Structural and Energetic Characterization of the Ankyrin Repeat Protein Family

Ankyrin repeat containing proteins are one of the most abundant solenoid folds. Usually implicated in specific protein-protein interactions, these proteins are readily amenable for design, with promising biotechnological and biomedical applications. Studying repeat protein families presents technical challenges due to the high sequence divergence among the repeating units. We developed and applied a systematic method to consistently identify and annotate the structural repetitions over the members of the complete Ankyrin Repeat Protein Family, with increased sensitivity over previous studies. We statistically characterized the number of repeats, the folding of the repeat-arrays, their structural variations, insertions and deletions. An energetic analysis of the local frustration patterns reveal the basic features underlying fold stability and its relation to the functional binding regions. We found a strong linear correlation between the conservation of the energetic features in the repeat arrays and their sequence variations, and discuss new insights into the organization and function of these ubiquitous proteins.Fil: Parra, Rodrigo Gonzalo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Espada, Rocío. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Verstraete, Nina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Ferreiro, Diego Ulises. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; Argentin

The Plant Short-Chain Dehydrogenase (SDR) superfamily:genome-wide inventory and diversification patterns

Background Short-chain dehydrogenases/reductases (SDRs) form one of the largest and oldest NAD(P)(H) dependent oxidoreductase families. Despite a conserved 'Rossmann-fold' structure, members of the SDR superfamily exhibit low sequence similarities, which constituted a bottleneck in terms of identification. Recent classification methods, relying on hidden-Markov models (HMMs), improved identification and enabled the construction of a nomenclature. However, functional annotations of plant SDRs remain scarce. Results Wide-scale analyses were performed on ten plant genomes. The combination of hidden Markov model (HMM) based analyses and similarity searches led to the construction of an exhaustive inventory of plant SDR. With 68 to 315 members found in each analysed genome, the inventory confirmed the over-representation of SDRs in plants compared to animals, fungi and prokaryotes. The plant SDRs were first classified into three major types --- 'classical', 'extended' and 'divergent' --- but a minority (10 % of the predicted SDRs) could not be classified into these general types ('unknown' or 'atypical' types). In a second step, we could categorize the vast majority of land plant SDRs into a set of 49 families. Out of these 49 families, 35 appeared early during evolution since they are commonly found through all the Green Lineage. Yet, some SDR families --- tropinone reductase-like proteins (SDR65C), 'ABA2-like'-NAD dehydrogenase (SDR110C), 'salutaridine/menthone-reductase-like' proteins (SDR114C), 'dihydroflavonol 4-reductase'-like proteins (SDR108E) and 'isoflavone-reductase-like' (SDR460A) proteins --- have undergone significant functional diversification within vascular plants since they diverged from Bryophytes. Interestingly, these diversified families are either involved in the secondary metabolism routes (terpenoids, alkaloids, phenolics) or participate in developmental processes (hormone biosynthesis or catabolism, flower development), in opposition to SDR families involved in primary metabolism which are poorly diversified. Conclusion The application of HMMs to plant genomes enabled us to identify 49 families that encompass all Angiosperms ('higher plants') SDRs, each family being sufficiently conserved to enable simpler analyses based only on overall sequence similarity. The multiplicity of SDRs in plant kingdom is mainly explained by the diversification of large families involved in different secondary metabolism pathways, suggesting that the chemical diversification that accompanied the emergence of vascular plants acted as a driving force for SDR evolution

Revealing ensemble state transition patterns in multi-electrode neuronal recordings using hidden Markov models

In order to harness the computational capacity of dissociated cultured neuronal networks, it is necessary to understand neuronal dynamics and connectivity on a mesoscopic scale. To this end, this paper uncovers dynamic spatiotemporal patterns emerging from electrically stimulated neuronal cultures using hidden Markov models (HMMs) to characterize multi-channel spike trains as a progression of patterns of underlying states of neuronal activity. However, experimentation aimed at optimal choice of parameters for such models is essential and results are reported in detail. Results derived from ensemble neuronal data revealed highly repeatable patterns of state transitions in the order of milliseconds in response to probing stimuli

Bioinformatic flowchart and database to investigate the origins and diversity of Clan AA peptidases

