Protein family clustering for structural genomics

A major goal of structural genomics is the provision of a structural template for a large fraction of protein domains. The magnitude of this task depends on the number and nature of protein sequence families. With a large number of bacterial genomes now fully sequenced, it is possible to obtain improved estimates of the number and diversity of families in that kingdom. We have used an automated clustering procedure to group all sequences in a set of genomes into protein families. Bench-marking shows the clustering method is sensitive at detecting remote family members, and has a low level of false positives. This comprehensive protein family set has been used to address the following questions. (1) What is the structure coverage for currently known families? (2) How will the number of known apparent families grow as more genomes are sequenced? (3) What is a practical strategy for maximizing structure coverage in future? Our study indicates that approximately 20% of known families with three or more members currently have a representative structure. The study indicates also that the number of apparent protein families will be considerably larger than previously thought: We estimate that, by the criteria of this work, there will be about 250,000 protein families when 1000 microbial genomes have been sequenced. However, the vast majority of these families will be small, and it will be possible to obtain structural templates for 70-80% of protein domains with an achievable number of representative structures, by systematically sampling the larger families.     

Describing RNA structure by libraries of clustered nucleotide doublets

The rapidly increasing wealth of structural information on RNA and knowledge of its varying roles in biology have facilitated the study of RNA structure using computational methods. Here, we present a new method to describe RNA structure based on nucleotide doublets, where a doublet is any two nucleotides in a structure. We restrict our search to doublets that are close together in space, but not necessarily in sequence, and obtain doublet libraries of various sizes by clustering a large set of doublets taken from a data set of high-resolution RNA structures. We demonstrate that these libraries are able to both capture structural features present in RNA and fit local RNA structure with a high level of accuracy. Libraries ranging in size from ten to 100 doublets are examined, and a detailed analysis shows that a library with as few as 30 doublets is sufficient to capture the most common structural features, while larger libraries would be more appropriate for accurate modeling. We anticipate many uses for these libraries, from annotation to structure refinement and prediction.     

Agent-based modeling of the context dependency in T cell recognition

Antigen recognition by T cells is a key event in the adaptive immune response. T cells scan the surface of antigen-presenting cells (APCs) or target cells for specific peptides bound to MHC molecules. In the physiological setting, a typical APC presents tens of thousands of diverse endogenous self-derived peptides complexed to MHC (pMHC complexes). When 'foreign' peptides are presented, they constitute a small fraction of the total surface peptide repertoire. As T cells seem to be capable of discerning minute amounts of 'foreign' peptides among a complex background of self-peptides, endogenous peptides are generally assumed to play no role in recognition. However, recent results suggest that these background peptides may alter the sensitivity of T cells to foreign peptides. Current experimental limitations preclude analysis of peptide mixtures approaching physiological complexity, making it difficult to further address the role of complex background peptides. In this paper, we present a computational model to test how complex, varied peptide populations on an APC could potentially modulate a T cell's ability to detect the presence of small numbers of agonist peptides among a diverse population. We use the model to investigate the notion that under physiological conditions, T cell recognition of foreign peptides is context dependent, that is, T cells process signals gathered from all pMHC interactions, not just from a few agonist peptides while ignoring all others.     

Multi-class clustering and prediction in the analysis of microarray data

DNA microarray technology provides tools for studying the expression profiles of a large number of distinct genes simultaneously. This technology has been applied to sample clustering and sample prediction. Because of a large number of genes measured, many of the genes in the original data set are irrelevant to the analysis. Selection of discriminatory genes is critical to the accuracy of clustering and prediction. This paper considers statistical significance testing approach to selecting discriminatory gene sets for multi-class clustering and prediction of experimental samples. A toxicogenomic data set with nine treatments (a control and eight metals, As, Cd, Ni, Cr, Sb, Pb, Cu, and AsV with a total of 55 samples) is used to illustrate a general framework of the approach. Among four selected gene sets, a gene set omega(I) formed by the intersection of the F-test and the set of the union of one-versus-all t-tests performs the best in terms of clustering as well as prediction. Hierarchical and two modified partition (k-means) methods all show that the set omega(I) is able to group the 55 samples into seven clusters reasonably well, in which the As and AsV samples are considered as one cluster (the same group) as are the Cd and Cu samples. With respect to prediction, the overall accuracy for the gene set omega(I) using the nearest neighbors algorithm to predict 55 samples into one of the nine treatments is 85%.     

Analysis of the plant architecture via tree-structured statistical models: the hidden Markov tree models

Plant architecture is the result of repetitions that occur through growth and branching processes. During plant ontogeny, changes in the morphological characteristics of plant entities are interpreted as the indirect translation of different physiological states of the meristems. Thus connected entities can exhibit either similar or very contrasted characteristics. We propose a statistical model to reveal and characterize homogeneous zones and transitions between zones within tree-structured data: the hidden Markov tree (HMT) model. This model leads to a clustering of the entities into classes sharing the same 'hidden state'. The application of the HMT model to two plant sets (apple trees and bush willows), measured at annual shoot scale, highlights ordered states defined by different morphological characteristics. The model provides a synthetic overview of state locations, pointing out homogeneous zones or ruptures. It also illustrates where within branching structures, and when during plant ontogeny, morphological changes occur. However, the labelling exhibits some patterns that cannot be described by the model parameters. Some of these limitations are addressed by two alternative HMT families.     

