A heterogeneous information network is an information network composed of multiple types of objects. Clustering on such a network may lead to better understanding of both hidden structures of the network and the individual role played by every object in each cluster. However, although clustering on homogeneous networks has been studied over decades, clustering on heterogeneous networks has not been addressed until recently.A recent study proposed a new algorithm, RankClus, for clustering on bi-typed heterogeneous networks. However, a real-world network may consist of more than two types, and the interactions among multi-typed objects play a key role at disclosing the rich semantics that a network carries. In this paper, we study clustering of multi-typed heterogeneous networks with a star network schema and propose a novel algorithm, NetClus, that utilizes links across multityped objects to generate high-quality net-clusters. An iterative enhancement method is developed that leads to effective ranking-based clustering in such heterogeneous networks. Our experiments on DBLP data show that NetClus generates more accurate clustering results than the baseline topic model algorithm PLSA and the recently proposed algorithm, RankClus. Further, NetClus generates informative clusters, presenting good ranking and cluster membership information for each attribute object in each net-cluster.

Lanthionine-containing peptides (lanthipeptides) are a family of ribosomally synthesized and posttranslationally modified peptides containing (methyl)lanthionine residues. Here we present a phylogenomic study of the four currently known classes of lanthipeptide synthetases (LanB and LanC for class I, LanM for class II, LanKC for class III, and LanL for class IV). Although they possess very similar cyclase domains, class II-IV synthetases have evolved independently, and LanB and LanC enzymes appear to not always have coevolved. LanM enzymes from various phyla that have three cysteines ligated to a zinc ion (as opposed to the more common CysCys-His ligand set) cluster together. Most importantly, the phylogenomic data suggest that for some scaffolds, the ring topology of the final lanthipeptides may be determined in part by the sequence of the precursor peptides and not just by the biosynthetic enzymes. This notion was supported by studies with two chimeric peptides, suggesting that the nisin and prochlorosin biosynthetic enzymes can produce the correct ring topologies of epilancin 15X and lacticin 481, respectively. These results highlight the potential of lanthipeptide synthetases for bioengineering and combinatorial biosynthesis. Our study also demonstrates unexplored areas of sequence space that may be fruitful for genome mining. molecular evolution | natural products | phylogeny | posttranslational modification | lantibiotics

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The enterococcal cytolysin is a virulence factor consisting of two post-translationally modified peptides that synergistically kill human immune cells. Both peptides are made by CylM, a member of the LanM lanthipeptide synthetases. CylM catalyzes seven dehydrations of Ser and Thr residues and three cyclization reactions during the biosynthesis of the cytolysin large subunit. We present here the 2.2 Å resolution structure of CylM, the first structural information on a LanM. Unexpectedly, the structure reveals that the dehydratase domain of CylM resembles the catalytic core of eukaryotic lipid kinases, despite the absence of clear sequence homology. The kinase and phosphate elimination active sites that affect net dehydration are immediately adjacent to each other. Characterization of mutants provided insights into the mechanism of the dehydration process. The structure is also of interest because of the interactions of human homologs of lanthipeptide cyclases with kinases such as mammalian target of rapamycin.DOI: http://dx.doi.org/10.7554/eLife.07607.001

Information network contains abundant knowledge about relationships among people or entities. Unfortunately, such kind of knowledge is often hidden in a network where different kinds of relationships are not explicitly categorized. For example, in a research publication network, the advisor-advisee relationships among researchers are hidden in the coauthor network. Discovery of those relationships can benefit many interesting applications such as expert finding and research community analysis. In this paper, we take a computer science bibliographic network as an example, to analyze the roles of authors and to discover the likely advisoradvisee relationships. In particular, we propose a time-constrained probabilistic factor graph model (TPFG), which takes a research publication network as input and models the advisor-advisee relationship mining problem using a jointly likelihood objective function. We further design an efficient learning algorithm to optimize the objective function. Based on that our model suggests and ranks probable advisors for every author. Experimental results show that the proposed approach infer advisor-advisee relationships efficiently and achieves a state-of-the-art accuracy (80-90%). We also apply the discovered advisor-advisee relationships to a specific expert finding task and empirical study shows that the search performance can be effectively improved (+4.09% by NDCG@5).

Lanthipeptides are natural products that belong to the family of ribosomally synthesized and posttranslationally modified peptides (RiPPs). They contain characteristic lanthionine (Lan) or methyllanthionine (MeLan) structures that contribute to their diverse biological activities. Despite its structurally diverse set of 30 substrates, the highly substrate-tolerant lanthipeptide synthetase ProcM is shown to display high selectivity for formation of a single product from selected substrates. Mutation of the active site zinc ligands to alanine or the unique zinc ligand Cys971 to histidine resulted in a decrease of the cyclization rate, especially for the second cyclization of the substrates ProcA1.1, ProcA2.8 and ProcA3.3. Surprisingly, for ProcA3.3 these mutations also altered the regioselectivity of cyclization resulting in a new major product. ProcM was not able to correct the ring topology of incorrectly cyclized intermediates and products, suggesting that thermodynamic control is not operational. Collectively, the data in this study suggest that the high regioselectivity of product formation is governed by the selectivity of the initially formed ring.

The mechanisms by which lanthipeptide synthetases control the order in which they catalyze multiple chemical processes are poorly understood. The lacticin 481 synthetase (LctM) cleaves eight chemical bonds and forms six new chemical bonds in a controlled and ordered process. Two general mechanisms have been suggested for the temporal and spatial control of these transformations. In the spatial positioning model, leader peptide binding promotes certain reactions by establishing the spatial orientation of the substrate peptide relative to the synthetase active sites. In the intermediate structure model, the LctM-catalyzed dehydration and cyclization reactions that occur in two distinct active sites orchestrate the overall process by imparting specific structure into the maturing peptide that facilitates the ensuing reaction. Using isotopically labeled LctA analogues with engineered lacticin 481 biosynthetic machinery and mass spectrometry analysis, we show here that the LctA leader peptide plays critical roles in establishing the modification order and enhancing the catalytic efficiency and fidelity of the synthetase. The data are most consistent with a mechanistic model for LctM where both spatial positioning and intermediate structure contribute to efficient biosynthesis.

With the development of Web applications, textual documents are not only getting richer, but also ubiquitously interconnected with users and other objects in various ways, which brings about text-rich heterogeneous information networks. Topic models have been proposed and shown to be useful for document analysis, and the interactions among multi-typed objects play a key role at disclosing the rich semantics of the network. However, most of topic models only consider the textual information while ignore the network structures or can merely integrate with homogeneous networks. None of them can handle heterogeneous information network well. In this paper, we propose a novel topic model with biased propagation (TMBP) algorithm to directly incorporate heterogeneous information network with topic modeling in a unified way. The underlying intuition is that multi-typed objects should be treated differently along with their inherent textual information and the rich semantics of the heterogeneous information network. A simple and unbiased topic propagation across such a heterogeneous network does not make much sense. Consequently, we investigate and develop two biased propagation frameworks, the biased random walk framework and the biased regularization framework, for the TMBP algorithm from different perspectives, which can discover latent topics and identify clusters of multi-typed objects simultaneously. We extensively evaluate the proposed approach and compare to the state-of-the-art techniques on several datasets. Experimental results demonstrate that the improvement in our proposed approach is consistent and promising.