scholarly journals TCRD and Pharos 2021: mining the human proteome for disease biology

2020 ◽  
Vol 49 (D1) ◽  
pp. D1334-D1346 ◽  
Author(s):  
Timothy K Sheils ◽  
Stephen L Mathias ◽  
Keith J Kelleher ◽  
Vishal B Siramshetty ◽  
Dac-Trung Nguyen ◽  
...  
Keyword(s):  
Protein Interactions ◽  
National Institutes Of Health ◽  
Data Sources ◽  
Protein Protein Interactions ◽  
Data Types ◽  
Drug Induced ◽  
Aggregated Data ◽  
Disease Biology ◽  
Central Resource ◽  
Druggable Genome

Abstract In 2014, the National Institutes of Health (NIH) initiated the Illuminating the Druggable Genome (IDG) program to identify and improve our understanding of poorly characterized proteins that can potentially be modulated using small molecules or biologics. Two resources produced from these efforts are: The Target Central Resource Database (TCRD) (http://juniper.health.unm.edu/tcrd/) and Pharos (https://pharos.nih.gov/), a web interface to browse the TCRD. The ultimate goal of these resources is to highlight and facilitate research into currently understudied proteins, by aggregating a multitude of data sources, and ranking targets based on the amount of data available, and presenting data in machine learning ready format. Since the 2017 release, both TCRD and Pharos have produced two major releases, which have incorporated or expanded an additional 25 data sources. Recently incorporated data types include human and viral-human protein–protein interactions, protein–disease and protein–phenotype associations, and drug-induced gene signatures, among others. These aggregated data have enabled us to generate new visualizations and content sections in Pharos, in order to empower users to find new areas of study in the druggable genome.

scholarly journals Parasites, proteomes and systems: has Descartes’ clock run out of time?

Parasitology ◽  
2012 ◽  
Vol 139 (9) ◽  
pp. 1103-1118 ◽  
Author(s):  
J. M. WASTLING ◽  
S. D. ARMSTRONG ◽  
R. KRISHNA ◽  
D. XIA
Keyword(s):  
Systems Biology ◽  
Protein Interactions ◽  
Biological Data ◽  
Protein Protein Interactions ◽  
Proteomics Data ◽  
Data Types ◽  
Quality Of Data ◽  
Post Translational Modifications ◽  
Systems Modelling ◽  
New Generation

SUMMARYSystems biology aims to integrate multiple biological data types such as genomics, transcriptomics and proteomics across different levels of structure and scale; it represents an emerging paradigm in the scientific process which challenges the reductionism that has dominated biomedical research for hundreds of years. Systems biology will nevertheless only be successful if the technologies on which it is based are able to deliver the required type and quality of data. In this review we discuss how well positioned is proteomics to deliver the data necessary to support meaningful systems modelling in parasite biology. We summarise the current state of identification proteomics in parasites, but argue that a new generation of quantitative proteomics data is now needed to underpin effective systems modelling. We discuss the challenges faced to acquire more complete knowledge of protein post-translational modifications, protein turnover and protein-protein interactions in parasites. Finally we highlight the central role of proteome-informatics in ensuring that proteomics data is readily accessible to the user-community and can be translated and integrated with other relevant data types.

scholarly journals GreeningDB: A Database of Host–Pathogen Protein–Protein Interactions and Annotation Features of the Bacteria Causing Huanglongbing HLB Disease

2021 ◽  
Vol 22 (19) ◽  
pp. 10897
Author(s):  
Cristian D. Loaiza ◽  
Naveen Duhan ◽  
Rakesh Kaundal
Keyword(s):  
Protein Interactions ◽  
Network Visualization ◽  
High Yield ◽  
Protein Protein Interactions ◽  
Citrus Industry ◽  
Citrus Greening Disease ◽  
Host Pathogen ◽  
Pathogen Protein ◽  
Central Resource ◽  
User Friendly

The Citrus genus comprises some of the most important and commonly cultivated fruit plants. Within the last decade, citrus greening disease (also known as huanglongbing or HLB) has emerged as the biggest threat for the citrus industry. This disease does not have a cure yet and, thus, many efforts have been made to find a solution to this devastating condition. There are challenges in the generation of high-yield resistant cultivars, in part due to the limited and sparse knowledge about the mechanisms that are used by the Liberibacter bacteria to proliferate the infection in Citrus plants. Here, we present GreeningDB, a database implemented to provide the annotation of Liberibacter proteomes, as well as the host–pathogen comparactomics tool, a novel platform to compare the predicted interactomes of two HLB host–pathogen systems. GreeningDB is built to deliver a user-friendly interface, including network visualization and links to other resources. We hope that by providing these characteristics, GreeningDB can become a central resource to retrieve HLB-related protein annotations, and thus, aid the community that is pursuing the development of molecular-based strategies to mitigate this disease’s impact. The database is freely available at http://bioinfo.usu.edu/GreeningDB/.

