Predicción de estructuras de proteínas basada en vecinos más cercanos

Programa de Doctorado en Biotecnología y Tecnología QuímicaLas proteínas son las biomoléculas que tienen mayor diversidad estructural y desempeñan multitud de importantes funciones en todos los organismos vivos. Sin embargo, en la formación de las proteínas se producen anomalías que provocan o facilitan el desarrollo de importantes enfermedades como el cáncer o el Alzheimer, siendo de vital importancia el diseño de fármacos que permitan evitar sus desastrosas consecuencias. En dicho diseño de fármacos se precisa disponer de modelos estructurales de proteínas que, pese a que su secuencia es conocida, en la mayoría de los casos su estructura aún se ignora. Es por ello que la predicción de la estructura de una proteína a partir de su secuencia de aminoácidos resulta clave para la cura de este tipo de enfermedades. En la presente Tesis se ha analizado profundamente el estado del arte del problema de la predicción de la estructura terciaria y cuaternaria de una proteína, aportando diversos aspectos y puntos de vista de los métodos más actuales y relevantes presentes en la literatura. Por otra parte, se propone un método nuevo para la predicción de mapas de distancias que representan estructuras proteínicas mediante un esquema de vecinos más cercanos empleando propiedades físico-químicas de aminoácidos como entrada. Se ha realizado una exhaustiva experimentación y se han analizado los resultados desde varios puntos de vista y destacando diversos aspectos de interés. Finalmente, se ha aplicado la propuesta metodológica a dos grupos de proteínas de interés biológico: las proteínas de virus y de mitocondrias, obteniéndose resultados muy prometedores en ambos casos.Universidad Pablo de Olavide. Centro de Estudios de Postgrad

Automatic Eligibility of Sellers in an Online Marketplace: A Case Study of Amazon Algorithm

Purchase processes on Amazon Marketplace begin at the Buy Box, which represents the buy click process through which numerous sellers compete. This study aimed to estimate empirically the relevant seller characteristics that Amazon could consider featuring in the Buy Box. To that end, 22 product categories from Italy’s Amazon web page were studied over a ten-month period, and the sellers were analyzed through their products featured in the Buy Box. Two different experiments were proposed and the results were analyzed using four classification algorithms (a neural network, random forest, support vector machine, and C5.0 decision trees) and a rule-based classification. The first experiment aimed to characterize sellers unspecifically by predicting their change at the Buy Box. The second one aimed to predict which seller would be featured in it. Both experiments revealed that the customer experience and the dynamics of the sellers’ prices were important features of the Buy Box. Additionally, we proposed a set of default features that Amazon could consider when no information about sellers was available. We also proposed the possible existence of a relationship or composition among important features that could be used for sellers to be featured in the Buy Box

Use of bioinformatics tools to find new genes involved in rare diseases

Motivation: Rare diseases are a big challenge of our community and it is important to find answers and put forward a cure in the medicine field. Thanks to the huge amount of data that transcriptomic researches provide to public databases, we can use bioinformatics tools to analyse and seek new paths to understand better their molecular mechanisms and find new molecules that are related to the disease in order to make a future drug discovery process to this kind of research.Methods: We use transcriptomic data from the Expression Atlas repository, searching for experiments where the gene related to rare diseases is differently expressed.. We use the fold change data to choose those proteins that the expression are correlated to the expression of our gene of interest (R2>= 0.95). Using enrichment tools from Reactome database, or DAVID computational tool, we can stablish a Gene Ontology (GO) study among which we we can choose those that belong to the same biological process and path. This first step means an approach to select from thousands of genes a few gene cluster that may be highly related with the gene that cause the disease. The use of analysis tool R with bioinformatics packages, such as Bioconductor, CompGo, RDavidWebService or Clusterprofiler, allow us to keep improving the methodology making a deep analysis of Gene Ontology of our gene cluster, crafting relationships between them.Results: The current status of this research consists in the analysis of all GO terms that are belonged to our genes of interest that were crossed with the terms of the gene related to the studied disease. This step is crucial in order to find genes that are also affected by rare diseases in their metabolic path. This methodology could discover new biomarkers or, in another case, new strategies to understand the correct operation of the biological process of rare diseases and most importantly, the possibility to find a possible cure for these conditions

A novel approach to forecast urban surface-level ozone considering heterogeneous locations and limited information

