Yeast Protein-protein Interaction Research Articles

Elucidating the metabolic roles of isoflavone synthase-mediated protein-protein interactions in yeast.

Transient plant enzyme complexes formed via protein-protein interactions (PPIs) play crucial regulatory roles in secondary metabolism. Complexes assembled on cytochrome P450s (CYPs) are challenging to characterize metabolically due to difficulties in decoupling the PPIs' metabolic impacts from the CYPs' catalytic activities. Here, we developed a yeast-based synthetic biology approach to elucidate the metabolic roles of PPIs between a soybean-derived CYP, isoflavone synthase (GmIFS2), and other enzymes in isoflavonoid metabolism. By reconstructing multiple complex variants with an inactive GmIFS2 in yeast, we found that GmIFS2-mediated PPIs can regulate metabolic flux between two competing pathways producing deoxyisoflavonoids and isoflavonoids. Specifically, GmIFS2 can recruit chalcone synthase (GmCHS7) and chalcone reductase (GmCHR5) to enhance deoxyisoflavonoid production or GmCHS7 and chalcone isomerase (GmCHI1B1) to enhance isoflavonoid production. Additionally, we identified and characterized two novel isoflavone O -methyltransferases interacting with GmIFS2. This study highlights the potential of yeast synthetic biology for characterizing CYP-mediated complexes.

A novel global clustering coefficient-dependent degree centrality (GCCDC) metric for large network analysis using real-world datasets

Nowadays, it is imperative to identify the combination of profitable products (nodes) within large product networks. Data exploration for such broad-spectrum product networks require sophisticated techniques for their analysis and meaningful inferences. The most commonly used techniques are the centrality metrics due to their efficiency in computation. Centrality metrics include Degree Centrality (DC), Closeness Centrality (CC), Betweenness Centrality (BC), Eigenvector Centrality (EVC), Katz Centrality (KC), and the local clustering coefficient-dependent degree centrality (LCCDC or LD). In this research, a novel approach the global clustering coefficient-dependent degree centrality (GCCDC or GD) method has been formulated for the analysis of links of the profitable products (nodes) in a large product network. GCCDC or GD is formulated by using the global clustering coefficient method which is efficient and accurate for large product networks such as product Amazon network. Furthermore, three correlation coefficients that are Pearson’s, Spearman’s, and Kendall’s have been used for evaluation. The results have shown that GD is preferable over LD to avoid uncertainties in computation of results for real-world datasets. To prove the scalability of the novel method, a dataset from different domain (biological yeast protein-protein interaction (PPI) dataset) was also analyzed using similar metrics and shown improved results.

Cost-based Feature Selection for Network Model Choice

Selecting a small set of informative features from a large number of possibly noisy candidates is a challenging problem with many applications in machine learning and approximate Bayesian computation. In practice, the cost of computing informative features also needs to be considered. This is particularly important for networks because the computational costs of individual features can span several orders of magnitude. We addressed this issue for the network model selection problem using two approaches. First, we adapted nine feature selection methods to account for the cost of features. We show for two classes of network models that the cost can be reduced by two orders of magnitude without considerably affecting classification accuracy (proportion of correctly identified models). Second, we selected features using pilot simulations with smaller networks. This approach reduced the computational cost by a factor of 50 without affecting classification accuracy. To demonstrate the utility of our approach, we applied it to three different yeast protein interaction networks and identified the best-fitting duplication divergence model. Supplementary materials, including computer code to reproduce our results, are available online.

Complex Prediction in Large PPI Networks Using Expansion and Stripe of Core Cliques.

The widespread availability and importance of large-scale protein-protein interaction (PPI) data demand a flurry of research efforts to understand the organisation of a cell and its functionality by analysing these data at the network level. In the bioinformatics and data mining fields, network clustering acquired a lot of attraction to examine a PPI network's topological and functional aspects. The clustering of PPI networks has been proven to be an excellent method for discovering functional modules, disclosing functions of unknown proteins, and other tasks in numerous research over the last decade. This research proposes a unique graph mining approach to detect protein complexes using dense neighbourhoods (highly connected regions) in an interaction graph. Our technique first finds size-3 cliques associated with each edge (protein interaction), and then these core cliques are expanded to form high-density subgraphs. Loosely connected proteins are stripped out from these subgraphs to produce a potential protein complex. Finally, the redundancy is removed based on the Jaccard coefficient. Computational results are presented on the yeast and human protein interaction dataset to highlight our proposed technique's efficiency. Predicted protein complexes of the proposed approach have a significantly higher score of similarity to those used as gold standards in the CYC-2008 and CORUM benchmark databases than other existing approaches.