scholarly journals Expanding Interactome Analyses beyond Model Eukaryotes

2021 ◽  
Author(s):  
Katherine James ◽  
Anil Wipat ◽  
Simon Cockell
Keyword(s):  
Genome Sequence ◽  
Protein Interactions ◽  
Model Organisms ◽  
Protein Protein Interactions ◽  
Data Types ◽  
Interaction Prediction ◽  
Diverse Species ◽  
Eukaryotic Species

Interactome analyses have traditionally been applied to yeast, human and other model organisms due to the availability of protein-protein interactions data for these species. Recently these techniques have been applied to more diverse species using computational interaction prediction from genome sequence and other data types. This review describes the various types of computational interactome networks that can be created and how they have been used in diverse eukaryotic species, highlighting some of the key interactome studies in non-model organisms.

scholarly journals ChlamDB: a comparative genomics database of the phylum Chlamydiae and other members of the Planctomycetes-Verrucomicrobiae-Chlamydiae superphylum

2019 ◽  
Author(s):  
Trestan Pillonel ◽  
Florian Tagini ◽  
Claire Bertelli ◽  
Gilbert Greub
Keyword(s):  
Comparative Genomics ◽  
Protein Interactions ◽  
Type Iii Secretion ◽  
Large Scale ◽  
Comparative Genomic ◽  
Protein Protein Interactions ◽  
Comprehensive Overview ◽  
Gene Annotations ◽  
Genomic Analyses ◽  
Central Resource

Abstract ChlamDB is a comparative genomics database containing 277 genomes covering the entire Chlamydiae phylum as well as their closest relatives belonging to the Planctomycetes-Verrucomicrobiae-Chlamydiae (PVC) superphylum. Genomes can be compared, analyzed and retrieved using accessions numbers of the most widely used databases including COG, KEGG ortholog, KEGG pathway, KEGG module, Pfam and InterPro. Gene annotations from multiple databases including UniProt (curated and automated protein annotations), KEGG (annotation of pathways), COG (orthology), TCDB (transporters), STRING (protein–protein interactions) and InterPro (domains and signatures) can be accessed in a comprehensive overview page. Candidate effectors of the Type III secretion system (T3SS) were identified using four in silico methods. The identification of orthologs among all PVC genomes allows users to perform large-scale comparative analyses and to identify orthologs of any protein in all genomes integrated in the database. Phylogenetic relationships of PVC proteins and their closest homologs in RefSeq, comparison of transmembrane domains and Pfam domains, conservation of gene neighborhood and taxonomic profiles can be visualized using dynamically generated graphs, available for download. As a central resource for researchers working on chlamydia, chlamydia-related bacteria, verrucomicrobia and planctomyces, ChlamDB facilitates the access to comprehensive annotations, integrates multiple tools for comparative genomic analyses and is freely available at https://chlamdb.ch/. Database URL: https://chlamdb.ch/

scholarly journals Modulating Target Protein Biology Through the Re-mapping of Conformational Distributions Using Small Molecules

2021 ◽  
Vol 9 ◽  
Author(s):  
Alastair D. G. Lawson ◽  
Malcolm MacCoss ◽  
Dominique L. Baeten ◽  
Alex Macpherson ◽  
Jiye Shi ◽  
...  
Keyword(s):  
Small Molecules ◽  
Protein Interactions ◽  
Three Dimensional ◽  
Conformational Sampling ◽  
Protein Protein Interactions ◽  
Computing Power ◽  
Disease Biology ◽  
New Chemical Entities ◽  
Wet Lab ◽  
Made In

Over the last 10 years considerable progress has been made in the application of small molecules to modulating protein-protein interactions (PPIs), and the navigation from “undruggable” to a host of candidate molecules in clinical trials has been well-charted in recent, comprehensive reviews. Structure-based design has played an important role in this scientific journey, with three dimensional structures guiding medicinal chemistry efforts. However, the importance of two additional dimensions: movement and time is only now being realised, as increasing computing power, closely aligned with wet lab validation, is applied to the challenge. Protein dynamics are fundamental to biology and disease, and application to PPI drug discovery has massively widened the scope for new chemical entities to influence function from allosteric, and previously unreported, sites. In this forward-looking perspective we highlight exciting, new opportunities for small molecules to modulate disease biology, by adjusting the frequency profile of natural conformational sampling, through the stabilisation of clinically desired conformers of target proteins.

