Functions Of Yeast Proteins Research Articles

Exploring absent protein function in yeast: assaying post translational modification and human genetic variation.

Yeast is a valuable eukaryotic model organism that has evolved many processes conserved up to humans, yet many protein functions, including certain DNA and protein modifications, are absent. It is this absence of protein function that is fundamental to approaches using yeast as an in vivo test system to investigate human proteins. Functionality of the heterologous expressed proteins is connected to a quantitative, selectable phenotype, enabling the systematic analyses of mechanisms and specificity of DNA modification, post-translational protein modifications as well as the impact of annotated cancer mutations and coding variation on protein activity and interaction. Through continuous improvements of yeast screening systems, this is increasingly carried out on a global scale using deep mutational scanning approaches. Here we discuss the applicability of yeast systems to investigate absent human protein function with a specific focus on the impact of protein variation on protein-protein interaction modulation.

Deciphering the role of N‐terminal methylation in modulating yeast protein function including the multitasking stress response protein, Hsp31

As the one of the most common protein post-translational modifications (PTMs), protein methylation, especially a-N-methylation is largely underexplored. a-N-methylation is conserved in both eukaryotes and prokaryotes. Identification of the two a-N-methyltransferases in humans, NTMT1 and NTMT2, and their homologue of high sequence similarity in yeast, Tae1, indicated a shared novel protein regulation mechanism in both species. Recently, NTMT1/2 are shown important functions in many biological pathways including cell division and dysregulation is implicated in cancer development. Structural studies and in vitro analysis support a canonical motif, X1-P2-K3 (X=A, S, P or G after initiating M is cleaved) on substrate proteins as the determinant for recognition by NTMT1/2 and Tae1. However, a-N-methylation on most of the potential substrates with the motif are not substantiated. The object of this study is using yeast as an ideal model to investigate the a-N-terminal methylome, taking advantage of its well observed phenotypes, fully sequenced genome, tractable genetics, and comparatively simplified N-terminal methyltransferase network. We combine bioinformatic searching, mass spectrometry PTM mapping, yeast phenotypic assay and biochemical assays in this study to approach our aims: (1) Screen for potential substrates and identify methylation events using a proteomic dataset repurposing approach, (2) Confirm a-N-methylation experimentally, (3) Investigate the Tae1 relevant phenotypes and biological pathways. (4) Exploration of mechanism in regulating substrate function. Initial bioinformatic research by motif mapping indicates that 45 proteins might be regulated by Tae1 through a-N-methylation. We have verified a-N-methylation on 6 proteins including Heat shock protein 31, Hsp31. Hsp31 and its three paralogues are members of the DJ-1/ThiJ/PfpI family with the human protein implicated in Parkinson's Disease. We demonstrate that TAE1 deficient yeast show susceptibility to inhibitors of protein synthesis or microtubule assembly. However, the same strain demonstrates resistance to heat stress and oxidative stress. Hsp31 is a multifunctional protein with chaperone, deglycase and methylglyoxalase activity. Thus, we hypothesize that Tae1 regulates yeast response to heat stress and oxidative stress potentially through its substrate, Hsp31. Further studies confirmed that Tae1 is responsible for a-N-methylation of Hsp31. Overexpression of Hsp31 N-terminal mutants with decreased methylation potential show elevated protective effect under both heat shock and methylglyoxalase (MGO) stress. However, MGO stress treatment on yeast show limited effect on methylglyoxalase activity of purified Hsp31. The exact mechanism of how a-N-methylation affects protein function is currently under assessment for Hsp31 and other potential substrates. The results of this study will reveal the more global view of N-terminal methyltransferase substrates, facilitate the translation to human substrates and further assist in investigating N-terminal methylation under various disease contexts and assist in optimizing drug design of disease targets.

