756 research outputs found

    Rational drug design using genetic algorithm: case of malaria disease

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    With the rapid development in the amount of molecular biological structures, computational molecular docking (CMD) approaches become one of the crucial tools in rational drug design (RDD). Currently, number of researchers are working in this filed to overcome the recent issues of docking by using genetic algorithm approach. Moreover, Genetic Algorithm facilities the researchers and scientists in molecular docking experiments. Since conducting the experiment in the laboratory considered as time consuming and costly, the scientists determined to use the computational techniques to simulate their experiments. In this paper, auto dock 4.2, well known docking simulation has been used to perform the experiment in specific disease called malaria. The genetic algorithm (GA) approach in the autodock4.2 has been used to search for the potential candidate drug in the twenty drugs. It shows the great impacts in the results obtained from the CMD simulation. In the experiment, we used falcipain-2 as our target protein (2GHU.pdb) obtained from the protein data bank and docked with twenty different available anti malaria drugs in order to find the effective and efficient drugs. Drug Diocopeltine A was found as the best lowest binding energy with the value of -8.64 Kcal/mol. Thus, it can be selected as the anti malaria drug candidate

    Optimización multi-objetivo en las ciencias de la vida.

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    Para conseguir este objetivo, en lugar de intentar incorporar nuevos algoritmos directamente en el código fuente de AutoDock, se utilizó un framework orientado a la resolución de problemas de optimización con metaheurísticas. Concretamente, se usó jMetal, que es una librería de código libre basada en Java. Ya que AutoDock está implementado en C++, se desarrolló una versión en C++ de jMetal (posteriormente distribuida públicamente). De esta manera, se consiguió integrar ambas herramientas (AutoDock 4.2 y jMetal) para optimizar la energía libre de unión entre compuesto químico y receptor. Después de disponer de una amplia colección de metaheurísticas implementadas en jMetalCpp, se realizó un detallado estudio en el cual se aplicaron un conjunto de metaheurísticas para optimizar un único objetivo minimizando la energía libre de unión, el cual es el resultado de la suma de todos los términos de energía de la función objetivo de energía de AutoDock 4.2. Por lo tanto, cuatro metaheurísticas tales como dos variantes de algoritmo genético gGA (Algoritmo Genético generacional) y ssGA (Algoritmo Genético de estado estacionario), DE (Evolución Diferencial) y PSO (Optimización de Enjambres de Partículas) fueron aplicadas para resolver el problema del acoplamiento molecular. Esta fase se dividió en dos subfases en las que se usaron dos conjuntos de instancias diferentes, utilizando como receptores HIV-proteasas con cadenas laterales de aminoacidos flexibles y como ligandos inhibidores HIV-proteasas flexibles. El primer conjunto de instancias se usó para un estudio de configuración de parámetros de los algoritmos y el segundo para comparar la precisión de las conformaciones ligando-receptor obtenidas por AutoDock y AutoDock+jMetalCpp. La siguiente fase implicó aplicar una formulación multi-objetivo para resolver problemas de acoplamiento molecular dados los resultados interesantes obtenidos en estudios previos existentes en los que dos objetivos como la energía intermolecular y la energía intramolecular fueron minimizados. Por lo tanto, se comparó y analizó el rendimiento de un conjunto de metaheurísticas multi-objetivo mediante la resolución de complejos flexibles de acoplamiento molecular minimizando la energía inter- e intra-molecular. Estos algoritmos fueron: NSGA-II (Algoritmo Genético de Ordenación No dominada) y su versión de estado estacionario (ssNSGA-II), SMPSO (Optimización Multi-objetivo de Enjambres de Partículas con Modulación de Velocidad), GDE3 (Tercera versión de la Evolución Diferencial Generalizada), MOEA/D (Algoritmo Evolutivo Multi-Objetivo basado en la Decomposición) y SMS-EMOA (Optimización Multi-objetivo Evolutiva con Métrica S). Después de probar enfoques multi-objetivo ya existentes, se probó uno nuevo. En concreto, el uso del RMSD como un objetivo para encontrar soluciones similares a la de la solución de referencia. Se replicó el estudio previo usando este conjunto diferente de objetivos. Por último, se analizó de forma detallada el algoritmo que obtuvo mejores resultados en los estudios previos. En concreto, se realizó un estudio de variantes del SMPSO minimizando la energía intermolecular y el RMSD. Este estudio proporcionó algunas pistas sobre cómo nuevos algoritmos basados en SMPSO pueden ser adaptados para mejorar los resultados de acoplamiento molecular para aquellas simulaciones que involucren ligandos y receptores flexibles. Esta tesis demuestra que la inclusión de técnicas metaheurísticas de jMetalCpp en la herramienta de acoplamiento molecular AutoDock incrementa las posibilidades a los usuarios de ámbito biológico cuando resuelven el problema del acoplamiento molecular. El uso de técnicas de optimización mono-objetivo diferentes aparte de aquéllas ampliamente usadas en las comunidades de acoplamiento molecular podría dar lugar a soluciones de mayor calidad. En nuestro caso de estudio mono-objetivo, el algoritmo de evolución diferencial obtuvo mejores resultados que aquellos obtenidos por AutoDock. También se propone diferentes enfoques multi-objetivo para resolver el problema del acoplamiento molecular, tales como la decomposición de los términos de la energía de unión o el uso del RMSD como un objetivo. Finalmente, se demuestra que el SMPSO, una metaheurística de optimización multi-objetivo de enjambres de partículas, es una técnica remarcable para resolver problemas de acoplamiento molecular cuando se usa un enfoque multi-objetivo, obteniendo incluso mejores soluciones que las técnicas mono-objetivo.Las herramientas de acoplamiento molecular han llegado a ser bastante eficientes en el descubrimiento de fármacos y en el desarrollo de la investigación de la industria farmacéutica. Estas herramientas se utilizan para elucidar la interacción de una pequeña molécula (ligando) y una macro-molécula (diana) a un nivel atómico para determinar cómo el ligando interactúa con el sitio de unión de la proteína diana y las implicaciones que estas interacciones tienen en un proceso bioquímico dado. En el desarrollo computacional de las herramientas de acoplamiento molecular los investigadores de este área se han centrado en mejorar los componentes que determinan la calidad del software de acoplamiento molecular: 1) la función objetivo y 2) los algoritmos de optimización. La función objetivo de energía se encarga de proporcionar una evaluación de las conformaciones entre el ligando y la proteína calculando la energía de unión, que se mide en kcal/mol. En esta tesis, se ha usado AutoDock, ya que es una de las herramientas de acoplamiento molecular más citada y usada, y cuyos resultados son muy precisos en términos de energía y valor de RMSD (desviación de la media cuadrática). Además, se ha seleccionado la función de energía de AutoDock versión 4.2, ya que permite realizar una mayor cantidad de simulaciones realistas incluyendo flexibilidad en el ligando y en las cadenas laterales de los aminoácidos del receptor que están en el sitio de unión. Se han utilizado algoritmos de optimización para mejorar los resultados de acoplamiento molecular de AutoDock 4.2, el cual minimiza la energía libre de unión final que es la suma de todos los términos de energía de la función objetivo de energía. Dado que encontrar la solución óptima en el acoplamiento molecular es un problema de gran complejidad y la mayoría de las veces imposible, se suelen utilizar algoritmos no exactos como las metaheurísticas, para así obtener soluciones lo suficientemente buenas en un tiempo razonable

