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At least 631 records · Page 35Linked to original sources
PubMed2006

High-resolution structures of Escherichia coli cDsbD in different redox states: A combined crystallographic, biochemical and computational study.

Escherichia coli DsbD transports electrons from cytoplasmic thioredoxin to periplasmic target proteins. DsbD is composed of an N-terminal (nDsbD) and a C-terminal (cDsbD) periplasmic domain, connected by a central transmembrane domain. Each domain possesses two cysteine residues essential for electron transport. The transport proceeds via disulfide exchange reactions from cytoplasmic thioredoxin to the central transmembrane domain and via cDsbD to nDsbD, which then reduces the periplasmic target proteins. We determined four high-resolution structures of cDsbD: oxidized (1.65 A resolution), chemically reduced (1.3 A), photo-reduced (1.1 A) and chemically reduced at pH increased from 4.6 to 7. The latter structure was refined at 0.99 A resolution, the highest achieved so far for a thioredoxin superfamily member. The data reveal unprecedented structural details of cDsbD, demonstrating that the domain is very rigid and undergoes hardly any conformational change upon disulfide reduction or interaction with nDsbD. In full agreement with the crystallographic results, guanidinium chloride-induced unfolding and refolding experiments indicate that oxidized and reduced cDsbD are equally stable. We confirmed the structural rigidity of cDsbD by molecular dynamics simulations. A remarkable feature of cDsbD is the pKa of 9.3 for the active site Cys461: this value, determined using two different experimental methods, surprisingly was around 2.5 units higher than expected on the basis of the redox potential. Additionally, taking advantage of the very high quality of the cDsbD structures, we carried out pKa calculations, which gave results in agreement with the experimental findings. In conclusion, our wide-scope analysis of cDsbD, encompassing atomic-resolution crystallography, computational chemistry and biophysical measurements, highlighted two so far unrecognized key aspects of this domain: its unusual redox properties and extreme rigidity. Both are likely to be correlated to the role of cDsbD as a covalently linked electron shuttle between the membrane domain and the N-terminal periplasmic domain of DsbD.

Amino Acid Sequence↗
PubMed2004

Experimental demonstration of a programmable quantum computer by NMR.

A programmable quantum computer is experimentally demonstrated by nuclear magnetic resonance using one qubit for the program and two qubits for data. A non-separable two-qubit operation is performed in a programmable way to show the successful demonstration. Projective measurements required in the programmable quantum computer are simulated by averaging the results of experiments just like when producing an effective pure state.

Computers↗
PubMed2005

The fumarate sensor DcuS: progress in rapid protein fold elucidation by combining protein structure prediction methods with NMR spectroscopy.

We illustrate how moderate resolution protein structures can be rapidly obtained by interlinking computational prediction methodologies with un- or partially assigned NMR data. To facilitate the application of our recently described method of ranking and subsequent refining alternative structural models using unassigned NMR data [Proc. Natl. Acad. Sci. USA 100 (2003) 15404] for such "structural genomics"-type experiments it is combined with protein models from several prediction techniques, enhanced to utilize partial assignments, and applied on a protein with an unknown structure and fold. From the original NMR spectra obtained for the 140 residue fumarate sensor DcuS, 1100 1H, 13C, and 15N chemical shift signals, 3000 1H-1H NOESY cross peak intensities, and 209 backbone residual dipolar couplings were extracted and used to rank models produced by de novo structure prediction and comparative modeling methods. The ranking proceeds in two steps: first, an optimal assignment of the NMR peaks to atoms is found for each model independently, and second, the models are ranked based on the consistency between the NMR data and the model assuming these optimal assignments. The low-resolution model selected using this ranking procedure had the correct overall fold and a global backbone RMSD of 6.0 angstrom, and was subsequently refined to 3.7 angstrom RMSD. With the incorporation of a small number of NOE and residual dipolar coupling constraints available very early in the traditional spectral assignment process, a model with an RMSD of 2.8 angstrom could rapidly be built. The ability to generate moderate resolution models within days of NMR data collection should facilitate large scale NMR structure determination efforts.

Algorithms↗
PubMed2006

High performance computing in biology: multimillion atom simulations of nanoscale systems.

Computational methods have been used in biology for sequence analysis (bioinformatics), all-atom simulation (molecular dynamics and quantum calculations), and more recently for modeling biological networks (systems biology). Of these three techniques, all-atom simulation is currently the most computationally demanding, in terms of compute load, communication speed, and memory load. Breakthroughs in electrostatic force calculation and dynamic load balancing have enabled molecular dynamics simulations of large biomolecular complexes. Here, we report simulation results for the ribosome, using approximately 2.64 million atoms, the largest all-atom biomolecular simulation published to date. Several other nano-scale systems with different numbers of atoms were studied to measure the performance of the NAMD molecular dynamics simulation program on the Los Alamos National Laboratory Q Machine. We demonstrate that multimillion atom systems represent a 'sweet spot' for the NAMD code on large supercomputers. NAMD displays an unprecedented 85% parallel scaling efficiency for the ribosome system on 1024 CPUs. We also review recent targeted molecular dynamics simulations of the ribosome that prove useful for studying conformational changes of this large biomolecular complex in atomic detail.

