Internetwork connectivity of molecular networks across species of life

AbstractMolecular interactions are studied as independent networks in systems biology. However, molecular networks do not exist independently of each other. In a network of networks approach (called multiplex), we study the joint organization of transcriptional regulatory network (TRN) and protein–protein interaction (PPI) network. We find that TRN and PPI are non-randomly coupled across five different eukaryotic species. Gene degrees in TRN (number of downstream genes) are positively correlated with protein degrees in PPI (number of interacting protein partners). Gene–gene and protein–protein interactions in TRN and PPI, respectively, also non-randomly overlap. These design principles are conserved across the five eukaryotic species. Robustness of the TRN–PPI multiplex is dependent on this coupling. Functionally important genes and proteins, such as essential, disease-related and those interacting with pathogen proteins, are preferentially situated in important parts of the human multiplex with highly overlapping interactions. We unveil the multiplex architecture of TRN and PPI. Multiplex architecture may thus define a general framework for studying molecular networks. This approach may uncover the building blocks of the hierarchical organization of molecular interactions.

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Internetwork connectivity of molecular networks across species of life

10.1101/2020.08.03.233304 ◽

2020 ◽

Author(s):

Tarun Mahajan ◽

Roy D Dar

Keyword(s):

Molecular Interactions ◽

Protein Interactions ◽

Transcriptional Regulatory Network ◽

Building Blocks ◽

Design Principles ◽

Hierarchical Organization ◽

Molecular Networks ◽

Interacting Protein ◽

Ppi Networks ◽

Eukaryotic Species

AbstractBackgroundMolecular interactions have been studied as independent complex networks in systems biology. However, molecular networks dont exist independently of each other. In a network of networks approach (called multiplex), we uncover the design principles for the joint organization of transcriptional regulatory network (TRN) and protein-protein interaction (PPI) network.ResultsWe find that TRN and PPI networks are non-randomly coupled in the TRN-PPI multiplex across five different eukaryotic species. Gene degrees in TRN (number of downstream genes) are positively correlated with protein degrees in PPI (number of interacting protein partners). Gene-gene interactions in TRN and protein-protein interactions in PPI also non-randomly overlap in the multiplex. These design principles are conserved across the five eukaryotic species. We show that the robustness of the TRN-PPI multiplex is dependent on these design principles. Further, functionally important genes and proteins, such as essential, disease-related and those involved in host-pathogen PPI networks, are preferentially situated in essential parts of the human multiplex with highly overlapping interactions.ConclusionWe unveil the multiplex architecture of TRN and PPI networks across different species. Multiplex architecture may thus define a general framework for studying molecular networks across the different species of life. This approach may uncover the building blocks of the hierarchical organization of molecular interactions.

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Sequence co-evolution gives 3D contacts and structures of protein complexes

10.1101/004762 ◽

2014 ◽

Cited By ~ 4

Author(s):

Thomas A. Hopf ◽

Charlotta P.I. Schärfe ◽

João P.G.L.M. Rodrigues ◽

Anna G. Green ◽

Chris Sander ◽

...

Keyword(s):

Protein Interactions ◽

Protein Complexes ◽

3D Structure ◽

Three Dimensional ◽

Dimensional Structure ◽

Evolutionary Sequence ◽

Protein Protein Interactions ◽

Interacting Protein ◽

Protein Protein Interaction ◽

Unknown Structure

Protein-protein interactions are fundamental to many biological processes. Experimental screens have identified tens of thousands of interactions and structural biology has provided detailed functional insight for select 3D protein complexes. An alternative rich source of information about protein interactions is the evolutionary sequence record. Building on earlier work, we show that analysis of correlated evolutionary sequence changes across proteins identifies residues that are close in space with sufficient accuracy to determine the three-dimensional structure of the protein complexes. We evaluate prediction performance in blinded tests on 76 complexes of known 3D structure, predict protein-protein contacts in 32 complexes of unknown structure, and demonstrate how evolutionary couplings can be used to distinguish between interacting and non-interacting protein pairs in a large complex. With the current growth of sequence databases, we expect that the method can be generalized to genome-wide elucidation of protein-protein interaction networks and used for interaction predictions at residue resolution.

