A statistical physics perspective on alignment-independent protein sequence comparison.

Motivation: Within bioinformatics, the textual alignment of amino acid sequences has long dominated the determination of similarity between proteins, with all that implies for shared structure, function, and evolutionary descent. Despite the relative success of modern-day sequence alignment algorithms, so-called alignment-free approaches offer a complementary means of determining and expressing similarity, with potential benefits in certain key applications, such as regression analysis of protein structure-function studies, where alignment-base similarity has performed poorly. Results: Here, we offer a fresh, statistical physics-based perspective focusing on the question of alignment-free comparison, in the process adapting results from “first passage probability distribution” to summarize statistics of ensemble averaged amino acid propensity values. In this paper, we introduce and elaborate this approach.

AllerTOP - a server for in silico prediction of allergens

Background: Allergy is a form of hypersensitivity to normally innocuous substances, such as dust, pollen, foods or drugs. Allergens are small antigens that commonly provoke an IgE antibody response. There are two types of bioinformatics-based allergen prediction. The first approach follows FAO/WHO Codex alimentarius guidelines and searches for sequence similarity. The second approach is based on identifying conserved allergenicity-related linear motifs. Both approaches assume that allergenicity is a linearly coded property. In the present study, we applied ACC pre-processing to sets of known allergens, developing alignment-independent models for allergen recognition based on the main chemical properties of amino acid sequences.Results: A set of 684 food, 1,156 inhalant and 555 toxin allergens was collected from several databases. A set of non-allergens from the same species were selected to mirror the allergen set. The amino acids in the protein sequences were described by three z-descriptors (z1, z2 and z3) and by auto- and cross-covariance (ACC) transformation were converted into uniform vectors. Each protein was presented as a vector of 45 variables. Five machine learning methods for classification were applied in the study to derive models for allergen prediction. The methods were: discriminant analysis by partial least squares (DA-PLS), logistic regression (LR), decision tree (DT), naïve Bayes (NB) and k nearest neighbours (kNN). The best performing model was derived by kNN at k = 3. It was optimized, cross-validated and implemented in a server named AllerTOP, freely accessible at http://www.pharmfac.net/allertop. AllerTOP also predicts the most probable route of exposure. In comparison to other servers for allergen prediction, AllerTOP outperforms them with 94% sensitivity.Conclusions: AllerTOP is the first alignment-free server for in silico prediction of allergens based on the main physicochemical properties of proteins. Significantly, as well allergenicity AllerTOP is able to predict the route of allergen exposure: food, inhalant or toxin. © 2013 Dimitrov et al.; licensee BioMed Central Ltd.

Visual suppression in intermittent exotropia during binocular alignment.

PURPOSE To investigate the cortical mechanisms that prevent diplopia in intermittent exotropia (X(T)) during binocular alignment (orthotropia). METHODS The authors studied 12 X(T) patients aged 5 to 22 years. Seventy-five percent had functional stereo vision with stereoacuity similar to that of 12 age-matched controls (0.2-3.7 min arc). Identical face images were presented to the two eyes for 400 ms. In one eye, the face was presented at the fovea; in the other, offset along the horizontal axis with up to 12° eccentricity. The task was to indicate whether one or two faces were perceived. RESULTS All X(T) patients showed normal diplopia when the nonfoveal face was presented to nasal hemiretina, though with a slightly larger fusional range than age-matched controls. However, 10 of 12 patients never experienced diplopia when the nonfoveal face was presented to temporal hemiretina (i.e., when the stimulus simulated exodeviation). Patients showed considerable variability when the single image was perceived. Some patients suppressed the temporal stimulus regardless of which eye viewed it, whereas others suppressed a particular eye even when it viewed the foveal stimulus. In two patients, the simulated exodeviation might have triggered a shift from normal to anomalous retinal correspondence. CONCLUSIONS Antidiplopic mechanisms in X(T) can be reliably triggered by purely retinal information during orthotropia, but the nature of these mechanisms varies between patients.

