Identification of proteins associated with amyloidosis by polarity index method

2015 ◽  
Vol 62 (1) ◽  
pp. 41-55 ◽  
Author(s):  
Carlos Polanco ◽  
José Lino Samaniego ◽  
Vladimir N. Uversky ◽  
Jorge Alberto Castañón-González ◽  
Thomas Buhse ◽  
...  
Keyword(s):  
Index Method ◽  
Polarity Index

scholarly journals Detection of selective antibacterial peptides by the Polarity Profile method.

2013 ◽  
Vol 60 (2) ◽  
Author(s):  
Carlos Polanco ◽  
Thomas Buhse ◽  
José Lino Samaniego ◽  
Jorge Alberto Castañón-González
Keyword(s):  
Antimicrobial Peptides ◽  
Mammalian Cells ◽  
False Negative ◽  
Antibacterial Peptides ◽  
Index Method ◽  
Profile Method ◽  
Bacterial Membranes ◽  
Polarity Index ◽  
Polarity Profile ◽  
Almost All

Antimicrobial peptides occupy a prominent place in the production of pharmaceuticals, because of their effective contribution to the protection of the immune system against almost all types of pathogens. These peptides are thoroughly studied by computational methods designed to shed light on their main functions. In this paper, we propose a computational approach, named the Polarity Profile method that represents an improvement to the former Polarity Index method. The Polarity Profile method is very effective in detecting the subgroup of antibacterial peptides called selective cationic amphipathic antibacterial peptides (SCAAP) that show high toxicity towards bacterial membranes and exhibit almost zero toxicity towards mammalian cells. Our study was restricted to the peptides listed in the antimicrobial peptides database (APD2) of December 19, 2012. Performance of the Polarity Profile method is demonstrated through a comparison to the former Polarity Index method by using the same sets of peptides. The efficiency of the Polarity Profile method exceeds 85% taking into account the false positive and/or false negative peptides.

Bioinformatics-based characterization of proteins related to SARS-CoV-2 using the Polarity Index Method® (PIM®) and Intrinsic Disorder Predisposition

2021 ◽  
Vol 18 ◽  
Author(s):  
Carlos Polanco ◽  
Vladimir N. Uversky ◽  
Guy W. Dayhoff II ◽  
Alberto Huberman ◽  
Thomas Buhse ◽  
...  
Keyword(s):  
Intrinsically Disordered Proteins ◽  
Intrinsic Disorder ◽  
Cell Penetrating Peptides ◽  
Disordered Proteins ◽  
Structural Proteins ◽  
Public Health Issue ◽  
Index Method ◽  
Current Time ◽  
Intrinsically Disordered ◽  
Polarity Index

Background: The global outbreak of the 2019 novel Coronavirus Disease (COVID-19) caused by the infection with the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), which appeared in China at the end of 2019, signifies a major public health issue at the current time. Objective: The objective of the present study is to characterize the physicochemical properties of the SARS-CoV-2 proteins at a residues level, and to generate a “bioinformatics fingerprint” in the form of a “PIM® profile” created for each sequence utilizing the Polarity Index Method® (PIM®), suitable for the identification of these proteins. Methods: Two different bioinformatics approaches were used to analyze sequence characteristics of these proteins at the residues level, an in-house bioinformatics system PIM®, and a set of the commonly used algorithms for the predic-tion of protein intrinsic disorder predisposition, such as PONDR® VLXT, PONDR® VL3, PONDR® VSL2, PONDR® FIT, IUPred_short and IUPred_long. The PIM® profile was generated for four SARS-CoV-2 structural proteins and compared with the corresponding profiles of the SARS-CoV-2 non-structural proteins, SARS-CoV-2 putative proteins, SARS-CoV proteins, MERS-CoV proteins, sets of bacterial, fungal, and viral proteins, cell-penetrating peptides, and a set of intrinsically disordered proteins. We also searched for the UniProt proteins with PIM® profiles similar to those of SARS-CoV-2 structural, non-structural, and putative proteins. Results: We show that SARS-CoV-2 structural, non-structural, and putative proteins are characterized by a unique PIM® profile. A total of 1736 proteins were identified from the 562,253 “reviewed” proteins from the UniProt database, whose PIM® profile was similar to that of the SARS-CoV-2 structural, non-structural, and putative proteins. Conclusion: The PIM® profile represents an important characteristic that might be useful for the identification of proteins similar to SARS-CoV-2 proteins.

