scholarly journals Proteins interacting with cloning scars: a source of false positive protein-protein interactions

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Charles A. S. Banks ◽  
Gina Boanca ◽  
Zachary T. Lee ◽  
Laurence Florens ◽  
Michael P. Washburn
Keyword(s):  
Protein Interactions ◽  
False Positive ◽  
Protein Protein Interactions

Protein-protein interactions: Analysis of a false positive GST pulldown result

2011 ◽  
Vol 79 (8) ◽  
pp. 2365-2371 ◽  
Author(s):  
Sandra Wissmueller ◽  
Josep Font ◽  
Chu Wai Liew ◽  
Edward Cram ◽  
Thilo Schroeder ◽  
...  
Keyword(s):  
Protein Interactions ◽  
False Positive ◽  
Protein Protein Interactions

scholarly journals A critical and Integrated View of the Yeast Interactome

2004 ◽  
Vol 5 (5) ◽  
pp. 382-402 ◽  
Author(s):  
Michael Cornell ◽  
Norman W. Paton ◽  
Stephen G. Oliver
Keyword(s):  
High Throughput ◽  
Protein Interactions ◽  
False Positive ◽  
Affinity Purification ◽  
Information Management System ◽  
Positive Interactions ◽  
Global Studies ◽  
Protein Protein Interactions ◽  
Integrative Analyses ◽  
Genome Information

Global studies of protein–protein interactions are crucial to both elucidating gene function and producing an integrated view of the workings of living cells. High-throughput studies of the yeast interactome have been performed using both genetic and biochemical screens. Despite their size, the overlap between these experimental datasets is very limited. This could be due to each approach sampling only a small fraction of the total interactome. Alternatively, a large proportion of the data from these screens may represent false-positive interactions. We have used the Genome Information Management System (GIMS) to integrate interactome datasets with transcriptome and protein annotation data and have found significant evidence that the proportion of false-positive results is high. Not all high-throughput datasets are similarly contaminated, and the tandem affinity purification (TAP) approach appears to yield a high proportion of reliable interactions for which corroborating evidence is available. From our integrative analyses, we have generated a set of verified interactome data for yeast.

scholarly journals From random to predictive: a context-specific interaction framework improves selection of drug protein-protein interactions for unknown drug pathways

2020 ◽  
Author(s):  
Jennifer Wilson ◽  
Alessio Gravina ◽  
Kevin Grimes
Keyword(s):  
Decision Making ◽  
Protein Interactions ◽  
False Positive ◽  
Specific Interaction ◽  
Interaction Network ◽  
Protein Protein Interactions ◽  
Data Set ◽  
Network Methods ◽  
Context Specific ◽  
Selection Of

With high drug attrition, interaction network methods are increasingly attractive as quick and inexpensive methods for prediction of drug safety and efficacy effects when a drug pathway is unknown. However, these methods suffer from high false positive rates for selecting drug phenotypic effects, their performance is often no better than random (AUROC ~0.5), and this limits the use of network methods in regulatory and industrial decision making. In contrast to many network engineering approaches that apply mathematical thresholds to discover phenotype associations, we hypothesized that interaction networks associated with true positive drug phenotypes are context specific. We tested this hypothesis on 16 designated medical event (DMEs) phenotypes which are a subset of adverse events that are of upmost concern to FDA review using a novel data set extracted from drug labels. We demonstrated that context-specific interactions (CSIs) distinguished true from false positive DMEs with an 50% improvement over non-context-specific approaches (AUROC 0.77 compared to 0.51). By reducing false positives, CSI analysis has the potential to advance network techniques to influence decision making in regulatory and industry settings.

scholarly journals Co-evolutionary analysis accurately predicts details of interactions between the Integrator complex subunits

2019 ◽  
Author(s):  
Bernard Fongang ◽  
Yingjie Zhu ◽  
Eric J. Wagner ◽  
Andrzej Kudlicki ◽  
Maga Rowicka
Keyword(s):  
Protein Interactions ◽  
False Positive ◽  
Protein Complexes ◽  
False Positive Rate ◽  
Attractive Alternative ◽  
Evolutionary Analysis ◽  
Protein Protein Interactions ◽  
Small Proteins ◽  
Positive Rate ◽  
Specificity Factor

