Protein Sequences Analysis Based on Smoothed PST

The primary structures of proteins can be determined by two distinct methods: directly by sequence analysis of the protein or indirectly by DNA sequence analysis of the corresponding gene or c-DNA. The direct protein sequence analysis methods are strongly influenced by the nature of the protein. They have been largely developed to accommodate water-soluble globular proteins, and methods for isolation and sequence analysis of hydrophobic proteins are relatively deficient and underdeveloped. The indirect sequence analysis by DNA sequence analysis is not influenced by the nature of the protein.

BackgroundThe World Health Organization (WHO) report stated that Acinetobacter baumannii had been classified as one of the most important pathogenic bacteria causing nosocomial infection in hospital patients due to multi-drug resistance (MDR). It is vital to find out new bacterial drug targets and annotated their structure and function for the exploration of new anti-bacterial agents. The present study utilized a systematic route to prioritize the potential drug targets that belong to Mur family of Acinetobacter baumannii and identify their homologous proteins using a computational approach such as sequence similarity search, multiple sequence alignment, phylogenetic analysis, protein sequence, and protein structure analysis. ResultsFrom the results of protein sequence analysis of eight Mur family proteins, they divided into three main enzymatic classes namely transferases (MurG, MurA and MraY), ligases (MurC, MurD, MurE, and MurF), and oxidoreductase (MurB). Based on the results of intra-comparative protein sequence analysis and enzymatic classification, we have chosen MurB, MurE, and MurG as the prioritized drug targets from A. baumannii and subjected them for further detailed studies of inter-species comparison. This inter-species comparison help us to explore the sequential and structural properties of homologous proteins in other species and hence, opens a gateway for new target identification and using common inhibitor for different bacterial species caused by various diseases. The pairwise sequence alignment results between A. baumannii’s MurB with A. calcoaceticus’s MurB, A. baumannii’s MurE with A. seifertii’s MurE, and A. baumannii’s MurG with A. pittii’s MurG showed that every group of the proteins are highly similar with each other and they showed sequence identity of 95.7% and sequence similarity of 97.2%. ConclusionTogether with the results of secondary and three-dimensional structure predictions explained that three selected proteins (MurB, MurE, and MurG) from A. baumannii and their related proteins (AcMurB, AsMurE, and ApMurG) belong to mixed αβ class and they are very similar.

Adsorptive Biphasic Column Technology for Protein Sequence Analysis and Protein Chemical Modification

The area of unsupervised activity categorization in computer vision is much less explored compared to the general practice of supervised learning of activity patterns. Recent works in the lines of activity discovery have proposed the use of probabilistic suffix trees (PST) and its variants which learn the activity models from temporally ordered sequences of object states. Such sequences often contain lots of objectstate self-transitions resulting in a large number of PST nodes in the learned activity models. We propose an alternative method of mining these sequences by avoiding to learn the self-transitions while maintaining the useful statistical properties of the sequences thereby forming a compressed suffix tree (CST). We show that, on arbitrary sequences with significant self-transitions, the CST achieves a much lesser size as compared to the polynomial growth of the PST. We further propose a distance metric between the CSTs using which, the learned activity models are categorized using hierarchical agglomerative clustering. CSTs learned from object trajectories extracted from two data sets are clustered for experimental verification of activity discovery.

