Oak-Associated Negativicute Equipped with Ancestral Aromatic Polyketide Synthase Produces Antimycobacterial Dendrubins

Anaerobic bacteria have only recently been recognized as a source of antibiotics; yet, the metabolic potential of Negativicutes (Gram-negative staining Firmicutes) such as the oak-associated Dendrosporobacter quercicolus has remained unknown. Genome mining of D. quercicolus and phylogenetic analyses revealed a gene cluster for a type II polyketide synthase (PKS) complex that belongs to the most ancestral enzyme systems of this type. Metabolic profiling, NMR analyses, and stable-isotope labeling led to the discovery of a new family of anthraquinone-type polyphenols, the dendrubins, which are diversified by acylation, methylation, and dimerization. Dendrubin A and B were identified as strong antibiotics against a range of clinically relevant, human-pathogenic mycobacteria.     

Reverse engineering of compact suffix trees and links: A novel algorithm

Invented in the 1970s, the Suffix Tree (ST) is a data structure that indexes all substrings of a text in linear space. Although more space demanding than other indexes, the ST remains likely an inspiring index because it represents substrings in a hierarchical tree structure. Along time, STs have acquired a central position in text algorithmics with myriad of algorithms and applications to for instance motif discovery, biological sequence comparison, or text compression. It is well known that different words can lead to the same suffix tree structure with different labels. Moreover, the properties of STs prevent all tree structures from being STs. Even the suffix links, which play a key role in efficient construction algorithms and many applications, are not sufficient to discriminate the suffix trees of distinct words. The question of recognising which trees can be STs has been raised and termed Reverse Engineering on STs. For the case where a tree is given with potential suffix links, a seminal work provides a linear time solution only for binary alphabets. Here, we also investigate the Reverse Engineering problem on ST with links and exhibit a novel approach and algorithm. Hopefully, this new suffix tree characterisation makes up a valuable step towards a better understanding of suffix tree combinatorics.     

Exploiting the performance of dictionary-based bio-entity name recognition in biomedical literature

Bio-entity name recognition is the key step for information extraction from biomedical literature. This paper presents a dictionary-based bio-entity name recognition approach. The approach expands the bio-entity name dictionary via the Abbreviation Definitions identifying algorithm, improves the recall rate through the improved edit distance algorithm and adopts some post-processing methods including Pre-keyword and Post-keyword expansion, Part of Speech expansion, merge of adjacent bio-entity names and the exploitation of the contextual cues to further improve the performance. Experiment results show that with this approach even an internal dictionary-based system could achieve a fairly good performance.     

Urinary exposure marker discovery for toxicants using ultra-high pressure liquid chromatography coupled with Orbitrap high resolution mass spectrometry and three untargeted metabolomics approaches

Human biomonitoring is the assessment of actual internal contamination of chemicals by measuring exposure markers, chemicals or their metabolites, in human urine, blood, serum, and other body fluids. However, the metabolism of chemicals within an organism is extremely complex. Therefore, the identification of metabolites is often difficult and laborious. Several untargeted metabolomics methods have been developed to perform objective searching/filtering of accurate-mass-based LC-MS data to facilitate metabolite identification. In this study, three metabolomics data processing approaches were used for chemical exposure marker discovery in urine with an LTQ-Orbitrap high-resolution mass spectrometry (HRMS) dataset; di-isononyl phthalate (DINP) was used as an example. The data processing techniques included the SMAIT, mass defect filtering (MDF), and XCMS Online. Sixteen, 83, and 139 probable DINP metabolite signals were obtained using the SMAIT, MDF, and XCMS procedures, respectively. Fourteen probable metabolite signals mined simultaneously by the three metabolomics approaches were confirmed as DINP metabolites by structural information provided by LC-MS/MS. Among them, 13 probable metabolite signals were validated as exposure-related markers in a rat model. Six (m/z 319.155, 361.127, 373.126, 389.157, 437.112 and 443.130) of the 13 exposure-related DINP metabolite signals have not previously been reported in the literature. Our data indicate that SMAIT provided an efficient method to discover effectively and systematically urinary exposure markers of toxicant. The DINP metabolism information can provide valuable information for further investigations of DINP toxicity, toxicokinetics, exposure assessment, and human health effects.     

SVM and SVR-based MHC-binding prediction using a mathematical presentation of peptide sequences

At present, there are a number of methods for the prediction of T-cell epitopes and major histocompatibility complex (MHC)-binding peptides. Despite numerous methods for predicting T-cell epitopes, there still exist limitations that affect the reliability of prevailing methods. For this reason, the development of models with high accuracy are crucial. An accurate prediction of the peptides that bind to specific major histocompatibility complex class I and II (MHC-I and MHC-II) molecules is important for an understanding of the functioning of the immune system and the development of peptide-based vaccines. Peptide binding is the most selective step in identifying T-cell epitopes. In this paper, we present a new approach to predicting MHC-binding ligands that takes into account new weighting schemes for position-based amino acid frequencies, BLOSUM and VOGG substitution of amino acids, and the physicochemical and molecular properties of amino acids. We have made models for quantitatively and qualitatively predicting MHC-binding ligands. Our models are based on two machine learning methods support vector machine (SVM) and support vector regression (SVR), where our models have used for feature selection, several different encoding and weighting schemes for peptides. The resulting models showed comparable, and in some cases better, performance than the best existing predictors. The obtained results indicate that the physicochemical and molecular properties of amino acids (AA) contribute significantly to the peptide-binding affinity.     