Inference of pan-cancer related genes by orthologs matching based on enhanced LSTM model.

Many disease-related genes have been found to be associated with cancer diagnosis, which is useful for understanding the pathophysiology of cancer, generating targeted drugs, and developing new diagnostic and treatment techniques. With the development of the pan-cancer project and the ongoing expansion of sequencing technology, many scientists are focusing on mining common genes from The Cancer Genome Atlas (TCGA) across various cancer types. In this study, we attempted to infer pan-cancer associated genes by examining the microbial model organism Saccharomyces Cerevisiae (Yeast) by homology matching, which was motivated by the benefits of reverse genetics. First, a background network of protein-protein interactions and a pathogenic gene set involving several cancer types in humans and yeast were created. The homology between the human gene and yeast gene was then discovered by homology matching, and its interaction sub-network was obtained. This was undertaken following the principle that the homologous genes of the common ancestor may have similarities in expression. Then, using bidirectional long short-term memory (BiLSTM) in combination with adaptive integration of heterogeneous information, we further explored the topological characteristics of the yeast protein interaction network and presented a node representation score to evaluate the node ability in graphs. Finally, homologous mapping for human genes matched the important genes identified by ensemble classifiers for yeast, which may be thought of as genes connected to all types of cancer. One way to assess the performance of the BiLSTM model is through experiments on the database. On the other hand, enrichment analysis, survival analysis, and other outcomes can be used to confirm the biological importance of the prediction results. You may access the whole experimental protocols and programs at https://github.com/zhuyuan-cug/AI-BiLSTM/tree/master.

Structural Determinants of Yeast Protein-Protein Interaction Interface Evolution at the Residue Level

Interfaces of contact between proteins play important roles in determining the proper structure and function of protein–protein interactions (PPIs). Therefore, to fully understand PPIs, we need to better understand the evolutionary design principles of PPI interfaces. Previous studies have uncovered that interfacial sites are more evolutionarily conserved than other surface protein sites. Yet, little is known about the nature and relative importance of evolutionary constraints in PPI interfaces. Here, we explore constraints imposed by the structure of the microenvironment surrounding interfacial residues on residue evolutionary rate using a large dataset of over 700 structural models of baker’s yeast PPIs. We find that interfacial residues are, on average, systematically more conserved than all other residues with a similar degree of total burial as measured by relative solvent accessibility (RSA). Besides, we find that RSA of the residue when the PPI is formed is a better predictor of interfacial residue evolutionary rate than RSA in the monomer state. Furthermore, we investigate four structure-based measures of residue interfacial involvement, including change in RSA upon binding (ΔRSA), number of residue-residue contacts across the interface, and distance from the center or the periphery of the interface. Integrated modeling for evolutionary rate prediction in interfaces shows that ΔRSA plays a dominant role among the four measures of interfacial involvement, with minor, but independent contributions from other measures. These results yield insight into the evolutionary design of interfaces, improving our understanding of the role that structure plays in the molecular evolution of PPIs at the residue level.

Clique Based Approach to Predict Complexes from Protein Interaction Network

The ample availability and importance of large-scale protein-protein interaction (PPI) data demand a flurry of research efforts to understand cells' organization, processes, and functioning by analyzing these data at the network level. In the bioinformatics and data mining fields, network clustering requires a lot of attraction to discover clusters of interacting proteins. Clustering proteins in a PPI network has been an excellent method for discovering functional modules, disclosing functions of unknown proteins, and other tasks in numerous research over the last decade. In this research, a unique graph mining approach is proposed to detect dense neighborhoods (highly connected regions) in an interaction graph, including protein complexes. Our technique first finds size-3 cliques and then expands these size-3 cliques based on their affinity to produce maximal dense regions. To highlight the efficiency of our suggested strategy, we present experimental results using yeast and human protein interaction data. Our predicted complexes match or overlap much better with the gold standard protein complexes in the CYC-2008 and CORUM benchmark databases than other existing approaches.

