14 resultados para Loop structure prediction
em Consorci de Serveis Universitaris de Catalunya (CSUC), Spain
Resumo:
Experimentally, Ce2O3 films are used to study cerium oxide in its fully or partially reduced state, as present in many applications. We have explored the space of low energy Ce2O3 nanofilms using structure prediction and density functional calculations, yielding more than 30 distinct nanofilm structures. First, our results help to rationalize the roles of thermodynamics and kinetics in the preparation of reduced ceria nanofilms with different bulk crystalline structures (e.g. A-type or bixbyite) depending on the support used. Second, we predict a novel, as yet experimentally unresolved, nanofilm which has a structure that does not correspond to any previously reported bulk A2B3 phase and which has an energetic stability between that of A-type and bixbyite. To assist identification and fabrication of this new Ce2O3 nanofilm we calculate some observable properties and propose supports for its epitaxial growth.
Resumo:
Background: Recent advances on high-throughput technologies have produced a vast amount of protein sequences, while the number of high-resolution structures has seen a limited increase. This has impelled the production of many strategies to built protein structures from its sequence, generating a considerable amount of alternative models. The selection of the closest model to the native conformation has thus become crucial for structure prediction. Several methods have been developed to score protein models by energies, knowledge-based potentials and combination of both.Results: Here, we present and demonstrate a theory to split the knowledge-based potentials in scoring terms biologically meaningful and to combine them in new scores to predict near-native structures. Our strategy allows circumventing the problem of defining the reference state. In this approach we give the proof for a simple and linear application that can be further improved by optimizing the combination of Zscores. Using the simplest composite score () we obtained predictions similar to state-of-the-art methods. Besides, our approach has the advantage of identifying the most relevant terms involved in the stability of the protein structure. Finally, we also use the composite Zscores to assess the conformation of models and to detect local errors.Conclusion: We have introduced a method to split knowledge-based potentials and to solve the problem of defining a reference state. The new scores have detected near-native structures as accurately as state-of-art methods and have been successful to identify wrongly modeled regions of many near-native conformations.
Resumo:
In a number of programs for gene structure prediction in higher eukaryotic genomic sequences, exon prediction is decoupled from gene assembly: a large pool of candidate exons is predicted and scored from features located in the query DNA sequence, and candidate genes are assembled from such a pool as sequences of nonoverlapping frame-compatible exons. Genes are scored as a function of the scores of the assembled exons, and the highest scoring candidate gene is assumed to be the most likely gene encoded by the query DNA sequence. Considering additive gene scoring functions, currently available algorithms to determine such a highest scoring candidate gene run in time proportional to the square of the number of predicted exons. Here, we present an algorithm whose running time grows only linearly with the size of the set of predicted exons. Polynomial algorithms rely on the fact that, while scanning the set of predicted exons, the highest scoring gene ending in a given exon can be obtained by appending the exon to the highest scoring among the highest scoring genes ending at each compatible preceding exon. The algorithm here relies on the simple fact that such highest scoring gene can be stored and updated. This requires scanning the set of predicted exons simultaneously by increasing acceptor and donor position. On the other hand, the algorithm described here does not assume an underlying gene structure model. Indeed, the definition of valid gene structures is externally defined in the so-called Gene Model. The Gene Model specifies simply which gene features are allowed immediately upstream which other gene features in valid gene structures. This allows for great flexibility in formulating the gene identification problem. In particular it allows for multiple-gene two-strand predictions and for considering gene features other than coding exons (such as promoter elements) in valid gene structures.
Resumo:
We describe the use of dynamic combinatorial chemistry (DCC) to identify ligands for the stem-loop structure located at the exon 10-5'-intron junction of Tau pre-mRNA, which is involved in the onset of several tauopathies including frontotemporal dementia with Parkinsonism linked to chromosome 17 (FTDP-17). A series of ligands that combine the small aminoglycoside neamine and heteroaromatic moieties (azaquinolone and two acridines) have been identified by using DCC. These compounds effectively bind the stem-loop RNA target (the concentration required for 50% RNA response (EC(50)): 2-58 μM), as determined by fluorescence titration experiments. Importantly, most of them are able to stabilize both the wild-type and the +3 and +14 mutated sequences associated with the development of FTDP-17 without producing a significant change in the overall structure of the RNA (as analyzed by circular dichroism (CD) spectroscopy), which is a key factor for recognition by the splicing regulatory machinery. A good correlation has been found between the affinity of the ligands for the target and their ability to stabilize the RNA secondary structure.
Resumo:
We describe the effect of guanidinylation of the aminoglycoside moiety on acridine-neamine-containing ligands for the stem-loop structure located at the exon 10-5′-intron junction of Tau pre-mRNA, an important regulatory element of tau gene alternative splicing. On the basis of dynamic combinatorial chemistry experiments, ligands that combine guanidinoneamine and two different acridines were synthesized and their RNA-binding properties were compared with those of their amino precursors. Fluorescence titration experiments and UV-monitored melting curves revealed that guanidinylation has a positive effect both on the binding affinity and specificity of the ligands for the stemloop RNA, as well as on the stabilization of all RNA sequences evaluated, particularly some mutated sequences associated with the development of FTDP-17 tauopathy. However, this correlation between binding affinity and stabilization due to guanidinylation was only found in ligands containing a longer spacer between the acridine and guanidinoneamine moieties, since a shorter spacer produced the opposite effect (e.g. lower binding affinity and lower stabilization). Furthermore, spectroscopic studies suggest that ligand binding does not significantly change the overall RNA structure upon binding (circular dichroism) and that the acridine moiety might intercalate near the bulged region of the stem->loop structure (UV-Vis and NMR spectroscopy).