Identification of antimicrobial peptides by using eigenvectors

2016 ◽  
Vol 63 (3) ◽  
Author(s):  
Carlos Polanco
Keyword(s):  
Antimicrobial Peptides ◽  
Cancer Cells ◽  
Potential Application ◽  
Mammalian Cells ◽  
Computational Method ◽  
Antibacterial Peptides ◽  
Index Method ◽  
Vector Method ◽  
Polarity Index ◽  
Wide Range

Antibacterial peptides are subject to broad research due to their potential application and the benefit they can provide for a wide range of diseases. In this work, a mathematical-computational method, called the Polarity Vector Method, is introduced that has a high discriminative level (>70%) to identify peptides associated with Gram (-) bacteria, Gram (+) bacteria, cancer cells, fungi, insects, mammalian cells, parasites, and viruses, taken from the Antimicrobial Peptides Database. This supervised method uses only eigenvectors from the incident polar matrix of the group studied. It was verified with a comparative study with another extensively verified method developed previously by our team, the Polarity Index Method. The number of positive hits of both methods was up to 98% in all the tests conducted.

scholarly journals A toy model of prebiotic peptide evolution: the possible role of relative amino acid abundances.

2013 ◽  
Vol 60 (2) ◽  
Author(s):  
Carlos Polanco ◽  
Thomas Buhse ◽  
José Lino Samaniego ◽  
Jorge Alberto Castañón González
Keyword(s):  
Amino Acid ◽  
Index Method ◽  
Principal Role ◽  
E Coli ◽  
Polarity Index ◽  
Sequential Information ◽  
Peptide Profiles ◽  
Dynamic Key ◽  
Self Replication

This paper presents a mathematical-computational toy model based on the assumed dynamic principles of prebiotic peptide evolution. Starting from a pool of amino acid monomers, the model describes in a generalized manner the generation of peptides and their sequential information. The model integrates the intrinsic and dynamic key elements of the initiation of biopolymerization, such as the relative amino acid abundances and polarities, as well as the oligomer reversibility, i.e. fragmentation and recombination, and peptide self-replication. Our modeling results suggest that the relative amino acid abundances, as indicated by Miller-Urey type electric discharge experiments, played a principal role in the early sequential information of peptide profiles. Moreover, the computed profiles display an astonishing similarity to peptide profiles observed in so-called biological common ancestors found in the following three microorganisms; E. coli, M. jannaschii, and S. cereviasiae. The prebiotic peptide fingerprint was obtained by the so-called polarity index method that was earlier reported as a tool for the identification of cationic amphipathic antibacterial short peptides.

Bioinformatics-based identification of proteins expressed by arthropod-borne viruses transmitted by Aedes aegypti mosquito

2020 ◽  
Vol 17 ◽  
Author(s):  
Carlos Polanco ◽  
Vladimir N. Uversky ◽  
Alberto Huberman ◽  
Leire Andrés ◽  
Thomas Buhse ◽  
...  
Keyword(s):  
Aedes Aegypti ◽  
Intrinsically Disordered Proteins ◽  
Intrinsic Disorder ◽  
Cell Penetrating Peptides ◽  
Disordered Proteins ◽  
Index Method ◽  
Intrinsically Disordered ◽  
Polarity Index ◽  
Uniprot Database ◽  
Prophylactic Measures

