scholarly journals Characterization of known protein complexes using k-connectivity and other topological measures

F1000Research ◽  
2013 ◽  
Vol 2 ◽  
pp. 172 ◽  
Author(s):  
Suzanne R Gallagher ◽  
Debra S Goldberg
Keyword(s):  
Protein Interactions ◽  
Protein Complexes ◽  
Clustering Coefficient ◽  
Edge Density ◽  
Protein Protein Interactions ◽  
X Ray Crystallography ◽  
Protein Protein Interaction ◽  
Unknown Protein ◽  
Topological Measures

Many protein complexes are densely packed, so proteins within complexes often interact with several other proteins in the complex. Steric constraints prevent most proteins from simultaneously binding more than a handful of other proteins, regardless of the number of proteins in the complex. Because of this, as complex size increases, several measures of the complex decrease within protein-protein interaction networks. However, k-connectivity, the number of vertices or edges that need to be removed in order to disconnect a graph, may be consistently high for protein complexes. The property of k-connectivity has been little used previously in the investigation of protein-protein interactions. To understand the discriminative power of k-connectivity and other topological measures for identifying unknown protein complexes, we characterized these properties in known Saccharomyces cerevisiae protein complexes in networks generated both from highly accurate X-ray crystallography experiments which give an accurate model of each complex, and also as the complexes appear in high-throughput yeast 2-hybrid studies in which new complexes may be discovered. We also computed these properties for appropriate random subgraphs. We found that clustering coefficient, mutual clustering coefficient, and k-connectivity are better indicators of known protein complexes than edge density, degree, or betweenness. This suggests new directions for future protein complex-finding algorithms.

scholarly journals Characterization of known protein complexes using k-connectivity and other topological measures

F1000Research ◽  
2015 ◽  
Vol 2 ◽  
pp. 172
Author(s):  
Suzanne R Gallagher ◽  
Debra S Goldberg
Keyword(s):  
Protein Interactions ◽  
Protein Complexes ◽  
Clustering Coefficient ◽  
Edge Density ◽  
Protein Protein Interactions ◽  
X Ray Crystallography ◽  
Protein Protein Interaction ◽  
Unknown Protein ◽  
Topological Measures

Many protein complexes are densely packed, so proteins within complexes often interact with several other proteins in the complex. Steric constraints prevent most proteins from simultaneously binding more than a handful of other proteins, regardless of the number of proteins in the complex. Because of this, as complex size increases, several measures of the complex decrease within protein-protein interaction networks. However, k-connectivity, the number of vertices or edges that need to be removed in order to disconnect a graph, may be consistently high for protein complexes. The property of k-connectivity has been little used previously in the investigation of protein-protein interactions. To understand the discriminative power of k-connectivity and other topological measures for identifying unknown protein complexes, we characterized these properties in known Saccharomyces cerevisiae protein complexes in networks generated both from highly accurate X-ray crystallography experiments which give an accurate model of each complex, and also as the complexes appear in high-throughput yeast 2-hybrid studies in which new complexes may be discovered. We also computed these properties for appropriate random subgraphs.We found that clustering coefficient, mutual clustering coefficient, and k-connectivity are better indicators of known protein complexes than edge density, degree, or betweenness. This suggests new directions for future protein complex-finding algorithms.

Decoding Protein-protein Interactions: An Overview

2020 ◽  
Vol 20 (10) ◽  
pp. 855-882
Author(s):  
Olivia Slater ◽  
Bethany Miller ◽  
Maria Kontoyianni
Keyword(s):  
Drug Discovery ◽  
Protein Interactions ◽  
Drug Repurposing ◽  
Protein Docking ◽  
Target Space ◽  
Protein Protein Interactions ◽  
X Ray Crystallography ◽  
Protein Protein Interaction ◽  
Interaction Sites ◽  
Long Time

