A massively parallel barcoded sequencing pipeline enables generation of the first ORFeome and interactome map for rice

Systematic mappings of protein interactome networks have provided invaluable functional information for numerous model organisms. Here we developPCR-mediatedLinkage of barcodedAdaptersTo nucleic acidElements forsequencing (PLATE-seq) that serves as a general tool to rapidly sequence thousands of DNA elements. We validate its utility by generating the ORFeome forOryza sativacovering 2,300 genes and constructing a high-quality protein–protein interactome map consisting of 322 interactions between 289 proteins, expanding the known interactions in rice by roughly 50%. Our work paves the way for high-throughput profiling of protein–protein interactions in a wide range of organisms.

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3D Genome Contributes to Protein-Protein Interactome

10.1101/2020.11.30.404517 ◽

2020 ◽

Author(s):

Yi Shi ◽

Song Cao ◽

Mingxuan Zhang ◽

Xianbin Su ◽

Zehua Guo ◽

...

Keyword(s):

Protein Interactions ◽

Interacting Proteins ◽

Protein Protein Interactions ◽

3D Genome ◽

Protein Interactome ◽

Dna Elements ◽

Genome Space ◽

Ppi Prediction ◽

Cellular Compartmentalization ◽

Genome Information

AbstractNumerous computational methods have been proposed to predict protein-protein interactions, none of which however, considers the original DNA loci of the interacting proteins in the perspective of 3D genome. Here we retrospect the DNA origins of the interacting proteins in the context of 3D genome and discovered that 1) if a gene pair is more proximate in 3D genome, their corresponding proteins are more likely to interact. 2) signal peptide involvement of PPI affects the corresponding gene-gene proximity in 3D genome space. 3) by incorporating 3D genome information, existing PPI prediction methods can be further improved in terms of accuracy. Combining our previous discoveries, we conjecture the existence of 3D genome driven cellular compartmentalization, meaning the co-localization of DNA elements lead to increased probability of the co-localization of RNA elements and protein elements.

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Multi-layered networks of SalmoNet2 enable strain comparisons of the Salmonella genus on a molecular level

10.1101/2021.12.20.473597 ◽

2021 ◽

Author(s):

Marton Laszlo Olbei ◽

Balazs Bohar ◽

David Fazekas ◽

Matthew Madgwick ◽

Padhmanand Sudhakar ◽

...

Keyword(s):

Host Range ◽

Protein Interactions ◽

Multidrug Resistant ◽

System Level ◽

Protein Protein Interactions ◽

High Quality Protein ◽

Transcriptional Regulatory ◽

Wide Range ◽

Computational Resources ◽

Systemic Infections

Serovars of the genus Salmonella primarily evolved as gastrointestinal pathogens in a wide range of hosts. Some serotypes later evolved further, adopting a more invasive lifestyle in a narrower host range associated with systemic infections. A system-level knowledge of these pathogens has the potential to identify the complex adaptations associated with the evolution of serovars with distinct pathogenicity, host range and risk to human health. This promises to aid the design of interventions and serve as a knowledge base in the Salmonella research community. Here we present SalmoNet2, a major update to SalmoNet, the first multi-layered interaction resource for Salmonella strains, containing protein-protein, transcriptional regulatory and enzyme-enzyme interactions. The new version extends the number of Salmonella genomes from 11 to 20, including strains such as Salmonella Typhimurium D23580, an epidemic multidrug-resistant strain leading to invasive non-typhoidal Salmonella Disease (iNTS), and a strain from Salmonella bongori, another species in the Salmonella genus. The database now uses strain specific metabolic models instead of a generalised model to highlight differences between strains. This has increased the coverage of high-quality protein-protein interactions, and enhances interoperability with other computational resources by adopting standardised formats. The resource website has been updated with tutorials to help researchers analyse their Salmonella data using molecular interaction networks from SalmoNet2. SalmoNet2 is accessible at http://salmonet.org/.</SPAN>

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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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Ways into Understanding HIF Inhibition

Cancers ◽

10.3390/cancers13010159 ◽

2021 ◽

Vol 13 (1) ◽

pp. 159

Author(s):

Tina Schönberger ◽

Joachim Fandrey ◽

Katrin Prost-Fingerle

Keyword(s):

Protein Interactions ◽

Target Genes ◽

Protein Protein Interactions ◽

Low Oxygen ◽

Drug Candidates ◽

Open Questions ◽

Wide Range ◽

Hif Pathway

Hypoxia is a key characteristic of tumor tissue. Cancer cells adapt to low oxygen by activating hypoxia-inducible factors (HIFs), ensuring their survival and continued growth despite this hostile environment. Therefore, the inhibition of HIFs and their target genes is a promising and emerging field of cancer research. Several drug candidates target protein–protein interactions or transcription mechanisms of the HIF pathway in order to interfere with activation of this pathway, which is deregulated in a wide range of solid and liquid cancers. Although some inhibitors are already in clinical trials, open questions remain with respect to their modes of action. New imaging technologies using luminescent and fluorescent methods or nanobodies to complement widely used approaches such as chromatin immunoprecipitation may help to answer some of these questions. In this review, we aim to summarize current inhibitor classes targeting the HIF pathway and to provide an overview of in vitro and in vivo techniques that could improve the understanding of inhibitor mechanisms. Unravelling the distinct principles regarding how inhibitors work is an indispensable step for efficient clinical applications and safety of anticancer compounds.

