scholarly journals Expanding Interactome Analyses beyond Model Eukaryotes

2021 ◽  
Author(s):  
Katherine James ◽  
Anil Wipat ◽  
Simon Cockell
Keyword(s):  
Genome Sequence ◽  
Protein Interactions ◽  
Model Organisms ◽  
Protein Protein Interactions ◽  
Data Types ◽  
Interaction Prediction ◽  
Diverse Species ◽  
Eukaryotic Species

Interactome analyses have traditionally been applied to yeast, human and other model organisms due to the availability of protein-protein interactions data for these species. Recently these techniques have been applied to more diverse species using computational interaction prediction from genome sequence and other data types. This review describes the various types of computational interactome networks that can be created and how they have been used in diverse eukaryotic species, highlighting some of the key interactome studies in non-model organisms.

scholarly journals An Improved Phenotype-Driven Tool for Rare Mendelian Variant Prioritization: Benchmarking Exomiser on Real Patient Whole-Exome Data

Genes ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 460
Author(s):  
Valentina Cipriani ◽  
Nikolas Pontikos ◽  
Gavin Arno ◽  
Panagiotis I. Sergouniotis ◽  
Eva Lenassi ◽  
...  
Keyword(s):  
Molecular Diagnosis ◽  
Protein Interactions ◽  
Model Organisms ◽  
Protein Protein Interactions ◽  
Sequencing Data ◽  
Variant Prioritization ◽  
Real Patient ◽  
Human Phenotype ◽  
Support Tool ◽  
Variant Frequency

Next-generation sequencing has revolutionized rare disease diagnostics, but many patients remain without a molecular diagnosis, particularly because many candidate variants usually survive despite strict filtering. Exomiser was launched in 2014 as a Java tool that performs an integrative analysis of patients’ sequencing data and their phenotypes encoded with Human Phenotype Ontology (HPO) terms. It prioritizes variants by leveraging information on variant frequency, predicted pathogenicity, and gene-phenotype associations derived from human diseases, model organisms, and protein–protein interactions. Early published releases of Exomiser were able to prioritize disease-causative variants as top candidates in up to 97% of simulated whole-exomes. The size of the tested real patient datasets published so far are very limited. Here, we present the latest Exomiser version 12.0.1 with many new features. We assessed the performance using a set of 134 whole-exomes from patients with a range of rare retinal diseases and known molecular diagnosis. Using default settings, Exomiser ranked the correct diagnosed variants as the top candidate in 74% of the dataset and top 5 in 94%; not using the patients’ HPO profiles (i.e., variant-only analysis) decreased the performance to 3% and 27%, respectively. In conclusion, Exomiser is an effective support tool for rare Mendelian phenotype-driven variant prioritization.

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

2020 ◽  
Vol 117 (21) ◽  
pp. 11836-11842 ◽  
Author(s):  
Shayne D. Wierbowski ◽  
Tommy V. Vo ◽  
Pascal Falter-Braun ◽  
Timothy O. Jobe ◽  
Lars H. Kruse ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Massively Parallel ◽  
Model Organisms ◽  
Protein Protein Interactions ◽  
Numerous Model ◽  
High Quality Protein ◽  
Protein Interactome ◽  
Wide Range ◽  
Dna Elements ◽  
General Tool

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.

scholarly journals Parasites, proteomes and systems: has Descartes’ clock run out of time?

Parasitology ◽  
2012 ◽  
Vol 139 (9) ◽  
pp. 1103-1118 ◽  
Author(s):  
J. M. WASTLING ◽  
S. D. ARMSTRONG ◽  
R. KRISHNA ◽  
D. XIA
Keyword(s):  
Systems Biology ◽  
Protein Interactions ◽  
Biological Data ◽  
Protein Protein Interactions ◽  
Proteomics Data ◽  
Data Types ◽  
Quality Of Data ◽  
Post Translational Modifications ◽  
Systems Modelling ◽  
New Generation

SUMMARYSystems biology aims to integrate multiple biological data types such as genomics, transcriptomics and proteomics across different levels of structure and scale; it represents an emerging paradigm in the scientific process which challenges the reductionism that has dominated biomedical research for hundreds of years. Systems biology will nevertheless only be successful if the technologies on which it is based are able to deliver the required type and quality of data. In this review we discuss how well positioned is proteomics to deliver the data necessary to support meaningful systems modelling in parasite biology. We summarise the current state of identification proteomics in parasites, but argue that a new generation of quantitative proteomics data is now needed to underpin effective systems modelling. We discuss the challenges faced to acquire more complete knowledge of protein post-translational modifications, protein turnover and protein-protein interactions in parasites. Finally we highlight the central role of proteome-informatics in ensuring that proteomics data is readily accessible to the user-community and can be translated and integrated with other relevant data types.

