Spatial abstraction for autonomous robot navigation.

Optimal navigation for a simulated robot relies on a detailed map and explicit path planning, an approach problematic for real-world robots that are subject to noise and error. This paper reports on autonomous robots that rely on local spatial perception, learning, and commonsense rationales instead. Despite realistic actuator error, learned spatial abstractions form a model that supports effective travel.

Conserved processes and lineage-specific proteins in fungal cell wall evolution.

The cell wall is a defining organelle that differentiates fungi from its sister clades in the opisthokont superkingdom. With a sensitive technique to align low-complexity protein sequences, we have identified 187 cell wall-related proteins in Saccharomyces cerevisiae and determined the presence or absence of homologs in 17 other fungal genomes. There were both conserved and lineage-specific cell wall proteins, and the degree of conservation was strongly correlated with protein function. Some functional classes were poorly conserved and lineage specific: adhesins, structural wall glycoprotein components, and unannotated open reading frames. These proteins are primarily those that are constituents of the walls themselves. On the other hand, glycosyl hydrolases and transferases, proteases, lipases, proteins in the glycosyl phosphatidyl-inositol-protein synthesis pathway, and chaperones were strongly conserved. Many of these proteins are also conserved in other eukaryotes and are associated with wall synthesis in plants. This gene conservation, along with known similarities in wall architecture, implies that the basic architecture of fungal walls is ancestral to the divergence of the ascomycetes and basidiomycetes. The contrasting lineage specificity of wall resident proteins implies diversification. Therefore, fungal cell walls consist of rapidly diversifying proteins that are assembled by the products of an ancestral and conserved set of genes.

Discovery of Recurrent Sequence Motifs in Saccharomyces cerevisiae Cell Wall Proteins.

This paper describes a procedure for the discovery of recurrent substrings in amino acid sequences of proteins, and its application to fungal cell walls. The evolutionary origins of fungal cell walls are an open biological question. This question can be approached by studies of similarity among the sequences and sub-sequences of fungal wall proteins and by comparison to proteins in animals. We describe here how we have discovered building blocks, represented as recurrent sequence motifs (sub-sequences), within fungal cell wall proteins. These motifs have not been systematically identified before, because the low Shannon entropy of the cell wall sequences has hindered searches for local sequence similarities by sequence alignments. Nonetheless, our new, composition-based scoring matrices for local alignment searches now support statistically valid alignments for such low entropy sequences (Coronado et al. 2006. Euk. Cell 5: 628-637). We have now searched for similarities in a set of 171 known and putative cell wall proteins from baker's yeast, Saccharomyces cerevisiae. The aligned segments were repeatedly subdivided and catalogued to identify 217 recurrent sequence motifs of length 8 amino acids or greater. 95% of these motifs occur in more than one cell wall protein. The median length of the motifs is 22 amino acid residues, considerably shorter than protein domains. For many cell wall proteins, these motifs collectively account for more than half of their amino acids. The prevalence of these motifs supports the idea of fungal cell wall proteins as assemblies of recurrent building blocks.

Comparative genomics reveals long, evolutionarily conserved, low-complexity islands in yeast proteins.

Eukaryotic proteomes abound in low-complexity sequences, including tandem repeats and regions with significantly biased amino acid compositions. We assessed the functional importance of compositionally biased sequences in the yeast proteome using an evolutionary analysis of 2838 orthologous open reading frame (ORF) families from three Saccharomyces species (S. cerevisiae, S. bayanus, and S. paradoxus). Sequence conservation was measured by the amino acid sequence variability and by the ratio of nonsynonymous-to-synonymous nucleotide substitutions (K(a)/K(s)) between pairs of orthologous ORFs. A total of 1033 ORF families contained one or more long (at least 45 residues), low-complexity islands as defined by a measure based on the Shannon information index. Low-complexity islands were generally less conserved than ORFs as a whole; on average they were 50% more variable in amino acid sequences and 50% higher in K(a)/K(s) ratios. Fast-evolving low-complexity sequences outnumbered conserved low-complexity sequences by a ratio of 10 to 1. Sequence differences between orthologous ORFs fit well to a selectively neutral Poisson model of sequence divergence. We therefore used the Poisson model to identify conserved low-complexity sequences. ORFs containing the 33 most conserved low-complexity sequences were overrepresented by those encoding nucleic acid binding proteins, cytoskeleton components, and intracellular transporters. While a few conserved low-complexity islands were known functional domains (e.g., DNA/RNA-binding domains), most were uncharacterized. We discuss how comparative genomics of closely related species can be employed further to distinguish functionally important, shorter, low-complexity sequences from the vast majority of such sequences likely maintained by neutral processes.

Composition-modified matrices improve identification of homologs of saccharomyces cerevisiae low-complexity glycoproteins.

Yeast glycoproteins are representative of low-complexity sequences, those sequences rich in a few types of amino acids. Low-complexity protein sequences comprise more than 10% of the proteome but are poorly aligned by existing methods. Under default conditions, BLAST and FASTA use the scoring matrix BLOSUM62, which is optimized for sequences with diverse amino acid compositions. Because low-complexity sequences are rich in a few amino acids, these tools tend to align the most common residues in nonhomologous positions, thereby generating anomalously high scores, deviations from the expected extreme value distribution, and small e values. This anomalous scoring prevents BLOSUM62-based BLAST and FASTA from identifying correct homologs for proteins with low-complexity sequences, including Saccharomyces cerevisiae wall proteins. We have devised and empirically tested scoring matrices that compensate for the overrepresentation of some amino acids in any query sequence in different ways. These matrices were tested for sensitivity in finding true homologs, discrimination against nonhomologous and random sequences, conformance to the extreme value distribution, and accuracy of e values. Of the tested matrices, the two best matrices (called E and gtQ) gave reliable alignments in BLAST and FASTA searches, identified a consistent set of paralogs of the yeast cell wall test set proteins, and improved the consistency of secondary structure predictions for cell wall proteins.