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Structure and function relationships of proteins based on polar profile: a review

100%
Polanco C., Buhse T., Uversky V.
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2016
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vol. 63
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issue 2
229-233
EN
Proteins in the post-genome era impose diverse research challenges, the main are the understanding of their structure-function mechanism, and the growing need for new pharmaceutical drugs, particularly antibiotics that help clinicians treat the ever- increasing number of Multidrug-Resistant Organisms (MDROs). Although, there is a wide range of mathematical-computational algorithms to satisfy the demand, among them the Quantitative Structure-Activity Relationship algorithms that have shown better performance using a characteristic training data of the property searched; their performance has stagnated regardless of the number of metrics they evaluate and their complexity. This article reviews the characteristics of these metrics, and the need to reconsider the mathematical structure that expresses them, directing their design to a more comprehensive algebraic structure. It also shows how the main function of a protein can be determined by measuring the polarity of its linear sequence, with a high level of accuracy, and how such exhaustive metric stands as a "fingerprint" that can be applied to scan the protein regions to obtain new pharmaceutical drugs, and thus to establish how the singularities led to the specialization of the protein groups known today.
2
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Classifying lipoproteins based on their polar profiles

88%
Polanco C., Castañón-González J., Buhse T., Uversky V., Amkie R.
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2016
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vol. 63
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issue 2
235-241
EN
The lipoproteins are an important group of cargo proteins known for their unique capability to transport lipids. By applying the Polarity index algorithm, which has a metric that only considers the polar profile of the linear sequences of the lipoprotein group, we obtained an analytical and structural differentiation of all the lipoproteins found in UniProt Database. Also, the functional groups of lipoproteins, and particularly of the set of lipoproteins relevant to atherosclerosis, were analyzed with the same method to reveal their structural preference, and the results of Polarity index analysis were verified by an alternate test, the Cumulative Distribution Function algorithm, applied to the same groups of lipoproteins.
3
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Electronegativity and intrinsic disorder of preeclampsia-related proteins

76%
Polanco C., Castañón-González J., Uversky V., Buhse T., Samaniego Mendoza J., Calva J.
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2017
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vol. 64
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issue 1
99-111
EN
Preeclampsia, hemorrhage, and infection are the leading causes of maternal death in underdeveloped countries. Since several proteins associated with preeclampsia are known, we conducted a computational study which evaluated the commonness and potential functionality of intrinsic disorder of these proteins and also made an attempt to characterize their origin. The origin of the preeclampsia-related proteins was assessed with a supervised technique, a Polarity Index Method (PIM), which evaluates the electronegativity of proteins based solely on their sequence. The commonness of intrinsic disorder was evaluated using several disorder predictors from the PONDR family, the charge-hydropathy plot (CH-plot) and cumulative distribution function (CDF) analyses, and using the MobiDB web-based tool, whereas potential functionality of intrinsic disorder was studied with the D2P2 resource and ANCHOR predictor of disorder-based binding sites, and the STRING tool was used to build the interactivity networks of the preeclampsia-related proteins. Peculiarities of the PIM-derived polar profile of the group of preeclampsia-related proteins were then compared with profiles of a group of lipoproteins, antimicrobial peptides, angiogenesis-related proteins, and the intrinsically disordered proteins. Our results showed a high graphical correlation between preeclampsia proteins, lipoproteins, and the angiogenesis proteins. We also showed that many preeclampsia-related proteins contain numerous functional disordered regions. Therefore, these bioinformatics results led us to assume that the preeclampsia proteins are highly associated with the lipoproteins group, and that some preeclampsia-related proteins contain significant amounts of functional disorders.
4
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Identification of proteins associated with amyloidosis by polarity index method

52%
Polanco C., Samaniego J., Uversky V., Castañón-González J., Buhse T., Leopold-Sordo M., Madero-Arteaga A., Morales-Reyes A., Tavera-Sierra L., González-Bernal J., Arias-Estrada M.
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2015
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vol. 62
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issue 1
41-55
EN
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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