Studying protein–protein interactions: progress, pitfalls and solutions

2016 ◽  
Vol 44 (4) ◽  
pp. 994-1004 ◽  
Author(s):  
Sheri Hayes ◽  
Beatrice Malacrida ◽  
Maeve Kiely ◽  
Patrick A. Kiely
Keyword(s):  
Protein Interactions ◽  
Protein Function ◽  
Large Scale ◽  
In Silico Analysis ◽  
Screening Tools ◽  
Protein Protein Interactions ◽  
Advantages And Disadvantages ◽  
Comprehensive Knowledge ◽  
Starting Point ◽  
The Many

Signalling proteins are intrinsic to all biological processes and interact with each other in tightly regulated and orchestrated signalling complexes and pathways. Characterization of protein binding can help to elucidate protein function within signalling pathways. This information is vital for researchers to gain a more comprehensive knowledge of cellular networks which can then be used to develop new therapeutic strategies for disease. However, studying protein–protein interactions (PPIs) can be challenging as the interactions can be extremely transient downstream of specific environmental cues. There are many powerful techniques currently available to identify and confirm PPIs. Choosing the most appropriate range of techniques merits serious consideration. The aim of this review is to provide a starting point for researchers embarking on a PPI study. We provide an overview and point of reference for some of the many methods available to identify interactions from in silico analysis and large scale screening tools through to the methods used to validate potential PPIs. We discuss the advantages and disadvantages of each method and we also provide a workflow chart to highlight the main experimental questions to consider when planning cell lysis to maximize experimental success.

scholarly journals Using deep maxout neural networks to improve the accuracy of function prediction from protein interaction networks

2018 ◽  
Author(s):  
Cen Wan ◽  
Domenico Cozzetto ◽  
Rui Fa ◽  
David T. Jones
Keyword(s):  
Neural Networks ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Function ◽  
Large Scale ◽  
Protein Function Prediction ◽  
Function Prediction ◽  
Network Embedding ◽  
Protein Protein Interactions ◽  
Functional Representations

Protein-protein interaction network data provides valuable information that infers direct links between genes and their biological roles. This information brings a fundamental hypothesis for protein function prediction that interacting proteins tend to have similar functions. With the help of recently-developed network embedding feature generation methods and deep maxout neural networks, it is possible to extract functional representations that encode direct links between protein-protein interactions information and protein function. Our novel method, STRING2GO, successfully adopts deep maxout neural networks to learn functional representations simultaneously encoding both protein-protein interactions and functional predictive information. The experimental results show that STRING2GO outperforms other network embedding-based prediction methods and one benchmark method adopted in a recent large scale protein function prediction competition.

scholarly journals Development of Large-scale Cross-linking Mass Spectrometry

2017 ◽  
Vol 17 (6) ◽  
pp. 1055-1066 ◽  
Author(s):  
Helena Maria Barysz ◽  
Johan Malmström
Keyword(s):  
Mass Spectrometry ◽  
Protein Interactions ◽  
Protein Function ◽  
Large Scale ◽  
Unmet Need ◽  
Large Data ◽  
Cross Linking ◽  
Data Sets ◽  
Protein Protein Interactions ◽  
Model Complex

Cross-linking mass spectrometry (CLMS) provides distance constraints to study the structure of proteins, multiprotein complexes and protein-protein interactions which are critical for the understanding of protein function. CLMS is an attractive technology to bridge the gap between high-resolution structural biology techniques and proteomic-based interactome studies. However, as outlined in this review there are still several bottlenecks associated with CLMS which limit its application on a proteome-wide level. Specifically, there is an unmet need for comprehensive software that can reliably identify cross-linked peptides from large data sets. In this review we provide supporting information to reason that targeted proteomics of cross-links may provide the required sensitivity to reliably detect and quantify cross-linked peptides and that a reporter ion signature for cross-linked peptides may become a useful approach to increase confidence in the identification process of cross-linked peptides. In addition, the review summarizes the recent advances in CLMS workflows using the analysis of condensin complex in intact chromosomes as a model complex.

scholarly journals Functional protein dynamics on uncharted time scales detected by nanoparticle-assisted NMR spin relaxation

