scholarly journals ApicoTFdb: the comprehensive web repository of apicomplexan transcription factors and transcription-associated co-factors

Database ◽  
2019 ◽  
Vol 2019 ◽  
Author(s):  
Rahila Sardar ◽  
Abhinav Kaushik ◽  
Rajan Pandey ◽  
Asif Mohmmed ◽  
Shakir Ali ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Protein Interactions ◽  
Drug Targets ◽  
Complex Life Cycle ◽  
Protein Protein Interactions ◽  
Apicomplexan Parasites ◽  
Reference Protein ◽  
Ap2 Family ◽  
Novel Drug ◽  
Novel Drug Targets

Abstract Despite significant progress in apicomplexan genome sequencing and genomics, the current list of experimentally validated transcription factors (TFs) in these genomes is incomplete and mainly consists of AP2 family of proteins, with only a limited number of non-AP2 family TFs and transcription-associated co-factors (TcoFs). We have performed a systematic bioinformatics-aided prediction of TFs and TcoFs in apicomplexan genomes and developed the ApicoTFdb database which consists of experimentally validated as well as computationally predicted TFs and TcoFs in 14 apicomplexan species. The predicted TFs are manually curated to complement the existing annotations. The current version of the database includes 1292 TFs which includes experimentally validated and computationally predicted TFs, representing 20 distinct families across 14 apicomplexan species. The predictions include TFs of TUB, NAC, BSD, HTH, Cupin/Jumonji, winged helix and FHA family proteins, not reported earlier as TFs in the genomes. Apart from TFs, ApicoTFdb also classifies TcoFs into three main subclasses: TRs, CRRs and RNARs, representing 2491 TcoFs in 14 apicomplexan species, are analyzed in this study. The database is designed to integrate different tools for comparative analysis. All entries in the database are dynamically linked with other databases, literature reference, protein–protein interactions, pathways and annotations associated with each protein. ApicoTFdb will be useful to the researchers interested in less-studied gene regulatory mechanisms mediating the complex life cycle of the apicomplexan parasites. The database will aid in the discovery of novel drug targets to much needed combat the growing drug resistance in the parasites.

scholarly journals ApicoTFdb: The comprehensive web repository of apicomplexan transcription factors and regulators

2019 ◽  
Author(s):  
Rahila Sardar ◽  
Abhinav Kaushik ◽  
Rajan Pandey ◽  
Asif Mohmmed ◽  
Shakir Ali ◽  
...  
Keyword(s):  
Drug Targets ◽  
Complex Life Cycle ◽  
Current Version ◽  
Winged Helix ◽  
Apicomplexan Parasites ◽  
Gene Regulatory ◽  
Ap2 Family ◽  
Novel Drug ◽  
Current List ◽  
Novel Drug Targets

AbstractDespite significant progress in apicomplexans genome sequencing and genomics, the current list of experimentally validated TFs in these genomes is incomplete and mainly consists of AP2 family of proteins, with only a limited number of non-AP2 family TFs and TAFs. We have performed systematic bioinformatics aided prediction of TFs and TAFs in apicomplexan genomes, and developed ApicoTFdb database which consists of experimentally validated as well as computationally predicted TFs and TAFs in 14 apicomplexan species. The predicted TFs are manually curated to complement the existing annotations. The current version of the database includes 1310 TFs, out of which 833 are novel and computationally predicted TFs, representing 22 distinct families across 14 apicomplexan species. The predictions include TFs of TUB, NAC, BSD, CCAAT, HTH, Cupin/Jumonji, winged-helix, and FHA family proteins, not reported earlier in the genomes.Apart from TFs, ApicoTFdb also classifies TAFs into three main subclasses-TRs, CRRs and RNARs, representing 3047 TAFs in 14 apicomplexan species are analyzed in this study. The database is equipped with a set of useful tools for comparative analysis of a user-defined list of the proteins. ApicoTFdb will be useful to the researchers interested in less-studied gene regulatory mechanisms mediating the complex life cycle of the apicomplexan parasites. The database will aid the discovery of novel drug targets to much needed combat the growing drug resistance in the parasites.

Pathogen and Host-Pathogen Protein Interactions Provide a Key to Identify Novel Drug Targets

2021 ◽  
pp. 543-553 ◽  
Author(s):  
Shishir K. Gupta ◽  
Özge Osmanoglu ◽  
Mugdha Srivastava ◽  
Elena Bencúrová ◽  
Thomas Dandekar
Keyword(s):  
Protein Interactions ◽  
Drug Targets ◽  
Host Pathogen ◽  
Pathogen Protein ◽  
Novel Drug ◽  
Novel Drug Targets

A Comparative Computational Analysis Approach to Predict Significant Protein-Protein Interactions of Human and Vancomycin Resistant Enterococcus faecalis (VRE) to Prioritize Potential Drug Targets