ABSTRACTSolving the structure of large, multi-subunit complexes is difficult despite recent advances in cryoEM, due to remaining challenges to express and purify complex subunits. Computational approaches that predict protein-protein interactions, including Direct Coupling Analysis (DCA), represent an attractive alternative to dissect interactions within protein complexes. However, due to high computational complexity and high false positive rate they are applicable only to small proteins. Here, we present a modified DCA to predict residues and domains involved in interactions of large proteins. To reduce false positive levels and increase accuracy of prediction, we use local Gaussian averaging and predicted secondary structure elements. As a proof-of-concept, we apply our method to two Integrator subunits, INTS9 and INTS11, which form a heterodimeric structure previously solved by crystallography. We accurately predict the domains of INTS9/11 interaction. We then apply this approach to predict the interaction domains of two complexes whose structure is currently unknown: 1) The heterodimer formed by the Cleavage and Polyadenylation Specificity Factor 100-kD (CPSF100) and 73-kD (CPSF73); 2) The heterotrimer formed by INTS4/9/11. Our predictions of interactions within these two complexes are supported by experimental data, demonstrating that our modified DCA is a useful method for predicting interactions and can easily be applied to other complexes.

Protein-protein interactions of the p53 family with the Bcl-2 family in hepatocellular carcinoma

2011 ◽  
Vol 49 (08) ◽  
Author(s):  
LC König ◽  
M Meinhard ◽  
C Sandig ◽  
MH Bender ◽  
A Lovas ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Protein Interactions ◽  
Protein Protein Interactions ◽  
P53 Family

Partial Purification of a Specific Plasminogen Activator from Porcine Parotid Glands

1974 ◽  
Vol 31 (03) ◽  
pp. 403-414 ◽  
Author(s):  
Terence Cartwright
Keyword(s):  
Nucleic Acid ◽  
Salivary Glands ◽  
Plasminogen Activator ◽  
Protein Interactions ◽  
Partial Purification ◽  
Protein Protein Interactions ◽  
Parotid Glands ◽  
Two Stage ◽  
Preliminary Characterization ◽  
Specific Inhibitors

SummaryA method is described for the extraction with buffers of near physiological pH of a plasminogen activator from porcine salivary glands. Substantial purification of the activator was achieved although this was to some extent complicated by concomitant extraction of nucleic acid from the glands. Preliminary characterization experiments using specific inhibitors suggested that the activator functioned by a similar mechanism to that proposed for urokinase, but with some important kinetic differences in two-stage assay systems. The lack of reactivity of the pig gland enzyme in these systems might be related to the tendency to protein-protein interactions observed with this material.

Target-Templated de novo Design of Macrocyclic D-/L-Peptides: Inhibitors of the PD-1/PD-L1 Interaction

2020 ◽  
Author(s):  
Salvador Guardiola ◽  
Monica Varese ◽  
Xavier Roig ◽  
Jesús Garcia ◽  
Ernest Giralt
Keyword(s):  
Protein Interactions ◽  
Cyclic Peptides ◽  
General Framework ◽  
Large Scale ◽  
De Novo ◽  
Inhibitory Effect ◽  
Original Text ◽  
Protein Protein Interactions ◽  
Retraction Notice ◽  
Pharmaceutical Properties

<p>NOTE: This preprint has been retracted by consensus from all authors. See the retraction notice in place above; the original text can be found under "Version 1", accessible from the version selector above.</p><p><br></p><p>------------------------------------------------------------------------</p><p><br></p><p>Peptides, together with antibodies, are among the most potent biochemical tools to modulate challenging protein-protein interactions. However, current structure-based methods are largely limited to natural peptides and are not suitable for designing target-specific binders with improved pharmaceutical properties, such as macrocyclic peptides. Here we report a general framework that leverages the computational power of Rosetta for large-scale backbone sampling and energy scoring, followed by side-chain composition, to design heterochiral cyclic peptides that bind to a protein surface of interest. To showcase the applicability of our approach, we identified two peptides (PD-<i>i</i>3 and PD-<i>i</i>6) that target PD-1, a key immune checkpoint, and work as protein ligand decoys. A comprehensive biophysical evaluation confirmed their binding mechanism to PD-1 and their inhibitory effect on the PD-1/PD-L1 interaction. Finally, elucidation of their solution structures by NMR served as validation of our <i>de novo </i>design approach. We anticipate that our results will provide a general framework for designing target-specific drug-like peptides.<i></i></p>

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