Applying Computational Scoring Functions to Assess Biomolecular Interactions in Food Science: Applications to the Estrogen Receptors

During the last decade, computational methods, which were for the most part developed to study protein-ligand interactions and especially to discover, design and develop drugs by and for medicinal chemists, have been successfully applied in a variety of food science applications [1,2]. It is now clear, in fact, that drugs and nutritional molecules behave in the same way when binding to a macromolecular target or receptor, and that many of the approaches used so extensively in medicinal chemistry can be easily transferred to the fields of food science. For instance, nuclear receptors are common targets for a number of drug molecules and could be, in the same way, affected by the interaction with food or food-like molecules. Thus, key computational medicinal chemistry methods like molecular dynamics can be used to decipher protein flexibility and to obtain stable models for docking and scoring in food-related studies, and virtual screening is increasingly being applied to identify molecules with potential to act as endocrine disruptors, food mycotoxins, and new nutraceuticals [3,4,5]. All of these methods and simulations are based on protein-ligand interaction phenomena, and represent the basis for any subsequent modification of the targeted receptor's or enzyme's physiological activity. We describe here the energetics of binding of biological complexes, providing a survey of the most common and successful algorithms used in evaluating these energetics, and we report case studies in which computational techniques have been applied to food science issues. In particular, we explore a handful of studies involving the estrogen receptors for which we have a long-term interest

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

Bioinformatics

This book is divided into different research areas relevant in Bioinformatics such as biological networks, next generation sequencing, high performance computing, molecular modeling, structural bioinformatics, molecular modeling and intelligent data analysis. Each book section introduces the basic concepts and then explains its application to problems of great relevance, so both novice and expert readers can benefit from the information and research works presented here

Advances in Human-Protein Interaction - Interactive and Immersive Molecular Simulations