Background: The female Aedes aegypti mosquito is a vector of several arthropodborne viruses, such as Mayaro, Dengue, Chikungunya, Yellow Fever, and Zika. These viruses cause the death of at least 600000 people a year and temporarily disable several millions more around the world. Up to date, there are no effective prophylactic measures that would prevent the contact and bite of this arthropod and, therefore, its consequential contagion. Objective: The objective of the present study was to search for the regularities of the proteins expressed by these five viruses, at residues level, and obtain a "bioinformatic fingerprint" to select them. Methods: We used two bioinformatic systems, our in-house bioinformatic system named Polarity Index Method® (PIM®) supported at residues level, and the commonly used algorithm for the prediction of intrinsic disorder predisposition, PONDR® FIT. We applied both programs to the 29 proteins that express the five groups of arboviruses studied, and we calculated for each of them their Polarity Index Method® profile and their intrinsic disorder predisposition. This information was then compared with analogous information for other protein groups, such as proteins from bacteria, fungi, viruses, and cell penetrating peptides from the UniProt database, and a set of intrinsically disordered proteins. Once the "fingerprint" of each group of arboviruses was obtained, these "fingerprints" were searched among the 559228 "reviewed" proteins from the UniProt database. Results: In total, 1736 proteins were identified from the 559228 “reviewed” proteins from UniProt database, with similar "PIM® profile" to the 29 mutated proteins that express the five groups of arboviruses. Conclusion: We propose that the “PIM® profile” of characterization of proteins might be useful for the identification of proteins expressed by arthropod-borne viruses transmitted by Aedes aegypti mosquito.

scholarly journals Possible computational filter to detect proteins associated to influenza A subtype H1N1.

2014 ◽  
Vol 61 (4) ◽  
Author(s):  
Carlos Polanco ◽  
Thomas Buhse ◽  
Jorge Alberto Castañón-González ◽  
José Lino Samaniego
Keyword(s):  
Influenza A ◽  
Quantitative Structure Activity Relationship ◽  
Peptide Sequence ◽  
Index Method ◽  
Linear Peptide ◽  
Polarity Index ◽  
Uniprot Database ◽  
Del Rio ◽  
Virus Subtype ◽  
Proteins And Peptides

The design of drugs with bioinformatics methods to identify proteins and peptides with a specific toxic action is increasingly recurrent. Here, we identify toxic proteins towards the influenza A virus subtype H1N1 located at the UniProt database. Our quantitative structure-activity relationship (QSAR) approach is based on the analysis of the linear peptide sequence with the so-called Polarity Index Method that shows an efficiency of 90% for proteins from the Uniprot Database. This method was exhaustively verified with the APD2, CPPsite, Uniprot, and AmyPDB databases as well as with the set of antibacterial peptides studied by del Rio et al. and Oldfield et al.

Bioinformatics-based identification of Selective Cationic Amphipatic Antibacterial Peptides: Aurein 1.2 variants case

2020 ◽  
Vol 17 ◽  
Author(s):  
Carlos Polanco ◽  
Alberto Huberman ◽  
Vladimir N. Uversky ◽  
Leire Andrés ◽  
Thomas Buhse ◽  
...  
Keyword(s):  
Mammalian Cells ◽  
Intrinsically Disordered Proteins ◽  
Intrinsic Disorder ◽  
Cell Penetrating Peptides ◽  
Disordered Proteins ◽  
Antibacterial Peptides ◽  
Index Method ◽  
Intrinsically Disordered ◽  
Polarity Index ◽  
Uniprot Database