scholarly journals Conservation and Variability of Synaptonemal Complex Proteins in Phylogenesis of Eukaryotes

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Tatiana M. Grishaeva ◽  
Yuri F. Bogdanov
Keyword(s):  
Synaptonemal Complex ◽  
Protein Interactions ◽  
Phylogenetic Trees ◽  
Flowering Plants ◽  
Model Organisms ◽  
Protein Protein Interactions ◽  
Lateral Element ◽  
Origin And Evolution ◽  
Eukaryotic Proteomes ◽  
Related Proteins

The problems of the origin and evolution of meiosis include the enigmatic variability of the synaptonemal complexes (SCs) which, being morphology similar, consist of different proteins in different eukaryotic phyla. Using bioinformatics methods, we monitored all available eukaryotic proteomes to find proteins similar to known SC proteins of model organisms. We found proteins similar to SC lateral element (LE) proteins and possessing the HORMA domain in the majority of the eukaryotic taxa and assume them the most ancient among all SC proteins. Vertebrate LE proteins SYCP2, SYCP3, and SC65 proved to have related proteins in many invertebrate taxa. Proteins of SC central space are most evolutionarily variable. It means that different protein-protein interactions can exist to connect LEs. Proteins similar to the known SC proteins were not found in Euglenophyta, Chrysophyta, Charophyta, Xanthophyta, Dinoflagellata, and primitive Coelomata. We conclude that different proteins whose common feature is the presence of domains with a certain conformation are involved in the formation of the SC in different eukaryotic phyla. This permits a targeted search for orthologs of the SC proteins using phylogenetic trees. Here we consider example of phylogenetic trees for protozoans, fungi, algae, mosses, and flowering plants.

scholarly journals NGPINT: A Next-generation protein-protein interaction software

2020 ◽  
Author(s):  
Sagnik Banerjee ◽  
Valeria Velásquez-Zapata ◽  
Gregory Fuerst ◽  
J. Mitch Elmore ◽  
Roger P. Wise
Keyword(s):  
Protein Interactions ◽  
Model Organisms ◽  
Cdna Libraries ◽  
Published Data ◽  
Data Sets ◽  
Next Generation ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Alternative Approach ◽  
Simulated Test

ABSTRACTMapping protein-protein interactions at a proteome scale is critical to understanding how cellular signaling networks respond to stimuli. Since eukaryotic genomes encode thousands of proteins, testing their interactions one-by-one is a challenging prospect. High-throughput yeast-two hybrid (Y2H) assays that employ next-generation sequencing to interrogate cDNA libraries represent an alternative approach that optimizes scale, cost, and effort. We present NGPINT, a robust and scalable software to identify all putative interactors of a protein using Y2H in batch culture. NGPINT combines diverse tools to align sequence reads to target genomes, reconstruct prey fragments and compute gene enrichment under reporter selection. Central to this pipeline is the identification of fusion reads containing sequences derived from both the Y2H expression plasmid and the cDNA of interest. To reduce false positives, these fusion reads are evaluated as to whether the cDNA fragment forms an in-frame translational fusion with the Y2H transcription factor. NGPINT successfully recognized 95% of interactions in simulated test runs. As proof of concept, NGPINT was tested using published data sets and recognized all validated interactions. NGPINT can be used in any organism with an available reference, thus facilitating the discovery of protein-protein interactions in non-model organisms.

scholarly journals Circadian Interactomics: How Research Into Protein-Protein Interactions Beyond the Core Clock Has Influenced the Model of Circadian Timekeeping

2021 ◽  
pp. 074873042110146
Author(s):  
Alexander E. Mosier ◽  
Jennifer M. Hurley
Keyword(s):  
Circadian Clock ◽  
Protein Interactions ◽  
Large Scale ◽  
Protein Complexes ◽  
Model Organisms ◽  
Protein Protein Interactions ◽  
Macromolecular Complexes ◽  
The Core ◽  
Circadian Control ◽  
Macromolecular Protein

