2D Polar Co-ordinate Representation of Amino Acid Sequences With some applications to Ebola virus, SARS and SARS-CoV-2 (COVID-19)

Dey, Tathagata; Biswas, Subhamoy; Chatterjee, Shreyans; Manna, Sourav; Nandy, Ashesh; Basak, Subhas

doi:10.3390/mol2net-06-06790

Cited by 4 publications

(3 citation statements)

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“…The computational complexity of the proposed method is relatively low, being linear in time as one increases the repetition number. The reconstruction is designed to be general for a polar coordinate system without any embedding context, so it can be broadly applied to circular clustering problems on different representations and in different fields [14,16,17,29,46].…”

Section: Discussionmentioning

confidence: 99%

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Circular Clustering With Polar Coordinate Reconstruction

Sun,

Sajda

2024

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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There is a growing interest in characterizing circular data found in biological systems. Such data are wide-ranging and varied, from the signal phase in neural recordings to nucleotide sequences in round genomes. Traditional clustering algorithms are often inadequate due to their limited ability to distinguish differences in the periodic component θ. Current clustering schemes for polar coordinate systems have limitations, such as being only angle-focused or lacking generality. To overcome these limitations, we propose a new analysis framework that utilizes projections onto a cylindrical coordinate system to represent objects in a polar coordinate system optimally. Using the mathematical properties of circular data, we show that our approach always finds the correct clustering result within the reconstructed dataset, given sufficient periodic repetitions of the data. This framework is generally applicable and adaptable to most state-of-the-art clustering algorithms. We demonstrate on synthetic and real data that our method generates more appropriate and consistent clustering results than standard methods. In summary, our proposed analysis framework overcomes the limitations of existing polar coordinate-based clustering methods and provides an accurate and efficient way to cluster circular data. We provide the code as open-source on GitHub.

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Section: Discussionmentioning

confidence: 99%

“…Additional applications of circular clustering can also be found in iridology for disease diagnosis [15,16]. Lately, the investigation of cluster analysis methods applied to protein sequences employing polar coordinate representations has also garnered increasing attention and exploration [17,18,19].…”

Section: Introductionmentioning

confidence: 99%

Circular Clustering With Polar Coordinate Reconstruction

Sun,

Sajda

2024

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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“…The graph radius, p R , this time defined as which is also the sequence descriptor, p R , is found to differ if there is any change in the sequence and thus can be used to filter out identical sequences. Another graphical representation is a 2D polar plot [53] where the amino acids are assigned along radii 18 degrees apart. Plotting a sequence in this graph represents amino acid distribution along the sequence and therefore allows comparison between protein sequences that have their amino acid distribution altered by mutations.…”

Section: Tools For Peptide Vaccine Designmentioning

confidence: 99%

A Bioinformatics Protocol for Rational Design of Peptide Vaccines and the COVID-19 Rampage

2020

Archives of Proteomics and Bioinformatics

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Recent decades have witnessed the emergence of many viral diseases some of which have resulted in epidemics and global pandemics. Design of drugs and vaccines for such diseases is expensive and time consuming. In this article we propose a new computer-assisted method for the fast design of a new class of vaccines, the peptide vaccines, based on the genetic sequences of the pathogens. It is expected that this approach will assist in fighting emerging pathogens.

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A Computational Search for Peptide Vaccines Using Novel Mathematical Descriptors of Sequences of Emerging Pathogens

Chatterjee

Dey

Nandy

et al. 2020

Biophysical and Computational Tools in Drug Discovery

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2D Polar Co-ordinate Representation of Amino Acid Sequences With some applications to Ebola virus, SARS and SARS-CoV-2 (COVID-19)

Abstract: We consider a novel approach to mathematically define a graphing method to represent amino acid sequences of proteins in two-dimensional plane and characterize them numerically. The amino acids are represented by their relative magnitude of their hydrophobicity.

Cited by 4 publications

References 5 publications

Circular Clustering With Polar Coordinate Reconstruction

Circular Clustering With Polar Coordinate Reconstruction

A Bioinformatics Protocol for Rational Design of Peptide Vaccines and the COVID-19 Rampage

A Computational Search for Peptide Vaccines Using Novel Mathematical Descriptors of Sequences of Emerging Pathogens

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