2019 ◽  
Vol 5 (8) ◽  
pp. eaax5560 ◽  
Author(s):  
Mouzhe Xie ◽  
Lei Yu ◽  
Lei Bruschweiler-Li ◽  
Xinyao Xiang ◽  
Alexandar L. Hansen ◽  
...  
Keyword(s):  
Time Scales ◽  
Spin Relaxation ◽  
Protein Interactions ◽  
Protein Function ◽  
Large Scale ◽  
Transverse Spin ◽  
Protein Protein Interactions ◽  
Functional Protein ◽  
Wide Range ◽  
Nmr Spin Relaxation

Protein function depends critically on intrinsic internal dynamics, which is manifested in distinct ways, such as loop motions that regulate protein recognition and catalysis. Under physiological conditions, dynamic processes occur on a wide range of time scales from subpicoseconds to seconds. Commonly used NMR spin relaxation in solution provides valuable information on very fast and slow motions but is insensitive to the intermediate nanosecond to microsecond range that exceeds the protein tumbling correlation time. Presently, very little is known about the nature and functional role of these motions. It is demonstrated here how transverse spin relaxation becomes exquisitely sensitive to these motions at atomic resolution when studying proteins in the presence of nanoparticles. Application of this novel cross-disciplinary approach reveals large-scale dynamics of loops involved in functionally critical protein-protein interactions and protein-calcium ion recognition that were previously unobservable.

scholarly journals Experimental and bioinformatic approaches for interrogating protein–protein interactions to determine protein function

2005 ◽  
Vol 34 (2) ◽  
pp. 263-280 ◽  
Author(s):  
Arnaud Droit ◽  
Guy G Poirier ◽  
Joanna M Hunter
Keyword(s):  
Protein Interactions ◽  
Protein Function ◽  
Large Scale ◽  
Protein Protein Interactions ◽  
Biologically Relevant ◽  
Protein Protein Interaction ◽  
Bioinformatic Approaches ◽  
Disparate Data ◽  
Protein Protein Interaction Networks ◽  
Novel Protein

An ambitious goal of proteomics is to elucidate the structure, interactions and functions of all proteins within cells and organisms. One strategy to determine protein function is to identify the protein–protein interactions. The increasing use of high-throughput and large-scale bioinformatics-based studies has generated a massive amount of data stored in a number of different databases. A challenge for bioinformatics is to explore this disparate data and to uncover biologically relevant interactions and pathways. In parallel, there is clearly a need for the development of approaches that can predict novel protein–protein interaction networks in silico. Here, we present an overview of different experimental and bioinformatic methods to elucidate protein–protein interactions.

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>

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>

scholarly journals Short loop functional commonality identified in leukaemia proteome highlights crucial protein sub-networks

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Sun Sook Chung ◽  
Joseph C F Ng ◽  
Anna Laddach ◽  
N Shaun B Thomas ◽  
Franca Fraternali
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Interaction Network ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Short Loop ◽  
New Strategy ◽  
Loop Network ◽  
Protein Protein Interaction Network

Abstract Direct drug targeting of mutated proteins in cancer is not always possible and efficacy can be nullified by compensating protein–protein interactions (PPIs). Here, we establish an in silico pipeline to identify specific PPI sub-networks containing mutated proteins as potential targets, which we apply to mutation data of four different leukaemias. Our method is based on extracting cyclic interactions of a small number of proteins topologically and functionally linked in the Protein–Protein Interaction Network (PPIN), which we call short loop network motifs (SLM). We uncover a new property of PPINs named ‘short loop commonality’ to measure indirect PPIs occurring via common SLM interactions. This detects ‘modules’ of PPI networks enriched with annotated biological functions of proteins containing mutation hotspots, exemplified by FLT3 and other receptor tyrosine kinase proteins. We further identify functional dependency or mutual exclusivity of short loop commonality pairs in large-scale cellular CRISPR–Cas9 knockout screening data. Our pipeline provides a new strategy for identifying new therapeutic targets for drug discovery.

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.

scholarly journals Investigating the Role of Large-Scale Domain Dynamics in Protein-Protein Interactions

2016 ◽  
Vol 3 ◽  
Author(s):  
Elise Delaforge ◽  
Sigrid Milles ◽  
Jie-rong Huang ◽  
Denis Bouvier ◽  
Malene Ringkjøbing Jensen ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Protein Protein Interactions ◽  
Domain Dynamics
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