Exploring Protein-Protein Interactions as Drug Targets for Anti-cancer Therapy with In Silico Workflows

We describe a computational protocol to aid the design of small molecule and peptide drugs that target protein-protein interactions, particularly for anti-cancer therapy. To achieve this goal, we explore multiple strategies, including finding binding hot spots, incorporating chemical similarity and bioactivity data, and sampling similar binding sites from homologous protein complexes. We demonstrate how to combine existing interdisciplinary resources with examples of semi-automated workflows. Finally, we discuss several major problems, including the occurrence of drug-resistant mutations, drug promiscuity, and the design of dual-effect inhibitors.Fil: Goncearenco, Alexander. National Institutes of Health; Estados UnidosFil: Li, Minghui. Soochow University; China. National Institutes of Health; Estados UnidosFil: Simonetti, Franco Lucio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Shoemaker, Benjamin A. National Institutes of Health; Estados UnidosFil: Panchenko, Anna R. National Institutes of Health; Estados Unido

Exposing Provenance Metadata Using Different RDF Models

A standard model for exposing structured provenance metadata of scientific assertions on the Semantic Web would increase interoperability, discoverability, reliability, as well as reproducibility for scientific discourse and evidence-based knowledge discovery. Several Resource Description Framework (RDF) models have been proposed to track provenance. However, provenance metadata may not only be verbose, but also significantly redundant. Therefore, an appropriate RDF provenance model should be efficient for publishing, querying, and reasoning over Linked Data. In the present work, we have collected millions of pairwise relations between chemicals, genes, and diseases from multiple data sources, and demonstrated the extent of redundancy of provenance information in the life science domain. We also evaluated the suitability of several RDF provenance models for this crowdsourced data set, including the N-ary model, the Singleton Property model, and the Nanopublication model. We examined query performance against three commonly used large RDF stores, including Virtuoso, Stardog, and Blazegraph. Our experiments demonstrate that query performance depends on both RDF store as well as the RDF provenance model

Industry-scale application and evaluation of deep learning for drug target prediction

Artificial intelligence (AI) is undergoing a revolution thanks to the breakthroughs of machine learning algorithms in computer vision, speech recognition, natural language processing and generative modelling. Recent works on publicly available pharmaceutical data showed that AI methods are highly promising for Drug Target prediction. However, the quality of public data might be different than that of industry data due to different labs reporting measurements, different measurement techniques, fewer samples and less diverse and specialized assays. As part of a European funded project (ExCAPE), that brought together expertise from pharmaceutical industry, machine learning, and high-performance computing, we investigated how well machine learning models obtained from public data can be transferred to internal pharmaceutical industry data. Our results show that machine learning models trained on public data can indeed maintain their predictive power to a large degree when applied to industry data. Moreover, we observed that deep learning derived machine learning models outperformed comparable models, which were trained by other machine learning algorithms, when applied to internal pharmaceutical company datasets. To our knowledge, this is the first large-scale study evaluating the potential of machine learning and especially deep learning directly at the level of industry-scale settings and moreover investigating the transferability of publicly learned target prediction models towards industrial bioactivity prediction pipelines.Web of Science121art. no. 2

Updates in metabolomics tools and resources: 2014-2015

Data processing and interpretation represent the most challenging and time-consuming steps in high-throughput metabolomic experiments, regardless of the analytical platforms (MS or NMR spectroscopy based) used for data acquisition. Improved machinery in metabolomics generates increasingly complex datasets that create the need for more and better processing and analysis software and in silico approaches to understand the resulting data. However, a comprehensive source of information describing the utility of the most recently developed and released metabolomics resources—in the form of tools, software, and databases—is currently lacking. Thus, here we provide an overview of freely-available, and open-source, tools, algorithms, and frameworks to make both upcoming and established metabolomics researchers aware of the recent developments in an attempt to advance and facilitate data processing workflows in their metabolomics research. The major topics include tools and researches for data processing, data annotation, and data visualization in MS and NMR-based metabolomics. Most in this review described tools are dedicated to untargeted metabolomics workflows; however, some more specialist tools are described as well. All tools and resources described including their analytical and computational platform dependencies are summarized in an overview Table

