Showing papers by "Mark Gerstein published in 1998"

A database of macromolecular motions

Abstract: We describe a database of macromolecular motions meant to be of general use to the structural community. The database, which is accessible on the World Wide Web with an entry point at http://bioinfo.mbb.yale.edu/ MolMovDB , attempts to systematize all instances of protein and nucleic acid movement for which there is at least some structural information. At present it contains >120 motions, most of which are of proteins. Protein motions are further classified hierarchically into a limited number of categories, first on the basis of size (distinguishing between fragment, domain and subunit motions) and then on the basis of packing. Our packing classification divides motions into various categories (shear, hinge, other) depending on whether or not they involve sliding over a continuously maintained and tightly packed interface. In addition, the database provides some indication about the evidence behind each motion (i.e. the type of experimental information or whether the motion is inferred based on structural similarity) and attempts to describe many aspects of a motion in terms of a standardized nomenclature (e.g. the maximum rotation, the residue selection of a fixed core, etc.). Currently, we use a standard relational design to implement the database. However, the complexity and heterogeneity of the information kept in the database makes it an ideal application for an object-relational approach, and we are moving it in this direction. Specifically, in terms of storing complex information, the database contains plausible representations for motion pathways, derived from restrained 3D interpolation between known endpoint conformations. These pathways can be viewed in a variety of movie formats, and the database is associated with a server that can automatically generate these movies from submitted coordinates.

A unified statistical framework for sequence comparison and structure comparison (sequence analysisystructure analysisyfold familyydatabase statisticsyprotein evolution)

Abstract: We present an approach for assessing the significance of sequence and structure comparisons by using nearly identical statistical formalisms for both se- quence and structure. Doing so involves an all-vs.-all com- parison of protein domains (taken here from the Structural Classification of Proteins (scop) database) and then fitting a simple distribution function to the observed scores. By using this distribution, we can attach a statistical signifi- cance to each comparison score in the form of a P value, the probability that a better score would occur by chance. As expected, we find that the scores for sequence matching follow an extreme-value distribution. The agreement, more- over, between the P values that we derive from this distri- bution and those reported by standard programs (e.g., BLAST and FASTA validates our approach. Structure comparison scores also follow an extreme-value distribution when the statistics are expressed in terms of a structural alignment score (essentially the sum of reciprocated distances between aligned atoms minus gap penalties). We find that the traditional metric of structural similarity, the rms deviation in atom positions after fitting aligned atoms, follows a different distribution of scores and does not perform as well as the structural alignment score. Comparison of the se- quence and structure statistics for pairs of proteins known to be related distantly shows that structural comparison is able to detect approximately twice as many distant rela- tionships as sequence comparison at the same error rate. The comparison also indicates that there are very few pairs with significant similarity in terms of sequence but not structure whereas many pairs have significant similarity in terms of structure but not sequence. Comparison is a most fundamental operation in biology. Measuring the similarities between ''things'' enables us to group them in families, cluster them in trees, and infer common ancestors and an evolutionary progression. Biological comparisons can take place at many levels, from that of whole organisms to that of individual molecules. We are concerned here with the comparison on the latter level, specifically, with comparisons of individual protein sequences and structures.

A unified statistical framework for sequence comparison and structure comparison

Abstract: We present an approach for assessing the significance of sequence and structure comparisons by using nearly identical statistical formalisms for both sequence and structure. Doing so involves an all-vs.-all comparison of protein domains [taken here from the Structural Classification of Proteins (scop) database] and then fitting a simple distribution function to the observed scores. By using this distribution, we can attach a statistical significance to each comparison score in the form of a P value, the probability that a better score would occur by chance. As expected, we find that the scores for sequence matching follow an extreme-value distribution. The agreement, moreover, between the P values that we derive from this distribution and those reported by standard programs (e.g., blast and fasta validates our approach. Structure comparison scores also follow an extreme-value distribution when the statistics are expressed in terms of a structural alignment score (essentially the sum of reciprocated distances between aligned atoms minus gap penalties). We find that the traditional metric of structural similarity, the rms deviation in atom positions after fitting aligned atoms, follows a different distribution of scores and does not perform as well as the structural alignment score. Comparison of the sequence and structure statistics for pairs of proteins known to be related distantly shows that structural comparison is able to detect approximately twice as many distant relationships as sequence comparison at the same error rate. The comparison also indicates that there are very few pairs with significant similarity in terms of sequence but not structure whereas many pairs have significant similarity in terms of structure but not sequence.