Probing the druggability of protein–protein interactions: targeting the Notch1 receptor ankyrin domain using a fragment-based approach

2011 ◽  
Vol 39 (5) ◽  
pp. 1327-1333 ◽  
Author(s):  
Noha Abdel-Rahman ◽  
Alfonso Martinez-Arias ◽  
Tom L. Blundell
Keyword(s):  
Crystal Structure ◽  
Protein Interactions ◽  
Binding Sites ◽  
Ternary Complex ◽  
Protein Protein Interactions ◽  
X Ray ◽  
Regulatory Systems ◽  
Protein Interfaces ◽  
Druggable Genome ◽  
Notch1 Receptor

In order to achieve greater selectivity in drug discovery, researchers in both academia and industry are targeting cell regulatory systems. This often involves targeting the protein–protein interactions of regulatory multiprotein assemblies. Protein–protein interfaces are widely recognized to be challenging targets as they tend to be large and relatively flat, and therefore usually do not have the concave binding sites that characterize the so-called ‘druggable genome’. One such prototypic multiprotein target is the Notch transcription complex, where an extensive network of protein–protein interactions stabilize the ternary complex comprising the ankyrin domain, CSL (CBF1/suppressor of Hairless/Lag-1) and MAML (Mastermind-like). Enhanced Notch activity is implicated in the development of T-ALL (T-cell acute lymphoblastic leukaemia) and selective inhibitors of Notch would be useful cancer medicines. In the present paper, we describe a fragment-based approach to explore the druggability of the ankyrin domain. Using biophysical methods and X-ray crystal structure analyses, we demonstrate that molecules can bind to the surface of the ankyrin domain at the interface region with CSL and MAML. We show that they probably represent starting points for designing larger compounds that can inhibit important protein–protein interactions that stabilize the Notch complex. Given the relatively featureless topography of the ankyrin domain, this unexpected development should encourage others to explore the druggability of such challenging multiprotein systems using fragment-based approaches.

scholarly journals A proximity biotinylation-based approach to identify protein-E3 ligase interactions induced by PROTACs and molecular glues

2022 ◽  
Vol 13 (1) ◽  
Author(s):  
Satoshi Yamanaka ◽  
Yuto Horiuchi ◽  
Saya Matsuoka ◽  
Kohki Kido ◽  
Kohei Nishino ◽  
...  
Keyword(s):  
Protein Interactions ◽  
E3 Ligase ◽  
High Specificity ◽  
General Strategy ◽  
Immunomodulatory Drugs ◽  
Protein Protein Interactions ◽  
Drug Induced ◽  
Inducible Protein ◽  
Substrate Proteins

AbstractProteolysis-targeting chimaeras (PROTACs) as well as molecular glues such as immunomodulatory drugs (IMiDs) and indisulam are drugs that induce interactions between substrate proteins and an E3 ubiquitin ligases for targeted protein degradation. Here, we develop a workflow based on proximity-dependent biotinylation by AirID to identify drug-induced neo-substrates of the E3 ligase cereblon (CRBN). Using AirID-CRBN, we detect IMiD-dependent biotinylation of CRBN neo-substrates in vitro and identify biotinylated peptides of well-known neo-substrates by mass spectrometry with high specificity and selectivity. Additional analyses reveal ZMYM2 and ZMYM2-FGFR1 fusion protein—responsible for the 8p11 syndrome involved in acute myeloid leukaemia—as CRBN neo-substrates. Furthermore, AirID-DCAF15 and AirID-CRBN biotinylate neo-substrates targeted by indisulam and PROTACs, respectively, suggesting that this approach has the potential to serve as a general strategy for characterizing drug-inducible protein–protein interactions in cells.

Study of Protein-Protein Interactions from Multiple Data Sources

2008 ◽  
pp. 280-307
Author(s):  
Tu Bao Ho ◽  
Thanh Phuong Nguyen ◽  
Tuan Nam Tran
Keyword(s):  
Protein Interactions ◽  
Inductive Logic Programming ◽  
Inductive Logic ◽  
Data Sources ◽  
Protein Protein Interactions ◽  
Prediction Rules ◽  
Multiple Data Sources ◽  
Protein Protein Interaction ◽  
Multiple Data ◽  
Domain Interactions

The objective of this paper is twofold. First is to provide a survey of computational methods for protein-protein interaction (PPI) study. Second is to introduce our work and results in using inductive logic programming to learn prediction rules for PPI and DDI (domain-domain interactions) from multiple data sources. We show advantages of ex-ploiting various types of data in these important problems of bioinformatics.

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