Resumo:
El proyecto está dedicado al estudio y diseño de filtros paso-banda y en banda dual con tecnología microstrip mediante estructuras resonantes de tipo open-loop. Se ha llevado a cabo el diseño de un filtro paso-banda con respuesta Chebyshev, un filtro pasobanda con ceros de transmisión y un filtro de banda dual para WCDMA y WiFi, empleado el método de diseño para filtros basados en resonadores inter-acoplados. Se presentan los modelos eléctricos de los filtros de RF simulados junto con sus respectivos layouts y se comparan las respuestas obtenidas de los dispositivos con las respuestas ideales. En el proyecto se realiza un estudio del comportamiento de los diferentes tipos de acoplamiento entre resonadores open-loop en función de la geometría de la estructura. Las tendencias de comportamiento de los acoplamientos permiten el diseño y colocación de los resonadores para satisfacer las especificaciones del filtro.
Resumo:
Conventional methods of gene prediction rely on the recognition of DNA-sequence signals, the coding potential or the comparison of a genomic sequence with a cDNA, EST, or protein database. Reasons for limited accuracy in many circumstances are species-specific training and the incompleteness of reference databases. Lately, comparative genome analysis has attracted increasing attention. Several analysis tools that are based on human/mouse comparisons are already available. Here, we present a program for the prediction of protein-coding genes, termed SGP-1 (Syntenic Gene Prediction), which is based on the similarity of homologous genomic sequences. In contrast to most existing tools, the accuracy of SGP-1 depends little on species-specific properties such as codon usage or the nucleotide distribution. SGP-1 may therefore be applied to nonstandard model organisms in vertebrates as well as in plants, without the need for extensive parameter training. In addition to predicting genes in large-scale genomic sequences, the program may be useful to validate gene structure annotations from databases. To this end, SGP-1 output also contains comparisons between predicted and annotated gene structures in HTML format. The program can be accessed via a Web server at http://soft.ice.mpg.de/sgp-1. The source code, written in ANSI C, is available on request from the authors.
Resumo:
One of the first useful products from the human genome will be a set of predicted genes. Besides its intrinsic scientific interest, the accuracy and completeness of this data set is of considerable importance for human health and medicine. Though progress has been made on computational gene identification in terms of both methods and accuracy evaluation measures, most of the sequence sets in which the programs are tested are short genomic sequences, and there is concern that these accuracy measures may not extrapolate well to larger, more challenging data sets. Given the absence of experimentally verified large genomic data sets, we constructed a semiartificial test set comprising a number of short single-gene genomic sequences with randomly generated intergenic regions. This test set, which should still present an easier problem than real human genomic sequence, mimics the approximately 200kb long BACs being sequenced. In our experiments with these longer genomic sequences, the accuracy of GENSCAN, one of the most accurate ab initio gene prediction programs, dropped significantly, although its sensitivity remained high. Conversely, the accuracy of similarity-based programs, such as GENEWISE, PROCRUSTES, and BLASTX was not affected significantly by the presence of random intergenic sequence, but depended on the strength of the similarity to the protein homolog. As expected, the accuracy dropped if the models were built using more distant homologs, and we were able to quantitatively estimate this decline. However, the specificities of these techniques are still rather good even when the similarity is weak, which is a desirable characteristic for driving expensive follow-up experiments. Our experiments suggest that though gene prediction will improve with every new protein that is discovered and through improvements in the current set of tools, we still have a long way to go before we can decipher the precise exonic structure of every gene in the human genome using purely computational methodology.
Resumo:
Evaluating other individuals with respect to personality characteristics plays a crucial role in human relations and it is the focus of attention for research in diverse fields such as psychology and interactive computer systems. In psychology, face perception has been recognized as a key component of this evaluation system. Multiple studies suggest that observers use face information to infer personality characteristics. Interactive computer systems are trying to take advantage of these findings and apply them to increase the natural aspect of interaction and to improve the performance of interactive computer systems. Here, we experimentally test whether the automatic prediction of facial trait judgments (e.g. dominance) can be made by using the full appearance information of the face and whether a reduced representation of its structure is sufficient. We evaluate two separate approaches: a holistic representation model using the facial appearance information and a structural model constructed from the relations among facial salient points. State of the art machine learning methods are applied to a) derive a facial trait judgment model from training data and b) predict a facial trait value for any face. Furthermore, we address the issue of whether there are specific structural relations among facial points that predict perception of facial traits. Experimental results over a set of labeled data (9 different trait evaluations) and classification rules (4 rules) suggest that a) prediction of perception of facial traits is learnable by both holistic and structural approaches; b) the most reliable prediction of facial trait judgments is obtained by certain type of holistic descriptions of the face appearance; and c) for some traits such as attractiveness and extroversion, there are relationships between specific structural features and social perceptions.