Comprehensive assessment of automatic structural alignment against a manual standard, the scop classification of proteins

Abstract: We apply a simple method for aligning protein sequences on the basis of a 3D structure, on a large scale, to the proteins in the scop classification of fold families. This allows us to assess, understand, and improve our automatic method against an objective, manually derived standard, a type of comprehensive evaluation that has not yet been possible for other structural alignment algorithms. Our basic approach directly matches the backbones of two structures, using repeated cycles of dynamic programming and least-squares fitting to determine an alignment minimizing coordinate difference. Because of simplicity, our method can be readily modified to take into account additional features of protein structure such as the orientation of side chains or the location-dependent cost of opening a gap. Our basic method, augmented by such modifications, can find reasonable alignments for all but 1.5% of the known structural similarities in scop, i.e., all but 32 of the 2,107 superfamily pairs. We discuss the specific protein structural features that make these 32 pairs so difficult to align and show how our procedure effectively partitions the relationships in scop into different categories, depending on what aspects of protein structure are involved (e.g., depending on whether or not consideration of side-chain orientation is necessary for proper alignment). We also show how our pairwise alignment procedure can be extended to generate a multiple alignment for a group of related structures. We have compared these alignments in detail with corresponding manual ones culled from the literature. We find good agreement (to within 95% for the core regions), and detailed comparison highlights how particular protein structural features (such as certain strands) are problematical to align, giving somewhat ambiguous results. With these improvements and systematic tests, our procedure should be useful for the development of scop and the future classification of protein folds. Supplementary material is available at http://bioinfo.mbb.yale.edu/align.

Patterns of Protein-Fold Usage in Eight Microbial Genomes: A Comprehensive Structural Census

Abstract: Eight microbial genomes are compared in terms of protein structure. Specifi- cally, yeast, H. influenzae, M. genitalium, M. jannaschii, Synechocystis, M. pneumoniae, H. pylori ,a ndE. coli are compared in terms of patterns of fold usage—whether a given fold occurs in a particular organism. Of the ,340 soluble protein folds currently in the structure databank (PDB), 240 occur in at least one of the eight genomes, and 30 are shared amongst all eight. The shared folds are depleted in all- helical structure and enriched in mixed helix- sheet structure compared to the folds in the PDB. The top-10 most common of the shared 30 are enriched in superfolds, uniting many non- homologous sequence families, and are espe- cially similar in overall architecture—eight having helices packed onto a central sheet. They are also very different from the common folds in the PBD, highlighting databank biases. Folds can be ranked in terms of expression as well as genome duplication. In yeast the top-10 most highly expressed folds are considerably different from the most highly duplicated folds. A tree can be constructed grouping genomes in terms of their shared folds. This has a remark- ably similar topology to more conventional classifications, based on very different mea- sures of relatedness. Finally, folds of mem- brane proteins can be analyzed through trans- membrane-helix (TM) prediction. All the genomes appear to have similar usage patterns for these folds, with the occurrence of a particu- lar fold falling off rapidly with increasing num- bers of TM-elements, according to a ''Zipf-like'' law. This implies there are no marked prefer- ences for proteins with particular numbers of TM-helices (e.g. 7-TM) in microbial genomes. Fur- ther information pertinent to this analysis is avail- able at http://bioinfo.mbb.yale.edu/genome. Pro- teins 33:518-534, 1998. r 1998 Wiley-Liss, Inc.