International audienc

Quantum Algorithms for Solving Hard Constrained Optimization Problems

En aquesta investigació, s'han examinat tècniques d'optimització per resoldre problemes de restriccions i s'ha fet un estudi de l'era quàntica i de les empreses líders del mercat, com ara IBM, D-Wave, Google, Xanadu, AWS-Braket i Microsoft. S'ha après sobre la comunitat, les plataformes, l'estat de les investigacions i s'han estudiat els postulats de la mecànica quàntica que serveixen per crear els sistemes i algorismes quàntics més eficients. Per tal de saber si és possible resoldre problemes de Problema de cerca de restriccions (CSP) de manera més eficient amb la computació quàntica, es va definir un escenari perquè tant la computació clàssica com la quàntica tinguessin un bon punt de referència. En primer lloc, la prova de concepte es centra en el problema de programació dels treballadors socials i més tard en el tema de la preparació per lots i la selecció de comandes com a generalització del Problema dels treballadors socials (SWP). El problema de programació dels treballadors socials és una mena de problema d'optimització combinatòria que, en el millor dels casos, es pot resoldre en temps exponencial; veient que el SWP és NP-Hard, proposa fer servir un altre enfoc més enllà de la computació clàssica per a la seva resolució. Avui dia, el focus a la computació quàntica ja no és només per la seva enorme capacitat informàtica sinó també, per l'ús de la seva imperfecció en aquesta era Noisy Intermediate-Scale Quantum (NISQ) per crear un poderós dispositiu d'aprenentatge automàtic que utilitza el principi variacional per resoldre problemes d'optimització en reduir la classe de complexitat. A la tesi es proposa una formulació (quadràtica) per resoldre el problema de l'horari dels treballadors socials de manera eficient utilitzant Variational Quantum Eigensolver (VQE), Quantum Approximate Optimization Algorithm (QAOA), Minimal Eigen Optimizer i ADMM optimizer. La viabilitat quàntica de l'algorisme s'ha modelat en forma QUBO, amb Docplex simulat Cirq, Or-Tools i provat a ordinadors IBMQ. Després d'analitzar els resultats de l'enfocament anterior, es va dissenyar un escenari per resoldre el SWP com a raonament basat en casos (qCBR), tant quànticament com clàssicament. I així poder contribuir amb un algorisme quàntic centrat en la intel·ligència artificial i l'aprenentatge automàtic. El qCBR és una tècnica d’aprenentatge automàtic basada en la resolució de nous problemes que utilitza l’experiència, com ho fan els humans. L'experiència es representa com una memòria de casos que conté qüestions prèviament resoltes i utilitza una tècnica de síntesi per adaptar millor l'experiència al problema nou. A la definició de SWP, si en lloc de pacients es tenen lots de comandes i en lloc de treballadors socials robots mòbils, es generalitza la funció objectiu i les restriccions. Per això, s'ha proposat una prova de concepte i una nova formulació per resoldre els problemes de picking i batching anomenat qRobot. Es va fer una prova de concepte en aquesta part del projecte mitjançant una Raspberry Pi 4 i es va provar la capacitat d'integració de la computació quàntica dins de la robòtica mòbil, amb un dels problemes més demandats en aquest sector industrial: problemes de picking i batching. Es va provar en diferents tecnologies i els resultats van ser prometedors. A més, en cas de necessitat computacional, el robot paral·lelitza part de les operacions en computació híbrida (quàntica + clàssica), accedint a CPU i QPU distribuïts en un núvol públic o privat. A més, s’ha desenvolupat un entorn estable (ARM64) dins del robot (Raspberry) per executar operacions de gradient i altres algorismes quàntics a IBMQ, Amazon Braket (D-Wave) i Pennylane de forma local o remota. Per millorar el temps d’execució dels algorismes variacionals en aquesta era NISQ i la següent, s’ha proposat EVA: un algorisme d’aproximació de Valor Exponencial quàntic. Fins ara, el VQE és el vaixell insígnia de la computació quàntica. Avui dia, a les plataformes líders del mercat de computació quàntica al núvol, el cost de l'experimentació dels circuits quàntics és proporcional al nombre de circuits que s'executen en aquestes plataformes. És a dir, amb més circuits més cost. Una de les coses que aconsegueix el VQE, el vaixell insígnia d'aquesta era de pocs qubits, és la poca profunditat en dividir el Hamiltonià en una llista de molts petits circuits (matrius de Pauli). Però aquest mateix fet, fa que simular amb el VQE sigui molt car al núvol. Per aquesta mateixa raó, es va dissenyar EVA per poder calcular el valor esperat amb un únic circuit. Tot i haver respost a la hipòtesi d'aquesta tesis amb tots els estudis realitzats, encara es pot continuar investigant per proposar nous algorismes quàntics per millorar problemes d'optimització.En esta investigación, se han examinado técnicas de optimización para resolver problemas de restricciones y se ha realizado un estudio de la era cuántica y de las empresas lideres del mercado, como IBM, D-Wave, Google, Xanadu, AWS-Braket y Microsoft. Se ha aprendido sobre su comunidad, sus plataformas, el estado de sus investigaciones y se han estudiado los postulados de la mecánica cuántica que sirven para crear los sistemas y algoritmos cuánticos más eficientes. Por tal de saber si es posible resolver problemas de Problema de búsqueda de restricciones (CSP) de manera más eficiente con la computación cuántica, se definió un escenario para que tanto la computación clásica como la cuántica tuvieran un buen punto de referencia. En primer lugar, la prueba de concepto se centra en el problema de programación de los trabajadores sociales y más tarde en el tema de la preparación por lotes y la selección de pedidos como una generalización del Problema de los trabajadores sociales (SWP). El problema de programación de los trabajadores sociales es una clase de problema de optimización combinatoria que, en el mejor de los casos, puede resolverse en tiempo exponencial; viendo que el SWP es NP-Hard, propone usar otro enfoque mas allá de la computación clásica para su resolución. Hoy en día, el foco en la computación cuántica ya no es sólo por su enorme capacidad informática sino también, por el uso de su imperfección en esta era Noisy Intermediate-Scale Quantum (NISQ) para crear un poderoso dispositivo de aprendizaje automático que usa el principio variacional para resolver problemas de optimización al reducir su clase de complejidad. En la tesis se propone una formulación (cuadrática) para resolver el problema del horario de los trabajadores sociales de manera eficiente usando Variational Quantum Eigensolver (VQE), Quantum Approximate Optimization Algorithm (QAOA), Minimal Eigen Optimizer y ADMM optimizer. La viabilidad cuántica del algoritmo se ha modelado en forma QUBO, con Docplex simulado Cirq, Or-Tools y probado en computadoras IBMQ. Después de analizar los resultados del enfoque anterior, se diseñó un escenario para resolver el SWP como razonamiento basado en casos (qCBR), tanto