Calicheamicin-DNA complexes: warhead alignment and saccharide recognition of the minor groove.

The solution structures of calicheamicin gamma 1I, its cycloaromatized analog (calicheamicin epsilon), and its aryl tetrasaccharide complexed to a common DNA hairpin duplex have been determined by NMR and distance-refined molecular dynamics computations. Sequence specificity is associated with carbohydrate-DNA recognition that places the aryl tetrasaccharide component of all three ligands in similar orientations in the minor groove at the d(T-C-C-T).d(A-G-G-A) segment. The complementary fit of the ligands and the DNA minor groove binding site creates numerous van der Waals contacts as well as hydrogen bonding interactions. Notable are the iodine and sulfur atoms of calicheamicin that hydrogen bond with the exposed amino proton of the 5'- and 3'-guanines, respectively, of the d(A-G-G-A) segment. The sequence-specific carbohydrate binding orients the enediyne aglycone of calicheamicin gamma 1I such that its C3 and C6 proradical centers are adjacent to the cleavage sites. While the enediyne aglycone of calicheamicin gamma 1I is tilted relative to the helix axis and spans the minor groove, the cycloaromatized aglycone is aligned approximately parallel to the helix axis in the respective complexes. Specific localized conformational perturbations in the DNA have been identified from imino proton complexation shifts and changes in specific sugar pucker patterns on complex formation. The helical parameters for the carbohydrate binding site are comparable with corresponding values in B-DNA fibers while a widening of the groove is observed at the adjacent aglycone binding site.

SfiI endonuclease activity is strongly influenced by the non-specific sequence in the middle of its recognition site

The SfiI endonuclease cleaves DNA at the sequence GGCCNNNN↓NGGCC, where N is any base and ↓ is the point of cleavage. Proteins that recognise discontinuous sequences in DNA can be affected by the unspecified sequence between the specified base pairs of the target site. To examine whether this applies to SfiI, a series of DNA duplexes were made with identical sequences apart from discrete variations in the 5 bp spacer. The rates at which SfiI cleaved each duplex were measured under steady-state conditions: the steady-state rates were determined by the DNA cleavage step in the reaction pathway. SfiI cleaved some of these substrates at faster rates than other substrates. For example, the change in spacer sequence from AACAA to AAACA caused a 70-fold increase in reaction rate. In general, the extrapolated values for kcat and Km were both higher on substrates with inflexible spacers than those with flexible structures. The dinucleotide at the site of cleavage was largely immaterial. SfiI activity is thus highly dependent on conformational variations in the spacer DNA.

Image recognition: Visual grouping, recognition, and learning

Vision extracts useful information from images. Reconstructing the three-dimensional structure of our environment and recognizing the objects that populate it are among the most important functions of our visual system. Computer vision researchers study the computational principles of vision and aim at designing algorithms that reproduce these functions. Vision is difficult: the same scene may give rise to very different images depending on illumination and viewpoint. Typically, an astronomical number of hypotheses exist that in principle have to be analyzed to infer a correct scene description. Moreover, image information might be extracted at different levels of spatial and logical resolution dependent on the image processing task. Knowledge of the world allows the visual system to limit the amount of ambiguity and to greatly simplify visual computations. We discuss how simple properties of the world are captured by the Gestalt rules of grouping, how the visual system may learn and organize models of objects for recognition, and how one may control the complexity of the description that the visual system computes.

Gene recognition via spliced sequence alignment.

Gene recognition is one of the most important problems in computational molecular biology. Previous attempts to solve this problem were based on statistics, and applications of combinatorial methods for gene recognition were almost unexplored. Recent advances in large-scale cDNA sequencing open a way toward a new approach to gene recognition that uses previously sequenced genes as a clue for recognition of newly sequenced genes. This paper describes a spliced alignment algorithm and software tool that explores all possible exon assemblies in polynomial time and finds the multiexon structure with the best fit to a related protein. Unlike other existing methods, the algorithm successfully recognizes genes even in the case of short exons or exons with unusual codon usage; we also report correct assemblies for genes with more than 10 exons. On a test sample of human genes with known mammalian relatives, the average correlation between the predicted and actual proteins was 99%. The algorithm correctly reconstructed 87% of genes and the rare discrepancies between the predicted and real exon-intron structures were caused either by short (less than 5 amino acids) initial/terminal exons or by alternative splicing. Moreover, the algorithm predicts human genes reasonably well when the homologous protein is nonvertebrate or even prokaryotic. The surprisingly good performance of the method was confirmed by extensive simulations: in particular, with target proteins at 160 accepted point mutations (PAM) (25% similarity), the correlation between the predicted and actual genes was still as high as 95%.