Local character of magnetic coupling in ionic solids

Magnetic interactions in ionic solids are studied using parameter-free methods designed to provide accurate energy differences associated with quantum states defining the Heisenberg constant J. For a series of ionic solids including KNiF3, K2NiF4, KCuF3, K2CuF4, and high- Tc parent compound La2CuO4, the J experimental value is quantitatively reproduced. This result has fundamental implications because J values have been calculated from a finite cluster model whereas experiments refer to infinite solids. The present study permits us to firmly establish that in these wide-gap insulators, J is determined from strongly local electronic interactions involving two magnetic centers only thus providing an ab initio support to commonly used model Hamiltonians.

Validação de método analítico livre de acetonitrila para análise de microcistinas por cromatografia líquida de alta eficiência

Blooms of cyanobacteria represent a public health risk due to their cyanotoxins such as microcystins. Liquid chromatography techniques to separate and quantify microcystins invariably use acetonitrile as the organic component of the mobile phase. The price and availability of acetonitrile together with its elevated toxicity encourage the validation of acetonitrile-free methods of microcystin analysis. In this work, methanol was employed as the organic solvent of the mobile phase and the validation method was performed with different environmental water samples. The method showed limits of detection between 0.17 and 0.25 µg/L and of quantification between 0.55 and 0.82 µg/L for the microcystin variants: -RR, -YR, -LR, -LA.

Local character of magnetic coupling in ionic solids

Magnetic interactions in ionic solids are studied using parameter-free methods designed to provide accurate energy differences associated with quantum states defining the Heisenberg constant J. For a series of ionic solids including KNiF3, K2NiF4, KCuF3, K2CuF4, and high- Tc parent compound La2CuO4, the J experimental value is quantitatively reproduced. This result has fundamental implications because J values have been calculated from a finite cluster model whereas experiments refer to infinite solids. The present study permits us to firmly establish that in these wide-gap insulators, J is determined from strongly local electronic interactions involving two magnetic centers only thus providing an ab initio support to commonly used model Hamiltonians.

Evolving Distributed Algorithms with Genetic Programming

Distributed systems are one of the most vital components of the economy. The most prominent example is probably the internet, a constituent element of our knowledge society. During the recent years, the number of novel network types has steadily increased. Amongst others, sensor networks, distributed systems composed of tiny computational devices with scarce resources, have emerged. The further development and heterogeneous connection of such systems imposes new requirements on the software development process. Mobile and wireless networks, for instance, have to organize themselves autonomously and must be able to react to changes in the environment and to failing nodes alike. Researching new approaches for the design of distributed algorithms may lead to methods with which these requirements can be met efficiently. In this thesis, one such method is developed, tested, and discussed in respect of its practical utility. Our new design approach for distributed algorithms is based on Genetic Programming, a member of the family of evolutionary algorithms. Evolutionary algorithms are metaheuristic optimization methods which copy principles from natural evolution. They use a population of solution candidates which they try to refine step by step in order to attain optimal values for predefined objective functions. The synthesis of an algorithm with our approach starts with an analysis step in which the wanted global behavior of the distributed system is specified. From this specification, objective functions are derived which steer a Genetic Programming process where the solution candidates are distributed programs. The objective functions rate how close these programs approximate the goal behavior in multiple randomized network simulations. The evolutionary process step by step selects the most promising solution candidates and modifies and combines them with mutation and crossover operators. This way, a description of the global behavior of a distributed system is translated automatically to programs which, if executed locally on the nodes of the system, exhibit this behavior. In our work, we test six different ways for representing distributed programs, comprising adaptations and extensions of well-known Genetic Programming methods (SGP, eSGP, and LGP), one bio-inspired approach (Fraglets), and two new program representations called Rule-based Genetic Programming (RBGP, eRBGP) designed by us. We breed programs in these representations for three well-known example problems in distributed systems: election algorithms, the distributed mutual exclusion at a critical section, and the distributed computation of the greatest common divisor of a set of numbers. Synthesizing distributed programs the evolutionary way does not necessarily lead to the envisaged results. In a detailed analysis, we discuss the problematic features which make this form of Genetic Programming particularly hard. The two Rule-based Genetic Programming approaches have been developed especially in order to mitigate these difficulties. In our experiments, at least one of them (eRBGP) turned out to be a very efficient approach and in most cases, was superior to the other representations.

