QSAR Classification Models for Predicting the Activity of Inhibitors of Beta-Secretase (BACE1) Associated with Alzheimer’s Disease

Alzheimer’s disease is one of the most common neurodegenerative disorders in elder population. The β-site amyloid cleavage enzyme 1 (BACE1) is the major constituent of amyloid plaques and plays a central role in this brain pathogenesis, thus it constitutes an auspicious pharmacological target for its treatment. In this paper, a QSAR model for identification of potential inhibitors of BACE1 protein is designed by using classification methods. For building this model, a database with 215 molecules collected from different sources has been assembled. This dataset contains diverse compounds with different scaffolds and physical-chemical properties, covering a wide chemical space in the drug-like range. The most distinctive aspect of the applied QSAR strategy is the combination of hybridization with backward elimination of models, which contributes to improve the quality of the final QSAR model. Another relevant step is the visual analysis of the molecular descriptors that allows guaranteeing the absence of information redundancy in the model. The QSAR model performances have been assessed by traditional metrics, and the final proposed model has low cardinality, and reaches a high percentage of chemical compounds correctly classified.Fil: Ponzoni, Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Sebastián Pérez, Víctor. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Consejo Superior de Investigaciones Científicas. Centro de Investigaciones Biológicas; EspañaFil: Martínez, María J.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Roca, Carlos. Consejo Superior de Investigaciones Científicas. Centro de Investigaciones Biológicas; EspañaFil: De la Cruz Pérez, Carlos. Consejo Superior de Investigaciones Científicas. Centro de Investigaciones Biológicas; EspañaFil: Cravero, Fiorella. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; ArgentinaFil: Vazquez, Gustavo Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Católica del Uruguay; UruguayFil: Páez, Juan A.. Consejo Superior de Investigaciones Científicas. Instituto de Química Médica; EspañaFil: Diaz, Monica Fatima. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería Química; ArgentinaFil: Campillo Martín, Nuria Eugenia. Consejo Superior de Investigaciones Científicas. Centro de Investigaciones Biológicas; Españ

Structure-based classification and ontology in chemistry

<p>Abstract</p> <p>Background</p> <p>Recent years have seen an explosion in the availability of data in the chemistry domain. With this information explosion, however, retrieving <it>relevant </it>results from the available information, and <it>organising </it>those results, become even harder problems. Computational processing is essential to filter and organise the available resources so as to better facilitate the work of scientists. Ontologies encode expert domain knowledge in a hierarchically organised machine-processable format. One such ontology for the chemical domain is ChEBI. ChEBI provides a classification of chemicals based on their structural features and a role or activity-based classification. An example of a structure-based class is 'pentacyclic compound' (compounds containing five-ring structures), while an example of a role-based class is 'analgesic', since many different chemicals can act as analgesics without sharing structural features. Structure-based classification in chemistry exploits elegant regularities and symmetries in the underlying chemical domain. As yet, there has been neither a systematic analysis of the types of structural classification in use in chemistry nor a comparison to the capabilities of available technologies.</p> <p>Results</p> <p>We analyze the different categories of structural classes in chemistry, presenting a list of patterns for features found in class definitions. We compare these patterns of class definition to tools which allow for automation of hierarchy construction within cheminformatics and within logic-based ontology technology, going into detail in the latter case with respect to the expressive capabilities of the Web Ontology Language and recent extensions for modelling structured objects. Finally we discuss the relationships and interactions between cheminformatics approaches and logic-based approaches.</p> <p>Conclusion</p> <p>Systems that perform intelligent reasoning tasks on chemistry data require a diverse set of underlying computational utilities including algorithmic, statistical and logic-based tools. For the task of automatic structure-based classification of chemical entities, essential to managing the vast swathes of chemical data being brought online, systems which are capable of hybrid reasoning combining several different approaches are crucial. We provide a thorough review of the available tools and methodologies, and identify areas of open research.</p

Development and prospective application of chemoinformatic tools to explore new ligand chemistry and protein biology