Deciphering the role of N‐terminal methylation in modulating yeast protein function including the multitasking stress response protein, Hsp31

The first cytosolic N‐terminal methyltransferase identified in yeast, Tae1, recognizes mainly a protein N‐terminal motif sequence of M‐X‐P‐K. This conserved sequence motif is found in multiple proteins including ribosomal proteins, histones and small heat shock proteins across species. The human homolog of Tae1, N‐terminal methyltransferase 1 (NTMT1/NRMT1) has important functions in cell division by regulating RCC1 and centromere proteins such as CENP‐A and is implicated as a cancer target. Although these studies have been informative, the biological function of alpha N‐terminal methylation in yeast and human remains largely unknown. Our research is directed at unravelling the role of N‐terminal methylation in regulating yeast protein function and is currently focused on Hsp31, a multifunctional chaperone and enzyme involved in oxidative stress response. We are also investigating N‐terminal methylation on a proteome‐wide basis.Immuno‐detection and tandem mass spectrometry (MS/MS) assay with overexpressed Hsp31‐ MORF or physiologically expressed Hsp31‐GFP purified from WT and Tae1 deficiency strains demonstrated that Hsp31‐MORF is methylated at the first alanine (initial methionine is efficiently removed by a native methionine aminopeptidase) after the protein is synthesized. We also observed that the methylation types are exclusively mono‐ or di‐ methylation, with no tri‐methylated species observed. This is the first demonstration that the Hsp31 chaperone protein is N‐terminally methylated. Interestingly, methylation of Hsp31 was decreased but not completely eliminated in Tae1 deficient strains. This is the first evidence of such an overlapping methyltransferase activity which suggests the presence of another enzyme with N‐terminal methyltransferase activity. It unclear how the biological function of Hsp31 is affected by methylation and the difference between mono‐/di‐ methylation and tri‐methylation. We also verified N‐terminal methylation on other potential Tae1 substrates by MS/MS, including Hsp33 and a xylose and arabinose reductase. We conclude that N‐terminal methylation occurs with diverse substrates and that yeast is an excellent model to probe the function of this post‐translational modification. Further investigation is ongoing to determine if and how N‐terminal methylation modulates Hsp31 functions using site‐directed mutagenesis and biological activity assays.Support or Funding InformationFunding: Purdue University MCMP Research Enhancement grantThis abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Cell scientist to watch – Maya Schuldiner

Maya Schuldiner pursued her PhD degree under the guidance of Prof. Nissim Benvenisty at the Hebrew University in Jerusalem. She carried out her postdoctoral training in the laboratory of Prof. Jonathan Weissman at the University of California, San Francisco, with support from the Human Frontiers Science Program and the Sandler Fellows Program. Since 2008, Maya has been running her own laboratory at the Weizmann Institute of Sciences in Rehovot, Israel. She received the Human Frontiers Science Program Career Development Award and the NIH Pathway to Independence Award, and she is a member of the EMBO Young Investigator programme and Faculty of 1000. Her current research interests are focused on unravelling novel functions of yeast proteins that are involved in organelle biology.

Hypergraph and Protein Function Prediction with Gene Expression Data

Most network-based protein (or gene) function prediction methods are based on the assumption that the labels of two adjacent proteins in the network are likely to be the same. However, assuming the pairwise relationship between proteins or genes is not complete. The information a group of genes that show very similar patterns of expression and tend to have similar functions (i.e. the functional modules) is missed. The natural way overcoming the information loss of the above assumption is to represent the gene expression data as the hypergraph. Thus, in this paper, the three un-normalized, random walk, and symmetric normalized hypergraph Laplacian based semi- supervised learning methods applied to hypergraph constructed from the gene expression data in order to predict the functions of yeast proteins are introduced. Experiment results show that the average accuracy performance measures of these three hypergraph Laplacian based semi-supervised learning methods are the same. However, their average accuracy performance measures of these three methods are much greater than the average accuracy performance measures of un-normalized graph Laplacian based semi-supervised learning method (i.e. the baseline method of this paper) applied to gene co-expression network created from the gene expression data. 