Kinetic study of pyrolysis of castor beans (Ricinuscommunis L) presscake: an alternative use for solid waste arising from the biodiesel production

This work investigated the effects of temperature and of rate of heating on the kinetic parameters of pyrolysis of castor beans presscake, a byproduct generated in the biodiesel production process. Pyrolysis process was investigated by thermogravimetric analysis, and parameters were obtained from nonisothermal experiments. The results obtained from the process of thermal decomposition indicated the elimination of humidity and the decomposition of organic components of the biomass. DTG curves showed that the heating rate affects the temperature of maximum decomposition of the material. Kinetic parameters such as activation energy and pre-exponential factor were obtained by model-free methods proposed by Flynn–Wall–Ozawa (FWO), Kissinger–Akahira–Sunose (KAS), and Kissinger. Experimental results showed that the kinetic parameters values of the FWO and KAS methods display good agreement and can be used to understand the mechanism of degradation of the cake. In a generalized way, the results contribute to better understanding of the processes of biomass pyrolysis.

Impedance-derived electrochemical capacitance spectroscopy for the evaluation of lectin-glycoprotein binding affinity

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Crystal Engineering of bright luminescent copper iodide clusters with phosphorus and nitrogen-based ligands

Copper(I) halide clusters are recently considered as good candidate for optoelectronic devices such as OLEDs . Although the copper halide clusters, in particular copper iodide, are very well known since the beginning of the 20th century, only in the late ‘70s the interest on these compounds grew dramatically due their particular photophysical behaviour. These complexes are characterized by a dual triplet emission bands, named Cluster Centred (3CC) and Halogen-to-Ligand charge transfer (3XLCT), the intensities of which are strictly related with the temperature. The CC transition, due to the presence of a metallophylic interactions, is prevalent at ambient temperature while the XLCT transition, located preferentially on the ligand part, became more prominent at low temperature. Since these pioneering works, it was easy to understand the photophysical properties of this compounds became more interesting in solid-state respect to solution with an improvement in emission efficiency. In this work we aim to characterize in SS organocopper(I)iodide compounds to valuate the correlation between the molecular crystal structure and the photophysical properties. It is also considered to hike new strategies to synthesize CuI complexes from the wet reactions to the more green solvent free methods. The advantages in using these strategies are evident but, obtain a single crystal suitable for SCXRD analysis from these batches is quite impossible. The structure solution still remains the key point in this research so we tackle this problem solving the structure by X-ray powder diffraction data. When the sample was fully characterized we moved to design and development of the associated OLED-device. Since copper iodide complexes are often insoluble in organic solvents, the high vacuum deposition technique is preferred. A new non-conventional deposition process have also been proposed to avoid the low complex stability in this practice with an in-situ complex formation in a layer-by layer deposition route.

C-3 Alkyl /Arylalkyl-2,3-dideoxy hex-2-enopyranosides as Antitubercular Agents: Synthesis, Biological Evaluation and QSAR Study

A series of C-3 alkyl and arylalky 2,3-dideoxy hex-2-enopyranoside derivatives were synthesized by Morita-Baylis-Hillman reaction using enulosides 4, 5 and 6 and various aliphatic and aromatic aldehydes. The compounds were evaluated in vitro for the complete inhibition of growth of Mycobacterium tuberculosis H37Rv. They exhibited moderate to good activity in the range of 25-1.56 µg/mL. Among these, 4d, 4h, 5c and 4hr showed activity at minimum inhibitory concentrations, 3.12, 6.25, 1.56 and 1.56µg/mL, respectively. These compounds were safe against cytotoxicity in VERO cell line and mouse macrophage cell line J 744A.1. A QSAR analysis by CP-MLR with alignment-free 3D-descriptors indicated the relevance of structure space comparable to the minimum energy conformation (from conformational analysis) of 5c to the activity. The study indicates that the compounds attaining conformational space 5c and reflecting some symmetry, minimum eccentricity and closely placed geometric and electronegativity centers therein are favorable for activity.