Multiple Conserved Cysteine Residues Are Required for Delta‐12 Fatty Acid Desaturase Function in Arabidopsis thaliana

Plants produce two essential fatty acids (FAs) for human nutrition: linoleic acid (18:2Δ9,12, an omega‐3 FA) and alpha‐linoleic acid (18:3Δ9,12,15, an omega‐6 FA). Plant FA desaturase 2 (FAD2) is a microsomal enzyme that introduces the carbon‐carbon double bond at the delta‐12 position through an oxygenated intermediate. As the structure of FAD2 has yet to be experimentally determined, we are using integrated structural modeling, sequence analyses, and biochemical approaches to better understand this essential membrane‐bound enzyme. For this project we are studying the role of cysteines (Cys), using Arabidopsis thaliana FAD2 as a model, given their potential involvement in disulfide bonds, catalysis, cofactor binding, and/or metal coordination. We have identified seven conserved Cys residues in planta that we hypothesize to play distinct roles in the structure and function of FAD2, including one located in a highly conserved catalytic histidine motif HXXXH. To elucidate their roles, each individual Cys has been mutated to alanine. Transient expression assays of FAD2 in yeast, followed by lipid compositional analysis by gas chromatography has demonstrated that all Cys mutations resulted in reduced enzymatic activity with two appearing essential. Analysis of homodimer protein‐protein interactions in yeast has identified one Cys required for dimerization and has highlighted potentially interacting regions. To expand upon these results, we are currently testing for the presence of intramolecular and intermolecular disulfide bonds and assessing protein stability over time. These results have demonstrated that several Cys are required for FAD2 function providing a new model for understanding the contribution of these residues to membrane‐bound FADs in plant systems. Ultimately, a better understanding of these enzymes will inform rational engineering strategies for producing essential lipids and high‐value bioproducts.

A Novel Method for Predicting Essential Proteins by Integrating Multidimensional Biological Attribute Information and Topological Properties

Background: Essential proteins are indispensable to the maintenance of life activities and play essential roles in the areas of synthetic biology. Identification of essential proteins by computational methods has become a hot topic in recent years because of its efficiency. Objective: Identification of essential proteins is of important significance and practical use in the areas of synthetic biology, drug targets, and human disease genes. Method: In this paper, a method called EOP (Edge clustering coefficient -Orthologous-Protein) is proposed to infer potential essential proteins by combining Multidimensional Biological Attribute Information of proteins with Topological Properties of the protein-protein interaction network. Results: The simulation results on the yeast protein interaction network show that the number of essential proteins identified by this method is more than the number identified by the other 12 methods (DC, IC, EC, SC, BC, CC, NC, LAC, PEC, CoEWC, POEM, DWE). Especially compared with DC (Degree Centrality), the SN (sensitivity) is 9% higher, when the candidate protein is 1%, the recognition rate is 34% higher, when the candidate protein is 5%, 10%, 15%, 20%, 25% the recognition rate is 36%, 22%, 15%, 11%, 8% higher, respectively. Conclusion: Experimental results show that our method can achieve satisfactory prediction results, which may provide references for future research.

Protein complex prediction in large protein–protein interaction network

Due to high computational complexity, the detection of protein complexes in large protein–protein interaction (PPI) networks remains a challenging problem. Finding the actual protein complexes from a large PPI network requires a sophisticated algorithm. The protein complexes exhibit in densely connected sub-graphs in a PPI network. This paper presents a novel algorithm based on a metaheuristic method for protein complex prediction in large PPI networks. The algorithm mimics the density-based graph clustering method with biological heuristics to identify the protein complexes. The algorithm is enhanced by a local search algorithm and three repair operators. A new function has been developed for computing cluster density. The method was applied to the yeast and human protein interaction data and compared with the state-of-the-art algorithms. The comparisons demonstrate the best performance of the proposed algorithm in terms of accuracy and f-measure.

Computed structures of core eukaryotic protein complexes.

Protein-protein interactions play critical roles in biology, but the structures of many eukaryotic protein complexes are unknown, and there are likely many interactions not yet identified. We take advantage of advances in proteome-wide amino acid coevolution analysis and deep-learning–based structure modeling to systematically identify and build accurate models of core eukaryotic protein complexes within the Saccharomyces cerevisiae proteome. We use a combination of RoseTTAFold and AlphaFold to screen through paired multiple sequence alignments for 8.3 million pairs of yeast proteins, identify 1505 likely to interact, and build structure models for 106 previously unidentified assemblies and 806 that have not been structurally characterized. These complexes, which have as many as five subunits, play roles in almost all key processes in eukaryotic cells and provide broad insights into biological function.

Visual detection of binary, ternary and quaternary protein interactions in fission yeast using a Pil1 co-tethering assay.

