Origin and Spread of Photosynthesis Based upon Conserved Sequence Features in Key Bacteriochlorophyll Biosynthesis Proteins

Background: Epigenetic repression mechanisms play an important role in gene regulation, specifically in cancer development. In many cases, a CpG island’s (CGI) susceptibility or resistance to methylation are shown to be contributed by local DNA sequence features. Objective: To develop unbiased machine learning models–individually and combined for different biological features–that predict the methylation propensity of a CGI. Methods: We developed our model consisting of CGI sequence features on a dataset of 75 sequences (28 prone, 47 resistant) representing a genome-wide methylation structure. We tested our model on two independent datasets that are chromosome (132 sequences) and disease (70 sequences) specific. Results: We provided improvements in prediction accuracy over previous models. Our results indicate that combined features better predict the methylation propensity of a CGI (area under the curve (AUC) ~0.81). Our global methylation classifier performs well on independent datasets reaching an AUC of ~0.82 for the complete model and an AUC of ~0.88 for the model using select sequences that better represent their classes in the training set. We report certain de novo motifs and transcription factor binding site (TFBS) motifs that are consistently better in separating prone and resistant CGIs. Conclusion: Predictive models for the methylation propensity of CGIs lead to a better understanding of disease mechanisms and can be used to classify genes based on their tendency to contain methylation prone CGIs, which may lead to preventative treatment strategies. MATLAB and Python™ scripts used for model building, prediction, and downstream analyses are available at https://github.com/dicleyalcin/methylProp_predictor.

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Analyzing the intervening sequences of pluripotency associated genes to identify conserved Sequence patterns in human using Integrative bioinformatics

International Journal of Pharma and Bio Sciences ◽

10.22376/ijpbs.2017.8.1.b582-587 ◽

2017 ◽

Vol 8 (1) ◽

Author(s):

PRIYANKA NARAD ◽

GULSHAN WADHWA ◽

KAILASH C UPADHYAYA

Keyword(s):

Conserved Sequence ◽

Intervening Sequences ◽

Sequence Patterns

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Regulation of vitamin B12 and bacteriochlorophyll biosynthesis in a facultative methylotroph, Protaminobacter ruber.

Journal of Nutritional Science and Vitaminology ◽

10.3177/jnsv.27.439 ◽

1981 ◽

Vol 27 (5) ◽

pp. 439-447 ◽

Cited By ~ 9

Author(s):

Kazuyoshi SATO ◽

Koichi ISHIDA ◽

Teruyuki KUNG ◽

Akihyro MIZUNO ◽

Shoichi SHIMIZU

Keyword(s):

Vitamin B12 ◽

Facultative Methylotroph ◽

Bacteriochlorophyll Biosynthesis

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Engineering subtilisin proteases that specifically degrade active RAS

Communications Biology ◽

10.1038/s42003-021-01818-7 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Yingwei Chen ◽

Eric A. Toth ◽

Biao Ruan ◽

Eun Jung Choi ◽

Richard Simmerman ◽

...

Keyword(s):

Active Form ◽

High Specificity ◽

Kinetic Properties ◽

Target Sequence ◽

Reporter System ◽

Human Cell Culture ◽

Conserved Sequence ◽

Cofactor Binding ◽

Selective Proteolysis

AbstractWe describe the design, kinetic properties, and structures of engineered subtilisin proteases that degrade the active form of RAS by cleaving a conserved sequence in switch 2. RAS is a signaling protein that, when mutated, drives a third of human cancers. To generate high specificity for the RAS target sequence, the active site was modified to be dependent on a cofactor (imidazole or nitrite) and protease sub-sites were engineered to create a linkage between substrate and cofactor binding. Selective proteolysis of active RAS arises from a 2-step process wherein sub-site interactions promote productive binding of the cofactor, enabling cleavage. Proteases engineered in this way specifically cleave active RAS in vitro, deplete the level of RAS in a bacterial reporter system, and also degrade RAS in human cell culture. Although these proteases target active RAS, the underlying design principles are fundamental and will be adaptable to many target proteins.

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