Resumo:
Type 1 diabetic patients depend on external insulin delivery to keep their blood glucose within near-normal ranges. In this work, two robust closed-loop controllers for blood glucose regulation are developed to prevent the life-threatening hypoglycemia, as well as to avoid extended hyperglycemia. The proposed controllers are designed by using the sliding mode control technique in a Smith predictor structure. To improve meal disturbance rejection, a simple feedforward controller is added to inject meal-time insulin bolus. Simulations scenarios were used to test the controllers, and showed the controllers ability to maintain the glucose levels within the safe limits in the presence of errors in measurements, modeling and meal estimation
Resumo:
The linear prediction coding of speech is based in the assumption that the generation model is autoregresive. In this paper we propose a structure to cope with the nonlinear effects presents in the generation of the speech signal. This structure will consist of two stages, the first one will be a classical linear prediction filter, and the second one will model the residual signal by means of two nonlinearities between a linear filter. The coefficients of this filter are computed by means of a gradient search on the score function. This is done in order to deal with the fact that the probability distribution of the residual signal still is not gaussian. This fact is taken into account when the coefficients are computed by a ML estimate. The algorithm based on the minimization of a high-order statistics criterion, uses on-line estimation of the residue statistics and is based on blind deconvolution of Wiener systems [1]. Improvements in the experimental results with speech signals emphasize on the interest of this approach.
Resumo:
Replication of human immunodeficiency virus (HIV) requires base pairing of the reverse transcriptase primer, human tRNA(Lys3), to the viral RNA. Although the major complementary base pairing occurs between the HIV primer binding sequence (PBS) and the tRNA's 3'-terminus, an important discriminatory, secondary contact occurs between the viral A-rich Loop I, 5'-adjacent to the PBS, and the modified, U-rich anticodon domain of tRNA(Lys3). The importance of individual and combined anticodon modifications to the tRNA/HIV-1 Loop I RNA's interaction was determined. The thermal stabilities of variously modified tRNA anticodon region sequences bound to the Loop I of viral sub(sero)types G and B were analyzed and the structure of one duplex containing two modified nucleosides was determined using NMR spectroscopy and restrained molecular dynamics. The modifications 2-thiouridine, s(2)U(34), and pseudouridine, Psi(39), appreciably stabilized the interaction of the anticodon region with the viral subtype G and B RNAs. The structure of the duplex results in two coaxially stacked A-form RNA stems separated by two mismatched base pairs, U(162)*Psi(39) and G(163)*A(38), that maintained a reasonable A-form helix diameter. The tRNA's s(2)U(34) stabilized the interaction between the A-rich HIV Loop I sequence and the U-rich anticodon, whereas the tRNA's Psi(39) stabilized the adjacent mismatched pairs.
Resumo:
Nitrophorins represent a unique class of heme proteins that are able to perform the delicate transportation and release of the free-radical gaseous messenger nitric oxide (NO) in a pH-triggered manner. Besides its ability to bind to phospholipid membranes, the N-terminus contains an additional Leu-Pro-Gly stretch, which is a unique sequence trait, and the heme cavity is significantly altered with respect to other nitrophorins. These distinctive features encouraged us to solve the X-ray crystallographic structures of NP7 at low and high pH and bound with different heme ligands (nitric oxide, histamine, imidazole). The overall fold of the lipocalin motif is well preserved in the different X-ray structures and resembles the fold of other nitrophorins. However, a chain-like arrangement in the crystal lattice due to a number of head-to-tail electrostatic stabilizing interactions is found in NP7. Furthermore, the X-ray structures also reveal ligand-dependent changes in the orientation of the heme, as well as in specific interactions between the A-B and G-H loops, which are considered to be relevant for the biological function of nitrophorins. Fast and ultrafast laser triggered ligand rebinding experiments demonstrate the pH-dependent ligand migration within the cavities and the exit route. Finally, the topological distribution of pockets located around the heme as well as from inner cavities present at the rear of the protein provides a distinctive feature in NP7, so that while a loop gated exit mechanism to the solvent has been proposed for most nitrophorins, a more complex mechanism that involves several interconnected gas hosting cavities is proposed for NP7.
Resumo:
During the process of language development, one of the most important tasks that children must face is that of identifying the grammatical category to which words in their language belong. This is essential in order to be able to form grammatically correct utterances. How do children proceed in order to classify words in their language and assign them to their corresponding grammatical category? The present study investigates the usefulness of phonological information for the categorization of nouns in English, given the fact that it is phonology the first source of information that might be available to prelinguistic infants who lack access to semantic information or complex morphosyntactic information. We analyse four different corpora containing linguistic samples of English speaking mothers addressing their children in order to explore the reliability with which words are represented in mothers’ speech based on several phonological criteria. The results of the analysis confirm the prediction that most of the words to which English learning infants are exposed during the first two years of life can be accounted for in terms of their phonological resemblance