Modeling Antibacterial Activity with Machine Learning and Fusion of Chemical Structure Information with Microorganism Metabolic Networks

Predicting the activity of new chemical compounds over pathogenic microorganisms with different metabolic reaction networks (MRNs) is an important goal due to the different susceptibility to antibiotics. The ChEMBL database contains >160 000 outcomes of preclinical assays of antimicrobial activity for 55 931 compounds with >365 parameters of activity (MIC, IC50, etc.) and >90 bacteria strains of >25 bacterial species. In addition, the Leong and Barabàsi data set includes >40 MRNs of microorganisms. However, there are no models able to predict antibacterial activity for multiple assays considering both drug and MRN structures at the same time. In this work, we combined perturbation theory, machine learning, and information fusion techniques to develop the first PTMLIF model. The best linear model found presented values of specificity = 90.31/90.40 and sensitivity = 88.14/88.07 in training/validation series. We carried out a comparison to nonlinear artificial neural network (ANN) techniques and previous models from the literature. Next, we illustrated the practical use of the model with an experimental case of study. We reported for the first time the isolation and characterization of terpenes from the plant Cissus incisa. The antibacterial activity of the terpenes was experimentally determined. The more active compounds were phytol and α-amyrin, with MIC = 100 μg/mL for Vancomycin-resistant Enterococcus faecium and Acinetobacter baumannii resistant to carbapenems. These compounds are already known from other sources. However, they have been isolated and evaluated for the first time here against several strains of multidrug-resistant bacteria including World Health Organization (WHO) priority pathogens. Last, we used the model to predict the activity of these compounds versus other microorganisms with different MRNs in order to find other potential targets.Ministerio de Economía y Competitividad (CTQ2016-74881-P) // Gobierno Vasco (IT1045-16

Aplicación de la topología molecular en la búsqueda de nuevos compuestos derivados del 4-nitro-imidazol activos frente al Tripanosoma brucei

Human African Trypanosomiasis, caused by the protozoan parasite Trypanosoma brucei, is characterized by a disabling chronic infectious process affecting millions of people worldwide. The therapeutic arsenal against this disease usually requires intravenous suministración, hindering accessibility and adherence to therapy. It has developed a topological mathematical model aimed to finding new compounds derived from 1-aryl-4-nitro-1H-imidazol with potential anti-trypanosome activity. Using linear discriminant analysis (LDA) was obtained a model capable of predicting correctly the activity of 93% of the studied compounds. The model has been subjected to an internal validation using the jack-knife test or leave-one-out and an internal cross-validation. Following a virtual sweep or virtual screening ten new imidazole derivatives are proposed, with potential anti-trypanosome activity.La Tripanosomiasis Humana Africana, causada por el parásito protozoario de la especie Trypanosoma brucei, está caracterizada por un proceso infectivo crónico discapacitante que afecta a millones de personas en todo el mundo. El arsenal terapéutico frente a esta enfermedad, requiere generalmente suministración por vía parenteral, lo que dificulta la adhesión y accesibilidad del paciente al tratamiento. Se ha desarrollado un modelo topológico-matemático encaminado a buscar nuevos compuestos derivados del 1-aril-4-nitro-1H-imidazol con potencial actividad anti-tripanosómica. Utilizando el análisis lineal discriminante se ha obtenido un modelo capaz de predecir correctamente la actividad del 93% de los compuestos estudiados. Se ha sometido al modelo a una validación interna por medio del test de Jack-knife y de una validación cruzada. Tras realizar un cribado molecular virtual se proponen diez nuevos derivados imidazólicos con potencial actividad anti-tripanosómica