Computational Biology↗
PubMed2004

Can the human brain do quantum computing?

The electrical membrane properties have been the key issues in the understanding of the cerebral physiology for more than almost two centuries. But, molecular neurobiology has now discovered that biochemical transactions play an important role in neuronal computations. Quantum computing (QC) is becoming a reality both from the theoretical point of view as well as from practical applications. Quantum mechanics is the most accurate description at atomic level and it lies behind all chemistry that provides the basis for biology ... maybe the magic of entanglement is also crucial for life. The purpose of the present paper is to discuss the dendrite spine as a quantum computing device, taking into account what is known about the physiology of the glutamate receptors and the cascade of biochemical transactions triggered by the glutamate binding to these receptors.

Algorithms↗
PubMed2004

Recurrence quantification analysis of sleep electoencephalogram in sleep apnea syndrome in humans.

The aim of this study is to elucidate whether the results of recurrence quantification analysis (RQA) of sleep EEGs in sleep apnea syndrome are valuable for analyzing sleep EEGs in sleep apnea syndrome. We investigated the ability of RQA to discriminate sleep stages and to characterize the different behaviors of sleep EEGs in sleep apnea syndrome. RQA was applied to EEG signals during sleep stages 1, 2, slow wave sleep (SWS), REM and the stage 'awake.' The sleep EEG signals were obtained from the MIT-BIH polysomnographic database. To examine the differences in the RQA measures for all sleep stages, one-way analysis of variance (ANOVA) and post hoc analysis were performed. From the results, all sleep stages could be distinctly discriminated by means of the RQA measure of %RATIO. We observed that stage 1 and REM had fewer recurrences, and that stage 2 was more autocorrelated than the other stages. The different dynamic behaviors of wakefulness and sleep EEG were also observed. Of significant interest was the observation that RQA was able to distinguish stage 1 from REM. In conclusion, we suggest that the information obtained from RQA of sleep EEGs in sleep apnea syndrome is valuable for its analysis, and that RQA constitutes a useful tool for analyzing sleep EEGs in subjects with sleep apnea syndrome.

Computing Methodologies↗
PubMed2005

Long-timescale simulation methods.

The outstanding challenges in computer simulations of biological macromolecules are related to their complexity. Part of the complexity of biological systems concerns their physical size. Enumerating atoms ranging from a few in small signal molecules to the millions of particles in biological complexes is an obvious example of biological hierarchy. Another aspect is the extremely broad range of timescales of life science processes (many orders of magnitude); this adds another dimension of complexity. This extended range of timescales may even be observed for a single biomolecular process. Consider, for example, the R to T transition in hemoglobin. The complete conformational change occurs in tens of microseconds. However, the system has more than one timescale. Considerable activity occurs on a range of timescales before the final event (heme relaxation, picoseconds; tertiary relaxation, nanoseconds; ligand escape from the protein matrix and rebinding, hundreds of nanoseconds and so on). Whereas the basic time-step of atomically detailed simulations is about a femtosecond, it is not difficult to find molecular processes in biology that span more than ten orders of magnitude of relevant times, making the straightforward simulation of these events very difficult. Several techniques have been developed in recent years to address these problems.

Algorithms↗
PubMed2005

Development of a multiple objective planning theory and system for sustainable air quality monitoring networks.

Air quality monitoring data are important bases for air quality management strategies planning and performance assessment. Therefore, the environmental protection authorities need to plan the air quality monitoring network effectively. However, in Taiwan, the national Environmental Protection Administration (EPA) and some county environmental protection bureaus (EPB) separately installed their own monitoring stations. This study developed an integrated methodology and computer system for planning air quality monitoring networks. The environmental, social, and economic objectives and sub-objectives, and their weights were identified using system analysis and multiple objective planning, based on the principles of sustainable development. A multiple objective optimization model and procedure for sustainable air quality monitoring networks planning are developed in this study. According to the procedure, a multiple objective planning system for sustainable air quality monitoring networks (MOPSSAQMN) is developed using computer software based on the modified bounded implicit enumeration algorithm with the constraint arrangement method. The air quality monitoring network of Taoyuan County, in northern Taiwan, was used as a case study to demonstrate the proposed method. Two satisfactory alternatives based on different conditions were generated using MOPSSAQMN. The compared results show that this study generated better alternatives than the current monitoring network. An installation schedule for the alternative was proposed, and its first step is now being implemented by the EPB of Taoyuan County Government. The procedure and computer system developed in this study can be used to assist the competent authorities to devise good and different alternatives for air quality monitoring networks planning.