cuántica como clásicamente. Y así, poder contribuir con un algoritmo cuántico centrado en la inteligencia artificial y el aprendizaje automático. El qCBR es una técnica de aprendizaje automático basada en la resolución de nuevos problemas que utiliza la experiencia, como lo hacen los humanos. La experiencia se representa como una memoria de casos que contiene cuestiones previamente resueltas y usa una técnica de síntesis para adaptar mejor la experiencia al nuevo problema. En la definición de SWP, si en lugar de pacientes se tienen lotes de pedidos y en lugar de trabajadores sociales robots móviles, se generaliza la función objetivo y las restricciones. Para ello, se ha propuesto una prueba de concepto y una nueva formulación para resolver los problemas de picking y batching llamado qRobot. Se hizo una prueba de concepto en esta parte del proyecto a través de una Raspberry Pi 4 y se probó la capacidad de integración de la computación cuántica dentro de la robótica móvil, con uno de los problemas más demandados en este sector industrial: problemas de picking y batching. Se probó en distintas tecnologías y los resultados fueron prometedores. Además, en caso de necesidad computacional, el robot paraleliza parte de las operaciones en computación híbrida (cuántica + clásica), accediendo a CPU y QPU distribuidos en una nube pública o privada. Además, desarrollamos un entorno estable (ARM64) dentro del robot (Raspberry) para ejecutar operaciones de gradiente y otros algoritmos cuánticos en IBMQ, Amazon Braket (D-Wave) y Pennylane de forma local o remota. Para mejorar el tiempo de ejecución de los algoritmos variacionales en esta era NISQ y la siguiente, se ha propuesto EVA: un algoritmo de Aproximación de Valor Exponencial cuántico. Hasta la fecha, el VQE es el buque insignia de la computación cuántica. Hoy en día, en las plataformas de computación cuántica en la nube líderes de mercado, el coste de la experimentación de los circuitos cuánticos es proporcional al número de circuitos que se ejecutan en dichas plataformas. Es decir, con más circuitos mayor coste. Una de las cosas que consigue el VQE, el buque insignia de esta era de pocos qubits, es la poca profundidad al dividir el Hamiltoniano en una lista de muchos pequeños circuitos (matrices de Pauli). Pero este mismo hecho, hace que simular con el VQE sea muy caro en la nube. Por esta misma razón, se diseñó EVA para poder calcular el valor esperado con un único circuito. Aún habiendo respuesto a la hipótesis de este trabajo con todos los estudios realizados, todavía se puede seguir investigando para proponer nuevos algoritmos cuánticos para mejorar problemas de optimización combinatoria.In this research, Combinatorial optimization techniques to solve constraint problems have been examined. A study of the quantum era and market leaders such as IBM, D-Wave, Google, Xanadu, AWS-Braket and Microsoft has been carried out. We have learned about their community, their platforms, the status of their research, and the postulates of quantum mechanics that create the most efficient quantum systems and algorithms. To know if it is possible to solve Constraint Search Problem (CSP) problems more efficiently with quantum computing, a scenario was defined so that both classical and quantum computing would have a good point of reference. First, the proof of concept focuses on the social worker scheduling problem and later on the issue of batch picking and order picking as a generalization of the Social Workers Problem (SWP). The social workers programming problem is a combinatorial optimization problem that can be solved exponentially at best; seeing that the SWP is NP-Hard, it claims using another approach beyond classical computation for its resolution. Today, the focus on quantum computing is no longer only on its enormous computing power but also on the use of its imperfection in this era Noisy Intermediate-Scale Quantum (NISQ) to create a powerful machine learning device that uses the variational principle to solve optimization problems by reducing their complexity class. In the thesis, a (quadratic) formulation is proposed to solve the problem of social workers' schedules efficiently using Variational Quantum Eigensolver (VQE), Quantum Approximate Optimization Algorithm (QAOA), Minimal Eigen Optimizer and ADMM optimizer. The quantum feasibility of the algorithm has been modelled in QUBO form, with Cirq simulated, Or-Tools and tested on IBMQ computers. After analyzing the results of the above approach, a scenario was designed to solve the SWP as quantum case-based reasoning (qCBR), both quantum and classically. And thus, to be able to contribute with a quantum algorithm focused on artificial intelligence and machine learning. The qCBR is a machine learning technique based on solving new problems that use experience, as humans do. The experience is represented as a memory of cases containing previously resolved questions and uses a synthesis technique to adapt the background to the new problem better. In the definition of SWP, if instead of patients there are batches of orders and instead of social workers mobile robots, the objective function and the restrictions are generalized. To do this, a proof of concept and a new formulation has been proposed to solve the problems of picking and batching called qRobot. A proof of concept was carried out in this part of the project through a Raspberry Pi 4 and the integration capacity of quantum computing within mobile robotics was tested, with one of the most demanded problems in this industrial sector: picking and batching problems. It was tested on different technologies, and the results were promising. Furthermore, in case of computational need, the robot parallelizes part of the operations in hybrid computing (quantum + classical), accessing CPU and QPU distributed in a public or private cloud. Furthermore, we developed a stable environment (ARM64) inside the robot (Raspberry) to run gradient operations and other quantum algorithms on IBMQ, Amazon Braket (D-Wave) and Pennylane locally or remotely. To improve the execution time of variational algorithms in this NISQ era and the next, EVA has been proposed: A quantum Exponential Value Approximation algorithm. To date, the VQE is the flagship of quantum computing. Today, in the market-leading quantum cloud computing platforms, the cost of experimenting with quantum circuits is proportional to the number of circuits running on those platforms. That is, with more circuits, higher cost. One of the things that the VQE, the flagship of this low-qubit era, achieves is shallow depth by dividing the Hamiltonian into a list of many small circuits (Pauli matrices). But this very fact makes simulating with VQE very expensive in the cloud. For this same reason, EVA was designed to calculate the expected value with a single circuit. Even having answered the hypothesis of this work with all the studies carried out, it is still possible to continue research to propose new quantum algorithms to improve combinatorial optimization