2021 ◽  
Vol 18 ◽  
Author(s):  
Reaz Uddin ◽  
Kanwal Khan
Keyword(s):  
Enterococcus Faecalis ◽  
Protein Interactions ◽  
Drug Targets ◽  
Vancomycin Resistant Enterococcus ◽  
Potential Drug ◽  
Protein Protein Interactions ◽  
Clp Protease ◽  
Vancomycin Resistant ◽  
Novel Drug ◽  
Potential Drug Targets

Background: Various challenges exist in the treatment of infectious diseases due to the significant rise in drug resistance, resulting in the failure of antibiotic treatment. As a consequence, a dire need has arisen for the rethinking of the drug discovery cycle because of the challenge of drug resistance. The underlying cause of the infectious diseases depends upon associations within the Host-pathogen Protein-Protein Interactions (HP-PPIs) network, which represents a key to unlock new pathogenesis mechanism. Hence, the elucidation of significant PPIs is a promising approach for the identification of potential drug targets. Objective: Identification of the most significant HP-PPIs and their partners, and target them to prioritize potential new drug targets against Vancomycin-resistant Enterococcus faecalis (VRE). Methods: We applied a computational approach based on one of the emerging techniques i.e. Interolog methodology to predict the significant Host-Pathogen PPIs. Structure-Based Studies were applied to model shortlisted protein structures and validate them through PSIPRED, PROCHECK, VERIFY3D, and ERRAT tools. Furthermore, 18,000 drug-like compounds from the ZINC library were docked against these proteins to study protein-chemical interactions using the AutoDock based molecular docking method. Results: Study resulted in the identification of 118 PPIs for Enterococcus faecalis, and prioritized two novel drug targets i.e. Exodeoxyribonuclease (ExoA) and ATP-dependent Clp protease proteolytic subunit (ClpP). Consequently, the docking program ranked 2,670 and 3,154 compounds as potential binders against Exodeoxyribonuclease and ATP-dependent Clp protease proteolytic subunit, respectively. Conclusion: Thereby, the current study enabled us to identify and prioritize potential PPIs in VRE and their interacting proteins in human hosts along with the pool of novel drug candidates.

Novel drug targets in 2019

2020 ◽  
Vol 19 (5) ◽  
pp. 300-300 ◽  
Author(s):  
Sorin Avram ◽  
Liliana Halip ◽  
Ramona Curpan ◽  
Tudor I. Oprea
Keyword(s):  
Drug Targets ◽  
Novel Drug ◽  
Novel Drug Targets

scholarly journals Identification of Polo-like kinases as potential novel drug targets for influenza A virus

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Marie O. Pohl ◽  
Jessica von Recum-Knepper ◽  
Ariel Rodriguez-Frandsen ◽  
Caroline Lanz ◽  
Emilio Yángüez ◽  
...  
Keyword(s):  
Influenza A Virus ◽  
Drug Targets ◽  
Influenza A ◽  
Novel Drug ◽  
Novel Drug Targets

scholarly journals Utilizing Functional Genomics Screening to Identify Potentially Novel Drug Targets in Cancer Cell Spheroid Cultures

10.3791/54738 ◽  
2016 ◽  
Author(s):  
Eamonn Morrison ◽  
Patty Wai ◽  
Andri Leonidou ◽  
Philip Bland ◽  
Saira Khalique ◽  
...  
Keyword(s):  
Functional Genomics ◽  
Cancer Cell ◽  
Drug Targets ◽  
Cell Spheroid ◽  
Novel Drug ◽  
Novel Drug Targets

scholarly journals Multi-omics data integration reveals novel drug targets in hepatocellular carcinoma

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Christos Dimitrakopoulos ◽  
Sravanth Kumar Hindupur ◽  
Marco Colombi ◽  
Dritan Liko ◽  
Charlotte K. Y. Ng ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Drug Targets ◽  
Mtor Signaling ◽  
Omics Data ◽  
Genetic Changes ◽  
Genetic Aberrations ◽  
Functional Consequences ◽  
Novel Drug ◽  
Novel Drug Targets ◽  
Omics Data Integration

Abstract Background Genetic aberrations in hepatocellular carcinoma (HCC) are well known, but the functional consequences of such aberrations remain poorly understood. Results Here, we explored the effect of defined genetic changes on the transcriptome, proteome and phosphoproteome in twelve tumors from an mTOR-driven hepatocellular carcinoma mouse model. Using Network-based Integration of multi-omiCS data (NetICS), we detected 74 ‘mediators’ that relay via molecular interactions the effects of genetic and miRNA expression changes. The detected mediators account for the effects of oncogenic mTOR signaling on the transcriptome, proteome and phosphoproteome. We confirmed the dysregulation of the mediators YAP1, GRB2, SIRT1, HDAC4 and LIS1 in human HCC. Conclusions This study suggests that targeting pathways such as YAP1 or GRB2 signaling and pathways regulating global histone acetylation could be beneficial in treating HCC with hyperactive mTOR signaling.

Sign in / Sign up

Export Citation Format

Share Document