Air Pollutants↗
PubMed2003

Support vector machines for predicting membrane protein types by using functional domain composition.

Membrane proteins are generally classified into the following five types: 1), type I membrane protein; 2), type II membrane protein; 3), multipass transmembrane proteins; 4), lipid chain-anchored membrane proteins; and 5), GPI-anchored membrane proteins. In this article, based on the concept of using the functional domain composition to define a protein, the Support Vector Machine algorithm is developed for predicting the membrane protein type. High success rates are obtained by both the self-consistency and jackknife tests. The current approach, complemented with the powerful covariant discriminant algorithm based on the pseudo-amino acid composition that has incorporated quasi-sequence-order effect as recently proposed by K. C. Chou (2001), may become a very useful high-throughput tool in the area of bioinformatics and proteomics.

Algorithms↗
PubMed2003

Hybrid global optimization algorithms for protein structure prediction: alternating hybrids.

Hybrid global optimization methods attempt to combine the beneficial features of two or more algorithms, and can be powerful methods for solving challenging nonconvex optimization problems. In this paper, novel classes of hybrid global optimization methods, termed alternating hybrids, are introduced for application as a tool in treating the peptide and protein structure prediction problems. In particular, these new optimization methods take the form of hybrids between a deterministic global optimization algorithm, the alphaBB, and a stochastically based method, conformational space annealing (CSA). The alphaBB method, as a theoretically proven global optimization approach, exhibits consistency, as it guarantees convergence to the global minimum for twice-continuously differentiable constrained nonlinear programming problems, but can benefit from computationally related enhancements. On the other hand, the independent CSA algorithm is highly efficient, though the method lacks theoretical guarantees of convergence. Furthermore, both the alphaBB method and the CSA method are found to identify ensembles of low-energy conformers, an important feature for determining the true free energy minimum of the system. The proposed hybrid methods combine the desirable features of efficiency and consistency, thus enabling the accurate prediction of the structures of larger peptides. Computational studies for met-enkephalin and melittin, employing sequential and parallel computing frameworks, demonstrate the promise for these proposed hybrid methods.

Algorithms↗
PubMed2003

Rules vs. analogy in English past tenses: a computational/experimental study.

Are morphological patterns learned in the form of rules? Some models deny this, attributing all morphology to analogical mechanisms. The dual mechanism model (Pinker, S., & Prince, A. (1998). On language and connectionism: analysis of a parallel distributed processing model of language acquisition. Cognition, 28, 73-193) posits that speakers do internalize rules, but that these rules are few and cover only regular processes; the remaining patterns are attributed to analogy. This article advocates a third approach, which uses multiple stochastic rules and no analogy. We propose a model that employs inductive learning to discover multiple rules, and assigns them confidence scores based on their performance in the lexicon. Our model is supported over the two alternatives by new "wug test" data on English past tenses, which show that participant ratings of novel pasts depend on the phonological shape of the stem, both for irregulars and, surprisingly, also for regulars. The latter observation cannot be explained under the dual mechanism approach, which derives all regulars with a single rule. To evaluate the alternative hypothesis that all morphology is analogical, we implemented a purely analogical model, which evaluates novel pasts based solely on their similarity to existing verbs. Tested against experimental data, this analogical model also failed in key respects: it could not locate patterns that require abstract structural characterizations, and it favored implausible responses based on single, highly similar exemplars. We conclude that speakers extend morphological patterns based on abstract structural properties, of a kind appropriately described with rules.

Computing Methodologies↗
PubMed1991

Absence of a direct effect of recombinant tumor necrosis factor-alpha on human sperm function and murine preimplantation development.

OBJECTIVE: This study was designed to test the in vitro effects of human recombinant tumor necrosis factor (rTNF) on sperm motility, fertilization, and preimplantation development. DESIGN: A sensitive enzyme immunoassay was used to determine half-lives of rTNF and confirm concentrations of cytokine throughout experimental conditions. Effect of rTNF on human sperm survival was measured by computer-assisted methodology, and effect on human sperm penetration was assessed by hamster ova penetration. Cytokine effect on murine gamete interaction was determined by in vitro fertilization (IVF). Murine preimplantation development was assessed by in vitro development of cryopreserved-thawed one-cell zygotes. RESULTS: The half-life of rTNF was reduced by the addition of sperm to culture media (P less than 0.001). Sperm motility (P = 0.245) and hamster ova penetration (P = 0.62) were not altered by incubations in the presence of concentrations of rTNF up to 10,000 U/mL. Mouse IVF (P = 0.60) and preimplantation development (P = 0.56) were not altered by rTNF in concentrations up to 5,000 U/mL. CONCLUSIONS: These results demonstrate rTNF by itself does not interfere with gamete function or early embryo development.

