A novel graphical representation and similarity analysis of protein sequences based on physicochemical properties

Mahmoodi-Reihani, Mehri; Abbasitabar, Fatemeh; Zare-Shahabadi, Vahid

doi:10.1016/j.physa.2018.07.011

Cited by 13 publications

(9 citation statements)

References 34 publications

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“…For a given peptide sequence, a numerical descriptive vector with the same size of the sequence length is calculated based on a set of physicochemical properties of AAs and PCA. 21 For example, if a data set containing n hexapeptides was considered, a descriptor data matrix with the number of rows and columns of n and 6, respectively, would be obtained (Figure 1). The values of the numerical descriptive vector are dependent on the types of the AA indices used in the computation step.…”

Section: ■ Results and Discussionmentioning

confidence: 99%

“…Five hundred fifty-three physicochemical properties of AAs were extracted from the AAindex1 database. For a given peptide sequence, a numerical descriptive vector with the same size of the sequence length is calculated based on a set of physicochemical properties of AAs and PCA . For example, if a data set containing n hexapeptides was considered, a descriptor data matrix with the number of rows and columns of n and 6, respectively, would be obtained (Figure ).…”

Section: Resultsmentioning

confidence: 99%

“…In the current research, for each peptide sequence, a numerical descriptive vector is calculated based on a set of physicochemical properties of AAs and PCA. 21 Consider a residue with n AAs. For each amino acid in the residue a numerical matrix X i with a size of N × L is created, where L is the number of considered physicochemical indices.…”

Section: ■ Computational Methodsmentioning

confidence: 99%

“…For more information, the reader should refer to our previous paper. 21 The physicochemical properties of AAs were extracted from the AAindex database. This database contains various physicochemical and biochemical properties of amino acids and pairs of amino acids.…”

Section: ■ Computational Methodsmentioning

confidence: 99%

“…In the current research, for each peptide sequence, a numerical descriptive vector is calculated based on a set of physicochemical properties of AAs and PCA . Consider a residue with n AAs.…”

Section: Computational Methodsmentioning

confidence: 99%

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In Silico Rational Design and Virtual Screening of Bioactive Peptides Based on QSAR Modeling

2020

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Predicting the bioactivity of peptides is an important challenge in drug development and peptide research. In this study, numerical descriptive vectors (NDVs) for peptide sequences were calculated based on the physicochemical properties of amino acids (AAs) and principal component analysis (PCA). The resulted NDV had the same length as the peptide sequence, so that each entry of NDV corresponded to one AA in the sequence. They were then applied to quantitative structure−activity relationship (QSAR) analysis of angiotensin-converting enzyme (ACE) inhibitor dipeptides, bitter-tasting dipeptides, and nonameric binding peptides of the human leukocyte antigens (HLA-A*0201). Multiple linear regression was used to construct the QSAR models. For each peptide set, a proper subset of physicochemical properties was chosen by the ant colony optimization algorithm. The leave-one-out cross-validation (q loo 2 ) values were 0.855, 0.936, and 0.642 and the root-mean-square errors (RMSEs) were 0.450, 0.149, and 0.461. Our results revealed that the new numerical descriptive vector can afford extensive characterization of peptide sequence so that it can be easily employed in peptide QSAR studies. Moreover, the proposed numerical descriptive vectors were able to determine hot spot residues in the peptides under study.

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Section: ■ Results and Discussionmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: ■ Computational Methodsmentioning

confidence: 99%

Section: ■ Computational Methodsmentioning

confidence: 99%

Section: Computational Methodsmentioning

confidence: 99%

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In Silico Rational Design and Virtual Screening of Bioactive Peptides Based on QSAR Modeling

2020

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A geometric characterization of DNA sequence

Gong

Fan

2019

Physica A: Statistical Mechanics and its Applications

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Mathematical Approach to Protein Sequence Comparison Based on Physiochemical Properties

Pal

Ghosh²,

Maji

et al. 2022

ACS Omega

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The difficult aspect of developing new protein sequence comparison techniques is coming up with a method that can quickly and effectively handle huge data sets of various lengths in a timely manner. In this work, we first obtain two numerical representations of protein sequences separately based on one physical property and one chemical property of amino acids. The lengths of all the sequences under comparison are made equal by appending the required number of zeroes. Then, fast Fourier transform is applied to this numerical time series to obtain the corresponding spectrum. Next, the spectrum values are reduced by the standard inter coefficient difference method. Finally, the corresponding normalized values of the reduced spectrum are selected as the descriptors for protein sequence comparison. Using these descriptors, the distance matrices are obtained using Euclidian distance. They are subsequently used to draw the phylogenetic trees using the UPGMA algorithm. Phylogenetic trees are first constructed for 9 ND4, 9 ND5, and 9 ND6 proteins using the polarity value as the chemical property and the molecular weight as the physical property. They are compared, and it is seen that polarity is a better choice than molecular weight in protein sequence comparison. Next, using the polarity property, phylogenetic trees are obtained for 12 baculovirus and 24 transferrin proteins. The results are compared with those obtained earlier on the identical sequences by other methods. Three assessment criteria are considered for comparison of the resultsquality based on rationalized perception, quantitative measures based on symmetric distance, and computational speed. In all the cases, the results are found to be more satisfactory.

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A novel graphical representation and similarity analysis of protein sequences based on physicochemical properties

Cited by 13 publications

References 34 publications

In Silico Rational Design and Virtual Screening of Bioactive Peptides Based on QSAR Modeling

In Silico Rational Design and Virtual Screening of Bioactive Peptides Based on QSAR Modeling

A geometric characterization of DNA sequence

Mathematical Approach to Protein Sequence Comparison Based on Physiochemical Properties

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