Structure- and Ligand-Based Design of Novel Antimicrobial Agents

The use of computer based techniques in the design of novel therapeutic agents is a rapidly emerging field. Although the drug-design techniques utilized by Computational Medicinal Chemists vary greatly, they can roughly be classified into structure-based and ligand-based approaches. Structure-based methods utilize a solved structure of the design target, protein or DNA, usually obtained by X-ray or NMR methods to design or improve compounds with activity against the target. Ligand-based methods use active compounds with known affinity for a target that may yet be unresolved. These methods include Pharmacophore-based searching for novel active compounds or Quantitative Structure-Activity Relationship (QSAR) studies. The research presented here utilized both structure and ligand-based methods against two bacterial targets: Bacillus anthracis and Mycobacterium tuberculosis. The first part of this thesis details our efforts to design novel inhibitors of the enzyme dihydropteroate synthase from B. anthracis using crystal structures with known inhibitors bound. The second part describes a QSAR study that was performed using a series of novel nitrofuranyl compounds with known, whole-cell, inhibitory activity against M. tuberculosis. Dihydropteroate synthase (DHPS) catalyzes the addition of p-amino benzoic acid (pABA) to dihydropterin pyrophosphate (DHPP) to form pteroic acid as a key step in bacterial folate biosynthesis. It is the traditional target of the sulfonamide class of antibiotics. Unfortunately, bacterial resistance and adverse effects have limited the clinical utility of the sulfonamide antibiotics. Although six bacterial crystal structures are available, the flexible loop regions that enclose pABA during binding and contain key sulfonamide resistance sites have yet to be visualized in their functional conformation. To gain a new understanding of the structural basis of sulfonamide resistance, the molecular mechanism of DHPS action, and to generate a screening structure for high-throughput virtual screening, molecular dynamics simulations were applied to model the conformations of the unresolved loops in the active site. Several series of molecular dynamics simulations were designed and performed utilizing enzyme substrates and inhibitors, a transition state analog, and a pterin-sulfamethoxazole adduct. The positions of key mutation sites conserved across several bacterial species were closely monitored during these analyses. These residues were shown to interact closely with the sulfonamide binding site. The simulations helped us gain new understanding of the positions of the flexible loops during inhibitor binding that has allowed the development of a DHPS structural model that could be used for high-through put virtual screening (HTVS). Additionally, insights gained on the location and possible function of key mutation sites on the flexible loops will facilitate the design of new, potent inhibitors of DHPS that can bypass resistance mutations that render sulfonamides inactive. Prior to performing high-throughput virtual screening, the docking and scoring functions to be used were validated using established techniques against the B. anthracis DHPS target. In this validation study, five commonly used docking programs, FlexX, Surflex, Glide, GOLD, and DOCK, as well as nine scoring functions, were evaluated for their utility in virtual screening against the novel pterin binding site. Their performance in ligand docking and virtual screening against this target was examined by their ability to reproduce a known inhibitor conformation and to correctly detect known active compounds seeded into three separate decoy sets. Enrichment was demonstrated by calculated enrichment factors at 1% and Receiver Operating Characteristic (ROC) curves. The effectiveness of post-docking relaxation prior to rescoring and consensus scoring were also evaluated. Of the docking and scoring functions evaluated, Surflex with SurflexScore and Glide with GlideScore performed best overall for virtual screening against the DHPS target. The next phase of the DHPS structure-based drug design project involved high-throughput virtual screening against the DHPS structural model previously developed and docking methodology validated against this target. Two general virtual screening methods were employed. First, large, virtual libraries were pre-filtered by 3D pharmacophore and modified Rule-of-Three fragment constraints. Nearly 5 million compounds from the ZINC databases were screened generating 3,104 unique, fragment-like hits that were subsequently docked and ranked by score. Second, fragment docking without pharmacophore filtering was performed on almost 285,000 fragment-like compounds obtained from databases of commercial vendors. Hits from both virtual screens with high predicted affinity for the pterin binding pocket, as determined by docking score, were selected for in vitro testing. Activity and structure-activity relationship of the active fragment compounds have been developed. Several compounds with micromolar activity were identified and taken to crystallographic trials. Finally, in our ligand-based research into M. tuberculosis active agents, a series of nitrofuranylamide and related aromatic compounds displaying potent activity was investigated utilizing 3-Dimensional Quantitative Structure-Activity Relationship (3D-QSAR) techniques. Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) methods were used to produce 3D-QSAR models that correlated the Minimum Inhibitory Concentration (MIC) values against M. tuberculosis with the molecular structures of the active compounds. A training set of 95 active compounds was used to develop the models, which were then evaluated by a series of internal and external cross-validation techniques. A test set of 15 compounds was used for the external validation. Different alignment and ionization rules were investigated as well as the effect of global molecular descriptors including lipophilicity (cLogP, LogD), Polar Surface Area (PSA), and steric bulk (CMR), on model predictivity. Models with greater than 70% predictive ability, as determined by external validation and high internal validity (cross validated r2 \u3e .5) were developed. Incorporation of lipophilicity descriptors into the models had negligible effects on model predictivity. The models developed will be used to predict the activity of proposed new structures and advance the development of next generation nitrofuranyl and related nitroaromatic anti-tuberculosis agents

