scholarly journals Insights into cancer severity from biomolecular interaction mechanisms

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Francesco Raimondi ◽  
Gurdeep Singh ◽  
Matthew J. Betts ◽  
Gordana Apic ◽  
Ranka Vukotic ◽  
...  
Keyword(s):  
Nucleic Acid ◽  
Small Molecule ◽  
High Throughput Sequencing ◽  
Somatic Mutations ◽  
Interaction Data ◽  
Cancer Genes ◽  
Histological Subtypes ◽  
Biomolecular Interaction ◽  
Cancer Mutations ◽  
Molecule Interactions

Abstract To attain a deeper understanding of diseases like cancer, it is critical to couple genetics with biomolecular mechanisms. High-throughput sequencing has identified thousands of somatic mutations across dozens of cancers, and there is a pressing need to identify the few that are pathologically relevant. Here we use protein structure and interaction data to interrogate nonsynonymous somatic cancer mutations, identifying a set of 213 molecular interfaces (protein-protein, -small molecule or –nucleic acid) most often perturbed in cancer, highlighting several potentially novel cancer genes. Over half of these interfaces involve protein-small-molecule interactions highlighting their overall importance in cancer. We found distinct differences in the predominance of perturbed interfaces between cancers and histological subtypes and presence or absence of certain interfaces appears to correlate with cancer severity.

scholarly journals Nucleic Acid Stability in Glycine Betaine Solutions: Correlating Small Molecule Interactions with Nucleic Acid Surfaces

2011 ◽  
Vol 100 (3) ◽  
pp. 57a
Author(s):  
Jeffrey J. Schwinefus ◽  
Elliot Schmidt ◽  
James Kohler ◽  
Alexandra Thomas
Keyword(s):  
Nucleic Acid ◽  
Small Molecule ◽  
Glycine Betaine ◽  
Acid Stability ◽  
Molecule Interactions

Nucleic acid-small molecule interactions. VII. Further characterization of deoxyribonucleic acid-diamino steroid complexes

Biochemistry ◽  
1968 ◽  
Vol 7 (4) ◽  
pp. 1568-1582 ◽  
Author(s):  
Henry R. Mahler ◽  
Gerald. Green ◽  
Robert. Goutarel ◽  
. Khuong-Huu-Qui
Keyword(s):  
Nucleic Acid ◽  
Small Molecule ◽  
Deoxyribonucleic Acid ◽  
Molecule Interactions

scholarly journals kataegis: an R package for identification and visualization of the genomic localized hypermutation regions using high-throughput sequencing

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Xue Lin ◽  
Yingying Hua ◽  
Shuanglin Gu ◽  
Li Lv ◽  
Xingyu Li ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Somatic Mutations ◽  
R Package ◽  
Frequency Of Occurrence ◽  
Link Type ◽  
Genomic Landscape ◽  
One Step ◽  
Flanking Regions

Abstract Background Genomic localized hypermutation regions were found in cancers, which were reported to be related to the prognosis of cancers. This genomic localized hypermutation is quite different from the usual somatic mutations in the frequency of occurrence and genomic density. It is like a mutations “violent storm”, which is just what the Greek word “kataegis” means. Results There are needs for a light-weighted and simple-to-use toolkit to identify and visualize the localized hypermutation regions in genome. Thus we developed the R package “kataegis” to meet these needs. The package used only three steps to identify the genomic hypermutation regions, i.e., i) read in the variation files in standard formats; ii) calculate the inter-mutational distances; iii) identify the hypermutation regions with appropriate parameters, and finally one step to visualize the nucleotide contents and spectra of both the foci and flanking regions, and the genomic landscape of these regions. Conclusions The kataegis package is available on Bionconductor/Github (https://github.com/flosalbizziae/kataegis), which provides a light-weighted and simple-to-use toolkit for quickly identifying and visualizing the genomic hypermuation regions.