Improving the prediction of yeast protein function using weighted protein-protein interactions

BackgroundBioinformatics can be used to predict protein function, leading to an understanding of cellular activities, and equally-weighted protein-protein interactions (PPI) are normally used to predict such protein functions. The present study provides a weighting strategy for PPI to improve the prediction of protein functions. The weights are dependent on the local and global network topologies and the number of experimental verification methods. The proposed methods were applied to the yeast proteome and integrated with the neighbour counting method to predict the functions of unknown proteins.ResultsA new technique to weight interactions in the yeast proteome is presented. The weights are related to the network topology (local and global) and the number of identified methods, and the results revealed improvement in the sensitivity and specificity of prediction in terms of cellular role and cellular locations. This method (new weights) was compared with a method that utilises interactions with the same weight and it was shown to be superior.ConclusionsA new method for weighting the interactions in protein-protein interaction networks is presented. Experimental results concerning yeast proteins demonstrated that weighting interactions integrated with the neighbor counting method improved the sensitivity and specificity of prediction in terms of two functional categories: cellular role and cell locations.

A Conditional Entropy Minimization Criterion for Dimensionality Reduction and Multiple Kernel Learning

Reducing the dimensionality of high-dimensional data without losing its essential information is an important task in information processing. When class labels of training data are available, Fisher discriminant analysis (FDA) has been widely used. However, the optimality of FDA is guaranteed only in a very restricted ideal circumstance, and it is often observed that FDA does not provide a good classification surface for many real problems. This letter treats the problem of supervised dimensionality reduction from the viewpoint of information theory and proposes a framework of dimensionality reduction based on class-conditional entropy minimization. The proposed linear dimensionality-reduction technique is validated both theoretically and experimentally. Then, through kernel Fisher discriminant analysis (KFDA), the multiple kernel learning problem is treated in the proposed framework, and a novel algorithm, which iteratively optimizes the parameters of the classification function and kernel combination coefficients, is proposed. The algorithm is experimentally shown to be comparable to or outperforms KFDA for large-scale benchmark data sets, and comparable to other multiple kernel learning techniques on the yeast protein function annotation task.

Enhanced protein fold recognition through a novel data integration approach

BackgroundProtein fold recognition is a key step in protein three-dimensional (3D) structure discovery. There are multiple fold discriminatory data sources which use physicochemical and structural properties as well as further data sources derived from local sequence alignments. This raises the issue of finding the most efficient method for combining these different informative data sources and exploring their relative significance for protein fold classification. Kernel methods have been extensively used for biological data analysis. They can incorporate separate fold discriminatory features into kernel matrices which encode the similarity between samples in their respective data sources.ResultsIn this paper we consider the problem of integrating multiple data sources using a kernel-based approach. We propose a novel information-theoretic approach based on a Kullback-Leibler (KL) divergence between the output kernel matrix and the input kernel matrix so as to integrate heterogeneous data sources. One of the most appealing properties of this approach is that it can easily cope with multi-class classification and multi-task learning by an appropriate choice of the output kernel matrix. Based on the position of the output and input kernel matrices in the KL-divergence objective, there are two formulations which we respectively refer to as MKLdiv-dc and MKLdiv-conv. We propose to efficiently solve MKLdiv-dc by a difference of convex (DC) programming method and MKLdiv-conv by a projected gradient descent algorithm. The effectiveness of the proposed approaches is evaluated on a benchmark dataset for protein fold recognition and a yeast protein function prediction problem.ConclusionOur proposed methods MKLdiv-dc and MKLdiv-conv are able to achieve state-of-the-art performance on the SCOP PDB-40D benchmark dataset for protein fold prediction and provide useful insights into the relative significance of informative data sources. In particular, MKLdiv-dc further improves the fold discrimination accuracy to 75.19% which is a more than 5% improvement over competitive Bayesian probabilistic and SVM margin-based kernel learning methods. Furthermore, we report a competitive performance on the yeast protein function prediction problem.

Approaching a complete repository of sequence-verified protein-encoding clones for Saccharomyces cerevisiae.

The availability of an annotated genome sequence for the yeast Saccharomyces cerevisiae has made possible the proteome-scale study of protein function and protein-protein interactions. These studies rely on availability of cloned open reading frame (ORF) collections that can be used for cell-free or cell-based protein expression. Several yeast ORF collections are available, but their use and data interpretation can be hindered by reliance on now out-of-date annotations, the inflexible presence of N- or C-terminal tags, and/or the unknown presence of mutations introduced during the cloning process. High-throughput biochemical and genetic analyses would benefit from a "gold standard" (fully sequence-verified, high-quality) ORF collection, which allows for high confidence in and reproducibility of experimental results. Here, we describe Yeast FLEXGene, a S. cerevisiae protein-coding clone collection that covers over 5000 predicted protein-coding sequences. The clone set covers 87% of the current S. cerevisiae genome annotation and includes full sequencing of each ORF insert. Availability of this collection makes possible a wide variety of studies from purified proteins to mutation suppression analysis, which should contribute to a global understanding of yeast protein function.

