Database Search Tools Research Articles

Constructing a Tandem Mass Spectral Library for Forensic Ricin Identification.

Ricin, a protein found in castor seeds, is a lethal toxin that is designated as a category 2 select agent, and cases of attempted ricin poisoning are relatively common. Many methods to detect protein toxins such as ricin use targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) to identify toxin peptides, usually tryptic peptides. The successful use of untargeted methods has also been reported. However, the use of untargeted proteomics methods, including database search, for peptide and protein identification is less common in forensic practice and may be unfamiliar to forensic science practitioners. Here, we propose a method to create spectral libraries of tryptic ricin peptides and use these libraries for ricin identification by spectral library search, which may be more familiar to forensic scientists because of the use of spectral libraries in small molecule identification. Peptide spectral libraries offer a direct comparison to an authentic standard, a key element of forensic analysis, but have not previously been used in a forensic context. To construct these spectral libraries, two pure ricin samples (one from a proposed standard reference material) were digested with trypsin and analyzed using a standard shotgun LC-MS/MS protocol. Spectral libraries were created from resulting tryptic peptides identified from filtered search results from four database search tools. The library was then used in a search using SpectraST on forensically realistic castor seed extracts. These castor seed samples were made using the crude methods commonly encountered in real-world ricin cases. Analysis showed that the spectral library search resulted in more peptides identified from crude castor seed samples compared to MS-GF+ and Sequest plus Percolator database searches. These results, the first published use of spectral library search to detect protein toxins in forensically relevant samples, suggest that computational comparison of putative ricin peptide spectra to library spectra can be an effective method to detect ricin in an unknown sample. Data are available via ProteomeXchange with identifier PXD013711.

Lilacs search strategy for systematic reviews of diagnostic test accuracy studies.

There are few publications on search strategies to identify diagnostic test accuracy (DTA) studies in lilacs. To translate and customise medline search strategies for use in lilacs and assess their retrieval of studies in Cochrane DTA systematic reviews. We developed a six-step process to translate and customise medline search strategies for use in lilacs (iAHx interface). We identified medline search strategies of published Cochrane DTA reviews, translated/customised them for use in lilacs, ran searches in lilacs and compared the retrieval results of our translated search strategy versus the one used in the published reviews. Our lilacs search strategies translated/customised from the medline strategies retrieved studies in 70 Cochrane DTA reviews. Only 29 of these reviews stated that they had searched the lilacs database and 21 published their lilacs search strategies. Few had used the lilacs database search tools, none exploded the subject headings, and 86% used only English terms. Translating and tailoring a medline search strategy for the lilacs database resulted in the retrieval of DTA studies that would have been missed otherwise.

Preprocessing Tandem Mass Spectra Using Genetic Programming for Peptide Identification.

One of the major challenges in proteomics is peptide identification from mass spectra containing high noise ratio and small number of signal (b-/y-ions) peaks. However, the accuracy and reliability of peptide identification in such highly imbalanced MS/MS data can be improved by applying a preprocessing step prior to peptide identification aiming at discriminating b-/y-ions from noise peaks in the spectra. In this study, we report a genetic programming (GP)-based preprocessing method for de-noising highly imbalanced and noisy CID MS/MS spectra. GP now becomes a popular machine learning method via automatic programming. GP preprocesses the highly noisy MS/MS spectra by classifying peaks as noise peaks or signal peaks in a binary classification manner. Meanwhile, a set of spectral fragment features based on the MS/MS fragmentation rules is extracted from the dataset to investigate their discriminating abilities by GP. A MS/MS spectral dataset containing thousands of spectra are used to train the GP model. As the GP tree-based representation has the capability for implicit feature selection during the evolutionary process, the evolved GP model with the selected features is compared with the best threshold-based method. The results show that the GP method improved the reliability of peptide identification and increased the identification rate of a de novo sequencing tool, PEAKS, to 99.4% from 80.1% achieved by the best threshold-based method. Moreover, the result of peptide identification by a database search tool, SEQUEST, using the data preprocessed by the GP method was statistically significant compared to the other methods.