Modelos bioinformáticos y estudio de receptores de proteínas mediante el uso de redes complejas para el desarrollo y diseño de fármacos eficaces en patologías del sistema nervioso central

La búsqueda y desarrollo de fármacos eficaces para el tratamiento de enfermedades neurodegenerativas ha generado grandes expectativas, debido a la relevancia que tienen sobre la economía de los sistemas sanitarios y la tremenda carga y desgaste que sufren familia y cuidadores. Por ello, la industria farmacéutica se ha volcado sobre estas patologías en las últimas tres décadas, pero las dificultades de realizar ensayos sobre el SN provoca que los gastos y tiempos de investigación se disparen, limitando de forma considerable la rentabilidad de los procesos tradicionales en el desarrollo de nuevos medicamentos. Es en este apartado donde realiza sus aportaciones el diseño de fármacos, dedicando una parte del mismo al desarrollo de modelos matemáticos que permitan predecir propiedades de interés para una gran variedad de sistemas químicos incluyendo moléculas de bajo peso molecular, polímeros, biopolímeros, sistemas heterogéneos, formulaciones farmacéuticas, conglomerados de moléculas e iones, materiales, nano-estructuras y otros. En dicho sentido, los estudios QSAR (Quantitative Structure-Activity-Relationships) son usados cada vez mas como herramientas para el descubrimiento molecular. Estos modelos QSAR pueden ser diseñados para que predigan la probabilidad de que un fármaco sea efectivo contra una enfermedad degenerativa determinada ya sea la enfermedad de Parkinson, Alzheimer o cualquier otra, actuando sobre una diana molecular específica. En esta memoria presentamos de manera conjunta la revisión de modelos previos y trabajos específicos novedosos, en los que se han introducido nuevos índices numéricos utilizados para describir tanto la estructura molecular de fármacos como la estructura macromolecular de sus dianas o receptores (proteínas y/o ADN/ARN). Con estos ITs hemos sido capaces de desarrollar nuevos modelos multiQSAR de gran interés por su doble función en la predicción de fármacos y sus dianas moleculares. Estos trabajos permitirán la introducción de nuevos conceptos teóricos y la evolución hacia modelos con posibles aplicaciones en la búsqueda de nuevos fármacos neuroprotectores útiles en el tratamiento de las enfermedades de Parkinson y Alzheimer y/o nuevas dianas moleculares para estos fármacos. Este tipo de investigación abarca un área general-básica en la que interactúan la Bioinformática y la Quimioinformática

