Integrated databanks access and sequence/structure analysis services at the PBIL.

Retrieval and analysis of nucleic acid and protein sequence

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.

Nucleic acid and protein sequence analysis: a practical approach

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.

We have developed a versatile program for the analysis of nucleic acid and protein sequences on the IBM Personal Computer. The program is interactive and self-instructing. It contains all the features generally found in sequence analysis programs on large computers, including extensive homology routines, as well as new procedures for the entry of sequence data. The program contains facilities to store and utilize the entire Nucleic Acid Sequence Data Bank. We have devised a new algorithm to find restriction enzyme sites, which allows our microcomputer program to find all sites on a small plasmid for 100 different enzymes in 1 to 2 minutes.

The enhanced graphic matrix procedure analyzes nucleic acid and amino acid sequences for features of possible biological interest and reveals the spatial patterns of such features. When a sequence is compared to itself the technique shows regions of self-complementarity, direct repeats, and palindromic subsequences. Comparison of two different sequences, exemplified by immunoglobulin kappa light chain genes, by using colored graphic matrices showed domains of similarity, regions of divergence, and features explainable by transpositions. Analysis of mouse constant domain immunoglobulin sequences revealed self-complementary regions that can be used to fold the molecule into a structure consistent with electron microscopic observations. Computer translation of nucleic acid sequences into all possible amino acid sequences followed by graphic matrix analysis provides a way to detect the most likely protein encoding regions and can predict the correct reading frames in sequences in which splicing patterns are not defined. Application of this technique to regions of simian virus 40 and polyoma virus demonstrates the frames of translation and shows the agreement of sequences determined in separate laboratories with different virus isolates. The graphic matrix technique can also be used to assemble fragmentary sequences during determination, to display local variations in base composition, to detect distant evolutionary relationships, and to display intragenic variation in rates of evolution.

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

One of the major goals of computational sequence analysis is to find sequence similarities, which could serve as evidence of structural and functional conservation, as well as of evolutionary relations among the sequences. Since the degree of similarity is usually assessed by the sequence alignment score, it is necessary to know if a score is high enough to indicate a biologically interesting alignment. A powerful approach to defining score cutoffs is based on the evaluation of the statistical significance of alignments. The statistical significance of an alignment score is frequently assessed by its P-value, which is the probability that this score or a higher one can occur simply by chance, given the probabilistic models for the sequences. In this review we discuss the general role of P-value estimation in sequence analysis, and give a description of theoretical methods and computational approaches to the estimation of statistical signifiance for important classes of sequence analysis problems. In particular, we concentrate on the P-value estimation techniques for single sequence studies (both score-based and score-free), global and local pairwise sequence alignments, multiple alignments, sequence-to-profile alignments and alignments built with hidden Markov models. We anticipate that the review will be useful both to researchers professionally working in bioinformatics as well as to biomedical scientists interested in using contemporary methods of DNA and protein sequence analysis.

A multiple sequence alignment (MSA) is a sequence alignment of three or more biological sequences, generally for Protein. MSA has wide range of applications and to cite few of them such as phylogenetic analysis, protein pattern identification, protein domain identification, prediction of protein structure, structural similarity of amino acids and to get evolutionary similarity. ClustalW2 is a general purpose global multiple sequence alignment program for proteins. It produces biologically meaningful multiple sequence alignments of divergent sequences. The output from ClustalW2 shows the best match for the selected sequences and lines up them in such a way that the identities, similarities and differences can be easily understood. Evolutionary relationships can be seen by creating Cladograms or Phylograms. Firstly, individual weights are assigned to each sequence in a partial alignment in order to down-weight near-duplicate sequences and up-weight the most divergent ones. Secondly, amino acid substitution matrices are varied at different alignment stages according to the divergence of the sequences to be aligned. Thirdly, residue-specific gap penalties and locally reduced gap penalties in hydrophilic regions encourage new gaps in potential loop regions rather than regular secondary structure. Fourthly, positions in early alignments where gaps have been opened receive locally reduced gap penalties to encourage the opening up of new gaps at these positions. In this paper, protein sequences of few legume species from UNIPROT database were taken and focused on MSA for protein sequences for these tree species of family Leguminosae, where ClustalW2 tool have used to generate biological data. The results are discussed with the help of Cladograms and Phylograms for selected tree species.

Co-evolution has an important function in the evolution of species and it is clearly manifested in certain scenarios such as host–parasite and predator–prey interactions, symbiosis and mutualism. The extrapolation of the concepts and methodologies developed for the study of species co-evolution at the molecular level has prompted the development of a variety of computational methods able to predict protein interactions through the characteristics of co-evolution. Particularly successful have been those methods that predict interactions at the genomic level based on the detection of pairs of protein families with similar evolutionary histories (similarity of phylogenetic trees: mirrortree). Future advances in this field will require a better understanding of the molecular basis of the co-evolution of protein families. Thus, it will be important to decipher the molecular mechanisms underlying the similarity observed in phylogenetic trees of interacting proteins, distinguishing direct specific molecular interactions from other general functional constraints. In particular, it will be important to separate the effects of physical interactions within protein complexes (‘co-adaptation') from other forces that, in a less specific way, can also create general patterns of co-evolution.

Gene annotation is a process that encompasses multiple approaches on the analysis of nucleic acids or protein sequences in order to assign structural and functional characteristics to gene models. When thousands of gene models are being described in an organism genome, construction and visualization of gene networks impose novel challenges in the understanding of complex expression patterns and the generation of new knowledge in genomics research. In order to take advantage of accumulated text data after conventional gene sequence analysis, this work applied semantics in combination with visualization tools to build transcriptome networks from a set of coffee gene annotations. A set of selected coffee transcriptome sequences, chosen by the quality of the sequence comparison reported by Basic Local Alignment Search Tool (BLAST) and Interproscan, were filtered out by coverage, identity, length of the query, and e-values. Meanwhile, term descriptors for molecular biology and biochemistry were obtained along the Wordnet dictionary in order to construct a Resource Description Framework (RDF) using Ruby scripts and Methontology to find associations between concepts. Relationships between sequence annotations and semantic concepts were graphically represented through a total of 6845 oriented vectors, which were reduced to 745 non-redundant associations. A large gene network connecting transcripts by way of relational concepts was created where detailed connections remain to be validated for biological significance based on current biochemical and genetics frameworks. Besides reusing text information in the generation of gene connections and for data mining purposes, this tool development opens the possibility to visualize complex and abundant transcriptome data, and triggers the formulation of new hypotheses in metabolic pathways analysis.

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)

Book reviewed in this article:G. G. B. Klaus (ed): Lymphocytes: A Practical Approach.C. Horvath (ed): High‐Performance Liquid Chromatography—Advances and Perspectives, Vol. 4.F. W. Doane and N. Anderson: Electron Microscopy in Diagnostic Virology. A Practical Guide and Atlas.N. R. Rose, H. Friedman and J. L. Fahey (eds): Manual of Clinical Immunology, 3rd edn.M. J. Bishop and C. J. Rawlings (eds): Nucleic Acid and Protein Sequence Analysis: A Practical Approach.

Nucleic acid and protein sequence analysis: A practical approach: edited by M. J. Bishop and C. J. Rawlings, IRL Press, 1987. £30/$54 hbk, £19/$34 pbk (xviii + 417 pages) ISBN 1 85221 006 0

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