Protein-protein interactions are vital for executing nearly all cellular processes. To facilitate the detection of protein-protein interactions in living cells of the fission yeast Schizosaccharomyces pombe, here we present an efficient and convenient method termed the Pil1 co-tethering assay. In its basic form, we tether a bait protein to mCherry-tagged Pil1, which forms cortical filamentary structures, and examine whether a GFP-tagged prey protein colocalizes with the bait. We demonstrate that this assay is capable of detecting pairwise protein-protein interactions of cytosolic proteins and nuclear proteins. Furthermore, we show that this assay can be used for detecting not only binary protein-protein interactions, but also ternary and quaternary protein-protein interactions. Using this assay, we systematically characterized the protein-protein interactions in the Atg1 complex and in the phosphatidylinositol 3-kinase (PtdIns3K) complexes and found that Atg38 is incorporated into the PtdIns3K complex I via an Atg38-Vps34 interaction. Our data show that this assay is a useful and versatile tool and should be added to the routine toolbox of fission yeast researchers. This article has an associated First Person interview with the first author of the paper.

First person – Zhong-Qiu Yu

ABSTRACT First Person is a series of interviews with the first authors of a selection of papers published in Journal of Cell Science, helping early-career researchers promote themselves alongside their papers. Zhong-Qiu Yu is first author on ‘ Visual detection of binary, ternary and quaternary protein interactions in fission yeast using a Pil1 co-tethering assay’, published in JCS. Zhong-Qiu conducted the research described in this article while a postdoc in Li-Lin Du's lab at National Institute of Biological Sciences, Beijing, China. He is now a research associate in the lab of David Rubinsztein at Department of Medical Genetics, Cambridge Institute for Medical Research, UK, investigating protein degradation pathways and neurodegeneration diseases.

APAL: Adjacency Propagation Algorithm for overlapping community detection in biological networks

We propose a novel method called Adjacency Propagation Algorithm (APAL) which considers the notion that the adjacent vertices are the best candidates for detecting overlapping communities in an undirected, unweighted, nontrivial graph. This is a compact algorithm with a single threshold parameter used to filter the detected communities according to their intraconnectivity property. In this study, APAL was tested rigorously using synthetic generators, such as the widely accepted LFR benchmark, as well as real data sets of yeast and human protein interactions networks. It was compared against the foremost algorithms in the field; the Clique Percolation Method (CPM), Community Overlap Propagation Algorithm (COPRA) and Neighbourhood-Inflated Seed Expansion (NISE). The results show that APAL outperforms its competitors for networks with increases in the number of memberships of the overlapping vertices. Such conditions are often found in biological networks, where a particular protein subunit may form part of several complexes. We believe that this shows the value of the implementation of APAL for protein interaction and other biological networks.

A Cross-entropy-based Method for Essential Protein Identification in Yeast Protein-protein Interaction Network

Background: Research has shown that essential proteins play important roles in the development and survival of organisms. Because of the high costs of traditional biological experiments, several computational prediction methods based on known protein-protein interactions (PPIs) have been recently proposed to detect essential proteins. Objective: Here, a novel prediction model called IoMCD is proposed to identify essential proteins by combining known PPIs with a variety of biological information about proteins, including gene expression data and homologous information of proteins. Methods: Compared to the traditional state-of-the-art prediction models, IoMCD involves two kinds of weights that are obtained, respectively, by extracting topological features of proteins from the original known protein–protein interaction (PPI) networks and calculating the Pearson correlation coefficients (PCCs) between the gene expression data of proteins. Based on these two kinds of weights and adopting a cross-entropy method, a unique weight is assigned to each protein. Subsequently, the homologous information of proteins is used to calculate an initial score for each protein. Finally, based on the unique weights and initial score of proteins, an iterative method is designed to measure the essentialities of proteins. Results: Intensive experiments were performed, and simulation results showed that the prediction accuracy of IoMCD, based on the dataset downloaded from the DIP and Gavin databases, was 92.16% and 89.71%, respectively, in the top 1% of the predicted essential proteins. Conclusion: Both simulation results demonstrated that IoMCD can achieve excellent prediction accuracy and could be an effective method for essential protein prediction.

An engineered protein-phosphorylation toggle network with implications for endogenous network discovery.

Synthetic biological networks comprising fast, reversible reactions could enable engineering of new cellular behaviors that are not possible with slower regulation. Here, we created a bistable toggle switch in Saccharomyces cerevisiae using a cross-repression topology comprising 11 protein-protein phosphorylation elements. The toggle is ultrasensitive, can be induced to switch states in seconds, and exhibits long-term bistability. Motivated by our toggle's architecture and size, we developed a computational framework to search endogenous protein pathways for other large and similar bistable networks. Our framework helped us to identify and experimentally verify five formerly unreported endogenous networks that exhibit bistability. Building synthetic protein-protein networks will enable bioengineers to design fast sensing and processing systems, allow sophisticated regulation of cellular processes, and aid discovery of endogenous networks with particular functions.

