A generalization of CGR representation for analyzing and comparing protein sequences

He, Ping‐an; Xu, S.C.; Dai, Qi; Yao, Yao

doi:10.1002/qua.25068

Cited by 15 publications

(12 citation statements)

References 30 publications

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“…Their method is applied successfully in their following papers and proved effective [25,26]. He et al present a new way of generalized Chaos Game Representation (CGR) method to outline a dynamic 3D graphical representation [27] which is analogous to the original CGR method proposed by Jeffrey for graphical representation of DNA [3]. El-Lakkani and Mahran introduce a two dimensional graphical representation of protein sequences.…”

Section: Introductionmentioning

confidence: 99%

A new method to analyze protein sequence similarity using Dynamic Time Warping

Hou

Pan

Peng³

et al. 2017

Genomics

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Sequences similarity analysis is one of the major topics in bioinformatics. It helps researchers to reveal evolution relationships of different species. In this paper, we outline a new method to analyze the similarity of proteins by Discrete Fourier Transform (DFT) and Dynamic Time Warping (DTW). The original symbol sequences are converted to numerical sequences according to their physico-chemical properties. We obtain the power spectra of sequences from DFT and extend the spectra to the same length to calculate the distance between different sequences by DTW. Our method is tested in different datasets and the results are compared with that of other software algorithms. In the comparison we find our scheme could amend some wrong classifications appear in other software. The comparison shows our approach is reasonable and effective.

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Section: Introductionmentioning

confidence: 99%

A new method to analyze protein sequence similarity using Dynamic Time Warping

Hou

Pan

Peng³

et al. 2017

Genomics

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“…In addition, the resulting dodecagon has been divided in 24 grids and the amount of points per grid has been used to predict the affiliation to protein families [99]. A further study reduced the amount of vertices to 8, whereby the grouping happened according to the respective physicochemical properties [100]. Moreover, He et al (2016) extended the illustration to three dimensions, which results in a cube, rather than a planar octagon [100].…”

Section: Encodingsmentioning

confidence: 99%

“…A further study reduced the amount of vertices to 8, whereby the grouping happened according to the respective physicochemical properties [100]. Moreover, He et al (2016) extended the illustration to three dimensions, which results in a cube, rather than a planar octagon [100]. The study investigated how this encoding could be employed for multiple sequence alignments.…”

Section: Encodingsmentioning

confidence: 99%

“…To this end, the authors introduced a method, which computes the euclidean distance between amino acid pairs of two encoded proteins. Finally, the similarity of two proteins is denoted by the sum of the distances [100]. Recently, one study used CGR in a 10D space, using a hypercube for the prediction of anticancer peptides [101] as well as for protein-protein interactions [102].…”

Section: Encodingsmentioning

confidence: 99%

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Encodings and models for antimicrobial peptide classification for multi-resistant pathogens

Spänig

Heider

2019

BioData Mining

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Antimicrobial peptides (AMPs) are part of the inherent immune system. In fact, they occur in almost all organisms including, e.g., plants, animals, and humans. Remarkably, they show effectivity also against multi-resistant pathogens with a high selectivity. This is especially crucial in times, where society is faced with the major threat of an ever-increasing amount of antibiotic resistant microbes. In addition, AMPs can also exhibit antitumor and antiviral effects, thus a variety of scientific studies dealt with the prediction of active peptides in recent years. Due to their potential, even the pharmaceutical industry is keen on discovering and developing novel AMPs. However, AMPs are difficult to verify in vitro, hence researchers conduct sequence similarity experiments against known, active peptides. Unfortunately, this approach is very time-consuming and limits potential candidates to sequences with a high similarity to known AMPs. Machine learning methods offer the opportunity to explore the huge space of sequence variations in a timely manner. These algorithms have, in principal, paved the way for an automated discovery of AMPs. However, machine learning models require a numerical input, thus an informative encoding is very important. Unfortunately, developing an appropriate encoding is a major challenge, which has not been entirely solved so far. For this reason, the development of novel amino acid encodings is established as a stand-alone research branch. The present review introduces state-of-the-art encodings of amino acids as well as their properties in sequence and structure based aggregation. Moreover, albeit a wellchosen encoding is essential, performant classifiers are required, which is reflected by a tendency towards specifically designed models in the literature. Furthermore, we introduce these models with a particular focus on encodings derived from support vector machines and deep learning approaches. Albeit a strong focus has been set on AMP predictions, not all of the mentioned encodings have been elaborated as part of antimicrobial research studies, but rather as general protein or peptide representations.

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“…Sequence analysis is fundamentally important in the omics era. In particular, how to represent protein or DNA sequences mathematically to capture their intrinsic features is an active area of research, with statistical methods and graphical representations as two main approaches . Compared to statistical methods, graphical representations are more intuitive and simpler to calculate.…”

Section: Introductionmentioning

confidence: 99%

A novel numerical model for protein sequences analysis based on spherical coordinates and multiple physicochemical properties of amino acids

Tang

et al. 2019

Biopolymers

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How to characterize short protein sequences to make an effective connection to their functions is an unsolved problem. Here we propose to map the physicochemical properties of each amino acid onto unit spheres so that each protein sequence can be represented quantitatively. We demonstrate the usefulness of this representation by applying it to the prediction of cell penetrating peptides. We show that its combination with traditional composition features yields the best performance across different datasets, among several methods compared. For the convenience of users, a web server has been established for automatic calculations of the proposed features at http://biophy.dzu.edu.cn/SNumD/.

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A generalization of CGR representation for analyzing and comparing protein sequences

Cited by 15 publications

References 30 publications

A new method to analyze protein sequence similarity using Dynamic Time Warping

A new method to analyze protein sequence similarity using Dynamic Time Warping

Encodings and models for antimicrobial peptide classification for multi-resistant pathogens

A novel numerical model for protein sequences analysis based on spherical coordinates and multiple physicochemical properties of amino acids

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