Identification of potential small molecule binding pockets on Rho family GTPases

Rho GTPases are conformational switches that control a wide variety of signaling pathways critical for eukaryotic cell development and proliferation. They represent attractive targets for drug design as their aberrant function and deregulated activity is associated with many human diseases including cancer. Extensive high-resolution structures (.100) and recent mutagenesis studies have laid the foundation for the design of new structure-based chemotherapeutic strategies. Although the inhibition of Rho signaling with drug-like compounds is an active area of current research, very little attention has been devoted to directly inhibiting Rho by targeting potential allosteric non-nucleotide binding sites. By avoiding the nucleotide binding site, compounds may minimize the potential for undesirable off-target interactions with other ubiquitous GTP and ATP binding proteins. Here we describe the application of molecular dynamics simulations, principal component analysis, sequence conservation analysis, and ensemble small-molecule fragment mapping to provide an extensive mapping of potential small-molecule binding pockets on Rho family members. Characterized sites include novel pockets in the vicinity of the conformationaly responsive switch regions as well as distal sites that appear to be related to the conformations of the nucleotide binding region. Furthermore the use of accelerated molecular dynamics simulation, an advanced sampling method that extends the accessible time-scale of conventional simulations, is found to enhance the characterization of novel binding sites when conformational changes are important for the protein mechanism.

Development and validation of AMANDA, a new algorithm for selecting highly relevant regions in Molecular Interaction Fields

Descriptors based on Molecular Interaction Fields (MIF) are highly suitable for drug discovery, but their size (thousands of variables) often limits their application in practice. Here we describe a simple and fast computational method that extracts from a MIF a handful of highly informative points (hot spots) which summarize the most relevant information. The method was specifically developed for drug discovery, is fast, and does not require human supervision, being suitable for its application on very large series of compounds. The quality of the results has been tested by running the method on the ligand structure of a large number of ligand-receptor complexes and then comparing the position of the selected hot spots with actual atoms of the receptor. As an additional test, the hot spots obtained with the novel method were used to obtain GRIND-like molecular descriptors which were compared with the original GRIND. In both cases the results show that the novel method is highly suitable for describing ligand-receptor interactions and compares favorably with other state-of-the-art methods.

A systolic array for linear MIMO detection based on an all-swap lattice reduction algorithm

A systolic array to implement lattice-reduction-aided lineardetection is proposed for a MIMO receiver. The lattice reductionalgorithm and the ensuing linear detections are operated in the same array, which can be hardware-efficient. All-swap lattice reduction algorithm (ASLR) is considered for the systolic design.ASLR is a variant of the LLL algorithm, which processes all lattice basis vectors within one iteration. Lattice-reduction-aided linear detection based on ASLR and LLL algorithms have very similarbit-error-rate performance, while ASLR is more time efficient inthe systolic array, especially for systems with a large number ofantennas.

ILS-ESP An efficient, simple, and parameter-free algorithm for solving the permutation flow-shop problem

From a managerial point of view, the more effcient, simple, and parameter-free (ESP) an algorithm is, the more likely it will be used in practice for solving real-life problems. Following this principle, an ESP algorithm for solving the Permutation Flowshop Sequencing Problem (PFSP) is proposed in this article. Using an Iterated Local Search (ILS) framework, the so-called ILS-ESP algorithm is able to compete in performance with other well-known ILS-based approaches, which are considered among the most effcient algorithms for the PFSP. However, while other similar approaches still employ several parameters that can affect their performance if not properly chosen, our algorithm does not require any particular fine-tuning process since it uses basic "common sense" rules for the local search, perturbation, and acceptance criterion stages of the ILS metaheuristic. Our approach defines a new operator for the ILS perturbation process, a new acceptance criterion based on extremely simple and transparent rules, and a biased randomization process of the initial solution to randomly generate different alternative initial solutions of similar quality -which is attained by applying a biased randomization to a classical PFSP heuristic. This diversification of the initial solution aims at avoiding poorly designed starting points and, thus, allows the methodology to take advantage of current trends in parallel and distributed computing. A set of extensive tests, based on literature benchmarks, has been carried out in order to validate our algorithm and compare it against other approaches. These tests show that our parameter-free algorithm is able to compete with state-of-the-art metaheuristics for the PFSP. Also, the experiments show that, when using parallel computing, it is possible to improve the top ILS-based metaheuristic by just incorporating to it our biased randomization process with a high-quality pseudo-random number generator.