Use of neural networks to model molecular structure and function

This thesis is a study of some applications of neural networks - a recent computer algorithm - to modelling the structure and function of biologically important molecules. In Chapter 1, an introduction to neural networks is given. An overview of quantitative structure activity relationships (QSARs) is presented. The applications of neural networks to QSAR and to the prediction of structural and functional features of protein and nucleic acid sequences are reviewed. The neural network algorithms used are discussed in Chapter 2. In Chapter 3, a two-layer feed-forward neural network has been trained to recognise an ATP/GTP-binding local sequence motif. A comparably sophisticated statistical method was developed, which performed marginally better than the neural network. In a second study, described in Chapters 4 and 5, one of the largest data sets available for developing a quantitative structure activity relationship - the inhibition of dihydrofolate reductase by 2,4-diamino-6,6-dimethyl-5-phenyldihydrotriazine derivatives has been used to benchmark several computational methods. A hidden-layer neural network, a decision tree and inductive logic programming have been compared with the more established methods of linear regression and nearest neighbour. The data were represented in two ways: by the traditional Hansch parameters and by a new set of descriptors designed to allow the formulation of rules relating the activity of the inhibitors to their chemical structure. The performance of neural networks has been assessed rigourously in two distinct areas of biomolecular modelling; sequence analysis and drug design. The conclusions of these studies are presented in Chapter 6

From a Molecule to a Drug: Chemical Features Enhancing Pharmacological Potential

This book collects contributions published in the Special Issue “From a Molecule to a Drug: Chemical Features Enhancing Pharmacological Potential” and dealing with successful stories of drug improvement or design using classic protocols, quantum mechanical mechanistic investigation, or hybrid approaches such as QM/MM or QM/ML (machine learning). In the last two decades, computer-aided modeling has strongly supported scientists’ intuition to design functional molecules. High-throughput screening protocols, mainly based on classical mechanics’ atomistic potentials, are largely employed in biology and medicinal chemistry studies with the aim of simulating drug-likeness and bioactivity in terms of efficient binding to the target receptors. The advantages of this approach are quick outcomes, the possibility of repurposing commercially available drugs, consolidated protocols, and the availability of large databases. On the other hand, these studies do not intrinsically provide reactivity information, which requires quantum mechanical methodologies that are only applicable to significantly smaller and simplified systems at present. These latter studies focus on the drug itself, considering the chemical properties related to its structural features and motifs. Overall, such simulations provide necessary insights for a better understanding of the chemistry principles that rule the diseases at the molecular level, as well as possible mechanisms for restoring the physiological equilibrium

El efecto de los aceites de oleaginosas en rumen en el sistema de fermentación in vitro

Los subproductos de plantas oleaginosas han sido utilizadas para mejorar el desempeño productivo en ruminates ya sea por la alta energía que proporciona o por su composición proteica, sin embargo, pocos artículos reportan la cantidad que debe proporcionarse en forma adecuada en la dieta de ruminates como suplemento. Es por lo anterior, que el trabajo fue elaborado para evaluar el efecto de cártamo (Carthamus tinctorius L., SFM)/canola (Brassica napus, CAS) con semilla de sorgo en una proporción (0%, 25%, 50%, 75%, 100%, respectivamente) en una proporción concentrado-forraje (1:1) en una prueba in vitro de fermentación ruminal de ovino, como es la cinética de producción de gas, nitrógeno amoniacal (NH3-N), pH, producción de metano (CH4) y la desaparición de materia seca in vitro (IVDMD) de rastrojo de maíz. Los resultados mostraron que la producción de gas, y de metano significativamente disminuyeron, pero IVDMD y pH incrementaron con el incremento de ambas proporciones de SFM y CAS en alimentación a base de concentrado. Para tomar en consideración el desempeño de fermentación y ambiente, nuestros resultados sugieren que la suplementación más adecuada de canola y cartamo en alimentación a base de concentrado son de 25% a 50%, y de 25% a 75%, respectivamente. Modificar el metabolismo microbiano en rumen a través de la adición de aceites derivados de plantas es una manera efectiva de aumentar los ácidos grasos funcionales de los productos derivados de ruminates. Poco es conocido de la influencia de ácidos grasos exogenos en los procesos del metabolismo de lípidos en las membranas de bacterias y protozoarios. Es por lo anterior, que el presente trabajo se enfocó en investigar los ácidos grasos de cadena larga (LCFA), ácidos grasos volétiles (VFA) y metano (CH4) a las 48 horas de suplemtento exógeno de aceites principalmente conteniendo ácidos grasos poli/mono insaturados, PUFA/MUFA, (C18:3, C18:2 y C18:1 de aceites de linaza, cártamo y canola respectivamente) y ácidos grasos de cadena mediana MCFA, (C12:0 de aceite de coco). Los resultados mostraron que la composición de ácidos grasos entre bacteria y protozoarios fueron diferentes. La suplementación de aceite de linaza, principalmente ácido linoleico (C18:3), al incrementar las proporciones de C18:2 n6c, C18:2 n6t, C18:1 n9c, C18:1 n9t y cis- ácidos grasos en la membrane de bacterias y protozoarios en diferente medida, mejora ligeramente las concentraciones de ácido acético y propiónico pero no tiene impacto en CH4. La suplementación con aceite de coco, aumenta la composición de MCFA, y por lo tanto aumenta la biosíntesis de MCFA en fracciones tanto de bacterias como de protozoarios (del C12:0 al C14:0), para inhibir la actividad de metanogénesis en cierta medida. Los mayores ácidos grasos saturados, saturados/insaturados, trans-, even-carbon insaturados fueron obtenidos de membranas de bacterias cuando se suplementaba con aceites de cártamo y canola, principalmente C18:2 y C18:1, respectivamente, pero más bajo que aceite de coco. Sin embargo, en ambos con cártamo y canola aumentaron las concentraciones de ácido acético y propiónico, disminuye la Ac/Pro ratio pero no el impacto de la actividad de metanogénesis. En resumen, los resultados implican que diferentes grados de ácidos grasos insaturados de cadena corta o larga puede impactar en la fermentación ruminal

