BIOINFORMATICS TOOL AND MODEL DEVELOPMENT FOR STUDYING BIOLOGICAL NETWORKS AND PROTEIN-PROTEIN INTERACTIONS

Ph.DDOCTOR OF PHILOSOPH

Development and use of databases for ligand-protein interaction studies

This project applies structure-activity relationship (SAR), structure-based and database mining approaches to study ligand-protein interactions. To support these studies, we have developed a relational database system called EDinburgh University Ligand Selection System (EDULISS 2.0) which stores the structure-data files of +5.5 million commercially available small molecules (+4.0 million are recognised as unique) and over 1,500 various calculated molecular properties (descriptors) for each compound. A user-friendly web-based interface for EDULISS 2.0 has been established and is available at http://eduliss.bch.ed.ac.uk/. We have utilised PubChem bioassay data from an NMR based screen assay for a human FKBP12 protein (PubChem AID: 608). A prediction model using a Logistic Regression approach was constructed to relate the assay result with a series of molecular descriptors. The model reveals 38 descriptors which are found to be good predictors. These are mainly 3D-based descriptors, however, the presence of some predictive functional groups is also found to give a positive contribution to the binding interaction. The application of a neural network technique called Self Organising Maps (SOMs) succeeded in visualising the similarity of the PubChem compounds based on the 38 descriptors and clustering the 36 % of active compounds (16 out of 44) in a cluster and discriminating them from 95 % of inactive compounds. We have developed a molecular descriptor called the Atomic Characteristic Distance (ACD) to profile the distribution of specified atom types in a compound. ACD has been implemented as a pharmacophore searching tool within EDULISS 2.0. A structure-based screen succeeded in finding inhibitors for pyruvate kinase and the ligand-protein complexes have been successfully crystallised. This study also discusses the interaction of metal-binding sites in metalloproteins. We developed a database system and web-based interface to store and apply geometrical information of these metal sites. The programme is called MEtal Sites in Proteins at Edinburgh UniverSity (MESPEUS; http://eduliss.bch.ed.ac.uk/MESPEUS/). MESPEUS is an exceptionally versatile tool for the collation and abstraction of data on a wide range of structural questions. As an example we carried out a survey using this database indicating that the most common protein types which contain Mg-OATP-phosphate site are transferases and the most common pattern is linkage through the β- and γ-phosphate groups

Estudio, desarrollo y aplicación de modelos de la teoría QSPR-QSAR en pesticidas

La presente tesis doctoral se enfoca en la construcción de modelos predictivos y que sean de utilidad como herramienta para asistir la búsqueda de estructuras químicas con valores favorables de la propiedad/actividad. La habilidad de predecir las propiedades fisicoquímicas y actividades biológicas de las sustancias químicas permite analizar de antemano las propiedades de compuestos nuevos, tóxicos o que demandan demasiado tiempo de evaluación experimental. Así, los modelos pueden ser utilizados para la predicción de las propiedades fisicoquímicas/actividades biológicas de nuevos compuestos químicos sintetizados en el laboratorio y carentes de datos experimentales. Entre los objetivos específicos del presente trabajo de tesis se citan: - desarrollar modelos matemáticos que resulten capaces de cuantificar relaciones hipotéticas entre la estructura química y la propiedad/actividad de pesticidas, a través de la técnica del análisis de regresión lineal multivariable aplicada a diferentes bases de datos de propiedades de interés agronómico extraídas de la literatura actualizada. Para ello, se utilizarán los mejores descriptores moleculares que surjan del análisis de miles de descriptores estructurales, obtenidos de programas computacionales de libre acceso - investigar el comportamiento de los descriptores flexibles u óptimos en los estudios QSPR-QSAR de pesticidas, e incorporarlos en los modelos en caso que resulten adecuados. Para ello, uno debe ser capaz de definir la construcción matemática del descriptor flexible, y debe elegir el procedimiento de ajuste de sus partes variables para alcanzar las mejores predicciones de la propiedad, evitando el sobreajuste del conjunto de calibración para así poder alcanzar una calidad predictiva aceptable y el modelo supere su validación externa - abordar el tratamiento de grandes conjuntos moleculares de alta diversidad estructural y que incluyan pesticidas - demostrar a través de los resultados encontrados que un enfoque basado en la representación estructural independiente de la conformación molecular permite alcanzar predicciones confiables de la propiedad/actividad estudiada La calidad de las predicciones conseguidas con estos estudios QSPR-QSAR de pesticidas se compara con la información experimental disponible y a través de las predicciones alcanzadas por metodologías teóricas alternativas de la literatura.Facultad de Ciencias Exacta