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Sequence co-evolution gives 3D contacts and structures of protein complexes

eLife ◽

10.7554/elife.03430 ◽

2014 ◽

Vol 3 ◽

Cited By ~ 254

Author(s):

Thomas A Hopf ◽

Charlotta P I Schärfe ◽

João P G L M Rodrigues ◽

Anna G Green ◽

Oliver Kohlbacher ◽

...

Keyword(s):

Protein Interactions ◽

Protein Complexes ◽

3D Structure ◽

Three Dimensional ◽

Dimensional Structure ◽

Evolutionary Sequence ◽

Protein Protein Interactions ◽

Interacting Protein ◽

Protein Protein Interaction ◽

Unknown Structure

Protein–protein interactions are fundamental to many biological processes. Experimental screens have identified tens of thousands of interactions, and structural biology has provided detailed functional insight for select 3D protein complexes. An alternative rich source of information about protein interactions is the evolutionary sequence record. Building on earlier work, we show that analysis of correlated evolutionary sequence changes across proteins identifies residues that are close in space with sufficient accuracy to determine the three-dimensional structure of the protein complexes. We evaluate prediction performance in blinded tests on 76 complexes of known 3D structure, predict protein–protein contacts in 32 complexes of unknown structure, and demonstrate how evolutionary couplings can be used to distinguish between interacting and non-interacting protein pairs in a large complex. With the current growth of sequences, we expect that the method can be generalized to genome-wide elucidation of protein–protein interaction networks and used for interaction predictions at residue resolution.

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An efficient transient assays system using Agrobacterium-mediated transformation of onion (Allium cepa) epidermal cells

Indian Journal of Genetics and Plant Breeding (The) ◽

10.31742/ijgpb.80.3.17 ◽

2020 ◽

Vol 80 (03) ◽

Author(s):

Yu-Miao Zhang ◽

Jun Wang ◽

Tao Wu

Keyword(s):

Subcellular Localization ◽

Protein Interaction ◽

Protein Interactions ◽

Epidermal Cells ◽

Cyclin Dependent Kinase ◽

Protein Subcellular Localization ◽

Protein Protein Interactions ◽

Efficient System ◽

Protein Protein Interaction ◽

Onion Epidermal Cells

In this study, the Agrobacterium infection medium, infection duration, detergent, and cell density were optimized. The sorghum-based infection medium (SbIM), 10-20 min infection time, addition of 0.01% Silwet L-77, and Agrobacterium optical density at 600 nm (OD600), improved the competence of onion epidermal cells to support Agrobacterium infection at >90% efficiency. Cyclin-dependent kinase D-2 (CDKD-2) and cytochrome c-type biogenesis protein (CYCH), protein-protein interactions were localized. The optimized procedure is a quick and efficient system for examining protein subcellular localization and protein-protein interaction.

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Decoding Protein-protein Interactions: An Overview

Current Topics in Medicinal Chemistry ◽

10.2174/1568026620666200226105312 ◽

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.

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

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqab010 ◽

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.

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The Protein Interactome of Glycolysis in Escherichia coli

Proteomes ◽

10.3390/proteomes9020016 ◽

2021 ◽

Vol 9 (2) ◽

pp. 16

Author(s):

Shomeek Chowdhury ◽

Stephen Hepper ◽

Mudassir K. Lodi ◽

Milton H. Saier ◽

Peter Uetz

Keyword(s):

Escherichia Coli ◽

Protein Interactions ◽

Allosteric Regulation ◽

Glycolytic Enzymes ◽

Protein Protein Interactions ◽

Post Translational Modification ◽

Interacting Protein ◽

E Coli ◽

Protein Interactome ◽

The Impact

Glycolysis is regulated by numerous mechanisms including allosteric regulation, post-translational modification or protein-protein interactions (PPI). While glycolytic enzymes have been found to interact with hundreds of proteins, the impact of only some of these PPIs on glycolysis is well understood. Here we investigate which of these interactions may affect glycolysis in E. coli and possibly across numerous other bacteria, based on the stoichiometry of interacting protein pairs (from proteomic studies) and their conservation across bacteria. We present a list of 339 protein-protein interactions involving glycolytic enzymes but predict that ~70% of glycolytic interactors are not present in adequate amounts to have a significant impact on glycolysis. Finally, we identify a conserved but uncharacterized subset of interactions that are likely to affect glycolysis and deserve further study.