Comparing genomes in terms of protein structure: surveys of a finite parts list

Abstract: We give an overview of the emerging field of structural genomics, describing how genomes can be compared in terms of protein structure. As the number of genes in a genome and the total number of protein folds are both quite limited, these comparisons take the form of surveys of a finite parts list, similar in respects to demographic censuses. Fold surveys have many similarities with other whole-genome characterizations, e.g., analyses of motifs or pathways. However, structure has a number of aspects that make it particularly suitable for comparing genomes, namely the way it allows for the precise definition of a basic protein module and the fact that it has a better defined relationship to sequence similarity than does protein function. An essential requirement for a structure survey is a library of folds, which groups the known structures into 'fold families.' This library can be built up automatically using a structure comparison program, and we described how important objective statistical measures are for assessing similarities within the library and between the library and genome sequences. After building the library, one can use it to count the number of folds in genomes, expressing the results in the form of Venn diagrams and 'top-10' statistics for shared and common folds. Depending on the counting methodology employed, these statistics can reflect different aspects of the genome, such as the amount of internal duplication or gene expression. Previous analyses have shown that the common folds shared between very different microorganisms, i.e., in different kingdoms, have a remarkably similar structure, being comprised of repeated strand-helix-strand super-secondary structure units. A major difficulty with this sort of 'fold-counting' is that only a small subset of the structures in a complete genome are currently known and this subset is prone to sampling bias. One way of overcoming biases is through structure prediction, which can be applied uniformly and comprehensively to a whole genome. Various investigators have, in fact, already applied many of the existing techniques for predicting secondary structure and transmembrane (TM) helices to the recently sequenced genomes. The results have been consistent: microbial genomes have similar fractions of strands and helices even though they have significantly different amino acid composition. The fraction of membrane proteins with a given number of TM helices falls off rapidly with more TM elements, approximately according to a Zipf law. This latter finding indicates that there is no preference for the highly studied 7-TM proteins in microbial genomes. Continuously updated tables and further information pertinent to this review are available over the web at http://bioinfo.mbb.yale.edu/genome.

Measurement of the effectiveness of transitive sequence comparison, through a third 'intermediate' sequence.

Abstract: MOTIVATION Transitive sequence matching expands the scope of sequence comparison by re-running the results of a given query against the databank as a new query. This sometimes results in the initial query sequence (Q) being related to a final match (M) indirectly, through a third, 'intermediate' sequence (Q --> I --> M ). This approach has often been suggested as providing greater sensitivity in sequence comparison; however, it has not yet been possible to gauge its improvement precisely. RESULTS Here, this improvement is comprehensively measured by seeing what fraction of the known structural relationships transitive sequence matching can uncover beyond that found by normal pairwise comparison (i.e. direct linkage). The structural relationships are taken from a well-characterized test set, the scop classification of protein structure. Specifically, 2055 known structural similarities (called 'pairs') between distantly related proteins constitute the basic test set. To make the measurement of transitive matching properly, special data sets, called 'baseline sets', are derived from this. They consist of pairs of sequences that have a clear structural relationship that cannot be found by normal sequence comparison (i.e. they cannot be directly linked). Specifically, using standard sequence comparison protocols (FASTA with an e-value cut-off of 0. 001), it is found that the baseline set consists of 1742 pairs. A third intermediate sequence can link 86 of these indirectly (5%), where this third sequence is drawn from the entire, current universe of protein sequences. The number of false positives is minimal. Furthermore, when one considers only the relationships within the test set that correspond to a close structural alignment, the coverage increases considerably. In particular, 862 of the baseline set pairs fit to better than 2.6 A RMS, and transitive matching can find 62 of these (9%). AVAILABILITY All the test data, including precise similarity values calculated from structural alignment, are available in tabular format over the Web from http://bioinfo.mbb. yale.edu/align. CONTACT Mark.Gerstein@yale.edu