Combining inference from evolution and geometric probability in protein structure evaluation

Starting from the hypothesis that evolutionarily important residues form a spatially limited cluster in a protein's native fold, we discuss the possibility of detecting a non-native structure based on the absence of such clustering. The relevant residues are determined using the Evolutionary Trace method. We propose a quantity to measure clustering of the selected residues on the structure and show that the exact values for its average and variance over several ensembles of interest can be found. This enables us to study the behavior of the associated z-scores. Since our approach rests on an analytic result, it proves to be general, customizable, and computationally fast. We find that clustering is indeed detectable in a large representative protein set. Furthermore, we show that non-native structures tend to achieve lower residue-clustering z-scores than those attained by the native folds. The most important conclusion that we draw from this work is that consistency between structural and evolutionary information, manifested in clustering of key residues, imposes powerful constraints on the conformational space of a protein.     

Unblocked statistical-coil tetrapeptides in aqueous solution: quantum-chemical computation of the carbon-13 NMR chemical shifts

We recently reported a theoretical characterization of representative ensembles of statistical-coil conformations for tetrapeptides with unblocked termini in aqueous solution, at pH 7. The results showed good agreement between the computed Boltzmann-averaged and experimentally-determined values for both the vicinal coupling constants ³J_NH and the -proton chemical shifts. Here, we carry out a cluster analysis of the ensembles of conformations generated in that study, and use them to compute the Boltzmann-averaged values of the quantum-chemical ¹³C chemical shifts for different amino acids in the unblocked tetrapeptides GGXA (where X stands for Phe, Arg, His, Glu, Ile, Lys, Gln, Tyr, Leu, Thr, Ala, Gly and Val). The values of the ¹³C chemical shifts in these thirteen amino acids (for which experimental data are available) were computed by using Density Functional Theory with a 6–311+G(2d,p) basis set. Good agreement is found in terms of both the correlation coefficient (R) and standard deviations of the difference between the computed Bolztmann-averaged and the NMR-determined values for the ¹³C chemical shifts. These results suggest that it may be possible to build a reliable theoretically-derived database of chemical shifts for statistical-coil residues. The results of the current study contribute to our understanding of the relations between chemical shifts, dihedral angles and vicinal coupling constants, ³J_NH. In addition, they can shed light as to how the statistical-coilconformation is related to the conformational preference of more structured states, such as the -helical conformation.     

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.     

The formation of synapses in the central nervous system

Interneuronal synapses are specialized contact zones formed between the transmitting pole of one neuron, usually an axon, and the receptive pole of another nerve cell, usually a dendritic process or the soma. The formation of these synaptic contacts is the result of cellular events related to neurite elongation, the establishment of polarity, axon guidance, and target recognition. A series of morphological rearrangements takes place once synaptic targets establish their initial contact. These changes include the clustering of synaptic vesicles in the presynaptic element and the formation of a specialized area capable of signal transduction at the postsynaptic target. The present review discusses the role of different synaptic proteins in the cellular events leading to the formation of synapses among neurons in the central nervous system.     

Anthrax toxin triggers endocytosis of its receptor via a lipid raft-mediated clathrin-dependent process

The protective antigen (PA) of the anthrax toxin binds to a cell surface receptor and thereby allows lethal factor (LF) to be taken up and exert its toxic effect in the cytoplasm. Here, we report that clustering of the anthrax toxin receptor (ATR) with heptameric PA or with an antibody sandwich causes its association to specialized cholesterol and glycosphingolipid-rich microdomains of the plasma membrane (lipid rafts). We find that although endocytosis of ATR is slow, clustering it into rafts either via PA heptamerization or using an antibody sandwich is necessary and sufficient to trigger efficient internalization and allow delivery of LF to the cytoplasm. Importantly, altering raft integrity using drugs prevented LF delivery and cleavage of cytosolic MAPK kinases, suggesting that lipid rafts could be therapeutic targets for drugs against anthrax. Moreover, we show that internalization of PA is dynamin and Eps15 dependent, indicating that the clathrin-dependent pathway is the major route of anthrax toxin entry into the cell. The present work illustrates that although the physiological role of the ATR is unknown, its trafficking properties, i.e., slow endocytosis as a monomer and rapid clathrin-mediated uptake on clustering, make it an ideal anthrax toxin receptor.     