A polynomial algorithm for special case of the one-machine scheduling problem with time-lags

The standard one-machine scheduling problem consists in schedulinga set of jobs in one machine which can handle only one job at atime, minimizing the maximum lateness. Each job is available forprocessing at its release date, requires a known processing timeand after finishing the processing, it is delivery after a certaintime. There also can exists precedence constraints between pairsof jobs, requiring that the first jobs must be completed beforethe second job can start. An extension of this problem consistsin assigning a time interval between the processing of the jobsassociated with the precedence constrains, known by finish-starttime-lags. In presence of this constraints, the problem is NP-hardeven if preemption is allowed. In this work, we consider a specialcase of the one-machine preemption scheduling problem with time-lags, where the time-lags have a chain form, and propose apolynomial algorithm to solve it. The algorithm consist in apolynomial number of calls of the preemption version of the LongestTail Heuristic. One of the applicability of the method is to obtainlower bounds for NP-hard one-machine and job-shop schedulingproblems. We present some computational results of thisapplication, followed by some conclusions.

On the efficiency and sensitivity of a pyramidal classification algorithm

In this paper we propose a Pyramidal Classification Algorithm,which together with an appropriate aggregation index producesan indexed pseudo-hierarchy (in the strict sense) withoutinversions nor crossings. The computer implementation of thealgorithm makes it possible to carry out some simulation testsby Monte Carlo methods in order to study the efficiency andsensitivity of the pyramidal methods of the Maximum, Minimumand UPGMA. The results shown in this paper may help to choosebetween the three classification methods proposed, in order toobtain the classification that best fits the original structureof the population, provided we have an a priori informationconcerning this structure.

A simple randomized algorithm for consistent sequential prediction of ergodic time series

We present a simple randomized procedure for the prediction of a binary sequence. The algorithm uses ideas from recent developments of the theory of the prediction of individual sequences. We show that if thesequence is a realization of a stationary and ergodic random process then the average number of mistakes converges, almost surely, to that of the optimum, given by the Bayes predictor.

MSISpIC: A Probabilistic Scan Matching Algorithm Using a Mechanical Scanned Imaging Sonar

This paper compares two well known scan matching algorithms: the MbICP and the pIC. As a result of the study, it is proposed the MSISpIC, a probabilistic scan matching algorithm for the localization of an Autonomous Underwater Vehicle (AUV). The technique uses range scans gathered with a Mechanical Scanning Imaging Sonar (MSIS), and the robot displacement estimated through dead-reckoning with the help of a Doppler Velocity Log (DVL) and a Motion Reference Unit (MRU). The proposed method is an extension of the pIC algorithm. Its major contribution consists in: 1) using an EKF to estimate the local path traveled by the robot while grabbing the scan as well as its uncertainty and 2) proposing a method to group into a unique scan, with a convenient uncertainty model, all the data grabbed along the path described by the robot. The algorithm has been tested on an AUV guided along a 600m path within a marina environment with satisfactory results

An Efficient Nominal Unification Algorithm

Nominal Unification is an extension of first-order unification where terms can contain binders and unification is performed modulo α equivalence. Here we prove that the existence of nominal unifiers can be decided in quadratic time. First, we linearly-reduce nominal unification problems to a sequence of freshness and equalities between atoms, modulo a permutation, using ideas as Paterson and Wegman for first-order unification. Second, we prove that solvability of these reduced problems may be checked in quadràtic time. Finally, we point out how using ideas of Brown and Tarjan for unbalanced merging, we could solve these reduced problems more efficiently

Validation of a clinical algorithm to identify low-risk patients with pulmonary embolism.

