Sequence analysis of membrane proteins

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.

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.

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.

This article reviews current knowledge about the organization of bacterial genomes, of which a number of components (replicons), namely chromosomes, plasmids and prophages, have been well characterized. The historical position of the acceptance of the idea of circularity and unit copy number of replicons in bacterial cells has been readdressed by new methods of genome analysis, particularly pulsed-field gcl electrophoresis, which have facilitated identification of variation in replicon number and distinction whether the replicons are circular or linear DNA structures. Much has also been learnt about the origins of DNA replication in replicons and how they function via the controlling role of specific proteins or RNA. A related aspect is thc problem of how the replication products are stabilized, segregated and partitioned into daughter cells at cell division. Our understanding of replicons has also been improved by application of state-of-the-art computer software methods of comparative DNA and protein sequence analysis. This knowledge has provided insights into the fundamental nature of these processes and their origin and evolution in single-cell and multicellular organisms.

Amino-terminal (N-terminal) sequence analysis is used to identify the order of amino acids of proteins or peptides, starting at their N-terminal end. This unit describes the sequence analysis of protein or peptide samples in solution or bound to PVDF membranes using a Perkin-Elmer Procise Sequencer. Sequence analysis of protein or peptide samples in solution or bound to PVDF membranes using a Hewlett-Packard Model G1005A sequencer is also described. Methods are provided for optimizing separation of PTH amino acid derivatives on Perkin-Elmer instruments and for increasing the proportion of sample injected onto the PTH analyzer on older Perkin-Elmer instruments by installing a modified sample loop. The amount of data obtained from a single sequencer run is substantial, and careful interpretation of this data by an experienced scientist familiar with the current operation performance of the instrument used for this analysis is critically important. A discussion of data interpretation is therefore provided. Finally, discussion of optimization of sequencer performance as well as possible solutions to frequently encountered problems is included.

Biology has progressed tremendously in the last decade due in part to the increased automation in the generation of data from sequences to genotypes to phenotypes. Biology is now very much an information science and bioinformatics provides the means to connect biological data to hypotheses. Bioinformatics: Tools and Applications provides up-to-date descriptions of the various areas of applied bioinformatics, from the analysis of sequence, literature, and functional data to the function and evolution of organisms. The ability to process and interpret large volumes of data is essential with the application of new high throughput DNA sequencers providing an overload of sequence data. Initial chapters provide an introduction to the analysis of DNA and protein sequences, from motif detection to gene prediction and annotation, with specific chapters on DNA and protein databases as well as data visualization. Additional chapters focus on gene expression analysis from the perspective of traditional microarrays and more recent sequence based approaches, followed by an introduction to the evolving field of phenomics, with specific chapters detailing advances in plant and microbial phenome analysis and a chapter dealing with the important issue of standards for functional genomics. Further chapters present the area of literature databases and associated mining tools which are becoming increasingly essential to interpret the vast volume of published biological information, while the final chapters present bioinformatics purely from a developers point of view, describing the various data and databases as well as common programming languages used for bioinformatics applications. These chapters provide an introduction and motivation to further avenues for implementation. Together, this volume aims to provide a resource for biology students wanting a greater understanding of the encroaching area of bioinformatics, as well as computer scientists who are interested learning more about the field of applied bioinformatics.

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

Sequence alignment algorithms, which are hardly to be efficient, are frequently used in protein sequences analysis. In order to improve the analyzing efficiency, an improved PST(Probabilistic Suffix Trees) model is proposed in this paper. Firstly, by analyzing the similarity between protein sequences analysis and sequences data mining, the idea of using PST model to analyze protein sequences is presented; And then the standard PST model is improved by smoothing operation according to the features of protein sequences analysis; Next, taking the smoothed PST as features of data set, the similarities degree between protein sequences are calculated by using the similarities of the normalized sequences; At last, the effectiveness and high efficiency of the algorithm are verified by some protein sequences analysis examples.

We have previously described programs for a variety of types of sequence analysis (1-4). These programs have now been integrated into a single package. They are written in the standard C programming language and run on virtually any computer system with a C compiler, such as the IBM/PC and other computers running under the MS/DOS and UNIX operating systems. The programs are widely distributed and may be obtained from the authors as described below.

The World Wide Web server of the PBIL (Pôle Bioinformatique Lyonnais) provides on-line access to sequence databanks and to many tools of nucleic acid and protein sequence analyses. This server allows to query nucleotide sequence banks in the EMBL and GenBank formats and protein sequence banks in the SWISS-PROT and PIR formats. The query engine on which our data bank access is based is the ACNUC system. It allows the possibility to build complex queries to access functional zones of biological interest and to retrieve large sequence sets. Of special interest are the unique features provided by this system to query the data banks of gene families developed at the PBIL. The server also provides access to a wide range of sequence analysis methods: similarity search programs, multiple alignments, protein structure prediction and multivariate statistics. An originality of this server is the integration of these two aspects: sequence retrieval and sequence analysis. Indeed, thanks to the introduction of re-usable lists, it is possible to perform treatments on large sets of data. The PBIL server can be reached at: http://pbil.univ-lyon1.fr.

A group of intrinsically disordered, hydrophilic proteins—Late Embryogenesis Abundant (LEA) proteins—has been linked to survival in plants and animals in periods of stress, putatively through safeguarding enzymatic function and prevention of aggregation in times of dehydration/heat. Yet despite decades of effort, the molecular-level mechanisms defining this protective function remain unknown. A recent effort to understand LEA functionality began with the unique application of phage display, wherein phage display and biopanning over recombinant Seed Maturation Protein homologs from Arabidopsis thaliana and Glycine max were used to retrieve client proteins at two different temperatures, with one intended to represent heat stress. From this previous study, we identified 21 client proteins for which clones were recovered, sometimes repeatedly. Here, we use sequence analysis and homology modeling of the client proteins to ascertain common sequence and structural properties that may contribute to binding affinity with the protective LEA protein. Our methods uncover what appears to be a predilection for protein-nucleic acid interactions among LEA client proteins, which is suggestive of subcellular residence. The results from this initial computational study will guide future efforts to uncover the protein protective mechanisms during heat stress, potentially leading to phage-display-directed evolution of synthetic LEA molecules.

Eighth international conference on methods in protein sequence analysis (Short communications)

Solid Phase Methods in Protein Sequence Analysis: Edited by A. Previero; M. A. Coletti-Previero. Inserm Symposium No 5. Pp 298. Elsevier/North-Holland Biomedical Press, Amsterdam and New York, 1977. $37.95 or Dfl. 87.00

We develop a novel multi-class classification method based on output codes for the problem of classifying a sequence of amino acids into one of many known protein structural classes, called folds. Our method learns relative weights between one-vs-all classifiers and encodes information about the protein structural hierarchy for multi-class prediction. Our code weighting approach significantly improves on the standard one-vs-all method for the fold recognition problem. In order to compare against widely used methods in protein sequence analysis, we also test nearest neighbor approaches based on the PSI-BLAST algorithm. Our code weight learning algorithm strongly outperforms these PSI-BLAST methods on every structure recognition problem we consider.

Methods in protein sequence analysis: K.A. Walsh Humana Press, Clifton, NJ, 1987, xxxv + 658 pp. ($79.50) ISBN 0-89603-118-7