Motivation: Modern sequencing technology now permits the sequencing of entire genomes, leading to thousands of new gene sequences in need of detailed annotation. It is too time consuming to predict the properties of each protein sequence manually and to organize the results of many prediction tools by hand. The prediction process must be automated so the predictions can be automatically organized, but the predictions must also be transparent. That is, the rationale for each prediction should be easily examinable by anyone that wishes to use the prediction. Results: Proteome Analyst (PA) is a web-based system for predicting the properties of each protein in a proteome. PA has three interesting features. First, it provides a single web-based tool that allows the user to select a wide range of analytic tools and automatically apply them to each protein in a proteome. In essence, PA provides one-stop automatic high-throughput analysis. Second, PA has the ability to explain its predictions to users. PA is based on established machine learning techniques, but makes every prediction transparent to its users. Third, PA allows users to easily create their own transparent custom predictors without programming. Availability: http://www.cs.ualberta.ca/~bioinfo/PA Supplementary Information: http://www.cs.ualberta.ca/~bioinfo/PA/Walkthrough http://www.cs.ualberta.ca/~bioinfo/PA/Experiments Contact: bioinfo@cs.ualberta.ca INTRODUCTION High-throughput sequencing technology has made it possible for even the smallest laboratory to sequence the genome of organelles, viruses and bacteria. Indeed there are now more than 1200 genome sequences deposited in public databases (http://www.ebi.ac.uk/genomes/) and this number is growing rapidly. Given the size and complexity of these data sets, most researchers are compelled to use automated annotation systems to identify or classify individual genes and proteins in their genomic data. A number of excellent systems have been developed over the past few years that permit automated genome-wide or proteome-wide annotation. These include GeneQuiz (Andrade et al., 1999), GeneAtlas (Kitson et al., 2002), EnsEMBL (Hubbard et al., 2002), PEDANT (Frishman et al., 2001), Genotator (Harris, 1997), Magpie (Gaasterland and Sensen, 1996) and GAIA (Overton et al., 1998). These use web-based tools to identify genes, parse data, translate sequences, search against public databases, identify domains or motifs and perform predictive analyses of protein sequences. Many of these packages provide user-customizable search schemas and graphical, hyperlinked output. The level of interpretation or inference offered by these annotation systems varies widely, with some offering only raw data (lists of homologues, calculated properties, etc.) in a consolidated format and others inferring function or ontology through detailed lexical analysis. Our PA system focuses on the task of prediction or classification. Our results show that classification/prediction can be used for many annotations. The most obvious is general-function, but we are also working on sub-cellular localization, specific function and protein-protein interaction are others we are working on. To use classification, we require an ontology or “dictionary” of class terms. Consequently, a number of controlled vocabularies or ontologies of protein function have started appearing. Among the first was the enzyme classification scheme (E.C. number) developed by the IUBMB and first employed in the ENZYME database (Bairoch, 1993). Later, Riley (1993) developed functional classification schemas for E. coli and other microbes. Variations on these functional vocabularies were added as other genomes and genome annotation systems were

Location prediction has attracted a significant amount of research effort. Given an object's recent movements and a future time, the goal of location prediction is to predict the location of this object at the future time specified. Prior works have elaborated on mining association relationships among regions, in which objects frequently appear, to predict locations. Association relationships among regions are represented as association rules. By exploring association relationships among regions, prior works are able to have a good accuracy for location prediction. However, with a large amount of trajectory data produced, a huge amount of association rules is expected. Furthermore, trajectory data has both the spatial and temporal information. To further enhance the accuracy of location prediction, one could utilize not only spatial information but also temporal information to estimate locations of objects. In this paper, we propose a spatial-temporal trajectory model (abbreviated as STT) to capture movement behaviors of objects. STT is represented as a probabilistic suffix tree with both spatial and temporal information of movements. Note that STT is able to discover sequential traversal relationships among regions and, for each region, STT derives the corresponding probabilities about the time when objects appear. With the nature of probabilistic suffix tree, we could use a compact structure to capture movement behavior of objects compared to association rules proposed. In light of STT, we further propose an algorithm to traverse STT for location prediction. By exploring both the spatial and temporal information of STT, the accuracy of location prediction is improved. To evaluate our proposed STT and prediction algorithm, we conduct experiments on the synthetic dataset generated from real datasets. Experimental results shows that our proposed STT is able to capture both spatial and temporal patterns of movement behaviors and, by exploring STT, our proposed prediction algorithm outperforms existing works.

The identification of useful temporal dependence structure in discrete time series data is an important component of algorithms applied to many tasks in statistical inference and machine learning, and used in a wide variety of problems across the spectrum of biological studies. Most of the early statistical approaches were ineffective in practice, because the amount of data required for reliable modelling grew exponentially with memory length. On the other hand, many of the more modern methodological approaches that make use of more flexible and parsimonious models result in algorithms that do not scale well and are computationally ineffective for larger data sets. In this paper we describe a class of novel methodological tools for effective Bayesian inference for general discrete time series, motivated primarily by questions regarding data originating from studies in genetics and neuroscience. Our starting point is the development of a rich class of Bayesian hierarchical models for variable-memory Markov chains. The particular prior structure we adopt makes it possible to design effective, linear-time algorithms that can compute most of the important features of the relevant posterior and predictive distributions without resorting to Markov chain Monte Carlo simulation. The origin of some of these algorithms can be traced to the family of Context Tree Weighting (CTW) algorithms developed for data compression since the mid-1990s. We have used the resulting methodological tools in numerous application-specific tasks (including prediction, segmentation, classification, anomaly detection, entropy estimation, and causality testing) on data from different areas of application. The results obtained compare quite favourably with those obtained using earlier approaches, such as Probabilistic Suffix Trees (PST), Variable-Length Markov Chains (VLMC), and the class of Markov Transition Distributions (MTD).