Functional Optical Coherence Tomography: In Vivo Applications in Dermatology and Neuroscience

Thesis (Ph.D.)--University of Washington, 2016-06

Present perspectives on the automated classification of the G-protein coupled receptors (GPCRs) at the protein sequence level

The G-protein coupled receptors--or GPCRs--comprise simultaneously one of the largest and one of the most multi-functional protein families known to modern-day molecular bioscience. From a drug discovery and pharmaceutical industry perspective, the GPCRs constitute one of the most commercially and economically important groups of proteins known. The GPCRs undertake numerous vital metabolic functions and interact with a hugely diverse range of small and large ligands. Many different methodologies have been developed to efficiently and accurately classify the GPCRs. These range from motif-based techniques to machine learning as well as a variety of alignment-free techniques based on the physiochemical properties of sequences. We review here the available methodologies for the classification of GPCRs. Part of this work focuses on how we have tried to build the intrinsically hierarchical nature of sequence relations, implicit within the family, into an adaptive approach to classification. Importantly, we also allude to some of the key innate problems in developing an effective approach to classifying the GPCRs: the lack of sequence similarity between the six classes that comprise the GPCR family and the low sequence similarity to other family members evinced by many newly revealed members of the family.

Uncovering changes in the redox protein interactome of PTEN

Phosphatase and tensin homolog (PTEN) is a redox-sensitive, dual-specificity protein phosphatase involved in regulating a number of cellular processes including metabolism, apoptosis, cell proliferation and survival. It acts as a tumor suppressor by negatively regulating the PI3K/Akt pathway. While direct evidence of a redox regulation of PTEN downstream signaling has been reported, the effect of cellular oxidative stress or direct PTEN oxidation on the PTEN interactome is still poorly defined. To investigate this, PTEN-GST fusion protein was prepared in its reduced form and an H2O2-oxidized form that was reversible by DTT treatment, and these were immobilized on a glutathione-sepharose-based support. The immobilized protein was incubated with cell lysate to capture interacting proteins. Captured proteins were eluted from the beads, analyzed by LC-MSMS and comparatively quantified using label-free methods. After subtraction of interactors that were also present in the resin and GST controls, 97 individual protein interactors were identified, including several that are novel. Fourteen interactors that varied significantly with the redox status of PTEN were identified, including thioredoxin and peroxiredoxin-1. Except for one interactor, their binding was higher for oxidized PTEN. Using western blotting, altered binding to PTEN was confirmed for 3 selected interactors (Prdx1, Trx, and Anxa2) and DDB1 was validated as a novel interactor with unaltered binding. Our results suggest that the redox status of PTEN causes a functional variation in the PTEN interactome which is important for the cellular function of PTEN. The resin capture method developed had distinct advantages in that the redox status of PTEN could be directly controlled and measured.

A predictor of membrane class:discriminating α-helical and β-barrel membrane proteins from non-membranous proteins

Accurate protein structure prediction remains an active objective of research in bioinformatics. Membrane proteins comprise approximately 20% of most genomes. They are, however, poorly tractable targets of experimental structure determination. Their analysis using bioinformatics thus makes an important contribution to their on-going study. Using a method based on Bayesian Networks, which provides a flexible and powerful framework for statistical inference, we have addressed the alignment-free discrimination of membrane from non-membrane proteins. The method successfully identifies prokaryotic and eukaryotic α-helical membrane proteins at 94.4% accuracy, β-barrel proteins at 72.4% accuracy, and distinguishes assorted non-membranous proteins with 85.9% accuracy. The method here is an important potential advance in the computational analysis of membrane protein structure. It represents a useful tool for the characterisation of membrane proteins with a wide variety of potential applications.