    METADOCK: A parallel metaheuristic schema for virtual screening methods

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    Virtual screening through molecular docking can be translated into an optimization problem, which can be tackled with metaheuristic methods. The interaction between two chemical compounds (typically a protein, enzyme or receptor, and a small molecule, or ligand) is calculated by using highly computationally demanding scoring functions that are computed at several binding spots located throughout the protein surface. This paper introduces METADOCK, a novel molecular docking methodology based on parameterized and parallel metaheuristics and designed to leverage heterogeneous computers based on heterogeneous architectures. The application decides the optimization technique at running time by setting a configuration schema. Our proposed solution finds a good workload balance via dynamic assignment of jobs to heterogeneous resources which perform independent metaheuristic executions when computing different molecular interactions required by the scoring functions in use. A cooperative scheduling of jobs optimizes the quality of the solution and the overall performance of the simulation, so opening a new path for further developments of virtual screening methods on high-performance contemporary heterogeneous platforms.Ingeniería, Industria y Construcció

    Tartarus: A Benchmarking Platform for Realistic And Practical Inverse Molecular Design

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    The efficient exploration of chemical space to design molecules with intended properties enables the accelerated discovery of drugs, materials, and catalysts, and is one of the most important outstanding challenges in chemistry. Encouraged by the recent surge in computer power and artificial intelligence development, many algorithms have been developed to tackle this problem. However, despite the emergence of many new approaches in recent years, comparatively little progress has been made in developing realistic benchmarks that reflect the complexity of molecular design for real-world applications. In this work, we develop a set of practical benchmark tasks relying on physical simulation of molecular systems mimicking real-life molecular design problems for materials, drugs, and chemical reactions. Additionally, we demonstrate the utility and ease of use of our new benchmark set by demonstrating how to compare the performance of several well-established families of algorithms. Surprisingly, we find that model performance can strongly depend on the benchmark domain. We believe that our benchmark suite will help move the field towards more realistic molecular design benchmarks, and move the development of inverse molecular design algorithms closer to designing molecules that solve existing problems in both academia and industry alike.Comment: 29+21 pages, 6+19 figures, 6+2 table