Analysis of Variance↗
PubMed2004

A VxD-based automatic blending system using multithreaded programming.

This paper discusses the object-oriented software design for an automatic blending system. By combining the advantages of a programmable logic controller (PLC) and an industrial control PC (ICPC), an automatic blending control system is developed for a chemical plant. The system structure and multithread-based communication approach are first presented in this paper. The overall software design issues, such as system requirements and functionalities, are then discussed in detail. Furthermore, by replacing the conventional dynamic link library (DLL) with virtual X device drivers (VxD's), a practical and cost-effective solution is provided to improve the robustness of the Windows platform-based automatic blending system in small- and medium-sized plants.

Algorithms↗
PubMed2001

Sequence alignment: an approximation law for the Z-value with applications to databank scanning.

The Z-value is an attempt to estimate the statistical significance of a Smith and Waterman dynamic programming alignment score (H-score) through the use of a Monte-Carlo procedure. In this paper, we give an approximation for the Z-value law deduced from the Poisson clumping heuristic developed by Waterman and Vingron (Stat. Sci. 9 (1994) 367) in the case of independent and identically distributed sequences comparison. As for non-gapped alignment scores, our approximation is of Gumbel type but with parameters that are sequence independent. This result makes clear the related experimental results mentioned by Comet et al. (Comput. Chem. 23 (1999) 317). Using 'quasi-real' sequences (i.e. randomly shuffled sequences of the same length and amino acid composition as the real ones) we investigate the relevance of our approximation result. Since the Monte-Carlo approach we use generates a bias for the Gumbel decay parameter estimation, a correction procedure is proposed. Applications to real sequences are considered and we show how our results can be used to detect the potential biological relationships between real sequences.

Computing Methodologies↗
PubMed1999

Significance of Z-value statistics of Smith-Waterman scores for protein alignments.

The Z-value is an attempt to estimate the statistical significance of a Smith-Waterman dynamic alignment score (SW-score) through the use of a Monte-Carlo process. It partly reduces the bias induced by the composition and length of the sequences. This paper is not a theoretical study on the distribution of SW-scores and Z-values. Rather, it presents a statistical analysis of Z-values on large datasets of protein sequences, leading to a law of probability that the experimental Z-values follow. First, we determine the relationships between the computed Z-value, an estimation of its variance and the number of randomizations in the Monte-Carlo process. Then, we illustrate that Z-values are less correlated to sequence lengths than SW-scores. Then we show that pairwise alignments, performed on 'quasi-real' sequences (i.e., randomly shuffled sequences of the same length and amino acid composition as the real ones) lead to Z-value distributions that statistically fit the extreme value distribution, more precisely the Gumbel distribution (global EVD, Extreme Value Distribution). However, for real protein sequences, we observe an over-representation of high Z-values. We determine first a cutoff value which separates these overestimated Z-values from those which follow the global EVD. We then show that the interesting part of the tail of distribution of Z-values can be approximated by another EVD (i.e., an EVD which differs from the global EVD) or by a Pareto law. This has been confirmed for all proteins analysed so far, whether extracted from individual genomes, or from the ensemble of five complete microbial genomes comprising altogether 16956 protein sequences.

Computing Methodologies↗
PubMed2003

Automatic recognition of explorative strategies in the Morris water maze.

Notwithstanding the development of reliable tracking systems, the quantification methodology of the Morris water maze (MWM) has witnessed an operational mismatch between the indexes used to quantify MWM performance and the cognitive concepts derived from these indexes. Indeed, escape latency is the main, and often unique, performance measure used for the quantification of behavior. Aim of the present work was to overcome this limitation by presenting a methodology that allows for automatic categorical pattern recognition of the behavioral strategies performed in the MWM. By selecting few a priori and user-defined behavioral categories, many quantitative variables and regions of interest (ROIs), we used discriminant analysis (DA) to obtain 97.9% of correct automatic recognition of categories. The developed discriminant model (DM) also allowed to predict category membership of newly recorded swim paths with the same statistical efficacy (96%), and to identify the variables that better discriminate between adjacent categories. The combination of DA with a tracking system, a selection of many variables, different ROIs and qualitative categorization, reduces the gap between the measurement process and the categories used to describe a given behavior, and offers a methodology to computationally reproduce the human categorization of behaviors in the MWM.

Animals↗