Using Bioinformatics to Understand Genetic Diseases

This Practical Guide in the Bringing Bioinformatics into the Classroom series outlines a number of basic bioinformatics approaches that can be used to understand the molecular basis of genetic diseases. A rare variation in the insulin gene is discussed, and the impact of the variation on the gene product, and how this results in disease, is explored. Specifically, this Guide introduces a range of commonly used bioinformatics tools and databases with which to analyse both DNA and protein sequences, and protein structures. On reading the Guide and completing the exercises, users will be able to: investigate the locations of genes on the human genome using a genome browser; compare DNA sequences using an alignment tool; translate a DNA sequence into its protein product; search a protein structure database, and visualise the 3D structure of insulin; and infer whether insulin is specific to humans by using protein sequence database search tools.

A rapid computational approach identifies SPICE1 as an Aurora kinase substrate.

Aurora kinases play a major role in mitosis by regulating diverse substrates. Defining their critical downstream targets is important in understanding Aurora kinase function. Here we have developed an unbiased computational approach to identify new Aurora kinase substrates based on phosphorylation site clustering, protein localization, protein structure, and species conservation. We validate the microtubule-associated proteins Clasp2, Elys, tubulin tyrosine ligase-like polyglutamylase residues 330–624 and spindle and centriole associated protein 1, residues 549–855 (SPICE1), as Aurora A and B kinases substrates in vitro. We also demonstrate that SPICE1 localization is regulated by Aurora kinases during mitosis. In the absence of Aurora kinase activity, SPICE1 remains at centrioles but does not target to the spindle. Similarly, a nonphosphorylatable SPICE1 mutant no longer localizes to the spindle. Finally, we show that misregulating SPICE1 phosphorylation results in abnormal centriole number, spindle multipolarity, and chromosome alignment defects. Overall, our work indicates that temporal and spatial Aurora kinase–mediated regulation of SPICE1 is important for correct chromosome segregation. In addition, our work provides a database-search tool that enables rapid identification of Aurora kinase substrates.

Efficacy and Safety of Direct Oral Anticoagulants in Patients with Cirrhosis— Single Institution Experience

IntroductionAnticoagulant therapy in individuals with cirrhosis is challenging, as their risk of hemorrhage is significant due to concomitant alterations in primary hemostasis, secondary hemostasis and fibrinolysis. For many years, warfarin and heparin (including low molecular weight heparin) were the only treatment options for patients with thrombosis. Since 2010, novel, direct-acting oral anticoagulants (DOAC) that inhibit either thrombin (dabigatran) or factor Xa (e.g., rivaroxaban, apixaban, edoxaban, and betrixaban) are available. These direct-acting oral anticoagulants do not require laboratory monitoring and have minimal food and drug interactions, which makes them appealing to use for many patients. Unfortunately, there is inadequate data about efficacy and safety of these direct oral anticoagulants in cirrhotic patients as this group was commonly excluded from clinical trials evaluating these medications.In our study we aim to describe a large single institution experience with use of DOACs in patients with cirrhosis and explore clinical characteristics as potential predictors for bleeding and thrombosis.Methods:We conducted a retrospective cohort study of patients with cirrhosis who were seen in our institution between September 1, 2010 and June 30, 2017 and were treated with a DOAC utilizing a database search tool, called the Advanced Cohort Explorer that allows electronic records to be reviewed in a time efficient manner through text search or code search functionality. Only patients with cirrhosis diagnosed by histopathologic evaluation or with clinical presentation consistent with cirrhosis and confirmed by a gastroenterologist or by radiologic imaging (MRI, CT) were included in the study.Results:In our study, 106 individuals (male=72) met the inclusion criteria. Ninety three (88%) patients were treated with an anti-Xa inhibitor (rivaroxaban or apixaban), and remaining 13 (12%) with direct thrombin inhibitor (dabigatran). Median age at starting anticoagulant was 66 (range 24-89) years. The most common indication for anticoagulation was atrial fibrillation/flutter (54%), followed by pulmonary embolism/deep vein thrombosis (19%) and splanchnic vein thrombosis (15%). At the time of DOAC initiation, the median Charlson comorbidity index (CCI) was 7 (range 3-15); MELD score was 10 (range 6-24); and platelet count 150,000/ul (range 50,000-432,000). Thirty six (34%) patients had objectively diagnosed varices. Median follow up was 563 days (range: 7-2646).Bleeding requiring medical evaluation occurred in 32 (30.2%) patients. Major bleeding as defined by ISTH was experienced by 12 (11.3%) individuals. The most common source of bleeding was gastrointestinal tract (21.7%); other organs were less commonly affected (table 1). Hemorrhagic complication usually occurred early after starting anticoagulation (median 101 days; range 4-1356), and was the most common reason for discontinuation of anticoagulant treatment (14%), followed by completion of treatment (10%). On Cox proportional hazard modeling, rising BMI (HR: 1.032 per unit increase in BMI; p<0.05) and elevated bilirubin (HR: 1.223 per 1 mg/dL increase) were associated with increased risk of bleeding. Other clinical factors, including sex, type of anticoagulant used, platelet count, PT/INR, albumin, MELD score, varices, portal hypertension, concomitant aspirin use, and Charlson comorbidity index were not associated with bleeding risk.Thrombotic events while on DOAC affected 7 (6.7%) individuals, which included venous thrombosis in 4 (3.8%) and arterial thrombosis in 3 (2.9%) patients. No clinical factors were associated with increased risk of thrombosis.Conclusions:Our study is the largest series of patients with cirrhosis treated with DOACs. Bleeding complications with DOAC use in this population is high. Providers considering starting DOACs in patients with cirrhosis should consider the risk of bleeding in this population. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

