Steps towards a repertoire of comprehensive maps of human protein interaction networks: the Human Proteotheque Initiative (HuPI)

A newly uncovered group of distantly related lysine methyltransferases preferentially interact with molecular chaperones to regulate their activity

Methylation is a post-translational modification that can affect numerous features of proteins, notably cellular localization, turnover, activity, and molecular interactions. Recent genome-wide analyses have considerably extended the list of human genes encoding putative methyltransferases. Studies on protein methyltransferases have revealed that the regulatory function of methylation is not limited to epigenetics, with many non-histone substrates now being discovered. We present here our findings on a novel family of distantly related putative methyltransferases. Affinity purification coupled to mass spectrometry shows a marked preference for these proteins to associate with various chaperones. Based on the spectral data, we were able to identify methylation sites in substrates, notably trimethylation of K135 of KIN/Kin17, K561 of HSPA8/Hsc70 as well as corresponding lysine residues in other Hsp70 isoforms, and K315 of VCP/p97. All modification sites were subsequently confirmed in vitro. In the case of VCP, methylation by METTL21D was stimulated by the addition of the UBX cofactor ASPSCR1, which we show directly interacts with the methyltransferase. This stimulatory effect was lost when we used VCP mutants (R155H, R159G, and R191Q) known to cause Inclusion Body Myopathy with Paget's disease of bone and Fronto-temporal Dementia (IBMPFD) and/or familial Amyotrophic Lateral Sclerosis (ALS). Lysine 315 falls in proximity to the Walker B motif of VCP's first ATPase/D1 domain. Our results indicate that methylation of this site negatively impacts its ATPase activity. Overall, this report uncovers a new role for protein methylation as a regulatory pathway for molecular chaperones and defines a novel regulatory mechanism for the chaperone VCP, whose deregulation is causative of degenerative neuromuscular diseases.

Methylation, or transfer of a single or multiple methyl groups (CH₃), is one of many post-translational modifications that occur on proteins. Such modifications can, in turn, affect numerous aspects of a protein, notably cellular localization, turnover, activity, and molecular interactions. In addition to post-translational modifications, the structural organization of a protein or protein complex can also have a significant impact on its function and stability. A group of factors known as “molecular chaperones” aid newly synthesized proteins in reaching their native conformation or alternating between physiologically relevant states. We present here a new family of factors that promote methylation of chaperones and show that, at least in one case, this modification translates into a modulation in the activity of the substrate chaperone. Our results not only characterize the function of previously unknown gene products, uncover a new role for protein methylation as a regulatory pathway for chaperones, and define a novel regulatory mechanism for the chaperone VCP, whose deregulation is causative of neuromuscular diseases, but also suggest the existence of a post-translational modification code that regulates molecular chaperones. Further decrypting this “chaperone code” will help understanding how the functional organization of the proteome is orchestrated.

Predicting direct protein interactions from affinity purification mass spectrometry data

Background

Affinity purification followed by mass spectrometry identification (AP-MS) is an increasingly popular approach to observe protein-protein interactions (PPI) in vivo. One drawback of AP-MS, however, is that it is prone to detecting indirect interactions mixed with direct physical interactions. Therefore, the ability to distinguish direct interactions from indirect ones is of much interest.

Results

We first propose a simple probabilistic model for the interactions captured by AP-MS experiments, under which the problem of separating direct interactions from indirect ones is formulated. Then, given idealized quantitative AP-MS data, we study the problem of identifying the most likely set of direct interactions that produced the observed data. We address this challenging graph theoretical problem by first characterizing signatures that can identify weakly connected nodes as well as dense regions of the network. The rest of the direct PPI network is then inferred using a genetic algorithm.

Our algorithm shows good performance on both simulated and biological networks with very high sensitivity and specificity. Then the algorithm is used to predict direct interactions from a set of AP-MS PPI data from yeast, and its performance is measured against a high-quality interaction dataset.

Conclusions

As the sensitivity of AP-MS pipeline improves, the fraction of indirect interactions detected will also increase, thereby making the ability to distinguish them even more desirable. Despite the simplicity of our model for indirect interactions, our method provides a good performance on the test networks.

Detection of locally over-represented GO terms in protein-protein interaction networks

High-throughput methods for identifying protein-protein interactions produce increasingly complex and intricate interaction networks. These networks are extremely rich in information, but extracting biologically meaningful hypotheses from them and representing them in a human-readable manner is challenging. We propose a method to identify Gene Ontology terms that are locally over-represented in a subnetwork of a given biological network. Specifically, we propose several methods to evaluate the degree of clustering of proteins associated to a particular GO term in both weighted and unweighted PPI networks, and describe efficient methods to estimate the statistical significance of the observed clustering. We show, using Monte Carlo simulations, that our best approximation methods accurately estimate the true p-value, for random scale-free graphs as well as for actual yeast and human networks. When applied to these two biological networks, our approach recovers many known complexes and pathways, but also suggests potential functions for many subnetworks. Online Supplementary Material is available at www.liebertonline.com.

Defining protein interactions that regulate disease progression

Over the past few years, the study of protein-protein interactions and protein complexes has shed more light on cellular processes and cell function. Because alterations in protein-protein interactions perturb the normal sequence of events in the cell and contribute to diseases such as cancer, the understanding of both the normal cellular protein-protein interaction networks and their modulation during the establishment of disease is a crucial issue in biomedical research, as it will facilitate the development of drugs to fight these diseases. In this article, the most commonly used approaches for studying protein-protein interactions are discussed as well as the direction in which the field of systematic characterization of protein interaction networks is progressing. We also discuss some success stories in the modulation of disease-related protein-protein interactions using small molecules.