A Geometric Representation of Protein Sequences

Wang, J.Z.; Du, Zhidian; Yu, Philip S.; Chen, Chin‐Fu

doi:10.1109/bibm.2007.22

Cited by 3 publications

(2 citation statements)

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“…Note that all these indices are related to amino acid burial and their hydrophobicity. These results are in agreement with the original findings of French and Robson [ 48 ], Swanson [ 49 ], Tomii and Kanehisa [ 42 ], and Gu et al [ 50 ]. The best correlations between the second and third components of the matrix BL62 0.2 with the amino acid indices contained in AAIndex are 0.77 and 0.70, respectively.…”

Section: Resultssupporting

confidence: 93%

A weighted string kernel for protein fold recognition

Nojoomi

Koehl²

2017

BMC Bioinformatics

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BackgroundAlignment-free methods for comparing protein sequences have proved to be viable alternatives to approaches that first rely on an alignment of the sequences to be compared. Much work however need to be done before those methods provide reliable fold recognition for proteins whose sequences share little similarity. We have recently proposed an alignment-free method based on the concept of string kernels, SeqKernel (Nojoomi and Koehl, BMC Bioinformatics, 2017, 18:137). In this previous study, we have shown that while Seqkernel performs better than standard alignment-based methods, its applications are potentially limited, because of biases due mostly to sequence length effects.MethodsIn this study, we propose improvements to SeqKernel that follows two directions. First, we developed a weighted version of the kernel, WSeqKernel. Second, we expand the concept of string kernels into a novel framework for deriving information on amino acids from protein sequences.ResultsUsing a dataset that only contains remote homologs, we have shown that WSeqKernel performs remarkably well in fold recognition experiments. We have shown that with the appropriate weighting scheme, we can remove the length effects on the kernel values. WSeqKernel, just like any alignment-based sequence comparison method, depends on a substitution matrix. We have shown that this matrix can be optimized so that sequence similarity scores correlate well with structure similarity scores. Starting from no information on amino acid similarity, we have shown that we can derive a scoring matrix that echoes the physico-chemical properties of amino acids.ConclusionWe have made progress in characterizing and parametrizing string kernels as alignment-based methods for comparing protein sequences, and we have shown that they provide a framework for extracting sequence information from structure.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-017-1795-5) contains supplementary material, which is available to authorized users.

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Section: Resultssupporting

confidence: 93%

A weighted string kernel for protein fold recognition

Nojoomi

Koehl²

2017

BMC Bioinformatics

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“…Other approaches start from the substitution matrices. However, with few exceptions, e.g., [ 9 ], most authors start by transforming them into distance matrices [ 4 , 10 - 14 ]. This approach is not devoid of difficulties.…”

Section: Introductionmentioning

confidence: 99%

Amino acid "little Big Bang": Representing amino acid substitution matrices as dot products of Euclidian vectors

Zimmermann

Gibrat

2010

BMC Bioinformatics

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BackgroundSequence comparisons make use of a one-letter representation for amino acids, the necessary quantitative information being supplied by the substitution matrices. This paper deals with the problem of finding a representation that provides a comprehensive description of amino acid intrinsic properties consistent with the substitution matrices.ResultsWe present a Euclidian vector representation of the amino acids, obtained by the singular value decomposition of the substitution matrices. The substitution matrix entries correspond to the dot product of amino acid vectors. We apply this vector encoding to the study of the relative importance of various amino acid physicochemical properties upon the substitution matrices. We also characterize and compare the PAM and BLOSUM series substitution matrices.ConclusionsThis vector encoding introduces a Euclidian metric in the amino acid space, consistent with substitution matrices. Such a numerical description of the amino acid is useful when intrinsic properties of amino acids are necessary, for instance, building sequence profiles or finding consensus sequences, using machine learning algorithms such as Support Vector Machine and Neural Networks algorithms.

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An efficient numerical method for protein sequences similarity analysis based on a new two-dimensional graphical representation

El-Lakkani

Mahran

2015

SAR and QSAR in Environmental Research

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A new two-dimensional graphical representation of protein sequences is introduced. Twenty concentric evenly spaced circles divided by n radial lines into equal divisions are selected to represent any protein sequence of length n. Each circle represents one of the different 20 amino acids, and each radial line represents a single amino acid of the protein sequence. An efficient numerical method based on the graph is proposed to measure the similarity between two protein sequences. To prove the accuracy of our approach, the method is applied to NADH dehydrogenase subunit 5 (ND5) proteins of nine different species and 24 transferrin sequences from vertebrates. High values of correlation coefficient between our results and the results of ClustalW are obtained (approximately perfect correlations). These values are higher than the values obtained in many other related works.

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A Geometric Representation of Protein Sequences

Cited by 3 publications

References 44 publications

A weighted string kernel for protein fold recognition

A weighted string kernel for protein fold recognition

Amino acid "little Big Bang": Representing amino acid substitution matrices as dot products of Euclidian vectors

An efficient numerical method for protein sequences similarity analysis based on a new two-dimensional graphical representation

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