In-Search Assignment of Monoisotopic Peaks Improves the Identification of Cross-Linked Peptides.

Cross-linking/mass spectrometry has undergone a maturation process akin to standard proteomics by adapting key methods such as false discovery rate control and quantification. A poorly evaluated search setting in proteomics is the consideration of multiple (lighter) alternative values for the monoisotopic precursor mass to compensate for possible misassignments of the monoisotopic peak. Here, we show that monoisotopic peak assignment is a major weakness of current data handling approaches in cross-linking. Cross-linked peptides often have high precursor masses, which reduces the presence of the monoisotopic peak in the isotope envelope. Paired with generally low peak intensity, this generates a challenge that may not be completely solvable by precursor mass assignment routines. We therefore took an alternative route by ‘”in-search assignment of the monoisotopic peak” in the cross-link database search tool Xi (Xi-MPA), which considers multiple precursor masses during database search. We compare and evaluate the performance of established preprocessing workflows that partly correct the monoisotopic peak and Xi-MPA on three publicly available data sets. Xi-MPA always delivered the highest number of identifications with ∼2 to 4-fold increase of PSMs without compromising identification accuracy as determined by FDR estimation and comparison to crystallographic models.

Combining High-Resolution and Exact Calibration To Boost Statistical Power: A Well-Calibrated Score Function for High-Resolution MS2 Data.

To achieve accurate assignment of peptide sequences to observed fragmentation spectra, a shotgun proteomics database search tool must make good use of the very high-resolution information produced by state-of-the-art mass spectrometers. However, making use of this information while also ensuring that the search engine's scores are well calibrated, that is, that the score assigned to one spectrum can be meaningfully compared to the score assigned to a different spectrum, has proven to be challenging. Here we describe a database search score function, the "residue evidence" (res-ev) score, that achieves both of these goals simultaneously. We also demonstrate how to combine calibrated res-ev scores with calibrated XCorr scores to produce a "combined p value" score function. We provide a benchmark consisting of four mass spectrometry data sets, which we use to compare the combined p value to the score functions used by several existing search engines. Our results suggest that the combined p value achieves state-of-the-art performance, generally outperforming MS Amanda and Morpheus and performing comparably to MS-GF+. The res-ev and combined p-value score functions are freely available as part of the Tide search engine in the Crux mass spectrometry toolkit ( http://crux.ms ).

Data-Dependent Scoring Parameter Optimization in MS-GF+ Using Spectrum Quality Filter.

Most database search tools for proteomics have their own scoring parameter sets depending on experimental conditions such as fragmentation methods, instruments, digestion enzymes, and so on. These scoring parameter sets are usually predefined by tool developers and cannot be modified by users. The number of different experimental conditions grows as the technology develops, and the given set of scoring parameters could be suboptimal for tandem mass spectrometry data acquired using new sample preparation or fragmentation methods. Here we introduce a new approach to optimize scoring parameters in a data-dependent manner using a spectrum quality filter. The new approach conducts a preliminary search for the spectra selected by the spectrum quality filter. Search results from the preliminary search are used to generate data-dependent scoring parameters; then, the full search over the entire input spectra is conducted using the learned scoring parameters. We show that the new approach yields more and better peptide-spectrum matches than the conventional search using built-in scoring parameters when compared at the same 1% false discovery rate.