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating, and enabling our understanding of natural phenomena at a wide range of spatial and temporal scales, giving rise to a new area of research known as AI for science (AI4Science). Being an emerging research paradigm, AI4Science is unique in that it is an enormous and highly interdisciplinary area. Thus, a unified and technical treatment of this field is needed yet challenging. This work aims to provide a technically thorough account of a subarea of AI4Science; namely, AI for quantum, atomistic, and continuum systems. These areas aim at understanding the physical world from the subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, climate, and subsurface) scales and form an important subarea of AI4Science. A unique advantage of focusing on these areas is that they largely share a common set of challenges, thereby allowing a unified and foundational treatment. A key common challenge is how to capture physics first principles, especially symmetries, in natural systems by deep learning methods. We provide an in-depth yet intuitive account of techniques to achieve equivariance to symmetry transformations. We also discuss other common technical challenges, including explainability, out-of-distribution generalization, knowledge transfer with foundation and large language models, and uncertainty quantification. To facilitate learning and education, we provide categorized lists of resources that we found to be useful. We strive to be thorough and unified and hope this initial effort may trigger more community interests and efforts to further advance AI4Science

TOWARDS THE ACCURATE PREDICTIONS OF NMR SHIELDING CONSTANTS

Ph.DDOCTOR OF PHILOSOPH

Fragmentation dynamics of ionised amino acids and neutral clusters of amino acids in the gas phase: a theoretical study

Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Ciencias, Departamento de Química. Fecha de lectura: 11-07-2017Esta tesis tiene embargado el acceso al texto completo hasta el 11-01-201

IN SILICO METHODS FOR DRUG DESIGN AND DISCOVERY

Computer-aided drug design (CADD) methodologies are playing an ever-increasing role in drug discovery that are critical in the cost-effective identification of promising drug candidates. These computational methods are relevant in limiting the use of animal models in pharmacological research, for aiding the rational design of novel and safe drug candidates, and for repositioning marketed drugs, supporting medicinal chemists and pharmacologists during the drug discovery trajectory.Within this field of research, we launched a Research Topic in Frontiers in Chemistry in March 2019 entitled “In silico Methods for Drug Design and Discovery,” which involved two sections of the journal: Medicinal and Pharmaceutical Chemistry and Theoretical and Computational Chemistry. For the reasons mentioned, this Research Topic attracted the attention of scientists and received a large number of submitted manuscripts. Among them 27 Original Research articles, five Review articles, and two Perspective articles have been published within the Research Topic. The Original Research articles cover most of the topics in CADD, reporting advanced in silico methods in drug discovery, while the Review articles offer a point of view of some computer-driven techniques applied to drug research. Finally, the Perspective articles provide a vision of specific computational approaches with an outlook in the modern era of CADD

NOTIFICATION !!!

All the content of this special edition is retrieved from the conference proceedings published by the European Scientific Institute, ESI. http://eujournal.org/index.php/esj/pages/view/books The European Scientific Journal, ESJ, after approval from the publisher re publishes the papers in a Special edition

NOTIFICATION!!!

The full content of this special edition is retrieved from the conference proceedings published by the European Scientific Institute, ESI. http://eujournal.org/index.php/esj/pages/view/books The European Scientific Journal, ESJ, after approval from the publisher re publishes the papers in a Special edition