Protein space: A natural method for realizing the nature of protein universe

Yu, Chenglong; Deng, Minghua; Cheng, Shiu Yuen; Yau, Shek Chung; He, Rong; Yau, Stephen S.-T.

doi:10.1016/j.jtbi.2012.11.005

Cited by 40 publications

(44 citation statements)

References 74 publications

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“…Protein kinase C (PKC) is a family of enzymes involved in controlling the function of other proteins through the phosphorylation of hydroxyl groups of serine and threonine amino acid residues on these proteins [10]. The entire PKC family can be divided into six subfamilies: cPKC, nPKC, aPKC, PKC μ , PKC1 and PRK.…”

Section: Resultsmentioning

confidence: 99%

“…The entire PKC family can be divided into six subfamilies: cPKC, nPKC, aPKC, PKC μ , PKC1 and PRK. There are 124 proteins in the set; descriptions and GenBank accession numbers are fully listed in the appendix of [10]. …”

Section: Resultsmentioning

confidence: 99%

“…In this figure, each protein is labeled by a unique integer from 1 to 124 corresponding to the index of the protein given in the table of [10]. From this figure, we can see that the six PKC subfamilies are clearly classified: PKC1 (upper left block, purple), nPKC (upper middle and bottom right blocks, red), cPKC (upper right block, green), PRK (center block, pink), PKC μ (below center block, orange), and aPKC (bottom left block, blue).…”

Section: Resultsmentioning

confidence: 99%

“…Among the various methods, many compute the biological distances and classify proteins using polygenetic-trees, since sequence and structural similarity is considered to be closely related to protein homology [2]. Some representatives of these methods are the natural vector method [10, 13, 14], protein map [8,9], K-string dictionary [11], and Yau-Hausdorff distance [6]. These methods are unsupervised that do not require the usage of labeled training data.…”

Section: Introductionmentioning

confidence: 99%

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An information-based network approach for protein classification

2017

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Protein classification is one of the critical problems in bioinformatics. Early studies used geometric distances and polygenetic-tree to classify proteins. These methods use binary trees to present protein classification. In this paper, we propose a new protein classification method, whereby theories of information and networks are used to classify the multivariate relationships of proteins. In this study, protein universe is modeled as an undirected network, where proteins are classified according to their connections. Our method is unsupervised, multivariate, and alignment-free. It can be applied to the classification of both protein sequences and structures. Nine examples are used to demonstrate the efficiency of our new method.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An information-based network approach for protein classification

2017

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“…We believe that clustering of patients based on their SNV profiles may provide valuable clues for prognostics, diagnostics, and therapeutics, as it takes into account all of their genetic data. The idea for this approach arose from distance-based phylogenetic analysis of DNA/protein sequences proposed by us earlier1718192021. In our proposed methodology we used a well-defined metric in mathematics, the Jaccard distance, to measure the similarity/dissimilarity between subjects using all the SNV information from each individual and from that we construct cluster trees based on the Jaccard distance matrices.…”

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confidence: 99%

A novel strategy for clustering major depression individuals using whole-genome sequencing variant data

Baune

Licinio

et al. 2017

Sci Rep

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Major depressive disorder (MDD) is highly prevalent, resulting in an exceedingly high disease burden. The identification of generic risk factors could lead to advance prevention and therapeutics. Current approaches examine genotyping data to identify specific variations between cases and controls. Compared to genotyping, whole-genome sequencing (WGS) allows for the detection of private mutations. In this proof-of-concept study, we establish a conceptually novel computational approach that clusters subjects based on the entirety of their WGS. Those clusters predicted MDD diagnosis. This strategy yielded encouraging results, showing that depressed Mexican-American participants were grouped closer; in contrast ethnically-matched controls grouped away from MDD patients. This implies that within the same ancestry, the WGS data of an individual can be used to check whether this individual is within or closer to MDD subjects or to controls. We propose a novel strategy to apply WGS data to clinical medicine by facilitating diagnosis through genetic clustering. Further studies utilising our method should examine larger WGS datasets on other ethnical groups.

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Establishing the phylogeny of Prochlorococcus with a new alignment‐free method

Zhao

Tian

et al. 2017

Ecology and Evolution

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Prochlorococcus marinus, one of the most abundant marine cyanobacteria in the global ocean, is classified into low‐light (LL) and high‐light (HL) adapted ecotypes. These two adapted ecotypes differ in their ecophysiological characteristics, especially whether adapted for growth at high‐light or low‐light intensities. However, some evolutionary relationships of Prochlorococcus phylogeny remain to be resolved, such as whether the strains SS120 and MIT9211 form a monophyletic group. We use the Natural Vector (NV) method to represent the sequence in order to identify the phylogeny of the Prochlorococcus. The natural vector method is alignment free without any model assumptions. This study added the covariances of amino acids in protein sequence to the natural vector method. Based on these new natural vectors, we can compute the Hausdorff distance between the two clades which represents the dissimilarity. This method enables us to systematically analyze both the dataset of ribosomal proteomes and the dataset of 16s‐23s rRNA sequences in order to reconstruct the phylogeny of Prochlorococcus. Furthermore, we apply classification to inspect the relationship of SS120 and MIT9211. From the reconstructed phylogenetic trees and classification results, we may conclude that the SS120 does not cluster with MIT9211. This study demonstrates a new method for performing phylogenetic analysis. The results confirm that these two strains do not form a monophyletic clade in the phylogeny of Prochlorococcus.

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Protein space: A natural method for realizing the nature of protein universe

Cited by 40 publications

References 74 publications

An information-based network approach for protein classification

An information-based network approach for protein classification

A novel strategy for clustering major depression individuals using whole-genome sequencing variant data

Establishing the phylogeny of Prochlorococcus with a new alignment‐free method

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