Venomics-Accelerated Cone Snail Venom Peptide Discovery.

Cone snail venoms are considered a treasure trove of bioactive peptides. Despite over 800 species of cone snails being known, each producing over 1000 venom peptides, only about 150 unique venom peptides are structurally and functionally characterized. To overcome the limitations of the traditional low-throughput bio-discovery approaches, multi-omics systems approaches have been introduced to accelerate venom peptide discovery and characterisation. This “venomic” approach is starting to unravel the full complexity of cone snail venoms and to provide new insights into their biology and evolution. The main challenge for venomics is the effective integration of transcriptomics, proteomics, and pharmacological data and the efficient analysis of big datasets. Novel database search tools and visualisation techniques are now being introduced that facilitate data exploration, with ongoing advances in related omics fields being expected to further enhance venomics studies. Despite these challenges and future opportunities, cone snail venomics has already exponentially expanded the number of novel venom peptide sequences identified from the species investigated, although most novel conotoxins remain to be pharmacologically characterised. Therefore, efficient high-throughput peptide production systems and/or banks of miniaturized discovery assays are required to overcome this bottleneck and thus enhance cone snail venom bioprospecting and accelerate the identification of novel drug leads.

Clinical leadership and nursing explored: A literature search.

To explore what we know of the concept of clinical leadership and what the term means. Clues to the definition of clinical leadership, the attributes of effective and less effective clinical leaders, models of clinical leadership and the barriers that hinder clinical leadership development were explored. While nursing leadership and healthcare leadership are terms that have been evident in nursing and health industry literature for many decades, clinical leadership is a relatively new term and is may still be misunderstood. A search was undertaken of formal and informal literature using a library database and a range of search engines for the words "clinical leadership" and "clinical leadership in nursing." In each case, the full search parameters were employed with searches between 1974-2016. Full-text articles were requested, and English was the preferred language. In total, 3,259 publications were located through seven database search tools, although these included a large number of duplications. Following further informal searches and removing irrelevant material, 27 research or literature review focused papers were retained that included 17 qualitative studies, one quantitative study, one mixed method study, one Delphi study and two that compared other research studies. As well, five literature reviews were retained in the synthesis. The data synthesis resulted five categories: definitions of clinical leadership, characteristics most likely or least associated with clinical leadership, models applied to clinical leadership and limits to clinical leadership development. Clinical leaders are recognised for having their values and beliefs parallel their actions and interventions. They are found across the spectrum of health organisations, often at the highest level for clinical interaction, but not commonly at the highest management level in a ward or unit team and they are seen in all clinical environments. Clinical Leadership and an understanding on how clinical leaders contribute to the health service is central to the application of values-based practice and how clinical leaders impact on innovation, change and making care better.

Introducing EzBioCloud: a taxonomically united database of 16S rRNA gene sequences and whole-genome assemblies.

The recent advent of DNA sequencing technologies facilitates the use of genome sequencing data that provide means for more informative and precise classification and identification of members of the Bacteria and Archaea. Because the current species definition is based on the comparison of genome sequences between type and other strains in a given species, building a genome database with correct taxonomic information is of paramount need to enhance our efforts in exploring prokaryotic diversity and discovering novel species as well as for routine identifications. Here we introduce an integrated database, called EzBioCloud, that holds the taxonomic hierarchy of the Bacteria and Archaea, which is represented by quality-controlled 16S rRNA gene and genome sequences. Whole-genome assemblies in the NCBI Assembly Database were screened for low quality and subjected to a composite identification bioinformatics pipeline that employs gene-based searches followed by the calculation of average nucleotide identity. As a result, the database is made of 61 700 species/phylotypes, including 13 132 with validly published names, and 62 362 whole-genome assemblies that were identified taxonomically at the genus, species and subspecies levels. Genomic properties, such as genome size and DNA G+C content, and the occurrence in human microbiome data were calculated for each genus or higher taxa. This united database of taxonomy, 16S rRNA gene and genome sequences, with accompanying bioinformatics tools, should accelerate genome-based classification and identification of members of the Bacteria and Archaea. The database and related search tools are available at www.ezbiocloud.net/.

