Computational Method for the Systematic Identification of Analog Series and Key Compounds Representing Series and Their Biological Activity Profiles

2016 ◽  
Vol 59 (16) ◽  
pp. 7667-7676 ◽  
Author(s):  
Dagmar Stumpfe ◽  
Dilyana Dimova ◽  
Jürgen Bajorath
Keyword(s):  
Biological Activity ◽  
Computational Method ◽  
Analog Series ◽  
Systematic Identification

scholarly journals EpiSAFARI: Sensitive detection of valleys in epigenetic signals for enhancing annotations of functional elements

2019 ◽  
Author(s):  
Arif Harmanci ◽  
Akdes Serin Harmanci ◽  
Jyothishmathi Swaminathan ◽  
Vidya Gopalakrishnan
Keyword(s):  
Transcription Factor ◽  
Regulatory Elements ◽  
Transcription Factor Binding ◽  
Computational Method ◽  
Sensitive Detection ◽  
Supplementary Information ◽  
Chip Sequencing ◽  
Factor Binding ◽  
Nucleotide Resolution ◽  
Systematic Identification

Abstract Motivation Functional genomics experiments generate genomewide signal profiles that are dense information sources for annotating the regulatory elements. These profiles measure epigenetic activity at the nucleotide resolution and they exhibit distinctive patterns as they fluctuate along the genome. Most notable of these patterns are the valley patterns that are prevalently observed in assays such as ChIP Sequencing and bisulfite sequencing. The genomic positions of valleys pinpoint locations of cis-regulatory elements such as enhancers and insulators. Systematic identification of the valleys provides novel information for delineating the annotation of regulatory elements. Nevertheless, the valleys are not reported by majority of the analysis pipelines. Results We describe EpiSAFARI, a computational method for sensitive detection of valleys from diverse types of epigenetic profiles. EpiSAFARI employs a novel smoothing method for decreasing noise in signal profiles and accounts for technical factors such as sparse signals, mappability, and nucleotide content. In performance comparisons, EpiSAFARI performs favorably in terms of accuracy. The histone modification valleys detected by EpiSAFARI exhibit high conservation, transcription factor binding, and they are enriched in nascent transcription. In addition, the large clusters of histone valleys are found to be enriched at the promoters of the developmentally associated genes. Differential histone valleys exhibit concordance with differential DNase signal at cell line specific valleys. DNA methylation valleys exhibit elevated conservation and high transcription factor binding. Specifically, we observed enriched binding of transcription factors associated with chromatin structure around methyl-valleys. Availability EpiSAFARI is publicly available at https://github.com/harmancilab/EpiSAFARI Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Systematic analysis of structural and activity relationships between conventional hierarchical and analog series-based scaffolds

RSC Advances ◽  
2017 ◽  
Vol 7 (30) ◽  
pp. 18718-18723 ◽  
Author(s):  
Dagmar Stumpfe ◽  
Dilyana Dimova ◽  
Jürgen Bajorath
Keyword(s):  
Biological Activity ◽  
Systematic Analysis ◽  
Analog Series ◽  
Hierarchical Scaffold

Three pairs of compounds (left) belonging to three different analog series that differ in their biological activity share a single conventional hierarchical scaffold (BM, middle) but have distinct ‘analog series-based’ (ASB) scaffold (right).

scholarly journals Computational method for estimating progression saturation of analog series

RSC Advances ◽  
2018 ◽  
Vol 8 (10) ◽  
pp. 5484-5492 ◽  
Author(s):  
Ryo Kunimoto ◽  
Tomoyuki Miyao ◽  
Jürgen Bajorath
Keyword(s):  
Chemical Space ◽  
Computational Method ◽  
Analog Series

Chemical space view of an analog series. Shown are inactive (red) and active (blue) analogs together with virtual candidate compounds (green) available to expand the series. Chemical neighborhoods of analogs are depicted in gray.

scholarly journals Tubulin Inhibitors: A Chemoinformatic Analysis Using Cell-Based Data

Molecules ◽  
2021 ◽  
Vol 26 (9) ◽  
pp. 2483
Author(s):  
Edgar López-López ◽  
Carlos M. Cerda-García-Rojas ◽  
José L. Medina-Franco
Keyword(s):  
Biological Activity ◽  
Content Analysis ◽  
Cell Lines ◽  
Quantitative Assessment ◽  
Chemical Space ◽  
Rational Approach ◽  
Tubulin Inhibitors ◽  
Narrow Region ◽  
Different Types ◽  
Analog Series

Inhibiting the tubulin-microtubules (Tub-Mts) system is a classic and rational approach for treating different types of cancers. A large amount of data on inhibitors in the clinic supports Tub-Mts as a validated target. However, most of the inhibitors reported thus far have been developed around common chemical scaffolds covering a narrow region of the chemical space with limited innovation. This manuscript aims to discuss the first activity landscape and scaffold content analysis of an assembled and curated cell-based database of 851 Tub-Mts inhibitors with reported activity against five cancer cell lines and the Tub-Mts system. The structure–bioactivity relationships of the Tub-Mts system inhibitors were further explored using constellations plots. This recently developed methodology enables the rapid but quantitative assessment of analog series enriched with active compounds. The constellations plots identified promising analog series with high average biological activity that could be the starting points of new and more potent Tub-Mts inhibitors.