This article presents a new approach to detect coiled coil and leucine zipper (L-Zip) motifs in protein sequences. The approach is based on protein scale calculation and sequence analysis. For this purpose, the wavelet-based local extrema extraction is employed, and window-based variations of local extrema afterward. This, in turn, provided a way to distinguish coiled coil subsequences and potential L-Zip motifs. The approach is validated on carefully chosen protein sequences that return inconclusive results within known frameworks for L-Zip detection, for example, 2ZIP. The results show that this new approach represents an improvement over previously presented approaches.

Dicer and Dicer like (DCLs) proteins are essential part of small RNA biogenesis pathway, is a type of RNase III digesting long dsRNA (pre-miRNA) to small RNA segments (miRNA). A total of 20 full length of Dicer like proteins (DCL1, DCL2, DCL3 and DCL4) from different organisms available in NCBI were evaluated by bioinformatics tools to investigate properties, structure of DCLs, domain analysis, multiple sequence alignment and phylogenetics tree construction. All DCLs protein sequences have Ribonuclease III protein family that contains RNaseIII domain including Helicase ATP-binding type-1, Helicase C-terminal, Dicer double-stranded RNA-binding fold, PAZ, Ribonuclease III, Double stranded RNA-binding domain (dsRB). Physicochemical analysis offers data such as pI, EC, Al, GRAVY and instability index about these enzymes. Putative phosphorylation sites were also identified which are found to be conserved in plant species and the results showed that the most abundant phosphorylation site is Serine residues in DCLs proteins. Patterns and profile analysis were performed using Prosite and conserved protein motifs subjected to MEME to obtain the best possible matches. The phylogenetics tree represented three major clusters and similar DCLs protein sequences of different plant species clustered together. The obtained results could be used for further in silico analysis and homology modeling studies. Keywords: Dicer, DCLs, miRNA, RNase, In silico analysis. *Corresponding Author: Ertugrul Filiz (e-mail: ertugrulfiliz@gmail.com). (Received: 25.05.2012 Accepted: 24.01.2013)

Statistical modeling of sequences is a central paradigm of machine learning that finds multiple uses in computational molecular biology and many other domains. The probabilistic automata typically built in these contexts are subtended by uniform, fixed-memory Markov models. In practice, such automata tend to be unnecessarily bulky and computationally imposing both during their synthesis and use. In [8], much more compact, tree-shaped variants of probabilistic automata are built which assume an underlying Markov process of variable memory length. In [3, 4], these variants, called Probabilistic Suffix Trees (PSTs) were successfully applied to learning and prediction of protein families. The process of learning the automaton from a given training set S of sequences requires Θ (Ln2) worst-case time, where n is the total length of the sequences in S and L is the length of a longest substring of S to be considered for a candidate state in the automaton. Once the automaton is built, predicting the likelihood of a query sequence of m characters may cost time Θ (m2) in the worst case.The main contribution of this paper is to introduce automata equivalent to PSTs but having the following properties: learning the automaton takes O (n) time.prediction of a string of m symbols by the automaton takes O (m) time.Along the way, the paper presents an evolving learning sheme, and addresses notions of empirical probability and related efficient computation,possibly a by-product of more general interest.

By two-dimensional polyacrylamide gel electrophoresis of 30S ribosomal subunit proteins (S proteins) from Haloarcula marismortui we identified 27 distinct spots and analyzed all of them by protein sequence analysis. We demonstrated that protein HmaS2 (HS2) is encoded by the open reading frame orfMSG and has sequence similarities to the S2 ribosomal protein family. The proteins HmaS5 and HmaS14 were identified as spots HS7 and HS21/HS22, respectively. Protein HS4 was characterized by amino-terminal sequence analysis. The spot HS25 was recognized as an individual protein and also characterized by sequence analysis. Furthermore, the complete primary sequence of HS26 is reported, showing similarity only to eukaryotic ribosomal proteins. The sequence data of a further basic protein shows a high degree of similarity to ribosomal protein S12, therefore, it was designated HmaS12. Slightly different results compared to published sequence data were obtained for the protein HS12 and HmaS19. The putative 'ribosomal' protein HSH could not be localized in the two-dimensional pattern of the total 30S ribosomal subunit proteins of H. marismortui. Therefore, it seems to be unlikely that this protein is a real constituent of the H. marismortui ribosome.