Summary Background: We previously derived a clinical prognostic algorithm to identify patients with pulmonary embolism (PE) who are at low-risk of short-term mortality who could be safely discharged early or treated entirely in an outpatient setting. Objectives: To externally validate the clinical prognostic algorithm in an independent patient sample. Methods: We validated the algorithm in 983 consecutive patients prospectively diagnosed with PE at an emergency department of a university hospital. Patients with none of the algorithm's 10 prognostic variables (age >/= 70 years, cancer, heart failure, chronic lung disease, chronic renal disease, cerebrovascular disease, pulse >/= 110/min., systolic blood pressure < 100 mm Hg, oxygen saturation < 90%, and altered mental status) at baseline were defined as low-risk. We compared 30-day overall mortality among low-risk patients based on the algorithm between the validation and the original derivation sample. We also assessed the rate of PE-related and bleeding-related mortality among low-risk patients. Results: Overall, the algorithm classified 16.3% of patients with PE as low-risk. Mortality at 30 days was 1.9% among low-risk patients and did not differ between the validation and the original derivation sample. Among low-risk patients, only 0.6% died from definite or possible PE, and 0% died from bleeding. Conclusions: This study validates an easy-to-use, clinical prognostic algorithm for PE that accurately identifies patients with PE who are at low-risk of short-term mortality. Low-risk patients based on our algorithm are potential candidates for less costly outpatient treatment.

A fast Bayesian reconstruction algorithm for emission tomography with entropy prior converging to feasible images

The development and tests of an iterative reconstruction algorithm for emission tomography based on Bayesian statistical concepts are described. The algorithm uses the entropy of the generated image as a prior distribution, can be accelerated by the choice of an exponent, and converges uniformly to feasible images by the choice of one adjustable parameter. A feasible image has been defined as one that is consistent with the initial data (i.e. it is an image that, if truly a source of radiation in a patient, could have generated the initial data by the Poisson process that governs radioactive disintegration). The fundamental ideas of Bayesian reconstruction are discussed, along with the use of an entropy prior with an adjustable contrast parameter, the use of likelihood with data increment parameters as conditional probability, and the development of the new fast maximum a posteriori with entropy (FMAPE) Algorithm by the successive substitution method. It is shown that in the maximum likelihood estimator (MLE) and FMAPE algorithms, the only correct choice of initial image for the iterative procedure in the absence of a priori knowledge about the image configuration is a uniform field.

Characterization of the role of Rho activating signalling complexes in G protein-coupled receptor induced cardiac fibrosis