    Combining evolutionary algorithms with reaction rules towards focused molecular design

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    Designing novel small molecules with desirable properties and feasible synthesis continues to pose a significant challenge in drug discovery, particularly in the realm of natural products. Reaction-based gradient-free methods are promising approaches for designing new molecules as they ensure synthetic feasibility and provide potential synthesis paths. However, it is important to note that the novelty and diversity of the generated molecules highly depend on the availability of comprehensive reaction templates. To address this challenge, we introduce ReactEA, a new open-source evolutionary framework for computer-aided drug discovery that solely utilizes biochemical reaction rules. ReactEA optimizes molecular properties using a comprehensive set of 22,949 reaction rules, ensuring chemical validity and synthetic feasibility. ReactEA is versatile, as it can virtually optimize any objective function and track potential synthetic routes during the optimization process. To demonstrate its effectiveness, we apply ReactEA to various case studies, including the design of novel drug-like molecules and the optimization of pre-existing ligands. The results show that ReactEA consistently generates novel molecules with improved properties and reasonable synthetic routes, even for complex tasks such as improving binding affinity against the PARP1 enzyme when compared to existing inhibitors.Centre of Biological Engineering (CEB, University of Minho) for financial and equipment support. Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UIDB/04469/2020 unit and through a Ph.D. scholarship awarded to João Correia (SFRH/BD/144314/2019). European Commission through the project SHIKIFACTORY100 - Modular cell factories for the production of 100 compounds from the shikimate pathway (Reference 814408).info:eu-repo/semantics/publishedVersio

    MACHINE LEARNING AND BIOINFORMATIC INSIGHTS INTO KEY ENZYMES FOR A BIO-BASED CIRCULAR ECONOMY

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    The world is presently faced with a sustainability crisis; it is becoming increasingly difficult to meet the energy and material needs of a growing global population without depleting and polluting our planet. Greenhouse gases released from the continuous combustion of fossil fuels engender accelerated climate change, and plastic waste accumulates in the environment. There is need for a circular economy, where energy and materials are renewably derived from waste items, rather than by consuming limited resources. Deconstruction of the recalcitrant linkages in natural and synthetic polymers is crucial for a circular economy, as deconstructed monomers can be used to manufacture new products. In Nature, organisms utilize enzymes for the efficient depolymerization and conversion of macromolecules. Consequently, by employing enzymes industrially, biotechnology holds great promise for energy- and cost-efficient conversion of materials for a circular economy. However, there is need for enhanced molecular-level understanding of enzymes to enable economically viable technologies that can be applied on a global scale. This work is a computational study of key enzymes that catalyze important reactions that can be utilized for a bio-based circular economy. Specifically, bioinformatics and data- mining approaches were employed to study family 7 glycoside hydrolases (GH7s), which are the principal enzymes in Nature for deconstructing cellulose to simple sugars; a cytochrome P450 enzyme (GcoA) that catalyzes the demethylation of lignin subunits; and MHETase, a tannase-family enzyme utilized by the bacterium, Ideonella sakaiensis, in the degradation and assimilation of polyethylene terephthalate (PET). Since enzyme function is fundamentally dependent on the primary amino-acid sequence, we hypothesize that machine-learning algorithms can be trained on an ensemble of functionally related enzymes to reveal functional patterns in the enzyme family, and to map the primary sequence to enzyme function such that functional properties can be predicted for a new enzyme sequence with significant accuracy. We find that supervised machine learning identifies important residues for processivity and accurately predicts functional subtypes and domain architectures in GH7s. Bioinformatic analyses revealed conserved active-site residues in GcoA and informed protein engineering that enabled expanded enzyme specificity and improved activity. Similarly, bioinformatic studies and phylogenetic analysis provided evolutionary context and identified crucial residues for MHET-hydrolase activity in a tannase-family enzyme (MHETase). Lastly, we developed machine-learning models to predict enzyme thermostability, allowing for high-throughput screening of enzymes that can catalyze reactions at elevated temperatures. Altogether, this work provides a solid basis for a computational data-driven approach to understanding, identifying, and engineering enzymes for biotechnological applications towards a more sustainable world

    Flavonol Glucosylation: A Structural Investigation of the Flavonol Specific 3-O Glucosyltransferase Cp3GT