Building synthetic protein–based switches

Synthetic Biology Synthetic circuits can potentially help to control complex biological processes, but systems based on regulating gene expression respond to stimuli at the minute to the hour time scale. Working in yeast cells, Mishra et al. report synthetic regulatory circuits based on protein phosphorylation reactions that respond to inputs within seconds (see the Perspective by Kholodenko and Okada). Multicomponent logic gates allowed ultrasensitive and stable switching between states. After validating their effective synthetic circuit, the authors searched known yeast protein interaction networks for similar regulatory motifs and found previously unrecognized circuits that function as native toggle switches in yeast. Science , aav0780, this issue p. [eaav0780][1]; see also abj5028, p. [25][2] [1]: /lookup/doi/10.1126/science.aav0780 [2]: /lookup/doi/10.1126/science.abj5028

VICTOR: A visual analytics web application for comparing cluster sets

Clustering is the process of grouping different data objects based on similar properties. Clustering has applications in various case studies from several fields such as graph theory, image analysis, pattern recognition, statistics and others. Nowadays, there are numerous algorithms and tools able to generate clustering results. However, different algorithms or parameterizations may produce quite dissimilar cluster sets. In this way, the user is often forced to manually filter and compare these results in order to decide which of them generate the ideal clusters. To automate this process, in this study, we present VICTOR, the first fully interactive and dependency-free visual analytics web application which allows the visual comparison of the results of various clustering algorithms. VICTOR can handle multiple cluster set results simultaneously and compare them using ten different metrics. Clustering results can be filtered and compared to each other with the use of data tables or interactive heatmaps, bar plots, correlation networks, sankey and circos plots. We demonstrate VICTOR's functionality using three examples. In the first case, we compare five different network clustering algorithms on a Yeast protein-protein interaction dataset whereas in the second example, we test four different parameters of the MCL clustering algorithm on the same dataset. Finally, as a third example, we compare four different meta-analyses with hierarchically clustered differentially expressed genes found to be involved in myocardial infarction.VICTOR is available at http://victor.pavlopouloslab.info or http://bib.fleming.gr:3838/VICTOR.

Molecular dynamics shows complex interplay and long-range effects of post-translational modifications in yeast protein interactions.

Post-translational modifications (PTMs) play a vital, yet often overlooked role in the living cells through modulation of protein properties, such as localization and affinity towards their interactors, thereby enabling quick adaptation to changing environmental conditions. We have previously benchmarked a computational framework for the prediction of PTMs' effects on the stability of protein-protein interactions, which has molecular dynamics simulations followed by free energy calculations at its core. In the present work, we apply this framework to publicly available data on Saccharomyces cerevisiae protein structures and PTM sites, identified in both normal and stress conditions. We predict proteome-wide effects of acetylations and phosphorylations on protein-protein interactions and find that acetylations more frequently have locally stabilizing roles in protein interactions, while the opposite is true for phosphorylations. However, the overall impact of PTMs on protein-protein interactions is more complex than a simple sum of local changes caused by the introduction of PTMs and adds to our understanding of PTM cross-talk. We further use the obtained data to calculate the conformational changes brought about by PTMs. Finally, conservation of the analyzed PTM residues in orthologues shows that some predictions for yeast proteins will be mirrored to other organisms, including human. This work, therefore, contributes to our overall understanding of the modulation of the cellular protein interaction networks in yeast and beyond.

Protein context shapes the specificity of SH3 domain-mediated interactions in vivo

Protein–protein interactions (PPIs) between modular binding domains and their target peptide motifs are thought to largely depend on the intrinsic binding specificities of the domains. The large family of SRC Homology 3 (SH3) domains contribute to cellular processes via their ability to support such PPIs. While the intrinsic binding specificities of SH3 domains have been studied in vitro, whether each domain is necessary and sufficient to define PPI specificity in vivo is largely unknown. Here, by combining deletion, mutation, swapping and shuffling of SH3 domains and measurements of their impact on protein interactions in yeast, we find that most SH3s do not dictate PPI specificity independently from their host protein in vivo. We show that the identity of the host protein and the position of the SH3 domains within their host are critical for PPI specificity, for cellular functions and for key biophysical processes such as phase separation. Our work demonstrates the importance of the interplay between a modular PPI domain such as SH3 and its host protein in establishing specificity to wire PPI networks. These findings will aid understanding how protein networks are rewired during evolution and in the context of mutation-driven diseases such as cancer.