Final Report of the ModSysC2020 Working Group - Data, Models and Theories for Complex Systems: new challenges and opportunities

Final Report of the ModSysC2020 Working Group at University Montpellier 2At University Montpellier 2, the modeling and simulation of complex systems has been identified as a major scientific challenge and one of the priority axes in interdisciplinary research, with major potential impact on training, economy and society. Many research groups and laboratories in Montpellier are already working in that direction, but typically in isolation within their own scientific discipline. Several local actions have been initiated in order to structure the scientific community with interdisciplinary projects, but with little coordination among the actions. The goal of the ModSysC2020 (modeling and simulation of complex systems in 2020) working group was to analyze the local situation (forces and weaknesses, current projects), identify the critical research directions and propose concrete actions in terms of research projects, equipment facilities, human resources and training to be encouraged. To guide this perspective, we decomposed the scientific challenge into four main themes, for which there is strong background in Montpellier: (1) modeling and simulation of complex systems; (2) algorithms and computing; (3) scientific data management; (4) production, storage and archiving of data from the observation of the natural and biological media. In this report, for each theme, we introduce the context and motivations, analyze the situation in Montpellier, identify research directions and propose specific actions in terms of interdisciplinary research projects and training. We also provide an analysis of the socio-economical aspects of modeling and simulation through use cases in various domains such as life science and healthcare, environmental science and energy. Finally, we discuss the importance of revisiting students training in fundamental domains such as modeling, computer programming and database which are typically taught too late, in specialized masters

