Integrating protein structural information

Dissertação apresentada para obtenção de Grau de Doutor em Bioquímica,Bioquímica Estrutural, pela Universidade Nova de Lisboa, Faculdade de Ciências e TecnologiaThe central theme of this work is the application of constraint programming and other artificial intelligence techniques to protein structure problems, with the goal of better combining experimental data with structure prediction methods. Part one of the dissertation introduces the main subjects of protein structure and constraint programming, summarises the state of the art in the modelling of protein structures and complexes, sets the context for the techniques described later on, and outlines the main points of the thesis: the integration of experimental data in modelling. The first chapter, Protein Structure, introduces the reader to the basic notions of amino acid structure, protein chains, and protein folding and interaction. These are important concepts to understand the work described in parts two and three. Chapter two, Protein Modelling, gives a brief overview of experimental and theoretical techniques to model protein structures. The information in this chapter provides the context of the investigations described in parts two and three, but is not essential to understanding the methods developed. Chapter three, Constraint Programming, outlines the main concepts of this programming technique. Understanding variable modelling, the notions of consistency and propagation, and search methods should greatly help the reader interested in the details of the algorithms, as described in part two of this book. The fourth chapter, Integrating Structural Information, is a summary of the thesis proposed here. This chapter is an overview of the objectives of this work, and gives an idea of how the algorithms developed here could help in modelling protein structures. The main goal is to provide a flexible and continuously evolving framework for the integration of structural information from a diversity of experimental techniques and theoretical predictions. Part two describes the algorithms developed, which make up the main original contribution of this work. This part is aimed especially at developers interested in the details of the algorithms, in replicating the results, in improving the method or in integrating them in other applications. Biochemical aspects are dealt with briefly and as necessary, and the emphasis is on the algorithms and the code

Satellite-based feature extraction and multivariate time-series prediction of biotoxin contamination in shellfish

Shellfish production constitutes an important sector for the economy of many Portuguese coastal regions, yet the challenge of shellfish biotoxin contamination poses both public health concerns and significant economic risks. Thus, predicting shellfish contamination levels holds great potential for enhancing production management and safeguarding public health. In our study, we utilize a dataset with years of Sentinel-3 satellite imagery for marine surveillance, along with shellfish biotoxin contamination data from various production areas along Portugal's western coastline, collected by Portuguese official control. Our goal is to evaluate the integration of satellite data in forecasting models for predicting toxin concentrations in shellfish given forecasting horizons up to four weeks, which implies extracting a small set of useful features and assessing their impact on the predictive models. We framed this challenge as a time-series forecasting problem, leveraging historical contamination levels and satellite images for designated areas. While contamination measurements occurred weekly, satellite images were accessible multiple times per week. Unsupervised feature extraction was performed using autoencoders able to handle non-valid pixels caused by factors like cloud cover, land, or anomalies. Finally, several Artificial Neural Networks models were applied to compare univariate (contamination only) and multivariate (contamination and satellite data) time-series forecasting. Our findings show that incorporating these features enhances predictions, especially beyond one week in lagoon production areas (RIAV) and for the 1-week and 2-week horizons in the L5B area (oceanic). The methodology shows the feasibility of integrating information from a high-dimensional data source like remote sensing without compromising the model's predictive ability.Comment: 19 page

Forecasting biotoxin contamination in mussels across production areas of the Portuguese coast with Artificial Neural Networks