Theme Issue Honoring Professor Robert Verpoorte's 75th Birthday: Past, Current and Future of Natural Products Research

This theme issue is to celebrate Professor Robert Verpoorte’s 75th birthday. Prof. Verpoorte has been working in Leiden University over 40 years. There is no need to dwell upon the contributions of Dr. Verpoorte to plant-derived natural products research during his whole life. Dr. Verpoorte was a highly productive scientist throughout his academic career, with over 800 scientific publications in the form of research papers, books, and book chapters. His research interests are very diverse, cover- ing numerous topics related to plant-based natural products such as plant cell biotech- nology, biosynthesis, metabolomics, genetic engineering, and green technology, as well as the isolation of new biologically active compounds. He has left indelible footprints in all these fields, and he is widely recognised as a pioneer in the work of the biosynthesis of indole alkaloids, NMR-based metabolomics, and green technology in natural products production. As close friends and colleagues who have been in nearly daily contact with him over the last 20 years viewing all of these remarkable scientific contributions, we felt compelled to recognize this by the publication of a Special Issue of this journal dedicated to him.Thus, this Special Issue has now finally been released with the help of many of his colleagues and former students as a token of our gratitude to his impressive work.The Special Issue covers five main natural products topics: (1) chemical profiling and metabolomics, (2) separation/isolation and identification of plant specialized metabolites, (3) pharmacognosy of natural products to identify bioactive molecules from natural prod- ucts, (4) novel formulation of natural products, and (5) overview of natural products as a source of bioactive molecules

V Jornadas de Investigación de la Facultad de Ciencia y Tecnología. 2016

171 p.I. Abstracts. Ahozko komunikazioak / Comunicaciones orales: 1. Biozientziak: Alderdi Molekularrak / Biociencias: Aspectos moleculares. 2. Biozientziak: Ingurune Alderdiak / Biociencias: Aspectos Ambientales. 3. Fisika eta Ingenieritza Elektronika / Física e Ingeniería Electrónica. 4. Geología / Geología. 5. Matematika / Matemáticas. 6. Kimika / Química. 7. Ingenieritza Kimikoa eta Kimika / Ingeniería Química y Química. II. Abstracts. Idatzizko Komunikazioak (Posterrak) / Comunicaciones escritas (Pósters): 1. Biozientziak / Biociencias. 2. Fisika eta Ingenieritza Elektronika / Física e Ingeniería Electrónica. 3. Geologia / Geologia. 4. Matematika / Matemáticas. 5. Kimika / Química. 6. Ingenieritza Kimikoa / Ingeniería Química