Protein sequence analysis is an important tool for researchers to study on bio-informatics and molecular biology, such as proteins structure and function prediction, phylogenetic classification and different conservation pattern recognition. It is a significant open issue to quickly efficiently find the similar proteins from a large scale of protein repository. This paper proposes a new method based on Discrete Wavelet Transform (DWT) to measure the similarity of protein sequences, i.e. the ACDWT model, as well as two amino acid encoding methods (HPC and ADCC) according to hydropathy properties and dissociation constants respectively. The model employs only the approximation coefficients of DWT so that the feature vector is short. That brings the proposed model a great running time promotion. According to the phylogenic trees about nine ND5 proteins made from our model and others, the experimental results show that our model is efficient and a little better than the others.

The alpha replicons of the multi-replicon plasmids pGSH500 and pLV1402 have been characterized by DNA sequence analysis. Analysis of the DNA sequence of a 3672 bp HIN:dIII fragment from pFDT100, which contains the pGSH500 alpha replicon, revealed similarity to a number of replicons belonging to, or related to, those of the IncFII family. The replicon region contains copA, tapA, repA and oriR, and replication initiation and termination sites are related to those from the IncFII replicon of R1. A copB gene was found to lie upstream of the HIN:dIII site in the parental plasmid pGSH500. Downstream of oriR, a 707 bp region shows 72.6% identity to a region of the Escherichia coli chromosome at 43.3', suggesting this region of pGSH500 may have been incorporated into the plasmid during a past chromosomal recombination event. Oligonucleotide primers homologous to consensus regions in the copB and repA genes, and the oriR regions from a number of IncFII-related replicons were used to amplify replication regions from pLV1402. Analysis of the amplified regions has shown the presence of copB, copA, tapA and repA genes. Phylogenetic analysis of Rep protein sequences from the RepFIIA family of antisense-control-regulated replicons revealed the presence of three distinct subgroups of Rep proteins. Comparative analysis of DNA and protein sequences from members of the RepFIIA family provides evidence supporting the roles of both non-selective divergence in co-integrate (multi-replicon) plasmids and Chi-mediated-recombination in replicon evolution, and in particular, that such processes may have been widespread in the evolution of the RepFIIA family.

Please cite this paper as: Pasricha et al. (2012) Comprehensive global amino acid sequence analysis of PB1F2 protein of influenza A H5N1 viruses and the Influenza A virus subtypes responsible for the 20th‐century pandemics. Influenza and Other Respiratory Viruses 7(4), 497–505. Background PB1F2 is the 11th protein of influenza A virus translated from +1 alternate reading frame of PB1 gene. Since the discovery, varying sizes and functions of the PB1F2 protein of influenza A viruses have been reported. Selection of PB1 gene segment in the pandemics, variable size and pleiotropic effect of PB1F2 intrigued us to analyze amino acid sequences of this protein in various influenza A viruses. Methods Amino acid sequences for PB1F2 protein of influenza A H5N1, H1N1, H2N2, and H3N2 subtypes were obtained from Influenza Research Database. Multiple sequence alignments of the PB1F2 protein sequences of the aforementioned subtypes were used to determine the size, variable and conserved domains and to perform mutational analysis. Results Analysis showed that 96·4% of the H5N1 influenza viruses harbored full‐length PB1F2 protein. Except for the 2009 pandemic H1N1 virus, all the subtypes of the 20th‐century pandemic influenza viruses contained full‐length PB1F2 protein. Through the years, PB1F2 protein of the H1N1 and H3N2 viruses has undergone much variation. PB1F2 protein sequences of H5N1 viruses showed both human‐ and avian host‐specific conserved domains. Global database of PB1F2 protein revealed that N66S mutation was present only in 3·8% of the H5N1 strains. We found a novel mutation, N84S in the PB1F2 protein of 9·35% of the highly pathogenic avian influenza H5N1 influenza viruses. Conclusions Varying sizes and mutations of the PB1F2 protein in different influenza A virus subtypes with pandemic potential were obtained. There was genetic divergence of the protein in various hosts which highlighted the host‐specific evolution of the virus. However, studies are required to correlate this sequence variability with the virulence and pathogenicity.

The emerging science of sequences: Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins, edited by Andreas Baxevanis and B. F. Francis Ouellette