Classifying behavior from short‐interval biologging data: An example with GPS tracking of birds

1. Recent advances in digital data collection have spurred accumulation of immense quantities of data that have potential to lead to remarkable ecological insight, but that also present analytic challenges. In the case of biologging data from birds, common analytical approaches to classifying movement behaviors are largely inappropriate for these massive data sets.
2. We apply a framework for using K‐means clustering to classify bird behavior using points from short time interval GPS tracks. K‐means clustering is a well‐known and computationally efficient statistical tool that has been used in animal movement studies primarily for clustering segments of consecutive points. To illustrate the utility of our approach, we apply K‐means clustering to six focal variables derived from GPS data collected at 1–11 s intervals from free‐flying bald eagles (Haliaeetus leucocephalus) throughout the state of Iowa, USA. We illustrate how these data can be used to identify behaviors and life‐stage‐ and age‐related variation in behavior.
3. After filtering for data quality, the K‐means algorithm identified four clusters in >2 million GPS telemetry data points. These four clusters corresponded to three movement states: ascending, flapping, and gliding flight; and one non‐moving state: perching. Mapping these states illustrated how they corresponded tightly to expectations derived from natural history observations; for example, long periods of ascending flight were often followed by long gliding descents, birds alternated between flapping and gliding flight.
4. The K‐means clustering approach we applied is both an efficient and effective mechanism to classify and interpret short‐interval biologging data to understand movement behaviors. Furthermore, because it can apply to an abundance of very short, irregular, and high‐dimensional movement data, it provides insight into small‐scale variation in behavior that would not be possible with many other analytical approaches.

     

Genetic diversity and gene flow in a Caribbean tree <Emphasis Type="Italic">Pterocarpus officinalis</Emphasis> Jacq.: a study based on chloroplast and nuclear microsatellites

We analysed the molecular diversity of Pterocarpus officinalis, a tree species distributed in Caribbean islands, South and Central America to quantify the genetic variation within island, to assess the pattern of differentiation and infer levels of gene flow; with the overall goal of defining a strategy of conservation. Two hundred two individuals of 9 populations were analysed using three chloroplast and six nuclear microsatellite markers. The observed heterozygosity varied markedly among the populations for nuclear (H _Onuc = 0.20–0.50) and chloroplast microsatellites (H _cp = 0.22–0.68). The continental population from French Guyana showed a higher value of H _Onuc than island populations, and the differences were significant in some cases. The fixation index F _IS ranged from −0.043 to 0.368; a significant heterozygote deficit was detected in 7 populations. The heterozygosity excess method suggested that two populations in Guadeloupe have undergone a recent bottleneck. Global and pairwise F _ST were high for both nuclear (F _STnuc = 0.29) and chloroplast microsatellites (F _STcp = 0.58). The neighbour-joining tree based on both markers, presented a differentiation pattern that can be explained by the seed dispersal by flotation and marine stream. The comparison of Bayesian approach and the method based on allelic frequency demonstrate a very limited number of migrants between populations.     

生物网络研究进展述评

生物网络是一类典型的复杂适应性系统,包含了许多个体的多层次的各种相互作用和关系,在过去的十年里,利用复杂网络理论对生物网络进行研究引起了人们的注意并获得了快速发展.本文首先从从度分布、聚类系数及鲁棒性等角度对现阶段生物网络性质的研究进行了简要介绍,后进一步对生物网络的聚类算法及主要建模理论做出了概括.今后的研究趋势在于如何建立合理的生物网络模型,以深入研究生物网络的各种性质.     

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.     

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.