Drug discovery and design is a tedious and expensive process whose small chances of success necessitates the development of novel chemoinformatic approaches and concepts. Their common goal is the efficient and robust identification of promising chemical matter and the reliable prediction of its properties. Computer-aided drug discovery and design (CADDD) and its multifarious installments throughout the different phases of the drug discovery pipeline contribute significantly to the expansion of the hits, the understanding of their structure-activity relationship and their rational diversification. They alleviate the development’s costs and its time-demand thus support the search for the needle in the haystack – a potent hit. The HTS-driven brute-force nature of current and of the decades’ past discovery and design strategies compelled researchers to develop ideas and algorithms in order to interfere with the pipeline and prevent its frequent failures. In the introduction, I describe the drug discovery and design pipeline and point out interfaces where CADDD contributes to its success. In Part 1 of this thesis, I present a novel methodology that supports the early-stage hit discovery processes through a fragment-based reduced graph similarity approach (RedFrag). It is a chimeric algorithm that combines fingerprint-based similarity calculation with scaffold-hopping-enabling graph isomorphism. We thoroughly investigated its performance retro- and prospectively. It uses a new type of reduced graph that does not suffer from information loss during its construction and bypasses the necessity of feature definitions. Built upon chemical epitopes resulting from molecule fragmentation, the reduced graph embodies physico-chemical and 2D-structural properties of a molecule. Reduced graphs are compared with a continuous-similarity-distance-driven maximal common subgraph algorithm, which calculates similarity at the fragmental and topological levels. The second chapter, Part 2, is dedicated to PrenDB: A digital compendium of the reaction space of prenyltransferases of the dimethylallyltryptophan synthase (DMATS) superfamily. Their catalytical transformations represent a major skeletal diversification step in the biosynthesis of secondary metabolites including the indole alkaloids. DMATS enzymes thus contribute significantly to the biological and pharmacological diversity of small molecule metabolites. The attachment of the prenyl donor to lead- or drug-like molecules renders the prenyltransferases useful in the access of chemical space that is difficult to reach by conventional synthesis. In PrenDB, we collected the substrates, enzymes and products. We then used a newly developed algorithm based on molecular fragmentation to automatically extract reactive chemical epitopes. The analysis of the collected data sheds light on the thus far explored substrate space of DMATS enzymes. We supplemented the browsable database with algorithmic prediction routines in order to assess the prenylability of novel compounds and did so for a set of 38 molecules. In a case study, Part 3, we investigated the regioselectivity of five prenyltransferases in the presence of unnatural prenyl donors. Detailed biochemical investigations revealed the acceptance of these dimethylallyl pyrophosphate (DMAPP) analogs by all tested enzymes with different relative activities and regioselectivities. In order to understand the activity profiles and their differences on a molecular level we investigated the interaction within the enzyme-prenyl donor-substrate system with molecular dynamics. Our experiments show that the reactivity of a prenyl donor strongly correlates with the distance of its electrophilic, reactive atom and the nucleophilic center of the substrate molecule. It renders the first step towards a better mechanistic understanding of the reactivity of prenyltransferases and expands significantly the potential usage and rational design of tryptophan prenylating enzymes as biocatalysts for Friedel–Crafts alkylation. Lastly, in Part 4, we present the synergistic potential of combined ligand- and structure-based drug discovery methodologies applied to the β2-adrenergic receptor (β2AR). The β2AR is a G protein-coupled receptor (GPCR) and a well-explored target. By the joint application of fingerprint-based similarity, substructure-based searches and docking we discovered 13 ligands – ten of which were novel – of this particular GPCR. Of note, two of the molecules used as starting points for the similarity and substructure searches distinguish themselves from other β2AR antagonists by their unique scaffold. Thus, the usage of a multistep hierarchical or parallel screening approach enabled us to use these unique structural features and discover novel chemical matter beyond the bounds of the ligand space known so far and emphasize the intrinsic complementarity of ligand- and structure-based approaches. The molecules described in this work allow us to explore the ligand space around the previously reported molecules in greater detail, leading to insights into their structure-activity relationship. In addition, we also characterized our hits with experimental binding and selectivity data and discussed it based on their putative binding modes derived by docking