The Chemical Translation Service—a web-based tool to improve standardization of metabolomic reports

Summary: Metabolomic publications and databases use different database identifiers or even trivial names which disable queries across databases or between studies. The best way to annotate metabolites is by chemical structures, encoded by the International Chemical Identifier code (InChI) or InChIKey. We have implemented a web-based Chemical Translation Service that performs batch conversions of the most common compound identifiers, including CAS, CHEBI, compound formulas, Human Metabolome Database HMDB, InChI, InChIKey, IUPAC name, KEGG, LipidMaps, PubChem CID+SID, SMILES and chemical synonym names. Batch conversion downloads of 1410 CIDs are performed in 2.5 min. Structures are automatically displayed

Ecotoxicity of plant extracts

Since ancient times, plants have been used by mankind as an important source of bioactive compounds with different uses ranging from traditional medicine, food, perfumery, and cosmetics. Currently, its use spreads to almost all economic sectors and new applications keep emerging. One of the great applications of compounds that can be extracted from plants is obtained in the form of essential oil. Plants that produce essential oils do it naturally and this production is linked to many factors such as response to stress, as a defence against pathogen attacks and even as a way to attract pollinators that play an important role in the reproduction of the plant, being also linked to the environmental and ecological conditions of the area in which the plant grows. These essential oils are obtainable industrially from the plants that produced them by a distillation process or in certain cases by a mechanical process. During the distillation process to obtain essential oils, another economically interesting product can be attained: hydrolates, which are generally composed of more hydrosoluble molecules that remain in the distillation water. Another product that can be obtained from plants are extracts, which are extracted by treating parts of a plant with a solvent. Global essential oil production has been continuously increasing over the last decade and it is expected to continue rising over the next years. This growing production can be linked to the ever-bigger demand by consumers for natural products. From an academic point of view, it can also be said that research for new compounds and new applications of products extracted from plants has been increasing. In this way, the “InovEP Project – Innovation with Plant Extracts” has the goal to connect the university and the industry, providing scientific knowledge that can be used by companies linked to the production of essential oils and other plant extracts, as well as companies that want to use these products to develop new applications based in scientific evidence. Among the studies performed, one of the goals is to study the environmental safety of these products. The work described in this dissertation focused on the ecotoxicological evaluation of essential oils, hydrolates and extracts obtained from several plants studied in the project: Cistus ladanifer; Cupressus lusitanica, Echinacea purpurea, Eucalyptus globulus, Hamamelis virginiana, Helichrysum italicum, Humulus lupulus, Matricaria chamomilla, Ocimum basilicum, Thymbra capitata, Thymus citriodorus and Syzygium aromaticum. The tests performed focused on the acute toxicity towards aquatic organisms using the cladoceran Daphnia magna as model organism. Commonly called “water-flea”, D. magna is one of the recommended organisms to perform toxicity tests in aquatic systems by several international organizations such as the European Union (EU), Organization for Co-operation and Economic Development (OECD), the American Society for Testing and Materials (ASTM), and the International Organization for Standardization (ISO). The results, in general, show that essential oils can cause effects at lower concentrations when compared to the studied extracts and hydrolates. The S. aromaticum essential oil caused effects at lower concentrations, followed by the T. capitata essential oil, E. globulus essential oil and C. ladanifer essential oil. Of all the essential oils, only the one from H. italicum did not cause effects up to the highest concentration tested. Of all the H. lupulus extracts tested, immobilisation of the test organisms was only observed with high concentrations of the chloroform extract, obtained from the flowers. All the other extracts did not cause immobilisation up to the highest concentrations tested, and the same trend was observed with all the hydrolates tested. In terms of classification of the acute toxicity of essential oils, extracts and hydrolates, the GHS (Globally Harmonized System for Classification and Labelling of Chemicals) proposed by the United Nations was followed, which is also used in the European Union. Thus, the S. aromaticum essential oil can be classified as toxic to aquatic systems under the “Acute 2” category, and the T. capitata and E. globulus essential oils under the “Acute 3” category. The essential oils from C. ladanifer, H. italicum and all the extracts and hydrolates tested can not be classified, being considered not toxic, as the obtained results are above the classification limits proposed in the GHS. The S. aromaticum, T. capitata and E. globulus essential oils can cause acute adverse effects in aquatic systems, particularly in organisms in the same trophic level as D. magna, and so, precautions should be taken to avoid accidental or intentional contaminations of aquatic systems. For