A computational model for predicting integrase catalytic domain of retrovirus

Integrase catalytic domain (ICD) is an essential part in the retrovirus for integration reaction, which enables its newly synthesized DNA to be incorporated into the DNA of infected cells. Owing to the crucial role of ICD for the retroviral replication and the absence of an equivalent of integrase in host cells, it is comprehensible that ICD is a promising drug target for therapeutic intervention. However, annotated ICDs in UniProtKB database have still been insufficient for a good understanding of their statistical characteristics so far. Accordingly, it is of great importance to put forward a computational ICD model in this work to annotate these domains in the retroviruses. The proposed model then discovered 11,660 new putative ICDs after scanning sequences without ICD annotations. Subsequently in order to provide much confidence in ICD prediction, it was tested under different cross-validation methods, compared with other database search tools, and verified on independent datasets. Furthermore, an evolutionary analysis performed on the annotated ICDs of retroviruses revealed a tight connection between ICD and retroviral classification. All the datasets involved in this paper and the application software tool of this model can be available for free download at https://sourceforge.net/projects/icdtool/files/?source=navbar.

MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry-based proteomics.

There is a need to better understand and handle the “dark matter” of proteomics – the vast diversity of post-translational and chemical modifications that are unaccounted in a typical analysis and thus remain unidentified. We present a novel fragment-ion indexing method, and its implementation in peptide identification tool MSFragger, that enables an over 100-fold improvement in speed over most existing tools. Using some of the largest proteomic datasets to date, we demonstrate how MSFragger empowers the open database search concept for comprehensive identification of peptides and all their modified forms, uncovering dramatic differences in the modification rates across experimental samples and conditions. We further illustrate its utility using protein-RNA crosslinked peptide data, and using affinity purification experiments where we observe on average a 300% increase in the number of identified spectra for enriched proteins. We also discuss the benefits of open searching for improved false discovery rate estimation in proteomics.

Protein sequence-similarity search acceleration using a heuristic algorithm with a sensitive matrix.

Protein database search for public databases is a fundamental step in the target selection of proteins in structural and functional genomics and also for inferring protein structure, function, and evolution. Most database search methods employ amino acid substitution matrices to score amino acid pairs. The choice of substitution matrix strongly affects homology detection performance. We earlier proposed a substitution matrix named MIQS that was optimized for distant protein homology search. Herein we further evaluate MIQS in combination with LAST, a heuristic and fast database search tool with a tunable sensitivity parameter m, where larger m denotes higher sensitivity. Results show that MIQS substantially improves the homology detection and alignment quality performance of LAST across diverse m parameters. Against a protein database consisting of approximately 15 million sequences, LAST with m = 105 achieves better homology detection performance than BLASTP, and completes the search 20 times faster. Compared to the most sensitive existing methods being used today, CS-BLAST and SSEARCH, LAST with MIQS and m = 106 shows comparable homology detection performance at 2.0 and 3.9 times greater speed, respectively. Results demonstrate that MIQS-powered LAST is a time-efficient method for sensitive and accurate homology search.Electronic supplementary materialThe online version of this article (doi:10.1007/s10969-016-9210-4) contains supplementary material, which is available to authorized users.

Evaluating the effect of database inflation in proteogenomic search on sensitive and reliable peptide identification