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Elucidating Protein-protein Interactions Through Computational Approaches and Designing Small Molecule Inhibitors Against them for Various Diseases

Current Topics in Medicinal Chemistry ◽

10.2174/1568026618666181025114903 ◽

2018 ◽

Vol 18 (20) ◽

pp. 1719-1736 ◽

Cited By ~ 3

Author(s):

Sharanya Sarkar ◽

Khushboo Gulati ◽

Manikyaprabhu Kairamkonda ◽

Amit Mishra ◽

Krishna Mohan Poluri

Keyword(s):

Small Molecule ◽

Protein Interactions ◽

Small Molecule Inhibitors ◽

Computational Techniques ◽

Protein Protein Interactions ◽

Computational Tools ◽

Computational Approaches ◽

Protein Protein Interaction ◽

Wide Range ◽

Therapeutic Measures

Background: To carry out wide range of cellular functionalities, proteins often associate with one or more proteins in a phenomenon known as Protein-Protein Interaction (PPI). Experimental and computational approaches were applied on PPIs in order to determine the interacting partners, and also to understand how an abnormality in such interactions can become the principle cause of a disease. Objective: This review aims to elucidate the case studies where PPIs involved in various human diseases have been proven or validated with computational techniques, and also to elucidate how small molecule inhibitors of PPIs have been designed computationally to act as effective therapeutic measures against certain diseases. Results: Computational techniques to predict PPIs are emerging rapidly in the modern day. They not only help in predicting new PPIs, but also generate outputs that substantiate the experimentally determined results. Moreover, computation has aided in the designing of novel inhibitor molecules disrupting the PPIs. Some of them are already being tested in the clinical trials. Conclusion: This review delineated the classification of computational tools that are essential to investigate PPIs. Furthermore, the review shed light on how indispensable computational tools have become in the field of medicine to analyze the interaction networks and to design novel inhibitors efficiently against dreadful diseases in a shorter time span.

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Metallocluster transactions: dynamic protein interactions guide the biosynthesis of Fe–S clusters in bacteria

Biochemical Society Transactions ◽

10.1042/bst20180365 ◽

2018 ◽

Vol 46 (6) ◽

pp. 1593-1603 ◽

Cited By ~ 9

Author(s):

Chenkang Zheng ◽

Patricia C. Dos Santos

Keyword(s):

Protein Interactions ◽

Protein Complexes ◽

Specific Protein ◽

Biological Synthesis ◽

Cluster Assembly ◽

Sequence Motifs ◽

Protein Protein Interactions ◽

Cysteine Desulfurase ◽

Wide Range ◽

Domains Of Life

Iron–sulfur (Fe–S) clusters are ubiquitous cofactors present in all domains of life. The chemistries catalyzed by these inorganic cofactors are diverse and their associated enzymes are involved in many cellular processes. Despite the wide range of structures reported for Fe–S clusters inserted into proteins, the biological synthesis of all Fe–S clusters starts with the assembly of simple units of 2Fe–2S and 4Fe–4S clusters. Several systems have been associated with the formation of Fe–S clusters in bacteria with varying phylogenetic origins and number of biosynthetic and regulatory components. All systems, however, construct Fe–S clusters through a similar biosynthetic scheme involving three main steps: (1) sulfur activation by a cysteine desulfurase, (2) cluster assembly by a scaffold protein, and (3) guided delivery of Fe–S units to either final acceptors or biosynthetic enzymes involved in the formation of complex metalloclusters. Another unifying feature on the biological formation of Fe–S clusters in bacteria is that these systems are tightly regulated by a network of protein interactions. Thus, the formation of transient protein complexes among biosynthetic components allows for the direct transfer of reactive sulfur and Fe–S intermediates preventing oxygen damage and reactions with non-physiological targets. Recent studies revealed the importance of reciprocal signature sequence motifs that enable specific protein–protein interactions and consequently guide the transactions between physiological donors and acceptors. Such findings provide insights into strategies used by bacteria to regulate the flow of reactive intermediates and provide protein barcodes to uncover yet-unidentified cellular components involved in Fe–S metabolism.

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Practical Model Selection for Prospective Virtual Screening

10.1101/337956 ◽

2018 ◽

Cited By ~ 1

Author(s):

Shengchao Liu ◽

Moayad Alnammi ◽

Spencer S. Ericksen ◽

Andrew F. Voter ◽

Gene E. Ananiev ◽

...