Drug discovery has focused on the paradigm “one drug, one target” for a long time. However, small molecules can act at multiple macromolecular targets, which serves as the basis for drug repurposing. In an effort to expand the target space, and given advances in X-ray crystallography, protein-protein interactions have become an emerging focus area of drug discovery enterprises. Proteins interact with other biomolecules and it is this intricate network of interactions that determines the behavior of the system and its biological processes. In this review, we briefly discuss networks in disease, followed by computational methods for protein-protein complex prediction. Computational methodologies and techniques employed towards objectives such as protein-protein docking, protein-protein interactions, and interface predictions are described extensively. Docking aims at producing a complex between proteins, while interface predictions identify a subset of residues on one protein that could interact with a partner, and protein-protein interaction sites address whether two proteins interact. In addition, approaches to predict hot spots and binding sites are presented along with a representative example of our internal project on the chemokine CXC receptor 3 B-isoform and predictive modeling with IP10 and PF4.

scholarly journals Characterization of major chromatin assembly proteins in tetrahymena thermophile using proeomic and molecular evolutionary analyses

2021 ◽  
Author(s):  
Syed N Shah
Keyword(s):  
Protein Interactions ◽  
Protein Complexes ◽  
Affinity Purification ◽  
Regulation Of Gene Expression ◽  
Chromatin Assembly ◽  
Histone Chaperones ◽  
Protein Protein Interactions ◽  
Selective Forces ◽  
Assembly Proteins

Histones H3/H4 are deposited onto DNA in a replication-dependent or independent fashion by the CAF1 and HIRA protein complexes. Despite the identification of these protein complexes, mechanistic details remain unclear. Recently, we showed that in T. thermophila histone chaperones Nrp1, Asf1 and the Impβ6 importin function together to transport newly synthesized H3/H4 from the cytoplasm to the nucleus. To characterize chromatin assembly proteins in T.thermophila, I used affinity purification combined with mass spectrometry to identify protein-protein interactions of Nrp1, Cac2 subunit of CAF1, HIRA and histone modifying Hat1-complex in T. thermophila. I found that the three-subunit T.thermophila CAF1 complex interacts with Casein Kinase 2 (CKII), possibly accounting for previously reported human CAF1phosphorylation. I also found that Hat2 subunit of HAT1 complex is also shared by CAF1 complex as its Cac3 subunit. This suggests that Hat2/Cac3 might exist in two separate pools of protein complexes. Remarkably, proteomic analysis of Hat2/Cac3 in turn revealed that it forms several complexes with other proteins including SIN3, RXT3, LIN9 and TESMIN, all of which have known roles in the regulation of gene expression. Finally, I asked how selective forces might have impacted on the function of proteins involved in H3/H4 transport. Focusing on NASP which possesses several TPR motifs, I showed that its protein-protein interactions are conserved in T. thermophila. Using molecular evolutionary methods I show that different TPRs in NASP evolve at different rates possibly accounting for the functional diversity observed among different family members.

scholarly journals Mass spectrometry-based methods for analysing the mitochondrial interactome in mammalian cells

2019 ◽  
Vol 167 (3) ◽  
pp. 225-231 ◽  
Author(s):  
Takumi Koshiba ◽  
Hidetaka Kosako
Keyword(s):  
Mass Spectrometry ◽  
Protein Interactions ◽  
Mammalian Cells ◽  
Protein Complexes ◽  
Living Cells ◽  
Protein Protein Interactions ◽  
Cellular Functions ◽  
Protein Protein Interaction ◽  
Membrane Protein Complexes ◽  
Protein Protein Interaction Networks

Abstract Protein–protein interactions are essential biologic processes that occur at inter- and intracellular levels. To gain insight into the various complex cellular functions of these interactions, it is necessary to assess them under physiologic conditions. Recent advances in various proteomic technologies allow to investigate protein–protein interaction networks in living cells. The combination of proximity-dependent labelling and chemical cross-linking will greatly enhance our understanding of multi-protein complexes that are difficult to prepare, such as organelle-bound membrane proteins. In this review, we describe our current understanding of mass spectrometry-based proteomics mapping methods for elucidating organelle-bound membrane protein complexes in living cells, with a focus on protein–protein interactions in mitochondrial subcellular compartments.

scholarly journals Evolutionary diversification of protein–protein interactions by interface add-ons

2017 ◽  
Vol 114 (40) ◽  
pp. E8333-E8342 ◽  
Author(s):  
Maximilian G. Plach ◽  
Florian Semmelmann ◽  
Florian Busch ◽  
Markus Busch ◽  
Leonhard Heizinger ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Complexes ◽  
Evolutionary Strategy ◽  
Structural Level ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Evolutionary Diversification