<p>Abstract</p> <p>Background</p> <p>Clan AA of aspartic peptidases relates the family of pepsin monomers evolutionarily with all dimeric peptidases encoded by eukaryotic LTR retroelements. Recent findings describing various pools of single-domain nonviral host peptidases, in prokaryotes and eukaryotes, indicate that the diversity of clan AA is larger than previously thought. The ensuing approach to investigate this enzyme group is by studying its phylogeny. However, clan AA is a difficult case to study due to the low similarity and different rates of evolution. This work is an ongoing attempt to investigate the different clan AA families to understand the cause of their diversity.</p> <p>Results</p> <p>In this paper, we describe in-progress database and bioinformatic flowchart designed to characterize the clan AA protein domain based on all possible protein families through ancestral reconstructions, sequence logos, and hidden markov models (HMMs). The flowchart includes the characterization of a major consensus sequence based on 6 amino acid patterns with correspondence with Andreeva's model, the structural template describing the clan AA peptidase fold. The set of tools is work in progress we have organized in a database within the GyDB project, referred to as Clan AA Reference Database <url>http://gydb.uv.es/gydb/phylogeny.php?tree=caard</url>.</p> <p>Conclusion</p> <p>The pre-existing classification combined with the evolutionary history of LTR retroelements permits a consistent taxonomical collection of sequence logos and HMMs. This set is useful for gene annotation but also a reference to evaluate the diversity of, and the relationships among, the different families. Comparisons among HMMs suggest a common ancestor for all dimeric clan AA peptidases that is halfway between single-domain nonviral peptidases and those coded by <it>Ty3/Gypsy </it>LTR retroelements. Sequence logos reveal how all clan AA families follow similar protein domain architecture related to the peptidase fold. In particular, each family nucleates a particular consensus motif in the sequence position related to the flap. The different motifs constitute a network where an alanine-asparagine-like variable motif predominates, instead of the canonical flap of the HIV-1 peptidase and closer relatives.</p> <p>Reviewers</p> <p>This article was reviewed by Daniel H. Haft, Vladimir Kapitonov (nominated by Jerry Jurka), and Ben M. Dunn (nominated by Claus Wilke).</p

Prediction of MHC-peptide binding: a systematic and comprehensive overview

T cell immune responses are driven by the recognition of peptide antigens (T cell epitopes) that are bound to major histocompatibility complex (MHC) molecules. T cell epitope immunogenicity is thus contingent on several events, including appropriate and effective processing of the peptide from its protein source, stable peptide binding to the MHC molecule, and recognition of the MHC-bound peptide by the T cell receptor. Of these three hallmarks, MHC-peptide binding is the most selective event that determines T cell epitopes. Therefore, prediction of MHC-peptide binding constitutes the principal basis for anticipating potential T cell epitopes. The tremendous relevance of epitope identification in vaccine design and in the monitoring of T cell responses has spurred the development of many computational methods for predicting MHC-peptide binding that improve the efficiency and economics of T cell epitope identification. In this report, we will systematically examine the available methods for predicting MHC-peptide binding and discuss their most relevant advantages and drawbacks

From harmful Microcystis blooms to multi-functional core-double-shell microsphere bio-hydrochar materials

Harmful algal blooms (HABs) induced by eutrophication is becoming a serious global environmental problem affecting public health and aquatic ecological sustainability. A novel strategy for the utilization of biomass from HABs was developed by converting the algae cells into hollow mesoporous biohydrochar microspheres via hydrothermal carbonization method. The hollow microspheres were used as microreactors and carriers for constructing CaO2 core-mesoporous shell-CaO2 shell microspheres (OCRMs). The CaO2 shells could quickly increase dissolved oxygen to extremely anaerobic water in the initial 40 min until the CaO2 shells were consumed. The mesoporous shells continued to act as regulators restricting the release of oxygen from CaO2 cores. The oxygen-release time using OCRMs was 7 times longer than when directly using CaO2. More interestingly, OCRMs presented a high phosphate removal efficiency (95.6%) and prevented the pH of the solution from rising to high levels in comparison with directly adding CaO2 due to the OH− controlled-release effect of OCRMs. The distinct core-doubleshell micro/nanostructure endowed the OCRMs with triple functions for oxygen controlled-release, phosphorus removal and less impact on water pH. The study is to explore the possibility to prepare smarter bio-hydrochar materials by utilizing algal blooms