Keyword(s):

Random Forest ◽

Virtual Screening ◽

Protein Interactions ◽

High Throughput Screening ◽

Screening Methods ◽

Protein Protein Interactions ◽

Screening Algorithm ◽

Screening Performance ◽

Wide Range ◽

Public Datasets

AbstractVirtual (computational) high-throughput screening provides a strategy for prioritizing compounds for experimental screens, but the choice of virtual screening algorithm depends on the dataset and evaluation strategy. We consider a wide range of ligand-based machine learning and docking-based approaches for virtual screening on two protein-protein interactions, PriA-SSB and RMI-FANCM, and present a strategy for choosing which algorithm is best for prospective compound prioritization. Our workflow identifies a random forest as the best algorithm for these targets over more sophisticated neural network-based models. The top 250 predictions from our selected random forest recover 37 of the 54 active compounds from a library of 22,434 new molecules assayed on PriA-SSB. We show that virtual screening methods that perform well in public datasets and synthetic benchmarks, like multi-task neural networks, may not always translate to prospective screening performance on a specific assay of interest.

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Exploring the Human-Nipah Virus Protein-Protein Interactome

Journal of Virology ◽

10.1128/jvi.01461-17 ◽

2017 ◽

Vol 91 (23) ◽

Cited By ~ 22

Author(s):

Luis Martinez-Gil ◽

Natalia M. Vera-Velasco ◽

Ismael Mingarro

Keyword(s):

Protein Interactions ◽

Affinity Purification ◽

Nipah Virus ◽

Productive Infection ◽

Virus Protein ◽

Protein Protein Interactions ◽

Data Set ◽

Wide Range ◽

Host Virus Interactions ◽

Virus Interactions

ABSTRACT Nipah virus is an emerging, highly pathogenic, zoonotic virus of the Paramyxoviridae family. Human transmission occurs by close contact with infected animals, the consumption of contaminated food, or, occasionally, via other infected individuals. Currently, we lack therapeutic or prophylactic treatments for Nipah virus. To develop these agents we must now improve our understanding of the host-virus interactions that underpin a productive infection. This aim led us to perform the present work, in which we identified 101 human-Nipah virus protein-protein interactions (PPIs), most of which (88) are novel. This data set provides a comprehensive view of the host complexes that are manipulated by viral proteins. Host targets include the PRP19 complex and the microRNA (miRNA) processing machinery. Furthermore, we explored the biologic consequences of the interaction with the PRP19 complex and found that the Nipah virus W protein is capable of altering p53 control and gene expression. We anticipate that these data will help in guiding the development of novel interventional strategies to counter this emerging viral threat. IMPORTANCE Nipah virus is a recently discovered virus that infects a wide range of mammals, including humans. Since its discovery there have been yearly outbreaks, and in some of them the mortality rate has reached 100% of the confirmed cases. However, the study of Nipah virus has been largely neglected, and currently we lack treatments for this infection. To develop these agents we must now improve our understanding of the host-virus interactions that underpin a productive infection. In the present work, we identified 101 human-Nipah virus protein-protein interactions using an affinity purification approach coupled with mass spectrometry. Additionally, we explored the cellular consequences of some of these interactions. Globally, this data set offers a comprehensive and detailed view of the host machinery's contribution to the Nipah virus's life cycle. Furthermore, our data present a large number of putative drug targets that could be exploited for the treatment of this infection.

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

Computational Knowledge Discovery for Bioinformatics Research ◽

10.4018/978-1-4666-1785-8.ch013 ◽

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.

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Chasing coevolutionary signals in intrinsically disordered proteins complexes

Scientific Reports ◽

10.1038/s41598-020-74791-6 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Javier A. Iserte ◽

Tamas Lazar ◽

Silvio C. E. Tosatto ◽

Peter Tompa ◽

Cristina Marino-Buslje

Keyword(s):

Protein Interactions ◽

Intrinsically Disordered Proteins ◽

Disordered Proteins ◽

Protein Protein Interactions ◽

Contact Prediction ◽

Intrinsically Disordered ◽

Regulatory Processes ◽

Wide Range ◽

Predict Protein Structure ◽

Biological Interpretation

Abstract Intrinsically disordered proteins/regions (IDPs/IDRs) are crucial components of the cell, they are highly abundant and participate ubiquitously in a wide range of biological functions, such as regulatory processes and cell signaling. Many of their important functions rely on protein interactions, by which they trigger or modulate different pathways. Sequence covariation, a powerful tool for protein contact prediction, has been applied successfully to predict protein structure and to identify protein–protein interactions mostly of globular proteins. IDPs/IDRs also mediate a plethora of protein–protein interactions, highlighting the importance of addressing sequence covariation-based inter-protein contact prediction of this class of proteins. Despite their importance, a systematic approach to analyze the covariation phenomena of intrinsically disordered proteins and their complexes is still missing. Here we carry out a comprehensive critical assessment of coevolution-based contact prediction in IDP/IDR complexes and detail the challenges and possible limitations that emerge from their analysis. We found that the coevolutionary signal is faint in most of the complexes of disordered proteins but positively correlates with the interface size and binding affinity between partners. In addition, we discuss the state-of-art methodology by biological interpretation of the results, formulate evaluation guidelines and suggest future directions of development to the field.

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