Dans certaines conditions pathologiques, telles que l'hypertension artérielle ou l'infarctus du myocarde, le coeur répond à une augmentation de la post-charge par des processus de remodelage aboutissant à une hypertrophie du ventricule gauche. L'hypertrophie cardiaque est caractérisée par une croissance hypertrophique des cardiomyocytes, ainsi que par une différenciation des fibroblastes en un phenotype présentant une capacité accrue de synthèse protéiques, nommés myofibroblastes. Ceci résulte en une accumulation excessive des constituants de la matrice extracellulaire, ou autrement dit fibrose. En raison de son effet délétère sur la contractilité du coeur, menant sur le long terme à une insuffisance cardiaque, de nombreux efforts ont été déployés, afin de définir les mécanismes moléculaires impliqués dans la réponse profibrotique. A ce jour, de nombreuses études indiquent que la petite GTPase RhoA pourrait être un médiateur important de la réponse profibrotique du myocarde. Cependant, les facteurs d'échanges impliqués dans la transduction de signaux profibrotiques, via la régulation de son activité au niveau des fibroblastes cardiaques, n'ont pas encore été identifiés. De précédentes études menées dans le laboratoire, ont identifiées une nouvelle protein d'ancrage de la PKA, exprimée majoritairement dans le coeur, nommée AKAP-Lbc. Il a été montré que cette protéine, en plus de sa fonction de protein d'ancrage, possédait une activité de facteur d'échange de nucléotide guanine (GEF) pour la petite GTPase RhoA. Au niveau des cardiomyocytes, il a été montré que l'AKAP-Lbc participe à une voie de signalisation pro-hypertrophique, incluant la sous-unité alpha de la protéine G hétérotrimerique G12 et RhoA. Chose intéressante, des observations antérieures à cette étude, indiquent que dans le coeur, l'AKAP-Lbc est également exprimée dans les fibroblastes. Cependant aucunes études n'a encore reporté de fonction pour ce facteur d'échange dans les fibroblastes cardiaques. Dans ce travail, les résultats obtenus indiquent que dans les fibroblastes cardiaques, I'activation de RhoA par l'AKAP-Lbc est impliquée dans la transmission de signaux profibrotiques, en aval des récépteurs à l'angiotensine II. En particulier, nous avons observé que la suppression de l'expression de l'AKAP-Lbc dans les fibroblastes ventriculaires de rat adultes, réduisait fortement Γ activation de Rho induite par l'angiotensine II, la déposition de collagène, la capacité migratoire des fibroblastes ainsi que leur différenciation en myofibroblastes. A notre connaissance, l'AKAP-Lbc est le premier RhoGEF identifié comme médiateur de la réponse profibrotique dans les fibroblastes cardiaques. - In pathological conditions such as chronic hypertension or myocardial infarction, the myocardium is subjected to various biomechanical and biochemical stresses, and undergoes an adverse ventricular remodelling process associated with cardiomyocytes hypertrophy and excess deposition of extracellular matrix proteins resulting in fibrosis. During the fibrotic response, cardiac fibroblasts differentiate into a more mobile and contractile phenotype termed myofibroblasts. These cells, possess a greater synthetic ability to produce ECM proteins and have been implicated in diseases with increased ECM deposition including cardiac fibrosis. Because fibrosis impairs myocardial contractility and is associated with the progression to heart failure, a major cause of lethality worldwide, many efforts have been made to define the molecular players involved in this process. During these last years, increasing evidence suggests a role for the small GTPase RhoA in mediating the fibrotic response in CFbs. However the identity of the exchange factors that modulate its activity and transduce fibrotic signals in CFbs is still unknown. Earlier work in our laboratory identified a novel PKA anchoring protein expressed in the heart termed AKAP-Lbc that has been shown to function as anchoring protein as well as a guanine nucleotide exchange factor (GEF) for the small GTPase RhoA. In response to several hypertrophic stimuli we have shown that RhoGEF activity of AKAP-Lbc mediated by Gan promotes the activation of a signaling pathway including RhoA, leading to cardiomyocytes hypertrophy. Within the heart, previous observations made in the laboratory indicated that AKAP-Lbc was also expressed in fibroblasts. However its role in cardiac fibroblasts remained to be determined. In the present study, we show that AKAP-Lbc is critical for activating RhoA and transducing profibrotic signals downstream of angiotensin II receptors in cardiac fibroblasts. In particular, our results indicate that suppression of AKAP-Lbc expression by infecting adult rat ventricular fibroblasts with lentiviruses encoding AKAP-Lbc specific short hairpin RNAs strongly reduces angiotensin II-induced RhoA activation, collagen deposition as well as cell migration and differentiation. These findings identify AKAP-Lbc as the first Rho-guanine nucleotide exchange factor involved in a profibrotic signalling pathway at the level of cardiac fibroblasts.

Numerical algorithm for Ginzburg-Landau equations with multiplicative noise: Application to domain growth

We consider stochastic partial differential equations with multiplicative noise. We derive an algorithm for the computer simulation of these equations. The algorithm is applied to study domain growth of a model with a conserved order parameter. The numerical results corroborate previous analytical predictions obtained by linear analysis.