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    Flavonoid glycosyltransferases (GTs), enzymes integral to plant ecological responses and human pharmacology, necessitate rigorous structural elucidation to decipher their mechanistic function and substrate specificity, particularly given their role in the biotransformation of diverse pharmacological agents and natural products. This investigation delved into a comprehensive exploration of the flavonol 3-O GT from Citrus paradisi (Cp3GT), scrutinizing the impact of a c-terminal c-myc/6x histidine tag on its enzymatic activity and substrate specificity, and successfully achieving its purification to apparent homogeneity. This established a strong foundation for potential future crystallographic and other structure/function analyses. Through the strategic implementation of site-directed mutagenesis, a thrombin cleavage site was incorporated proximal to the tag, followed by cloning in Pichia pastoris, methanol-induced expression, and cobalt-affinity chromatography for initial purification stages. Notably, the recombinant tags did not exhibit a discernible influence on Cp3GT kinetics, substrate preference, pH optima, or metal interactions, maintaining its specificity towards flavonols at the 3-OH position and favoring glucosylation of quercetin and kaempferol. Subsequent purification steps, including MonoQ anion exchange and size-exclusion chromatography, yielded Cp3GT with ≥95% homogeneity. In silico molecular models of Cp3GT and its truncated variants, Cp3GTΔ80 and Cp3GTΔ10, were constructed using D-I-TASSER and COFACTOR to assess binding interactions with quercetin and kaempferol. Results indicated minimal interference of c-myc/6x-his tags with the native Cp3GT structure. This study not only lays a foundation for impending crystallographic studies, aiming to solidify the understanding of Cp3GT\u27s stringent 3-O flavonol specificity, but also accentuates the potential of microbial expression platforms and plant metabolic engineering in producing beneficial compounds. To this end, a thorough review of four pivotal classes of plant secondary metabolites, flavonoids, alkaloids, betalains, and glucosinolates, was conducted. This will open avenues for further research and applications in biotechnological, medical, and agricultural domains

    The Structural Annotations of The Mir-122 Non-Coding RNA from The Tilapia Fish (Oreochromis niloticus)

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    Tilapia (Oreochromis niloticus) is an important fisheries commodity. Scientific efforts have been done to increase its quality. One of them is staging a premium diet such as a fat-enriched diet. The transcriptomics approach is able to provide the signatures of the diet outcomes by observing the micro(mi)RNA signature in transcriptional regulation. Hence, it was found that the availability of mir-122 is essential in the regulation of a high-fat diet in tilapia. However, this transcriptomics signature is lacking structural annotations and the complete interaction annotations with its silencing(si)RNA. RNAcentral website was navigated for the latest annotation of mir-122 from tilapia and other species as a comparison. MEGA X was employed to comprehend the miRNA evolutionary repertoire. The RNA secondary structure prediction tools from the Vienna RNA package and the RNA tertiary structure prediction tools from simRNA and modeRNA are secured with default parameters. The HNADOCK tools were leveraged to observe the interaction between mir-122 and its siRNA. The post-processing was conducted with the Chimera visualization tool. The secondary and tertiary structure of the mir-122 and its siRNA could be elucidated, docked, and visualized. In this end, further effort to develop a comprehensive molecular breeding tool could be secured with the structural annotation information

    Computational Modeling Methods for Understanding the Interaction of Lignin and Its Derivatives with Oxidoreductases as Biocatalysts

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    This chapter will be presented as follow. First, a brief introduction to structure and characterization of lignin and its derivatives is presented, as well as their importance as chemical scaffolds for obtaining value-added products in chemical, food, pharmaceutical and agriculture industry. Second, an extensive review of different reports using computational modeling methods—like molecular dynamics simulations, quantum mechanics and hybrid calculation methods, among others—in the understanding of enzyme-substrate interaction and biocatalysis will be presented. Third, and as last part of chapter, some hand picked examples from literature will be chosen as successful cases where the interplay between experiment and computation has given as a result protein engineered oxidoreductases with improved catalytic capabilities

    MDM2 Case Study: Computational Protocol Utilizing Protein Flexibility Improves Ligand Binding Mode Predictions

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    Recovery of the P53 tumor suppressor pathway via small molecule inhibitors of onco-protein MDM2 highlights the critical role of computational methodologies in targeted cancer therapies. Molecular docking programs in particular, provide a quantitative ranking of predicted binding geometries based on binding free energy allowing for the screening of large chemical libraries in search of lead compounds for cancer therapeutics. This study found improved binding mode predictions of medicinal compounds to MDM2 using the popular docking programs AutoDock and AutoDock Vina, while adopting a rigid-ligand/flexible-receptor protocol. Crystal structures representing small molecule inhibitors bound to MDM2 were selected and a total of 12 rotatable bonds was supplied to each complex and distributed systematically between the ligand and binding site residues. Docking results were evaluated in terms of the top ranked binding free energy and corresponding RMSD values from the experimentally known binding site. Results show lowest RMSD values coincide with a rigid ligand, while the protein retained the majority of flexibility. This study suggests the future implementation of a rigid-ligand/flexible-receptor protocol may improve accuracy of high throughput screenings of potential cancer drugs targeting the MDM2 protein, while maintaining manageable computational costs
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