Nonlinear signal analysis to understand the dynamics of the protein sequences

Angadi, Savita; Kulkarni, Abhijit J.

doi:10.1140/epjst/e2008-00840-6

Cited by 4 publications

(2 citation statements)

References 25 publications

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“…Recurrence plots (RP) and recurrence quantification analysis (RQA) have been used extensively to study proteins over the years . Most of the work investigated the sequence‐structure relationship in proteins.…”

Section: Introductionmentioning

confidence: 99%

Detecting protein atom correlations using correlation of probability of recurrence

Fataftah

Karain

2014

Proteins

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The dynamic cross-correlation Map(DCCM) technique has been used extensively to study protein dynamics. In this work, we introduce the use of the method of correlation of probability of recurrence (CPR) as a complementary method to detect correlations between protein residue atoms. Time series of the distances of the Cα atoms of the β-lactamase inhibitory protein (BLIP) from a reference position are analyzed using CPR and mutual information (MI). The results are compared to those provided by DCCM. In comparison to MI, CPR is found to detect more of the correlations present in DCCM. It is also able to detect a small number of significant correlations between distant residues that are not detected by DCCM.

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Section: Introductionmentioning

confidence: 99%

Detecting protein atom correlations using correlation of probability of recurrence

Fataftah

Karain

2014

Proteins

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“…Techniques like recurrence plots are used to visualize time series in a meaningful manner and give idea about the nature of the time series. Recurrence quantification analysis (RQA) has earlier been applied to study structure activity relationships in various proteins [3,4].and for other protein science and engineering applications providing interesting insights [5].…”

Section: Introductionmentioning

confidence: 99%

Identification of Defensins Employing Recurrence Quantification Analysis and Random Forest Classifiers

Karnik

Prasad

Diwevedi

et al. 2009

Lecture Notes in Computer Science

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Defensins represent a class of antimicrobial peptides synthesized in the body acting against various microbes. In this paper we study defensins using a non-linear signal analysis method Recurrence Quantication Analysis (RQA). We used the descriptors calculated employing RQA for the classification of defensins with Random Forest Classifier.The RQA descriptors were able to capture patterns peculiar to defensins leading to an accuracy rate of 78.12% using 10-fold cross validation.

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Analysis of Intrinsically Disordered Regions in Proteins Using Recurrence Quantification Analysis

Kulkarni¹,

Karnik

Angadi³

2011

Int. J. Bifurcation Chaos

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Identification of disordered regions in protein sequence(s) is a very important problem and a long standing puzzle to biologists. In many proteins, these structural disorders (an outcome of improper protein folding) inside an otherwise ordered structure exhibit many important biological functions. Classical approaches to identify these regions in a sequence rely mainly on the pattern recognition algorithms where amino acid residues are classified as ordered or disordered. In this work, we employ recurrence quantification analysis based approach to understand the complex dynamics underlying these regions. We hypothesize and demonstrate that proteins with disordered regions show a strong evidence of the order-chaos-order transition pattern by using windowed recurrence quantification analysis on a database of 476 proteins available from .

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Nonlinear signal analysis to understand the dynamics of the protein sequences

Cited by 4 publications

References 25 publications

Detecting protein atom correlations using correlation of probability of recurrence

Detecting protein atom correlations using correlation of probability of recurrence

Identification of Defensins Employing Recurrence Quantification Analysis and Random Forest Classifiers

Analysis of Intrinsically Disordered Regions in Proteins Using Recurrence Quantification Analysis

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