Transcriptional profiling of the sperm storage organs of Drosophila melanogaster.

The occurrence of female sperm storage across taxa indicates the importance of this complex and dynamic process. Organs responsible for sperm storage (SSOs) and proteins expressed therein, are important in fundamental aspects of reproduction and could play a major role in evolutionary processes such as post-mating sexual selection. Given the essential role of SSOs, it is surprising that the process of sperm storage is so poorly understood. This study investigated the transcriptome of female Drosophila melanogaster SSOs (seminal receptacle and spermathecae). Spermathecae were enriched for proteases and metabolic enzymes while the seminal receptacle was enriched for genes involved in localization, signaling and ion transport. Differences in functional gene categories indicate that these organs play unique roles in sperm storage.

Large-scale predictions of secretory proteins from mammalian genomic and EST sequences.

Machine learning techniques have improved predictions of secretory proteins from protein, genomic and expressed sequence tag (EST) sequences. Artificial neural networks, physical sequence analysis using high-performance optimization, and hidden Markov models identify extremely variable signal peptides (the vehicles of protein transport across the endoplasmic reticulum membrane), transmembrane segments, and specific extracellular and intracellular domains as indicators of possible roles in the intercellular and intracellular chemical signaling pathways. The major role of peptide hormones, blood coagulation factors, carcinogenesis agents, and other secretory proteins in orchestrating multicellular life indicates pharmacological potential in the cure of major diseases and numerous biotechnological applications.

PHYSEAN: PHYsical SEquence ANalysis for the identification of protein domains on the basis of physical and chemical properties of amino acids.

MOTIVATION: PHYSEAN predicts protein classes with highly variable sequences on the basis of their physical, chemical and biological characteristics such as diverse hydrophobicity, structural propensity and steric properties. These characteristics, calculated from multiple positions in a sequence, may be conserved even between sequences that fail to produce alignments at any acceptable level of statistical significance. PHYSEAN complements methods that require sequence alignments (BLAST, FASTA, dynamic programming) by adding less residue- and position-specific physicochemical information on the protein or the domain. RESULTS: We predict proteins or their domains like signal peptides using physical, chemical, geometric, and biological properties of the 20 amino acids. This comprehensive set of properties may cover the diagnostic functional and structural aspects of a domain or a protein class. We automatically select and weight a subset of properties so as to discriminate between, e.g., signal peptides and amino-termini of cytosolic proteins with the lowest number of incorrect predictions. This optimal selection of properties and their weights significantly decreases the number of incorrect predictions as compared to any single property or any combination of unweighted properties. Weights have been optimized by high-performance linear programming models that systematically find the optimal solution from among an astronomic number of property/weight combinations. PHYSEAN's performance is demonstrated by highly accurate predictions of signal peptides (the vehicles for protein transport across membranes) and their cleavage sites. The results indicate reliable predictions are possible even in the lack of sequence conservation using an automated physical and chemical analysis of proteins.

Amino acid substitutions preserve protein folding by conserving steric and hydrophobicity properties.

We present a comprehensive analysis of amino acid substitution patterns (sets of residues in a position of a multiple alignment) and conservation of physicochemical properties in alignments of protein sequences. Of the one million possible substitution patterns, only a few hundred account for the majority of aligned positions. Very similar distributions of substitution patterns are observed in all but one of the diverse databases of multiple alignments. In these substitution patterns we analyzed the conservation of 511 physicochemical and steric amino acid properties. Highest conservation was observed in those steric and transfer free energy-related properties that are crucial for folding. The best conserved steric properties include the minimal width of the side chains and their interactions with other residues. Among the hydrophobicity-related properties, charge and those properties that provide information on propensities to form secondary structures or side chain conformation, appear to be better conserved than pure hydrophobicity measures. Physicochemical sequence analysis based on the most conserved properties is expected to aid searching a protein sequence query against a database of multiple alignments, prediction of secondary and tertiary structures and protein engineering.

FASTA-SWAP and FASTA-PAT: pattern database searches using combinations of aligned amino acids, and a novel scoring theory.

We introduce two new pattern database search tools that utilize statistical significance and information theory to improve protein function identification. Both the general pattern scoring theory with the specific matrices introduced here and the low redundancy of pattern databases increase search sensitivity and selectivity. Pattern scoring preferentially rewards matches at conserved positions in a pattern with higher scores than matches at variable positions, and assigns more negative scores to mismatches at conserved positions than to mismatches at variable positions. The theory of pattern scoring can be used to create log-odds pattern scores for patterns derived from any set of multiple alignments. This theoretical framework can be used to adapt existing sequence database search tools to pattern analysis. Our FASTA-SWAP and FASTA-PAT tools are extensions of the FASTA program that search a sequence query against a pattern database. In the first step, FASTA-SWAP searches the diagonals of the query sequence and the library pattern for high-scoring segments, while FASTA-PAT performs an extended version of hashing. In the second step, both methods refine the alignments and the scores using dynamic programming. The tools utilize an extremely compact binary representation of all possible combinations of amino acid residues in aligned positions. Our FASTA-SWAP and FASTA-PAT tools are well suited for functional identification of distant relatives that may be missed by sequence database search methods. FASTA-SWAP and FASTA-PAT searches can be performed using our World-Wide Web Server (http://dot.imgen.bcm.tmc.edu:9331/seq-search/Op tions/fastapat.html).