Background: Selective Cationic Amphipathic Antibacterial Peptides (SCAAPs) occupy a prominent place in the production of new drugs on account of their high toxicity towards bacteria and low toxicity towards mammalian cells, low hemolytic activity, and contribution to the protection of the human immune system. Introduction: Their number in nature is very low and experimental tests are very protracted and costly. Therefore, it would be useful to have bioinformatics tools that would identify them in the existing databases and also propose new synthetic SCAAPs. Method: In order to reduce the costs of identification and/or chemical synthesis. To know the physicochemical characteristics of SCAAPs at a residues level and to obtain a “bioiformatics fingerprint” suitable for their selection, we have modified the Polarity Index Method® (PIM®) to include the α-helical configuration of each sequence to determine their individual “PIM® profile”. We have also used a set of computer program to determine their “Intrinsic Disorder Predisposition”. This information was then compared with other protein groups such as bacteria, fungi, virus and cell penetrating peptides (CPP) from the UniProt database and a set of intrinsically disordered proteins. Once the “fingerprint” of SCAAPs was obtained, it was used for searching among the 559228 “reviewed” proteins from the UniProt database and a set of synthetic SCAAPs characterized by the predefined “PIM® profile” selected. Results: Our results showed that the metric named “PIM® profile” can identify, with a high level of accuracy, a group of bacterial SCAAPs. This bioinformatics study was supported at residues level, using the in-house bioinformatics system Polarity Index Method the commonly used algorithm for the prediction of intrinsic disorder predisposition, PONDR® FIT. Conclusions: The Polarity Index Method seems highly efficient identifying SCAAP candidates.

scholarly journals Electronegativity and intrinsic disorder of preeclampsia-related proteins

2016 ◽  
Vol 64 (1) ◽  
Author(s):  
Carlos Polanco ◽  
Jorge Alberto Castañón-González ◽  
Vladimir N. Uversky ◽  
Thomas Buhse ◽  
José Lino Samaniego Mendoza ◽  
...  
Keyword(s):  
Antimicrobial Peptides ◽  
Maternal Death ◽  
Computational Study ◽  
Intrinsic Disorder ◽  
Functional Disorder ◽  
Index Method ◽  
Underdeveloped Countries ◽  
Polarity Index ◽  
Intrinsically Disorder ◽  
Related Proteins

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 in which evaluated the commonness and potential functionality of intrinsic disorder in these proteins and also made an attempt to characterize their origin. To this end, we used a several supervised techniques, as a Polarity Index Method (PIM), which evaluates the electronegativity of proteins from their sequence alone. Peculiarities of resulting 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 disorder proteins. Our results showed a high graphical correlation between preeclampsia proteins, lipoproteins, and the angiogenesis proteins. These results lead us to strongly assume that the preeclampsia proteins are lipoproteins. We also show that several preeclampsia-related proteins contain significant amounts of functional disorder.

Detecting Random Responses in a Personality Scale Using IRT-Based Person-Fit Indices

2019 ◽  
Vol 35 (1) ◽  
pp. 126-136 ◽  
Author(s):  
Tour Liu ◽  
Tian Lan ◽  
Tao Xin
Keyword(s):  
Experimental System ◽  
Real Data ◽  
Fit Indices ◽  
Random Response ◽  
Person Fit ◽  
Index Method ◽  
Single Person ◽  
Person Fit Index ◽  
Random Responses ◽  
Response Behaviors

Abstract. Random response is a very common aberrant response behavior in personality tests and may negatively affect the reliability, validity, or other analytical aspects of psychological assessment. Typically, researchers use a single person-fit index to identify random responses. This study recommends a three-step person-fit analysis procedure. Unlike the typical single person-fit methods, the three-step procedure identifies both global misfit and local misfit individuals using different person-fit indices. This procedure was able to identify more local misfit individuals than single-index method, and a graphical method was used to visualize those particular items in which random response behaviors appear. This method may be useful to researchers in that it will provide them with more information about response behaviors, allowing better evaluation of scale administration and development of more plausible explanations. Real data were used in this study instead of simulation data. In order to create real random responses, an experimental test administration was designed. Four different random response samples were produced using this experimental system.

Sign in / Sign up

Export Citation Format

Share Document