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Acta Biochim Pol. 2015;62(1):41-55. Epub 2015 Feb 12.

Identification of proteins associated with amyloidosis by polarity index method.

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Facultad de Ciencias de la Salud, Universidad Anáhuac, Anáhuac, México.
Department of Molecular Medicine and USF Health Byrd Alzheimer's Research Institute, Morsani College of Medicine, University of South Florida, USA; Department of Biological Sciences, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia; Institute for Biological Instrumentation, Russian Academy of Sciences, Pushchino, Moscow Region, Russia.
Centro de Investigaciones Químicas, Universidad Autónoma del Estado de Morelos, Cuernavaca, Morelos México.
Departamento de Ciencias Computacionales, Instituto Nacional de Astrofísica, Óptica y Electrónica, Puebla, México.
Departamento de Matemáticas, Facultad de Ciencias, Universidad Nacional Autónoma de México, México.


There is a natural protein form, insoluble and resistant to proteolysis, adopted by many proteins independently of their amino acid sequences via specific misfolding-aggregation process. This dynamic process occurs in parallel with or as an alternative to physiologic folding, generating toxic protein aggregates that are deposited and accumulated in various organs and tissues. These proteinaceous deposits typically represent bundles of β-sheet-enriched fibrillar species known as the amyloid fibrils that are responsible for serious pathological conditions, including but not limited to neurodegenerative diseases, grouped under the term amyloidoses. The proteins that might adopt this fibrillar conformation are some globular proteins and natively unfolded (or intrinsically disordered) proteins. Our work shows that intrinsically disordered and intrinsically ordered proteins can be reliably identified, discriminated, and differentiated by analyzing their polarity profiles generated using a computational tool known as the polarity index method (Polanco & Samaniego, 2009; Polanco et al., 2012; 2013; 2013a; 2014; 2014a; 2014b; 2014c; 2014d). We also show that proteins expressed in neurons can be differentiated from proteins in these two groups based on their polarity profiles, and also that this computational tool can be used to identify proteins associated with amyloidoses. The efficiency of the proposed method is high (i.e. 70%) as evidenced by the analysis of peptides and proteins in the APD2 database (2012), AVPpred database (2013), and CPPsite database (2013), the set of selective antibacterial peptides from del Rio et al. (2001), the sets of natively unfolded and natively folded proteins from Oldfield et al. (2005), the set of human revised proteins expressed in neurons, and non-human revised proteins expressed in neurons, from the Uniprot database (2014), and also the set of amyloidogenic proteins from the AmyPDB database (2014).

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