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Universal Screening Methods and Applications of ThermoFluor®

CrossRef Listing of Deleted DOIs ◽

10.1177/1087057106292746 ◽

2006 ◽

Vol 11 (7) ◽

pp. 854-863 ◽

Cited By ~ 124

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.

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Trifunctional cross-linker for mapping protein-protein interaction networks and comparing protein conformational states

eLife ◽

10.7554/elife.12509 ◽

2016 ◽

Vol 5 ◽

Cited By ~ 56

Author(s):

Dan Tan ◽

Qiang Li ◽

Mei-Jun Zhang ◽

Chao Liu ◽

Chengying Ma ◽

...

Keyword(s):

Protein Interactions ◽

Structural Information ◽

Affinity Purification ◽

Protein Protein Interactions ◽

C Elegans ◽

Protein Protein Interaction ◽

Lysine Residues ◽

Spacer Arm ◽

Cross Linker ◽

Protein Nucleic Acid

To improve chemical cross-linking of proteins coupled with mass spectrometry (CXMS), we developed a lysine-targeted enrichable cross-linker containing a biotin tag for affinity purification, a chemical cleavage site to separate cross-linked peptides away from biotin after enrichment, and a spacer arm that can be labeled with stable isotopes for quantitation. By locating the flexible proteins on the surface of 70S ribosome, we show that this trifunctional cross-linker is effective at attaining structural information not easily attainable by crystallography and electron microscopy. From a crude Rrp46 immunoprecipitate, it helped identify two direct binding partners of Rrp46 and 15 protein-protein interactions (PPIs) among the co-immunoprecipitated exosome subunits. Applying it to E. coli and C. elegans lysates, we identified 3130 and 893 inter-linked lysine pairs, representing 677 and 121 PPIs. Using a quantitative CXMS workflow we demonstrate that it can reveal changes in the reactivity of lysine residues due to protein-nucleic acid interaction.

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Structure-aware protein–protein interaction site prediction using deep graph convolutional network

Bioinformatics ◽

10.1093/bioinformatics/btab643 ◽

2021 ◽

Author(s):

Qianmu Yuan ◽

Jianwen Chen ◽

Huiying Zhao ◽

Yaoqi Zhou ◽

Yuedong Yang

Keyword(s):

Protein Interactions ◽

Spatial Information ◽

Screening Tools ◽

Supplementary Information ◽

Protein Protein Interactions ◽

Convolutional Network ◽

Source Codes ◽

Site Prediction ◽

Protein Protein Interaction ◽

Mapping Techniques

Abstract Motivation Protein–protein interactions (PPI) play crucial roles in many biological processes, and identifying PPI sites is an important step for mechanistic understanding of diseases and design of novel drugs. Since experimental approaches for PPI site identification are expensive and time-consuming, many computational methods have been developed as screening tools. However, these methods are mostly based on neighbored features in sequence, and thus limited to capture spatial information. Results We propose a deep graph-based framework deep Graph convolutional network for Protein–Protein-Interacting Site prediction (GraphPPIS) for PPI site prediction, where the PPI site prediction problem was converted into a graph node classification task and solved by deep learning using the initial residual and identity mapping techniques. We showed that a deeper architecture (up to eight layers) allows significant performance improvement over other sequence-based and structure-based methods by more than 12.5% and 10.5% on AUPRC and MCC, respectively. Further analyses indicated that the predicted interacting sites by GraphPPIS are more spatially clustered and closer to the native ones even when false-positive predictions are made. The results highlight the importance of capturing spatially neighboring residues for interacting site prediction. Availability and implementation The datasets, the pre-computed features, and the source codes along with the pre-trained models of GraphPPIS are available at https://github.com/biomed-AI/GraphPPIS. The GraphPPIS web server is freely available at https://biomed.nscc-gz.cn/apps/GraphPPIS. Supplementary information Supplementary data are available at Bioinformatics online.

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