An integrated probabilistic model for functional prediction of proteins.

We develop an integrated probabilistic model to combine protein physical interactions, genetic interactions, highly correlated gene expression networks, protein complex data, and domain structures of individual proteins to predict protein functions. The model is an extension of our previous model for protein function prediction based on Markovian random field theory. The model is flexible in that other protein pairwise relationship information and features of individual proteins can be easily incorporated. Two features distinguish the integrated approach from other available methods for protein function prediction. One is that the integrated approach uses all available sources of information with different weights for different sources of data. It is a global approach that takes the whole network into consideration. The second feature is that the posterior probability that a protein has the function of interest is assigned. The posterior probability indicates how confident we are about assigning the function to the protein. We apply our integrated approach to predict functions of yeast proteins based upon MIPS protein function classifications and upon the interaction networks based on MIPS physical and genetic interactions, gene expression profiles, tandem affinity purification (TAP) protein complex data, and protein domain information. We study the recall and precision of the integrated approach using different sources of information by the leave-one-out approach. In contrast to using MIPS physical interactions only, the integrated approach combining all of the information increases the recall from 57% to 87% when the precision is set at 57%-an increase of 30%.

Quantitative microscopy of green fluorescent protein-labeled yeast

This chapter discusses the quantitative microscopy of green fluorescent protein-labeled yeast. With the development of methods to tag proteins using green fluorescent protein (GFP), fluorescence microscopy has become increasingly important for characterizing protein function in yeast. The chapter describes the use of three-dimensional (3-D) deconvolution microscopy to perform fixed- and live-cell analysis of cells carrying GFP-tagged proteins. The chapter also discusses the process of deconvolution in fluorescence microscopy. Despite the focus on high-performance imaging, the methods described in the chapter are applicable to a wide range of experiments using conventional wide-field microscopes. The chapter describes the various steps for preparing biological samples, such as strain construction, mounting cells for microscopy, preparing slides with agar pads for live-cell microscopy, mounting cells on agar pads, mounting live cells without agar pads, fixing cells with paraformaldehyde and mounting, optimizing microscope optics, selecting filters, selecting cover glass, temperature control, minimizing spherical aberration through oil matching, acquiring images, and viewing and printing the image.

SURVEY AND SUMMARY: Saccharomyces cerevisiae basic helix-loop-helix proteins regulate diverse biological processes.

Basic helix-loop-helix (bHLH) proteins are among the most well studied and functionally important regulatory proteins in all eukaryotes. The HLH domain dictates dimerization to create homo- and heterodimers. Dimerization juxtaposes the basic regions of the two monomers to create a DNA interaction surface that recognizes the consensus sequence called the E-box, 5'-CANNTG-3'. Several bHLH proteins have been identified in the yeast Saccharomyces cerevisiae using traditional genetic methodologies. These proteins regulate diverse biological pathways. The completed sequence of the yeast genome, combined with novel methodologies allowing whole-genome expression studies, now offers a unique opportunity to study the function of these bHLH proteins. It is the purpose of this review to summarize the current knowledge of bHLH protein function in yeast.

Detection of nonfunctional overexpression gene products using two-dimensional gel electrophoresis with a narrowed pH range.

A commonly used technique in the analysis of yeast protein function is the overexpression of cloned genes. In the case that overexpression does not lead to an altered phenotype a stable synthesis of the protein has to be demonstrated. Here an example is shown where overexpression of the yeast HYP2 genes, coding for a hypusine-containing protein, was alternatively analyzed by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) alone or by two-dimensional (2-D) gel electrophoresis, using the narrowed pH range of 4.5-5.4. The results of the SDS-PAGE suggested a stable overproduction of HYP gene product, whereas 2-D analysis revealed an accumulation of nonfunctional protein isoforms.