Surface ozone (O3) is considered an hazard to human health, affecting vegetation crops and ecosystems. Accurate time and location O3 forecasting can help to protect citizens to unhealthy exposures when high levels are expected. Usually, forecasting models use numerous O3 precursors as predictors, limiting the reproducibility of these models to the availability of such information from data providers. This study introduces a 24 h-ahead hourly O3 concentrations forecasting methodology based on bagging and ensemble learning, using just two predictors with lagged O3 concentrations. This methodology was applied on ten-year time series (2006–2015) from three major urban areas of Andalusia (Spain). Its forecasting performance was contrasted with an algorithm especially designed to forecast time series exhibiting temporal patterns. The proposed methodology outperforms the contrast algorithm and yields comparable results to others existing in literature. Its use is encouraged due to its forecasting performance and wide applicability, but also as benchmark methodology

Prediction of protein distance maps by assembling fragments according to physicochemical similarities

The prediction of protein structures is a current issue of great significance in structural bioinformatics. More specifically, the prediction of the tertiary structure of a protein consists of determining its three-dimensional conformation based solely on its amino acid sequence. This study proposes a method in which protein fragments are assembled according to their physicochemical similarities, using information extracted from known protein structures. Many approaches cited in the literature use the physicochemical properties of amino acids, generally hydrophobicity, polarity and charge, to predict structure. In our method, implemented with parallel multithreading, a set of 30 physicochemical amino acid properties selected from the AAindex database were used. Several protein tertiary structure prediction methods produce a contact map. Our proposed method produces a distance map, which provides more information about the structure of a protein than a contact map. The results of experiments with several non-homologous protein sets demonstrate the generality of this method and its prediction quality using the amino acid properties considered

An Efficient Nearest Neighbor Method for Protein Contact Prediction

A variety of approaches for protein inter-residue contact pre diction have been developed in recent years. However, this problem is far from being solved yet. In this article, we present an efficient nearest neigh bor (NN) approach, called PKK-PCP, and an application for the protein inter-residue contact prediction. The great strength of using this approach is its adaptability to that problem. Furthermore, our method improves considerably the efficiency with regard to other NN approaches. Our NN-based method combines parallel execution with k-d tree as search algorithm. The input data used by our algorithm is based on structural features and physico-chemical properties of amino acids besides of evo lutionary information. Results obtained show better efficiency rates, in terms of time and memory consumption, than other similar approaches.Ministerio de Educación y Ciencia TIN2011-28956-C02-0

An Evolutionary Approach for Protein Contact Map Prediction

In this study, we present a residue-residue contact prediction approach based on evolutionary computation. Some amino acid properties are employed according to their importance in the folding process: hydrophobicity, polarity, charge and residue size. Our evolutionary algorithm provides a set of rules which determine different cases where two amino acids are in contact. A rule represents two windows of three amino acids. Each amino acid is characterized by these four properties. We also include a statistical study for the propensities of contacts between each pair of amino acids, according to their types, hydrophobicity and polarity. Different experiments were also performed to determine the best selection of properties for the structure prediction among the cited properties.Junta de Andalucía P07-TIC-02611Ministerio de Ciencia y Tecnología TIN2007-68084-C02-0

Alpha Helix Prediction Based on Evolutionary Computation

Multiple approaches have been developed in order to predict the protein secondary structure. In this paper, we propose an approach to such a problem based on evolutionary computation. The proposed ap proach considers various amino acids properties in order to predict the secondary structure of a protein. In particular, we will consider the hy drophobicity, the polarity and the charge of amino acids. In this study, we focus on predicting a particular kind of secondary structure: α-helices. The results of our proposal will be a set of rules that will identify the beginning or the end of such a structure.Junta de Andalucía P07-TIC-02611Ministerio de Ciencia y Tecnología TIN2007-68084-C02-0

Evolutionary decision rules for predicting protein contact maps

Protein structure prediction is currently one of the main open challenges in Bioinformatics. The protein contact map is an useful, and commonly used, represen tation for protein 3D structure and represents binary proximities (contact or non-contact) between each pair of amino acids of a protein. In this work, we propose a multi objective evolutionary approach for contact map prediction based on physico-chemical properties of amino acids. The evolutionary algorithm produces a set of decision rules that identifies contacts between amino acids. The rules obtained by the algorithm impose a set of conditions based on amino acid properties to predict contacts. We present results obtained by our approach on four different protein data sets. A statistical study was also performed to extract valid conclusions from the set of prediction rules generated by our algorithm. Results obtained confirm the validity of our proposal