the other essential oils, extracts and hydrolates, the same precautions should be taken since, although they can not be classified as toxic, the effects that they can cause in organisms from different trophic levels remain unknown.Desde a antiguidade, as plantas têm sido utilizadas pela Humanidade como uma importante fonte de compostos bioativos com diversas utilizações desde a medicina tradicional, passando pela alimentação, perfumaria e cosmética. Atualmente, a sua utilização distribui-se por quase todos os setores económicos e novas aplicações continuam a emergir. Algumas das mais importantes aplicações de compostos bioativos extraídos de plantas são realizadas recorrendo a óleos essenciais. Plantas que produzem óleos essenciais fazem-no naturalmente e esta produção está ligada a diversos fatores como resposta a stress, defesa contra ataques de agentes patogénicos e até como forma de atrair polinizadores que desempenham um papel fundamental na reprodução da planta, estando também ligada às condições ambientais e ecológicas em que a planta cresce. Estes óleos essenciais são obtidos industrialmente das plantas que os produzem por um processo de destilação ou em certos casos por um processo mecânico. Durante o processo de destilação para a obtenção de óleos essenciais, outro produto com interesse económico pode ser obtido: os hidrolatos, que são geralmente compostos por moléculas mais hidrossolúveis que permanecem na água de destilação. Outro produto que pode ser obtido das plantas são os extratos, que são obtidos através do tratamento de partes específicas de uma planta com um solvente. A produção global de óleos essenciais tem crescido continuamente ao longo da última década e estima-se que esta tendência continue nos próximos anos. Esta crescente produção pode ser ligada à cada vez maior preocupação por parte dos consumidores na utilização de compostos naturais. Do ponto de vista académico, também podemos afirmar que a pesquisa por novos compostos e novas aplicações de produtos extraídos de plantas tem mostrado uma tendência crescente. Desta forma, o Projeto “InovEP – Inovação com Extratos de Plantas” tem como objetivo fazer a ligação entre a academia e a indústria, produzindo conhecimento científico que possa ser utilizado por empresas ligadas à produção de óleos essenciais e outros extratos de plantas, assim como empresas que queiram utilizar estes produtos para desenvolver novas aplicações baseando-se em evidências científicas. Entre os vários estudos efetuados, um deles tem como objetivo o estudo da segurança ambiental destes produtos. O trabalho descrito na presente dissertação focou-se em avaliar os efeitos ecotoxicológicos de óleos essenciais, hidrolatos e extratos obtidos de várias plantas estudadas no projeto: Cistus ladanifer; Cupressus lusitanica, Echinacea purpurea, Eucalyptus globulus, Hamamelis virginiana, Helichrysum italicum, Humulus lupulus, Matricaria chamomilla, Ocimum basilicum, Thymbra capitata, Thymus citriodorus e Syzygium aromaticum. Os testes realizados focaram-se na toxicidade aguda em organismos aquáticos utilizando como organismo modelo o cladócero Daphnia magna. Popularmente chamada de “pulga de água”, é um dos organismos preferenciais para a realização de testes de toxicidade em ambiente aquático e é recomendada por várias organizações internacionais como a União Europeia (UE), a Organização para a Cooperação e Desenvolvimento Económico (OCDE), a Sociedade Americana para a Testagem e Materiais (ASTM) e a Organização Internacional para Padronização (ISO). De uma forma geral, os resultados obtidos demostraram que os óleos essenciais são capazes de causar efeitos em concentrações mais baixas comparativamente aos extratos e aos hidrolatos estudados. O óleo essencial de S. aromaticum foi o que causou efeitos a concentrações mais baixas, seguido pelo óleo essencial de T. capitata, óleo essencial de E. globulus e óleo essencial de C. ladanifer. De todos os óleos essenciais apenas o de H. italicum não causou efeitos até à máxima concentração testada. De todos os extratos de H. lupulus testados, apenas se verificou imobilização dos organismos teste com o extrato clorofórmico, obtido das flores da planta, a altas concentrações. Todos os outros extratos não causaram imobilização até às máximas concentrações testadas, verificando-se a mesma tendência com todos os hidrolatos testados. Em termos de classificação da toxicidade aguda dos óleos essenciais, extratos e hidrolatos testados foi seguido o sistema GHS (Globally Harmonized System for Classification and Labelling of Chemicals) proposto pelas Nações Unidas e utilizado também na União Europeia. Assim o óleo essencial de S. aromaticum pode ser classificado como tóxico para sistemas aquáticos na categoria “Agudo 2”, e os óleos essenciais de T. capitata e E. globulus na categoria “Agudo 3”. Os óleos essenciais de C. ladanifer, H. italicum, tal como todos os extratos e hidrolatos testados não podem ser classificados, sendo considerados não tóxicos, uma vez que os resultados obtidos são superiores aos limites de classificação propostos pela GHS. Os óleos essenciais de S. aromaticum, T. capitata e E globulus podem causar efeitos agudos adversos em sistemas aquáticos, particularmente em organismos do mesmo nível trófico que D. magna e precauções devem ser tomadas de forma a evitar contaminações acidentais ou intencionais de sistemas aquáticos. Para os restantes óleos essenciais, extratos e hidrolatos, as mesmas precauções devem ser tomadas uma vez que, apesar de não serem classificados como tóxicos, os efeitos que podem causar em organismos de outros níveis tróficos são ainda desconhecidos