Small Molecule Interactions with Protein−Tyrosine Phosphatase PTP1B and Their Use in Inhibitor Design†

Biochemistry ◽  
1996 ◽  
Vol 35 (50) ◽  
pp. 15989-15996 ◽  
Author(s):  
Terrence R. Burke, ◽  
Bin Ye ◽  
Xinjian Yan ◽  
Shaomeng Wang ◽  
Zongchao Jia ◽  
...  
Keyword(s):  
Small Molecule ◽  
Protein Tyrosine Phosphatase ◽  
Tyrosine Phosphatase ◽  
Inhibitor Design ◽  
Protein Tyrosine ◽  
Molecule Interactions

scholarly journals Identification of a Hydrophobic Cleft in the LytTR Domain of AgrA as a Locus for Small Molecule Interactions That Inhibit DNA Binding

Biochemistry ◽  
2012 ◽  
Vol 51 (50) ◽  
pp. 10035-10043 ◽  
Author(s):  
Paul G. Leonard ◽  
Ian F. Bezar ◽  
David J. Sidote ◽  
Ann M. Stock
Keyword(s):  
Dna Binding ◽  
Small Molecule ◽  
Molecule Interactions ◽  
Hydrophobic Cleft

scholarly journals A Statistical Framework for Evolutionary Analysis of Recurrent Somatic Mutations in Cancers

2020 ◽  
Author(s):  
Xun Gu
Keyword(s):  
Somatic Mutations ◽  
Cancer Genomics ◽  
Computational Procedure ◽  
The Cancer Genome Atlas ◽  
Component Model ◽  
Evolutionary Analysis ◽  
Cancer Genes ◽  
Component Mixture ◽  
Two Component ◽  
Empirical Bayesian Method

AbstractCurrent cancer genomics databases have accumulated millions of somatic mutations that remain to be further explored, faciltating enormous high throuput analyses to explore the underlying mechanisms that may contribute to malignant initiation or progression. In the context of over-dominant passenger mutations (unrelated to cancers), the challenge is to identify somatic mutations that are cancer-driving. Under the notion that carcinogenesis is a form of somatic-cell evolution, we developed a two-component mixture model that enables to accomplish the following analyses. (i) We formulated a quasi-likelihood approach to test whether the two-component model is significantly better than a single-component model, which can be used for new cancer gene predicting. (ii) We implemented an empirical Bayesian method to calculate the posterior probabilities of a site to be cancer-driving for all sites of a gene, which can be used for new driving site predicting. (iii) We developed a computational procedure to calculate the somatic selection intensity at driver sites and passenger sites, respectively, as well as site-specific profiles for all sites. Using these newly-developed methods, we comprehensively analyzed 294 known cancer genes based on The Cancer Genome Atlas (TCGA) database.

scholarly journals Discovering the drivers of clonal hematopoiesis

2020 ◽  
Author(s):  
Oriol Pich ◽  
Iker Reyes-Salazar ◽  
Abel Gonzalez-Perez ◽  
Nuria Lopez-Bigas
Keyword(s):  
Positive Selection ◽  
Molecular Mechanisms ◽  
Somatic Mutations ◽  
Cancer Genomics ◽  
Variant Calling ◽  
Selective Advantage ◽  
Cancer Genes ◽  
Driver Genes ◽  
Hematopoietic Stem ◽  
Clonal Hematopoiesis

AbstractMutations in genes that confer a selective advantage to hematopoietic stem cells (HSCs) in certain conditions drive clonal hematopoiesis (CH). While some CH drivers have been identified experimentally or through epidemiological studies, the compendium of all genes able to drive CH upon mutations in HSCs is far from complete. We propose that identifying signals of positive selection in blood somatic mutations may be an effective way to identify CH driver genes, similarly as done to identify cancer genes. Using a reverse somatic variant calling approach, we repurposed whole-genome and whole-exome blood/tumor paired samples of more than 12,000 donors from two large cancer genomics cohorts to identify blood somatic mutations. The application of IntOGen, a robust driver discovery pipeline, to blood somatic mutations across both cohorts, and more than 24,000 targeted sequenced samples yielded a list of close to 70 genes with signals of positive selection in CH, available at http://www.intogen.org/ch. This approach recovers all known CH genes, and discovers novel candidates. Generating this compendium is an essential step to understand the molecular mechanisms of CH and to accurately detect individuals with CH to ascertain their risk to develop related diseases.

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