Cells contain a multitude of protein complexes whose subunits interact with high specificity. However, the number of different protein folds and interface geometries found in nature is limited. This raises the question of how protein–protein interaction specificity is achieved on the structural level and how the formation of nonphysiological complexes is avoided. Here, we describe structural elements called interface add-ons that fulfill this function and elucidate their role for the diversification of protein–protein interactions during evolution. We identified interface add-ons in 10% of a representative set of bacterial, heteromeric protein complexes. The importance of interface add-ons for protein–protein interaction specificity is demonstrated by an exemplary experimental characterization of over 30 cognate and hybrid glutamine amidotransferase complexes in combination with comprehensive genetic profiling and protein design. Moreover, growth experiments showed that the lack of interface add-ons can lead to physiologically harmful cross-talk between essential biosynthetic pathways. In sum, our complementary in silico, in vitro, and in vivo analysis argues that interface add-ons are a practical and widespread evolutionary strategy to prevent the formation of nonphysiological complexes by specializing protein–protein interactions.

scholarly journals Identification of β-Strand Mediated Protein-Protein Interaction Inhibitors Using Ligand-Directed Fragment Ligation

2020 ◽  
Author(s):  
Zsófia Hegedüs Zsófia Hegedüs ◽  
Fruzsina Hobor ◽  
Deborah K. Shoemark ◽  
sergio Celis ◽  
Lu-Yun Lian ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Electrostatic Interactions ◽  
Significant Proportion ◽  
Selective Inhibition ◽  
Peptide Sequence ◽  
Protein Protein Interactions ◽  
X Ray Crystallography ◽  
Protein Protein Interaction ◽  
Ligand Discovery ◽  
Dynamics Simulations

β-Strand mediated protein-protein interactions (PPIs) represent underexploited targets for chemical probe development despite representing a significant proportion of known and therapeutically relevant PPI targets. β-strand mimicry is challenging given that both amino acid side-chains and backbone hydrogen-bonds are typically required for molecular recognition, yet these are oriented along perpendicular vectors. This paper describes an alternative approach using GKAP/SHANK1 PDZ as a model and dynamic ligation screening to identify small-molecule replacements for tranches of peptide sequence. A peptide truncation of GKAP functionalized at the N- and C-termini with acylhydrazone groups was used as an anchor. Reversible acylhydrazone bond exchange with a library of aldehyde fragments in the presence of the protein as template and <i>in situ</i> screening using a fluorescence anisotropy (FA) assay identified peptide hybrid hits with comparable affinity to the GKAP peptide binding sequence. Identified hits were validated using FA, ITC, NMR and X-ray crystallography to confirm selective inhibition of the target PDZ-mediated PPI and mode of binding. These analyses together with molecular dynamics simulations demonstrated the ligands make transient interactions with an unoccupied basic patch through electrostatic interactions, establishing proof-of-concept that this unbiased approach to ligand discovery represents a powerful addition to the armory of tools that can be used to identify PPI modulators.

scholarly journals Prediction of Protein-Protein Interactions Related to Protein Complexes Based on Protein Interaction Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-9
Author(s):  
Peng Liu ◽  
Lei Yang ◽  
Daming Shi ◽  
Xianglong Tang
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Complexes ◽  
Statistical Tests ◽  
Interaction Network ◽  
Biological Data ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Small Parts

A method for predicting protein-protein interactions based on detected protein complexes is proposed to repair deficient interactions derived from high-throughput biological experiments. Protein complexes are pruned and decomposed into small parts based on the adaptivek-cores method to predict protein-protein interactions associated with the complexes. The proposed method is adaptive to protein complexes with different structure, number, and size of nodes in a protein-protein interaction network. Based on different complex sets detected by various algorithms, we can obtain different prediction sets of protein-protein interactions. The reliability of the predicted interaction sets is proved by using estimations with statistical tests and direct confirmation of the biological data. In comparison with the approaches which predict the interactions based on the cliques, the overlap of the predictions is small. Similarly, the overlaps among the predicted sets of interactions derived from various complex sets are also small. Thus, every predicted set of interactions may complement and improve the quality of the original network data. Meanwhile, the predictions from the proposed method replenish protein-protein interactions associated with protein complexes using only the network topology.