BackgroundProteogenomics is a promising approach for various tasks ranging from gene annotation to cancer research. Databases for proteogenomic searches are often constructed by adding peptide sequences inferred from genomic or transcriptomic evidence to reference protein sequences. Such inflation of databases has potential of identifying novel peptides. However, it also raises concerns on sensitive and reliable peptide identification. Spurious peptides included in target databases may result in underestimated false discovery rate (FDR). On the other hand, inflation of decoy databases could decrease the sensitivity of peptide identification due to the increased number of high-scoring random hits. Although several studies have addressed these issues, widely applicable guidelines for sensitive and reliable proteogenomic search have hardly been available.ResultsTo systematically evaluate the effect of database inflation in proteogenomic searches, we constructed a variety of real and simulated proteogenomic databases for yeast and human tandem mass spectrometry (MS/MS) data, respectively. Against these databases, we tested two popular database search tools with various approaches to search result validation: the target-decoy search strategy (with and without a refined scoring-metric) and a mixture model-based method. The effect of separate filtering of known and novel peptides was also examined. The results from real and simulated proteogenomic searches confirmed that separate filtering increases the sensitivity and reliability in proteogenomic search. However, no one method consistently identified the largest (or the smallest) number of novel peptides from real proteogenomic searches.ConclusionsWe propose to use a set of search result validation methods with separate filtering, for sensitive and reliable identification of peptides in proteogenomic search.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3327-5) contains supplementary material, which is available to authorized users.

Top-down protein identification using isotopic envelope fingerprinting

For top-down protein database search and identification from tandem mass spectra, our isotopic envelope fingerprinting search algorithm and ProteinGoggle search engine have demonstrated their strength of efficiently resolving heavily overlapping data as well separating non-ideal data with non-ideal isotopic envelopes from ideal ones with ideal isotopic envelopes. Here we report our updated ProteinGoggle 2.0 for intact protein database search with full-capacity. The indispensable updates include users' optional definition of dynamic post-translational modifications and static chemical labeling during database creation, comprehensive dissociation methods and ion series, as well as a Proteoform Score for each proteoform. ProteinGoggle has previously been benchmarked with both collision-based dissociation (CID, HCD) and electron-based dissociation (ETD) data of either intact proteins or intact proteomes. Here we report our further benchmarking of the new version of ProteinGoggle with publically available photon-based dissociation (UVPD) data (http://hdl.handle.net/2022/17316) of intact E. coli ribosomal proteins. Biological significanceProtein species (aka proteoforms) function at their molecular level, and diverse structures and biological roles of every proteoform come from often co-occurring proteolysis, amino acid variation and post-translational modifications. Complete and high-throughput capture of this combinatorial information of proteoforms has become possible in evolving top-down proteomics; yet, various methods and technologies, especially database search and bioinformatics identification tools, in the top-down pipeline are still in their infancy stages and demand intensive research and development.

Bioinformatic Identification of Rare Codon Clusters (RCCs) in HBV Genome and Evaluation of RCCs in Proteins Structure of Hepatitis B Virus.

BackgroundHepatitis B virus (HBV) as an infectious disease that has nine genotypes (A - I) and a ‘putative’ genotype J.ObjectivesThe aim of this study was to identify the rare codon clusters (RCC) in the HBV genome and to evaluate these RCCs in the HBV proteins structure.MethodsFor detection of protein family accession numbers (Pfam) in HBV proteins, the UniProt database and Pfam search tool were used. Protein family accession numbers is a comprehensive and accurate collection of protein domains and families. It contains annotation of each family in the form of textual descriptions, links to other resources and literature references. Genome projects have used Pfam extensively for large-scale functional annotation of genomic data; Pfam database is a large collection of protein families, each represented by multiple sequence alignments and hidden Markov models (HMMs). The Pfam search tools are databases that identify Pfam of proteins. These Pfam IDs were analyzed in Sherlocc program and the location of RCCs in HBV genome and proteins were detected and reported as translated EMBL nucleotide sequence data library (TrEMBL) entries. The TrEMBL is a computer-annotated supplement of SWISS-PROT that contains all the translations of European molecular biology laboratory (EMBL) nucleotide sequence entries not yet integrated in SWISS-PROT. Furthermore, the structures of TrEMBL entries proteins were studied in the PDB database and 3D structures of the HBV proteins and locations of RCCs were visualized and studied using Swiss PDB Viewer software®.ResultsThe Pfam search tool found nine protein families in three frames. Results of Pfams studies in the Sherlocc program showed that this program has not identified RCCs in the external core antigen (PF08290) and truncated HBeAg gene (PF08290) of HBV. By contrast, the RCCs were identified in gene of hepatitis core antigen (PF00906 and the residues 224 - 234 and 251 - 255), large envelope protein S (PF00695 and the residues 53-56 and 70 - 84), X protein (PF00739 and the residues 10 - 24, 29 - 83, 95 - 99. 122 - 129, 139 - 143), DNA polymerase (viral) N-terminal domain (PF00242 and the residues 59 - 62, 214 - 217, 407 - 413) and protein P (Pf00336 and the residues 225 - 228). In HBV genome, seven RCCs were identified in the gene area of hepatitis core antigen, large envelope protein S and DNA polymerase, while protein structures of TrEMBL entries sequences found in Sherlocc program outputs were not complete.ConclusionsBased on the location of detected RCCs in the structure of HBV proteins, it was found that these RCCs may have a critical role in correct folding of HBV proteins and can be considered as drug targets. The results of this study provide new and deep perspectives about structure of HBV proteins for further researches and designing new drugs for treatment of HBV.

