scholarly journals The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets

2020 ◽  
Vol 49 (D1) ◽  
pp. D605-D612 ◽  
Author(s):  
Damian Szklarczyk ◽  
Annika L Gable ◽  
Katerina C Nastou ◽  
David Lyon ◽  
Rebecca Kirsch ◽  
...  
Keyword(s):  
Text Mining ◽  
Protein Interactions ◽  
Functional Characterization ◽  
Protein Networks ◽  
Protein Protein Interactions ◽  
Genomic Context ◽  
Mining System ◽  
String Database ◽  
System A ◽  
Physical Interactions

Abstract Cellular life depends on a complex web of functional associations between biomolecules. Among these associations, protein–protein interactions are particularly important due to their versatility, specificity and adaptability. The STRING database aims to integrate all known and predicted associations between proteins, including both physical interactions as well as functional associations. To achieve this, STRING collects and scores evidence from a number of sources: (i) automated text mining of the scientific literature, (ii) databases of interaction experiments and annotated complexes/pathways, (iii) computational interaction predictions from co-expression and from conserved genomic context and (iv) systematic transfers of interaction evidence from one organism to another. STRING aims for wide coverage; the upcoming version 11.5 of the resource will contain more than 14 000 organisms. In this update paper, we describe changes to the text-mining system, a new scoring-mode for physical interactions, as well as extensive user interface features for customizing, extending and sharing protein networks. In addition, we describe how to query STRING with genome-wide, experimental data, including the automated detection of enriched functionalities and potential biases in the user's query data. The STRING resource is available online, at https://string-db.org/.

scholarly journals Universal Screening Methods and Applications of ThermoFluor®

2006 ◽  
Vol 11 (7) ◽  
pp. 854-863 ◽  
Author(s):  
Maxwell D. Cummings ◽  
Michael A. Farnum ◽  
Marina I. Nelen
Keyword(s):  
Protein Interactions ◽  
Protein Function ◽  
Protein Unfolding ◽  
Direct Detection ◽  
Functional Characterization ◽  
Screening Methods ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Bacterial Enzyme ◽  
Research Problems

The genomics revolution has unveiled a wealth of poorly characterized proteins. Scientists are often able to produce milligram quantities of proteins for which function is unknown or hypothetical, based only on very distant sequence homology. Broadly applicable tools for functional characterization are essential to the illumination of these orphan proteins. An additional challenge is the direct detection of inhibitors of protein-protein interactions (and allosteric effectors). Both of these research problems are relevant to, among other things, the challenge of finding and validating new protein targets for drug action. Screening collections of small molecules has long been used in the pharmaceutical industry as 1 method of discovering drug leads. Screening in this context typically involves a function-based assay. Given a sufficient quantity of a protein of interest, significant effort may still be required for functional characterization, assay development, and assay configuration for screening. Increasingly, techniques are being reported that facilitate screening for specific ligands for a protein of unknown function. Such techniques also allow for function-independent screening with better characterized proteins. ThermoFluor®, a screening instrument based on monitoring ligand effects on temperature-dependent protein unfolding, can be applied when protein function is unknown. This technology has proven useful in the decryption of an essential bacterial enzyme and in the discovery of a series of inhibitors of a cancer-related, protein-protein interaction. The authors review some of the tools relevant to these research problems in drug discovery, and describe our experiences with 2 different proteins.

Mining Protein Interactome Networks to Measure Interaction Reliability and Select Hub Proteins

2013 ◽  
pp. 222-238
Author(s):  
Young-Rae Cho ◽  
Aidong Zhang
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Functional Characterization ◽  
Flow Simulation ◽  
Protein Protein Interactions ◽  
Systematic Analysis ◽  
Graph Theoretic ◽  
Interactome Network ◽  
Protein Interactome ◽  
Hub Proteins

High-throughput techniques involve large-scale detection of protein-protein interactions. This interaction data set from the genome-scale perspective is structured into an interactome network. Since the interaction evidence represents functional linkage, various graph-theoretic computational approaches have been applied to the interactome networks for functional characterization. However, this data is generally unreliable, and the typical genome-wide interactome networks have a complex connectivity. In this paper, the authors explore systematic analysis of protein interactome networks, and propose a $k$-round signal flow simulation algorithm to measure interaction reliability from connection patterns of the interactome networks. This algorithm quantitatively characterizes functional links between proteins by simulating the propagation of information signals through complex connections. In this regard, the algorithm efficiently estimates the strength of alternative paths for each interaction. The authors also present an algorithm for mining the complex interactome network structure. The algorithm restructures the network by hierarchical ordering of nodes, and this structure re-formatting process reveals hub proteins in the interactome networks. This paper demonstrates that two rounds of simulation accurately scores interaction reliability in terms of ontological correlation and functional consistency. Finally, the authors validate that the selected structural hubs represent functional core proteins.

scholarly journals Cloning, functional characterization, and mechanism of action of the B-cell-specific transcriptional coactivator OCA-B.

1995 ◽  
Vol 15 (8) ◽  
pp. 4115-4124 ◽  
Author(s):  
Y Luo ◽  
R G Roeder
Keyword(s):  
B Cell ◽  
Mechanism Of Action ◽  
Protein Interactions ◽  
Functional Characterization ◽  
Indirect Evidence ◽  
Transcriptional Coactivator ◽  
Protein Protein Interactions ◽  
Biochemical Purification ◽  
Activation Domain ◽  
Sequence Relationships

Biochemical purification and cognate cDNA cloning studies have revealed that the previously described transcriptional coactivator OCA-B consists of a 34- or 35-kDa polypeptide with sequence relationships to known coactivators that function by protein-protein interactions. Studies with a recombinant protein have proved that a single OCA-B polypeptide is the main determinant for B-cell-specific activation of immunoglobulin (Ig) promoters and provided additional insights into its mechanism of action. Recombinant OCA-B can function equally well with Oct-1 or Oct-2 on an Ig promoter, but while corresponding POU domains are sufficient for OCA-B interaction, and for octamer-mediated transcription of a histone H2B promoter, an additional Oct-1 or Oct-2 activation domain(s) is necessary for functional synergy with OCA-B. Further studies additional Oct-1 or Oct-2 activation domain(s) is necessary for functional synergy with OCA-B. Further studies show that Ig promoter activation by Oct-1 and OCA-B requires still other general (USA-derived) cofactors and also provide indirect evidence that distinct Oct-interacting cofactors regulate H2B transcription.

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