The circadian clock is the broadly conserved, protein-based, timekeeping mechanism that synchronizes biology to the Earth’s 24-h light-dark cycle. Studies of the mechanisms of circadian timekeeping have placed great focus on the role that individual protein-protein interactions play in the creation of the timekeeping loop. However, research has shown that clock proteins most commonly act as part of large macromolecular protein complexes to facilitate circadian control over physiology. The formation of these complexes has led to the large-scale study of the proteins that comprise these complexes, termed here “circadian interactomics.” Circadian interactomic studies of the macromolecular protein complexes that comprise the circadian clock have uncovered many basic principles of circadian timekeeping as well as mechanisms of circadian control over cellular physiology. In this review, we examine the wealth of knowledge accumulated using circadian interactomics approaches to investigate the macromolecular complexes of the core circadian clock, including insights into the core mechanisms that impart circadian timing and the clock’s regulation of many physiological processes. We examine data acquired from the investigation of the macromolecular complexes centered on both the activating and repressing arm of the circadian clock and from many circadian model organisms.

scholarly journals pathDIP 4: an extended pathway annotations and enrichment analysis resource for human, model organisms and domesticated species

2019 ◽  
Author(s):  
Sara Rahmati ◽  
Mark Abovsky ◽  
Chiara Pastrello ◽  
Max Kotlyar ◽  
Richard Lu ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Enrichment Analysis ◽  
Human Model ◽  
Model Organisms ◽  
Pathway Enrichment Analysis ◽  
Protein Protein Interactions ◽  
Proteome Coverage ◽  
Pathway Annotation ◽  
Human Proteins ◽  
Integrated Pathway

Abstract PathDIP was introduced to increase proteome coverage of literature-curated human pathway databases. PathDIP 4 now integrates 24 major databases. To further reduce the number of proteins with no curated pathway annotation, pathDIP integrates pathways with physical protein–protein interactions (PPIs) to predict significant physical associations between proteins and curated pathways. For human, it provides pathway annotations for 5366 pathway orphans. Integrated pathway annotation now includes six model organisms and ten domesticated animals. A total of 6401 core and ortholog pathways have been curated from the literature or by annotating orthologs of human proteins in the literature-curated pathways. Extended pathways are the result of combining these pathways with protein-pathway associations that are predicted using organism-specific PPIs. Extended pathways expand proteome coverage from 81 088 to 120 621 proteins, making pathDIP 4 the largest publicly available pathway database for these organisms and providing a necessary platform for comprehensive pathway-enrichment analysis. PathDIP 4 users can customize their search and analysis by selecting organism, identifier and subset of pathways. Enrichment results and detailed annotations for input list can be obtained in different formats and views. To support automated bioinformatics workflows, Java, R and Python APIs are available for batch pathway annotation and enrichment analysis. PathDIP 4 is publicly available at http://ophid.utoronto.ca/pathDIP.

scholarly journals Internetwork connectivity of molecular networks across species of life

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tarun Mahajan ◽  
Roy D. Dar
Keyword(s):  
Molecular Interactions ◽  
Protein Interactions ◽  
Transcriptional Regulatory Network ◽  
Building Blocks ◽  
Hierarchical Organization ◽  
Molecular Networks ◽  
Protein Protein Interactions ◽  
Interacting Protein ◽  
Protein Protein Interaction ◽  
Eukaryotic Species

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.

scholarly journals RAPPPID: Towards Generalisable Protein Interaction Prediction with AWD-LSTM Twin Networks

2021 ◽  
Author(s):  
Joseph Szymborski ◽  
Amin Emad
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Information Leakage ◽  
Supplementary Information ◽  
Protein Protein Interactions ◽  
Training Time ◽  
Interaction Prediction ◽  
Protein Protein Interaction ◽  
Protein Interaction Prediction ◽  
Time To Learn

Motivation: Computational methods for the prediction of protein-protein interactions, while important tools for researchers, are plagued by challenges in generalising to unseen proteins. Datasets used for modelling protein-protein predictions are particularly predisposed to information leakage and sampling biases. Results: In this study, we introduce RAPPPID, a method for the Regularised Automatic Prediction of Protein-Protein Interactions using Deep Learning. RAPPPID is a twin AWD-LSTM network which employs multiple regularisation methods during training time to learn generalised weights. Testing on stringent interaction datasets composed of proteins not seen during training, RAPPPID outperforms state-of-the-art methods. Further experiments show that RAPPPID's performance holds regardless of the particular proteins in the testing set and its performance is higher for biologically supported edges. This study serves to demonstrate that appropriate regularisation is an important component of overcoming the challenges of creating models for protein-protein interaction prediction that generalise to unseen proteins. Availability and Implementation: Code and datasets are freely available at https://github.com/jszym/rapppid. Contact: [email protected] Supplementary Information: Online-only supplementary data is available at the journal's website.

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