Comparisons of eukaryotic genomic sequences.

A method for assessing genomic similarity based on relative abundances of short oligonucleotides in large DNA samples is introduced. The method requires neither homologous sequences nor prior sequence alignments. The analysis centers on (i) dinucleotide (and tri- and tetra-) relative abundance extremes in genomic sequences, (ii) distances between sequences based on all dinucleotide relative abundance values, and (iii) a multidimensional partial ordering protocol. The emphasis in this paper is on assessments of general relatedness of genomes as distinguished from phylogenetic reconstructions. Our methods demonstrate that the relative abundance distances almost always differ more for genomic interspecific sequence comparisons than for genomic intraspecific sequence comparisons, indicating congruence over different genome sequence samples. The genomic comparisons are generally concordant with accepted phylogenies among vertebrate and among fungal species sequences. Several unexpected relationships between the major groups of metazoa, fungal, and protist DNA emerge, including the following. (i) Schizosaccharomyces pombe and Saccharomyces cerevisiae in dinucleotide relative abundance distances are as similar to each other as human is to bovine. (ii) S. cerevisiae, although substantially far from, is significantly closer to the vertebrates than are the invertebrates (Drosophila melanogaster, Bombyx mori, and Caenorhabditis elegans). This phenomenon may suggest variable evolutionary rates during the metazoan radiations and slower changes in the fungal divergences, and/or a polyphyletic origin of metazoa. (iii) The genomic sequences of D. melanogaster and Trypanosoma brucei are strikingly similar. This DNA similarity might be explained by some molecular adaptation of the parasite to its dipteran (tsetse fly) host, a host-parasite gene transfer hypothesis. Robustness of the methods may be due to a genomic signature of dinucleotide relative abundance values reflecting DNA structures related to dinucleotide stacking energies, constraints of DNA curvature, and mechanisms attendant to replication, repair, and recombination.

Heterogeneity of genomes: measures and values.

Genomic homogeneity is investigated for a broad base of DNA sequences in terms of dinucleotide relative abundance distances (abbreviated delta-distances) and of oligonucleotide compositional extremes. It is shown that delta-distances between different genomic sequences in the same species are low, only about 2 or 3 times the distance found in random DNA, and are generally smaller than the between-species delta-distances. Extremes in short oligonucleotides include underrepresentation of TpA and overrepresentation of GpC in most temperate bacteriophage sequences; underrepresentation of CTAG in most eubacterial genomes; underrepresentation of GATC in most bacteriophage; CpG suppression in vertebrates, in all animal mitochondrial genomes, and in many thermophilic bacterial sequences; and overrepresentation of GpG/CpC in all animal mitochondrial sets and chloroplast genomes. Interpretations center on DNA structures (dinucleotide stacking energies, DNA curvature and superhelicity, nucleosome organization), context-dependent mutational events, methylation effects, and processes of replication and repair.

Phylogenetic continuum indicates "galaxies" in the protein universe: preliminary results on the natural group structures of proteins.

The markedly nonuniform, even systematic distribution of sequences in the protein "universe" has been analyzed by methods of protein taxonomy. Mapping of the natural hierarchical system of proteins has revealed some dense cores, i.e., well-defined clusterings of proteins that seem to be natural structural groupings, possibly seeds for a future protein taxonomy. The aim was not to force proteins into more or less man-made categories by discriminant analysis, but to find structurally similar groups, possibly of common evolutionary origin. Single-valued distance measures between pairs of superfamilies from the Protein Identification Resource were defined by two chi 2-like methods on tripeptide frequencies and the variable-length subsequence identity method derived from dot-matrix comparisons. Distance matrices were processed by several methods of cluster analysis to detect phylogenetic continuum between highly divergent proteins. Only well-defined clusters characterized by relatively unique structural, intracellular environmental, organismal, and functional attribute states were selected as major protein groups, including subsets of viral and Escherichia coli proteins, hormones, inhibitors, plant, ribosomal, serum and structural proteins, amino acid synthases, and clusters dominated by certain oxidoreductases and apolar and DNA-associated enzymes. The limited repertoire of functional patterns due to small genome size, the high rate of recombination, specific features of the bacterial membranes, or of the virus cycle canalize certain proteins of viruses and Gram-negative bacteria, respectively, to organismal groups.

Improving signal peptide prediction accuracy by simulated neural network.

The accuracy of distinguishing amino-terminal signal peptides from cytosolic proteins has been improved to 95% by combining a neural network classifier with von Heijne's statistical prediction, the latter is itself 85-90% reliable. The network processed not the cleavage site, but amino-terminal 20-residue segments by the 'tiling' algorithm. Concordant positive predictions of both methods led to the safe identification of 496 novel signal peptides from the Protein Identification Resources.