scholarly journals Protein-Protein Interactions Prediction Based on Iterative Clique Extension with Gene Ontology Filtering

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Lei Yang ◽  
Xianglong Tang
Keyword(s):  
Gene Ontology ◽  
Protein Interactions ◽  
Protein Complexes ◽  
False Negative ◽  
Biological Database ◽  
Interacting Proteins ◽  
Ppi Network ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Biological Meaning

Cliques (maximal complete subnets) in protein-protein interaction (PPI) network are an important resource used to analyze protein complexes and functional modules. Clique-based methods of predicting PPI complement the data defection from biological experiments. However, clique-based predicting methods only depend on the topology of network. The false-positive and false-negative interactions in a network usually interfere with prediction. Therefore, we propose a method combining clique-based method of prediction and gene ontology (GO) annotations to overcome the shortcoming and improve the accuracy of predictions. According to different GO correcting rules, we generate two predicted interaction sets which guarantee the quality and quantity of predicted protein interactions. The proposed method is applied to the PPI network from the Database of Interacting Proteins (DIP) and most of the predicted interactions are verified by another biological database, BioGRID. The predicted protein interactions are appended to the original protein network, which leads to clique extension and shows the significance of biological meaning.

scholarly journals Improving Identification of In-organello Protein-Protein Interactions Using an Affinity-enrichable, Isotopically Coded, and Mass Spectrometry-cleavable Chemical Crosslinker

2020 ◽  
Vol 19 (4) ◽  
pp. 624-639 ◽  
Author(s):  
Karl A. T. Makepeace ◽  
Yassene Mohammed ◽  
Elena L. Rudashevskaya ◽  
Evgeniy V. Petrotchenko ◽  
F.-Nora Vögtle ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Protein Interactions ◽  
Ex Vivo ◽  
Protein Complexes ◽  
Protein Protein Interactions ◽  
Mass Spectral ◽  
Transport Chain ◽  
Unknown Protein ◽  
Modified Peptides ◽  
Wide Scale

An experimental and computational approach for identification of protein-protein interactions by ex vivo chemical crosslinking and mass spectrometry (CLMS) has been developed that takes advantage of the specific characteristics of cyanurbiotindipropionylsuccinimide (CBDPS), an affinity-tagged isotopically coded mass spectrometry (MS)-cleavable crosslinking reagent. Utilizing this reagent in combination with a crosslinker-specific data-dependent acquisition strategy based on MS2 scans, and a software pipeline designed for integrating crosslinker-specific mass spectral information led to demonstrated improvements in the application of the CLMS technique, in terms of the detection, acquisition, and identification of crosslinker-modified peptides. This approach was evaluated on intact yeast mitochondria, and the results showed that hundreds of unique protein-protein interactions could be identified on an organelle proteome-wide scale. Both known and previously unknown protein-protein interactions were identified. These interactions were assessed based on their known sub-compartmental localizations. Additionally, the identified crosslinking distance constraints are in good agreement with existing structural models of protein complexes involved in the mitochondrial electron transport chain.

scholarly journals In Vivo Methods to Study Protein–Protein Interactions as Key Players in Mycobacterium Tuberculosis Virulence

Pathogens ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 173 ◽  
Author(s):  
Veyron-Churlet ◽  
Locht
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Protein Interactions ◽  
Protein Protein Interactions ◽  
Cellular Processes ◽  
Unknown Protein ◽  
Protein Functions ◽  
Transient Interactions ◽  
Protein Complementation

Studies on protein–protein interactions (PPI) can be helpful for the annotation of unknown protein functions and for the understanding of cellular processes, such as specific virulence mechanisms developed by bacterial pathogens. In that context, several methods have been extensively used in recent years for the characterization of Mycobacterium tuberculosis PPI to further decipher tuberculosis (TB) pathogenesis. This review aims at compiling the most striking results based on in vivo methods (yeast and bacterial two-hybrid systems, protein complementation assays) for the specific study of PPI in mycobacteria. Moreover, newly developed methods, such as in-cell native mass resonance and proximity-dependent biotinylation identification, will have a deep impact on future mycobacterial research, as they are able to perform dynamic (transient interactions) and integrative (multiprotein complexes) analyses.

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