Computational approaches to complex biological networks

The need of understanding and modeling the biological networks is one of the raisons d'\ueatre and of the driving forces behind the emergence of Systems Biology. Because of its holistic approach and because of the widely different level of complexity of the networks, different mathematical methods have been developed during the years. Some of these computational methods are used in this thesis in order to investigate various properties of different biological systems. The first part deals with the prediction of the perturbation of cellular metabolism induced by drugs. Using Flux Balance Analysis to describe the reconstructed genome-wide metabolic networks, we consider the problem of identifying the most selective drug synergisms for given therapeutic targets. The second part of this thesis considers gene regulatory and large social networks as signed graphs (activation/deactivation or friendship/hostility are rephrased as positive/negative coupling between spins). Using the analogy with an Ising spin glass an analysis of the energy landscape and of the content of \u201cdisorder\u201d 'is carried out. Finally, the last part concerns the study of the spatial heterogeneity of the signaling pathway of rod photoreceptors. The electrophysiological data produced by our collaborators in the Neurobiology laboratory have been analyzed with various dynamical systems giving an insight into the process of ageing of photoreceptors and into the role diffusion in the pathway

A hybrid algorithm for Bayesian network structure learning with application to multi-label learning

We present a novel hybrid algorithm for Bayesian network structure learning, called H2PC. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. The algorithm is based on divide-and-conquer constraint-based subroutines to learn the local structure around a target variable. We conduct two series of experimental comparisons of H2PC against Max-Min Hill-Climbing (MMHC), which is currently the most powerful state-of-the-art algorithm for Bayesian network structure learning. First, we use eight well-known Bayesian network benchmarks with various data sizes to assess the quality of the learned structure returned by the algorithms. Our extensive experiments show that H2PC outperforms MMHC in terms of goodness of fit to new data and quality of the network structure with respect to the true dependence structure of the data. Second, we investigate H2PC's ability to solve the multi-label learning problem. We provide theoretical results to characterize and identify graphically the so-called minimal label powersets that appear as irreducible factors in the joint distribution under the faithfulness condition. The multi-label learning problem is then decomposed into a series of multi-class classification problems, where each multi-class variable encodes a label powerset. H2PC is shown to compare favorably to MMHC in terms of global classification accuracy over ten multi-label data sets covering different application domains. Overall, our experiments support the conclusions that local structural learning with H2PC in the form of local neighborhood induction is a theoretically well-motivated and empirically effective learning framework that is well suited to multi-label learning. The source code (in R) of H2PC as well as all data sets used for the empirical tests are publicly available.Comment: arXiv admin note: text overlap with arXiv:1101.5184 by other author

A methodology for robust optimization of low-thrust trajectories in multi-body environments

Issued as final reportThales Alenia Spac

Optimal Identification of Semi-Rigid Domains in Macromolecules from Molecular Dynamics Simulation

Biological function relies on the fact that biomolecules can switch between different conformations and aggregation states. Such transitions involve a rearrangement of parts of the biomolecules involved that act as dynamic domains. The reliable identification of such domains is thus a key problem in biophysics. In this work we present a method to identify semi-rigid domains based on dynamical data that can be obtained from molecular dynamics simulations or experiments. To this end the average inter-atomic distance-deviations are computed. The resulting matrix is then clustered by a constrained quadratic optimization problem. The reliability and performance of the method are demonstrated for two artificial peptides. Furthermore we correlate the mechanical properties with biological malfunction in three variants of amyloidogenic transthyretin protein, where the method reveals that a pathological mutation destabilizes the natural dimer structure of the protein. Finally the method is used to identify functional domains of the GroEL-GroES chaperone, thus illustrating the efficiency of the method for large biomolecular machines