Parasail: SIMD C library for global, semi-global, and local pairwise sequence alignments.

BackgroundSequence alignment algorithms are a key component of many bioinformatics applications.Though various fast Smith-Waterman local sequence alignment implementations have been developed for x86 CPUs, most are embedded into larger database search tools. In addition, fast implementations of Needleman-Wunsch global sequence alignment and its semi-global variants are not as widespread. This article presents the first software library for local, global, and semi-global pairwise intra-sequence alignments and improves the performance of previous intra-sequence implementations.ResultsA faster intra-sequence local pairwise alignment implementation is described and benchmarked, including new global and semi-global variants. Using a 375 residue query sequence a speed of 136 billion cell updates per second (GCUPS) was achieved on a dual Intel Xeon E5-2670 24-core processor system, the highest reported for an implementation based on Farrar’s ‘striped’ approach. Rognes’s SWIPE optimal database search application is still generally the fastest available at 1.2 to at best 2.4 times faster than Parasail for sequences shorter than 500 amino acids. However, Parasail was faster for longer sequences. For global alignments, Parasail’s prefix scan implementation is generally the fastest, faster even than Farrar’s ‘striped’ approach, however the opal library is faster for single-threaded applications. The software library is designed for 64 bit Linux, OS X, or Windows on processors with SSE2, SSE41, or AVX2. Source code is available from https://github.com/jeffdaily/parasail under the Battelle BSD-style license.ConclusionsApplications that require optimal alignment scores could benefit from the improved performance. For the first time, SIMD global, semi-global, and local alignments are available in a stand-alone C library.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-0930-z) contains supplementary material, which is available to authorized users.

A Systems Biology Approach Reveals Converging Molecular Mechanisms that Link Different POPs to Common Metabolic Diseases.

Background:A number of epidemiological studies have identified statistical associations between persistent organic pollutants (POPs) and metabolic diseases, but testable hypotheses regarding underlying molecular mechanisms to explain these linkages have not been published.Objectives:We assessed the underlying mechanisms of POPs that have been associated with metabolic diseases; three well-known POPs [2,3,7,8-tetrachlorodibenzodioxin (TCDD), 2,2´,4,4´,5,5´-hexachlorobiphenyl (PCB 153), and 4,4´-dichlorodiphenyldichloroethylene (p,p´-DDE)] were studied. We used advanced database search tools to delineate testable hypotheses and to guide laboratory-based research studies into underlying mechanisms by which this POP mixture could produce or exacerbate metabolic diseases.Methods:For our searches, we used proprietary systems biology software (MetaCore™/MetaDrug™) to conduct advanced search queries for the underlying interactions database, followed by directional network construction to identify common mechanisms for these POPs within two or fewer interaction steps downstream of their primary targets. These common downstream pathways belong to various cytokine and chemokine families with experimentally well-documented causal associations with type 2 diabetes.Conclusions:Our systems biology approach allowed identification of converging pathways leading to activation of common downstream targets. To our knowledge, this is the first study to propose an integrated global set of step-by-step molecular mechanisms for a combination of three common POPs using a systems biology approach, which may link POP exposure to diseases. Experimental evaluation of the proposed pathways may lead to development of predictive biomarkers of the effects of POPs, which could translate into disease prevention and effective clinical treatment strategies.Citation:Ruiz P, Perlina A, Mumtaz M, Fowler BA. 2016. A systems biology approach reveals converging molecular mechanisms that link different POPs to common metabolic diseases. Environ Health Perspect 124:1034–1041; http://dx.doi.org/10.1289/ehp.1510308