Regionally isolated populations of an imperiled Caribbean coral, Acropora palmata

The movements of larvae between marine populations are difficult to follow directly and have been the subject of much controversy, especially in the Caribbean. The debate centres on the degree to which populations are demographically open, such that depleted populations can be replenished by recruitment from distant healthy populations, or demographically closed and thus in need of local management. Given the depressed state of many tropical reef populations, the understanding of these movements now bears critically on the number, placement, and size of marine reserves. Most genetic analyses assume that dispersal patterns have been stable for thousands of generations, thus they commonly reflect past colonization histories more than ongoing dispersal. Recently developed multilocus genotyping approaches, however, have the demonstrated ability to detect both migration and population isolation over far shorter timescales. Previously, we developed five microsatellite markers and demonstrated them to be both Mendelian and coral-specific. Using these markers and Bayesian analyses, we show here that populations of the imperiled reef-building coral, Acropora palmata, have experienced little or no recent genetic exchange between the western and the eastern Caribbean. Puerto Rico is identified as an area of mixing between the two subregions. As a consequence of this regional isolation, populations in the western and eastern Caribbean should have the potential to adapt to local conditions and will require population-specific management strategies.     

Geometric preferences of crosslinked protein-derived cofactors reveal a high propensity for near-sequence pairs

Protein-derived cofactors that are composed of covalently crosslinked amino acid side chains are of increasing importance in protein science. These crosslinked protein-derived cofactors (CPDC) are formed either through direct oxidation by metal/O(2)-derived intermediates or through outer sphere oxidation by highly oxidizing cofactors. CPDCs that are formed by outer sphere oxidation do not require side-chain precursors to be coordinated by a metal center, and therefore are more difficult to identify than those formed by direct oxidation. To better understand the propensity for CPDC formation by outer sphere oxidation, the geometrical preferences of CPDCs were examined. The Dezymer algorithm has been used to identify all putative CPDC-forming mutations in 500 proteins. Geometrically, although chemically unrelated, these CPDCs were found to be similar to disulfide-bonded cysteine pairs. Additionally, the percentage of near-sequence pairs (i and i +1 to i and i + 5) increased as the average C(alpha)-C(alpha) distance between the amino acid pairs increased. This survey also examined the protein databank for proteins with pre-attack conformations for CPDCs, using non-bonded contacts reported by Procheck. A total of 323 unique proteins was identified, with 55 being near-sequence amino acid pairs. The high geometric propensity of near-sequence amino acid pairs for forming CPDCs is significant due to difficulties associated with detection by structural or mass spectrometric methods.     

Effect of cultural methods on leaf spot (<Emphasis Type="Italic">Mycosphaerella fragariae</Emphasis>) and gray mold (<Emphasis Type="Italic">Botrytis cinerea</Emphasis>) damage in strawberries

Damage of leaf spot, caused by Mycosphaerella fragariae and gray mold also called Botrytis fruit rot, caused by Botrytis cinerea, average fruit weight and yield were evaluated with regard to cultural methods over 2years. Leaf spot damage decreased significantly by around 90% due to leaf sanitation (removal of dead and leaf spot infected leaves in early spring) and by 50% due to plantation in a one-row-system instead of a two-row-system. When all leaves including the healthy green ones were removed in early spring, average fruit weight decreased significantly by 10%. Fruit sanitation – the third treatment – did not influence any of the measured parameters. Neither leaf sanitation nor fruit sanitation (removal of damaged fruits during harvest) reduced B. cinerea damage significant. Only the combination of a one-row-system, leaf sanitation and fruit sanitation almost halved (not significantly) B. cinerea damage in the first crop year compared to a two-row-system without leaf and fruit sanitation. B. cinerea damage correlated significantly and positively with the biomass of plants by R²= 0.47. According to this study and the cited literature it is suggested for humid Central European conditions to apply a one-row-system combined with leaf sanitation in early spring and fruit sanitation during harvest if fruit density is high, to reduce the risk of damages in larger dimension caused by M. fragariae and B. cinerea.     

Delineating fine-scale genetic units in amphibians:Probing the primacy of ponds

The population structure of pond-breeding amphibians is shaped by their distinct breeding foci, but it is unclear to what extent this is reflected in the fine-scale distribution of genetic diversity. We used microsatellite genotypes to investigate the genetic signatures of 24 populations of European newts, Triturus cristatus and T. marmoratus, inhabiting 21 ponds in a confined study area (7.5×3.5km) in western France. Employing a Bayesian clustering approach based on individual genotypes that minimises departures from Hardy–Weinberg equilibrium and linkage disequilibrium, no evidence was found for within-pond substructuring. Subjecting all sampled ponds simultaneously to this procedure revealed a clear signal of partitioning, with the most likely number of clusters however below the actual number of ponds (seven in T. cristatus, three in T. marmoratus). A more hierarchical Bayesian approach, with pond as analysis unit, was achieved to separate ponds from genetically more meaningful units, and reduced the T. cristatus populations to 11 clusters, and the T. marmoratus populations to five clusters. We were unable to specify a minimum nearest-neighbour distance where ponds are separate units, probably due to both historical and current demographic processes. The implications for strategies to manage and conserve endangered amphibians in human-altered landscapes are discussed.     

Changes of enzyme activity in lipid signaling pathways related to substrate reordering

The static fluid mosaic model of biological membranes has been progressively complemented by a dynamic membrane model that includes phospholipid reordering in domains that are proposed to extend from nanometers to microns. Kinetic models for lipolytic enzymes have only been developed for homogeneous lipid phases. In this work, we develop a generalization of the well-known surface dilution kinetic theory to cases where, in a same lipid phase, both domain and nondomain phases coexist. Our model also allows understanding the changes in enzymatic activity due to a decrease of free substrate concentration when domains are induced by peptides. This lipid reordering and domain dynamics can affect the activity of lipolytic enzymes, and can provide a simple explanation for how basic peptides, with a strong direct interaction with acidic phospholipids (such as beta-amyloid peptide), may cause a complex modulation of the activities of many important enzymes in lipid signaling pathways.