Pair-wise Probabilistic Analysis. PLoS Comput. Biol., 5, e1000333.
Conchillo-Solé,O. et al. (2007) AGGRESCAN: a server for the prediction and evaluation of ‘hot spots’ of aggregation in polypeptides. BMC Bioinformat-ics, 8, 65.
Fernandez-Escamilla,A.-M. et al. (2004) Prediction of sequence-dependent and mutational effects on the aggregation of peptides and proteins. Nat. Biotech-nol., 22, 13026.
Galzitskaya,O. V. et al. (2006) Prediction of amyloidogenic and disordered re-gions in protein chains. PLoS Comput. Biol., 2, 16391648.
Gasior, P. and Kotulska, M. (2014) FISH Amyloid a new method for finding amyloidogenic segments in proteins based on site specific co-occurrence of amino acids. BMC Bioinformatics, 15, 54.
Hamodrakas,S.J. et al. (2007) Consensus prediction of amyloidogenic determi-nants in amyloid fibril-forming proteins. Int. J. Biol. Macromol., 41, 295300.
Maurer-Stroh,S. et al. (2010) Exploring the sequence determinants of amyloid structure using position-specific scoring matrices. Nat. Methods, 7, 237242.
Palato,L.M. et al. (2019) Amyloidogenicity of naturally occurring full-length animal IAPP variants. J. Pept. Sci., 25, 18.
Pastor,M.T. et al. (2007) Hacking the code of amyloid formation: the amyloid stretch hypothesis. Prion, 1, 914.
López de la Paz,M. and Serrano,L. (2004) Sequence determinants of amyloid fibril formation. Proc. Natl. Acad. Sci. U. S. A., 101, 8792.
Rose,A.S. et al. (2018) NGL viewer: web-based molecular graphics for large complexes. Bioinformatics, 34, 37553758.
Saelices,L. et al. (2018) Crystal structures of amyloidogenic segments of human transthyretin. Protein Sci., 27, 1295-1303.
Saelices,L. et al. (2015) Uncovering the mechanism of aggregation of human transthyretin. J. Biol. Chem., 290, 2893228943.
Tartaglia,G.G. et al. (2005) Prediction of aggregation rate and aggregation-prone segments in polypeptide sequences. Protein Sci., 14, 27232734.
Tian,J. et al. (2009) Prediction of amyloid fibril-forming segments based on a support vector machine. BMC Bioinformatics, 10, S45.
Tsolis,A.C. et al. (2013) A Consensus Method for the Prediction of ‘Aggregation-Prone’ Peptides in Globular Proteins. PLoS One, 8, e54175.
Varadi,M. et al. (2017) AmyPro: a database of proteins with validated amyloido-genic regions. Nucleic Acids Res., 46, D387-D392.
Walsh,I. et al. (2014) PASTA 2.0: An improved server for protein aggregation prediction. Nucleic Acids Res., 42, 301307.
Wozniak,P.P. and Kotulska,M. (2015) AmyLoad: Website dedicated to amyloidogenic protein fragments. Bioinformatics, 31, 33953397.
Zambrano,R. et al. (2015) AGGRESCAN3D (A3D): Server for prediction of aggregation properties of protein structures. Nucleic Acids Res., 43, W306W313.
Zhang Z, Chen H, Lai L (2007) Identification of amyloid fibril-forming segments based on structure and residue-based statistical potential. Bioinformatics 23: 22182225.