scholarly journals STUDY OF THE AFFINITY OF N-ACYL DERIVATIVES OF 2-OXO-1-PYRROLIDINE ACETAMIDE TO THE BINDING SITE OF NMDA RECEPTOR BY MOLECULAR DOCKING METHOD

2019 ◽  
pp. 4-6
Author(s):  
A.S. Chiriapkin ◽  
A.A. Glushko ◽  
I.P. Kodonidi
Keyword(s):  
Biological Activity ◽  
Molecular Docking ◽  
Nmda Receptor ◽  
Binding Site ◽  
Computational Method ◽  
In Vivo Studies ◽  
Docking Method ◽  
Derivatives Of ◽  
Acyl Derivatives

The search for new compounds with а high nootropic biological activity is a promising scientific direction. A modern computational method for predicting pharmacological activity of the studied compounds is the molecular docking. The aim of this work is to study the affinity of new N-acyl derivatives of 2-oxo-1-pyrrolidine acetamide to the binding site of NMDA receptor to search of new effective nootropic drugs. As objects of study, there are used the new N-acyl derivatives of 2-oxo-1-pyrrolidine acetamide, racetams, glutamate and a virtual model of NMDA receptor of organism Rattus norvegicus with an identification code 2A5S from the RCSB PDB database. The results of the computational experiment indicate the presence of high nootropic biological activity in the compounds under study. Substance 3 has the greatest affinity of the N-acyl derivatives of 2-oxo-1-pyrrolidineethanol to the binding site of NMDA receptor Thus, we consider it is appropriate to conduct pharmacological in vivo studies of these compounds for the presence of nootropic biological activity.

scholarly journals EpiSAFARI: Sensitive detection of valleys in epigenetic signals for enhancing annotations of functional elements

2019 ◽  
Author(s):  
Arif Harmanci ◽  
Akdes Serin Harmanci ◽  
Jyothishmathi Swaminathan ◽  
Vidya Gopalakrishnan
Keyword(s):  
Bisulfite Sequencing ◽  
Regulatory Elements ◽  
Computational Method ◽  
Sensitive Detection ◽  
Sparse Signals ◽  
High Conservation ◽  
Technical Factors ◽  
Nucleotide Resolution ◽  
Large Clusters ◽  
Systematic Identification

AbstractThe genomewide signal profiles from functional genomics experiments are dense information sources for annotating the regulatory elements. These profiles measure epigenetic activity at the nucleotide resolution and they exhibit distinct patterns along the genome. Most notable of these patterns are the valley patterns that are prevalently observed in many epigenetic assays such as ChIP-Seq and bisulfite sequencing. Valleys mark locations of cis-regulatory elements such as enhancers. Systematic identification of the valleys provides novel information for delineating the annotation of regulatory elements using epigenetic data. Nevertheless, the valleys are generally not reported by analysis pipelines. Here, we describe EpiSAFARI, a computational method for sensitive detection of valleys from diverse types of epigenetic profiles. EpiSAFARI employs a novel smoothing method for decreasing noise in signal profiles and accounts for technical factors such as sparse signals, mappability, and nucleotide content. In performance comparisons, EpiSAFARI performs favorably in terms of accuracy. The histone modification and DNA methylation valleys detected by EpiSAFARI exhibit high conservation, transcription factor binding, and they are enriched in nascent transcription. In addition, the large clusters of histone valleys are found to be enriched at the promoters of the developmentally associated genes.

scholarly journals Identification of HCC-Related Genes Based on Differential Partial Correlation Network

2021 ◽  
Vol 12 ◽  
Author(s):  
Yuyao Gao ◽  
Xiao Chang ◽  
Jie Xia ◽  
Shaoyan Sun ◽  
Zengchao Mu ◽  
...  
Keyword(s):  
Functional Module ◽  
Disease Diagnosis ◽  
Computational Method ◽  
The Cancer Genome Atlas ◽  
Control Group ◽  
Disease Genes ◽  
Mechanism Study ◽  
Cancer Genome Atlas ◽  
Disease Related Genes ◽  
Systematic Identification

Hepatocellular carcinoma (HCC) is one of the most common causes of cancer-related death, but its pathogenesis is still unclear. As the disease is involved in multiple biological processes, systematic identification of disease genes and module biomarkers can provide a better understanding of disease mechanisms. In this study, we provided a network-based approach to integrate multi-omics data and discover disease-related genes. We applied our method to HCC data from The Cancer Genome Atlas (TCGA) database and obtained a functional module with 15 disease-related genes as network biomarkers. The results of classification and hierarchical clustering demonstrate that the identified functional module can effectively distinguish between the disease and the control group in both supervised and unsupervised methods. In brief, this computational method to identify potential functional disease modules could be useful to disease diagnosis and further mechanism study of complex diseases.

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