Harmful algal blooms (HABs) and the consequent contamination of shellfish are complex processes depending on several biotic and abiotic variables, turning prediction of shellfish contamination into a challenging task. Not only the information of interest is dispersed among multiple sources, but also the complex temporal relationships between the time-series variables require advanced machine methods to model such relationships. In this study, multiple time-series variables measured in Portuguese shellfish production areas were used to forecast shellfish contamination by diarrhetic she-llfish poisoning (DSP) toxins one to four weeks in advance. These time series included DSP con-centration in mussels (Mytilus galloprovincialis), toxic phytoplankton cell counts, meteorological, and remotely sensed oceanographic variables. Several data pre-processing and feature engineering methods were tested, as well as multiple autoregressive and artificial neural network (ANN) models. The best results regarding the mean absolute error of prediction were obtained for a bivariate long short-term memory (LSTM) neural network based on biotoxin and toxic phytoplankton measurements, with higher accuracy for short-term forecasting horizons. When evaluating all ANNs model ability to predict the contamination state (below or above the regulatory limit for contamination) and changes to this state, multilayer perceptrons (MLP) and convolutional neural networks (CNN) yielded improved predictive performance on a case-by-case basis. These results show the possibility of extracting relevant information from time-series data from multiple sources which are predictive of DSP contamination in mussels, therefore placing ANNs as good candidate models to assist the production sector in anticipating harvesting interdictions and mitigating economic losses.(c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).info:eu-repo/semantics/publishedVersio

Synechocystis ferredoxin/ferredoxin-NADP+-reductase/NADP+ complex: Structural model obtained by NMR-restrained docking

FEBS Letters 579 (2005) 4585–4590Abstract Ferredoxin (Fd) and ferredoxin-NADP+-reductase(FNR) are two terminal physiological partners of the photosynthetic electron transport chain. Based on a nuclear magnetic resonance(NMR)-restrained-docking approach, two alternative structural models of the Fd–FNR complex in the presence of NADP+ are proposed. The protein docking simulations were performed with the software BiGGER. NMR titration revealed a 1:1 stoichiometry for the complex and allowed the mapping of the interacting residues at the surface of Fd. The NMR chemical shifts were encoded into distance constraints and used with theoretically calculated electronic coupling between the redox cofactors to propose experimentally validated docked complexes

Job exposure matrices and human biomonitoring

Em epidemiologia ocupacional é importante conhecer e caracterizar a exposição aos fatores de risco e estabelecer o nexo de causalidade com as alterações na saúde. De entre as várias formas de avaliar a exposição no local de trabalho, contam-se as matrizes de exposição ocupacional (MEO). Uma MEO consiste num sistema de classificação da exposição, para uma ou mais substâncias ou agentes, em distintas ocupações e por ramos específicos de atividade. As MEO requerem estimativas qualificadas, sistemáticas e detalhadas das potenciais exposições e podem ser construídas com informação primária ou secundária. No essencial, trata- se de reconstruir, com o rigor possível, a exposição a que os trabalhadores estiveram sujeitos durante a sua vida profissional. Embora os dados da biomonitorização humana (BMH) sejam frequentemente usados para efeitos de validação de MEO, podem igualmente ser usados para o seu desenvolvimento. Este trabalho pretende descrever a abordagem baseada em matrizes de exposição ocupacional na avaliação da exposição ocupacional e o contributo dos dados de biomonitorização humana nessa avaliação. As MEO constituem, sem dúvida, um importante instrumento a ser utilizado quer em estudos populacionais, quer em Saúde Ocupacional, potenciando vários estudos e monitorizações, com destaque para a biomonitorização humana.In occupational epidemiology, it is important to know and characterize exposure to risk factors and establish the causal link with changes in health. Among the various ways of assessing exposure in the workplace are job exposure matrices (JEM). A JEM consists of an exposure classification system, for one or more substances or agents, in different occupations and by specific branches of activity. A JEM requires qualified, systematic and detailed estimates of potential exposures and can be constructed with primary or secondary information. Essentially, it is a question of reconstructing, as rigorously as possible, the exposure to which workers were subjected during their professional lives. Although human biomonitoring (HBM) data are often used for JEM validation purposes, they can also be used for the development of JEM. This work intends to describe the technical development of occupational exposure matrices, as an exposure assessment tool, and the role that human biomonitoring can play in this development. JEM are undoubtedly an important instrument to be used both in population studies and in occupational health, enhancing several studies and monitoring, especially human biomonitoring.info:eu-repo/semantics/publishedVersio