NSAMD: A new approach to discover structured contiguous substrings in sequence datasets using Next-Symbol-Array

In many sequence data mining applications, the goal is to find frequent substrings. Some of these applications like extracting motifs in protein and DNA sequences are looking for frequently occurring approximate contiguous substrings called simple motifs. By approximate we mean that some mismatches are allowed during similarity test between substrings, and it helps to discover unknown patterns. Structured motifs in DNA sequences are frequent structured contiguous substrings which contains two or more simple motifs. There are some works that have been done to find simple motifs but these works have problems such as low scalability, high execution time, no guarantee to find all patterns, and low flexibility in adaptation to other application. The Flame is the only algorithm that can find all unknown structured patterns in a dataset and has solved most of these problems but its scalability for very large sequences is still weak. In this research a new approach named Next-Symbol-Array based Motif Discovery (NSAMD) is represented to improve scalability in extracting all unknown simple and structured patterns. To reach this goal a new data structure has been presented called Next-Symbol-Array. This data structure makes change in how to find patterns by NSAMD in comparison with Flame and helps to find structured motif faster. Proposed algorithm is as accurate as Flame and extracts all existing patterns in dataset. Performance comparisons show that NSAMD outperforms Flame in extracting structured motifs in both execution time (51% faster) and memory usage (more than 99%). Proposed algorithm is slower in extracting simple motifs but considerable improvement in memory usage (more than 99%) makes NSAMD more scalable than Flame. This advantage of NSAMD is very important in biological applications in which very large sequences are applied.     

Phosphate recognition in structural biology

Drug-discovery research in the past decade has seen an increased selection of targets with phosphate recognition sites, such as protein kinases and phosphatases, in the past decade. This review attempts, with the help of database-mining tools, to give an overview of the most important principles in molecular recognition of phosphate groups by enzymes. A total of 3003 X-ray crystal structures from the RCSB Protein Data Bank with bound organophosphates has been analyzed individually, in particular for H-bonding interactions between proteins and ligands. The various known binding motifs for phosphate binding are reviewed, and similarities to phosphate complexation by synthetic receptors are highlighted. An analysis of the propensities of amino acids in various classes of phosphate-binding enzymes showed characteristic distributions of amino acids used for phosphate binding. This review demonstrates that structure-based lead development and optimization should carefully address the phosphate-binding-site environment and also proposes new alternatives for filling such sites.     

Anomaly detection in a mobile communication network

Mobile communication networks produce massive amounts of data which may be useful in identifying the location of an emergency situation and the area it affects. We propose a one pass clustering algorithm for quickly identifying anomalous data points. We evaluate this algorithm’s ability to detect outliers in a data set and describe how such an algorithm may be used as a component of an emergency response management system.

A data mining and ab initio study of the interaction between the aromatic and backbone amide groups in proteins

Interactions between aromatic groups and backbone amide groups in protein environments are characterized both through data mining analyses of X‐ray protein structures and through ab initio molecular orbital calculations on a model complex. The data mining analyses of 1029 X‐ray protein structures elucidate the configurational characteristics of the interaction as well as the positions of the interacting moieties involved. On a statistical average, more than seven such interactions occur in a typical protein structure. The configurations of these interactions are restricted with the face‐to‐face orientation as the preferred arrangement. The interaction occurs mainly within a single peptide chain. Both α‐helix and β‐strand secondary structures provide an almost equal number of backbone amides to participate within this interaction. The interaction energy was evaluated with the supermolecular ab initio method at the MP2 level. It is shown that aromatic–amide(backbone) interactions identified in proteins can achieve a stabilization energy of 3.3 kcal/mol. The interaction involves the entirety of the backbone amide rather than only its amine portion. This study concludes that the interaction between aromatic and backbone amide groups is of general significance to protein structure due to its strength and common occurrence. © 2000 John Wiley & Sons, Inc. Int J Quant Chem 80: 44–60, 2000     

典型煤的近红外反射光谱特征的方向性研究

高光谱遥感是煤矿区探测的有效方法,对于煤炭资源调查、矿区环境监测等具有重要意义,其中煤、矸石、植被、水体等被遥测物各个方向的反射光谱特征是煤矿高光谱遥感的基础,为此有必要针对典型煤的方向反射光谱特征进行研究.从我国不同矿区收集了无烟煤、烟煤、褐煤三大类煤中的4种典型煤样,4种煤样按煤阶由高到低顺序包括无烟煤一号、贫煤、...