Foodinformatic prediction of the retention time of pesticide residues detected in fruits and vegetables using UHPLC/ESI Q-Orbitrap

The present work describes the development of an in silico model to predict the retention time (tR) of a large Compound DataBase (CDB) of pesticides detected in fruits and vegetables. The model utilizes ultrahigh-performance liquid chromatography electrospray ionization quadrupole-Orbitrap (UHPLC/ESI Q-Orbitrap) mass spectrometry (MS) data. The available CDB was properly curated, and the pesticides were represented by conformation-independent molecular descriptors. In an attempt to improve the model predictions, the best four MLR models obtained were subjected to a consensus analysis. The optimal model was evaluated by means of the coefficient of determination and the residual standard deviation in calibration, validation, and prediction, along other internal and external validation criteria to accomplish the guidelines defined by the Organization for Economic Co-operation and Development. Finally, the in silico model was applied to predict the tR of an external set of 57 pesticides.Fil: Rojas, Cristian. Universidad del Azuay; EcuadorFil: Aranda, José Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; ArgentinaFil: Pacheco Jaramillo, Elisa. Universidad del Azuay; EcuadorFil: Losilla Bermejo, Irene. Universidad de Extremadura; España. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Tripaldi, Piercosimo. Universidad del Azuay; EcuadorFil: Duchowicz, Pablo Román. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; ArgentinaFil: Castro, Eduardo Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; Argentin

Chemically Aware Model Builder (camb): an R package for property and bioactivity modelling of small molecules.

BACKGROUND: In silico predictive models have proved to be valuable for the optimisation of compound potency, selectivity and safety profiles in the drug discovery process. RESULTS: camb is an R package that provides an environment for the rapid generation of quantitative Structure-Property and Structure-Activity models for small molecules (including QSAR, QSPR, QSAM, PCM) and is aimed at both advanced and beginner R users. camb's capabilities include the standardisation of chemical structure representation, computation of 905 one-dimensional and 14 fingerprint type descriptors for small molecules, 8 types of amino acid descriptors, 13 whole protein sequence descriptors, filtering methods for feature selection, generation of predictive models (using an interface to the R package caret), as well as techniques to create model ensembles using techniques from the R package caretEnsemble). Results can be visualised through high-quality, customisable plots (R package ggplot2). CONCLUSIONS: Overall, camb constitutes an open-source framework to perform the following steps: (1) compound standardisation, (2) molecular and protein descriptor calculation, (3) descriptor pre-processing and model training, visualisation and validation, and (4) bioactivity/property prediction for new molecules. camb aims to speed model generation, in order to provide reproducibility and tests of robustness. QSPR and proteochemometric case studies are included which demonstrate camb's application.Graphical abstractFrom compounds and data to models: a complete model building workflow in one package