3,477 research outputs found

    Paradigms for computational nucleic acid design

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    The design of DNA and RNA sequences is critical for many endeavors, from DNA nanotechnology, to PCR‐based applications, to DNA hybridization arrays. Results in the literature rely on a wide variety of design criteria adapted to the particular requirements of each application. Using an extensively studied thermodynamic model, we perform a detailed study of several criteria for designing sequences intended to adopt a target secondary structure. We conclude that superior design methods should explicitly implement both a positive design paradigm (optimize affinity for the target structure) and a negative design paradigm (optimize specificity for the target structure). The commonly used approaches of sequence symmetry minimization and minimum free‐energy satisfaction primarily implement negative design and can be strengthened by introducing a positive design component. Surprisingly, our findings hold for a wide range of secondary structures and are robust to modest perturbation of the thermodynamic parameters used for evaluating sequence quality, suggesting the feasibility and ongoing utility of a unified approach to nucleic acid design as parameter sets are refined further. Finally, we observe that designing for thermodynamic stability does not determine folding kinetics, emphasizing the opportunity for extending design criteria to target kinetic features of the energy landscape

    Nonlinear Dimension Reduction for Micro-array Data (Small n and Large p)

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    Computational structure‐based drug design: Predicting target flexibility

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    The role of molecular modeling in drug design has experienced a significant revamp in the last decade. The increase in computational resources and molecular models, along with software developments, is finally introducing a competitive advantage in early phases of drug discovery. Medium and small companies with strong focus on computational chemistry are being created, some of them having introduced important leads in drug design pipelines. An important source for this success is the extraordinary development of faster and more efficient techniques for describing flexibility in three‐dimensional structural molecular modeling. At different levels, from docking techniques to atomistic molecular dynamics, conformational sampling between receptor and drug results in improved predictions, such as screening enrichment, discovery of transient cavities, etc. In this review article we perform an extensive analysis of these modeling techniques, dividing them into high and low throughput, and emphasizing in their application to drug design studies. We finalize the review with a section describing our Monte Carlo method, PELE, recently highlighted as an outstanding advance in an international blind competition and industrial benchmarks.We acknowledge the BSC-CRG-IRB Joint Research Program in Computational Biology. This work was supported by a grant from the Spanish Government CTQ2016-79138-R.J.I. acknowledges support from SVP-2014-068797, awarded by the Spanish Government.Peer ReviewedPostprint (author's final draft

    Integrative Modeling of Transcriptional Regulation in Response to Autoimmune Desease Therapies

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    Die rheumatoide Arthritis (RA) und die Multiple Sklerose (MS) werden allgemein als Autoimmunkrankheiten eingestuft. Zur Behandlung dieser Krankheiten werden immunmodulatorische Medikamente eingesetzt, etwa TNF-alpha-Blocker (z.B. Etanercept) im Falle der RA und IFN-beta-Präparate (z.B. Betaferon und Avonex) im Falle der MS. Bis heute sind die molekularen Mechanismen dieser Therapien weitestgehend unbekannt. Zudem ist ihre Wirksamkeit und Verträglichkeit bei einigen Patienten unzureichend. In dieser Arbeit wurde die transkriptionelle Antwort im Blut von Patienten auf jede dieser drei Therapien untersucht, um die Wirkungsweise dieser Medikamente besser zu verstehen. Dabei wurden Methoden der Netzwerkinferenz eingesetzt, mit dem Ziel, die genregulatorischen Netzwerke (GRNs) der in ihrer Expression veränderten Gene zu rekonstruieren. Ausgangspunkt dieser Analysen war jeweils ein Genexpressions- Datensatz. Daraus wurden zunächst Gene gefiltert, die nach Therapiebeginn hoch- oder herunterreguliert sind. Anschließend wurden die genregulatorischen Regionen dieser Gene auf Transkriptionsfaktor-Bindestellen (TFBS) analysiert. Um schließlich GRN-Modelle abzuleiten, wurde ein neuer Netzwerkinferenz-Algorithmus (TILAR) verwendet. TILAR unterscheidet zwischen Genen und TF und beschreibt die regulatorischen Effekte zwischen diesen durch ein lineares Gleichungssystem. TILAR erlaubt dabei Vorwissen über Gen-TF- und TF-Gen-Interaktionen einzubeziehen. Im Ergebnis wurden komplexe Netzwerkstrukturen rekonstruiert, welche die regulatorischen Beziehungen zwischen den Genen beschreiben, die im Verlauf der Therapien differentiell exprimiert sind. Für die Etanercept-Therapie wurde ein Teilnetz gefunden, das Gene enthält, die niedrigere Expressionslevel bei RA-Patienten zeigen, die sehr gut auf das Medikament ansprechen. Die Analyse von GRNs kann somit zu einem besseren Verständnis Therapie-assoziierter Prozesse beitragen und transkriptionelle Unterschiede zwischen Patienten aufzeigen

    Evolutionary Computation and QSAR Research

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    [Abstract] The successful high throughput screening of molecule libraries for a specific biological property is one of the main improvements in drug discovery. The virtual molecular filtering and screening relies greatly on quantitative structure-activity relationship (QSAR) analysis, a mathematical model that correlates the activity of a molecule with molecular descriptors. QSAR models have the potential to reduce the costly failure of drug candidates in advanced (clinical) stages by filtering combinatorial libraries, eliminating candidates with a predicted toxic effect and poor pharmacokinetic profiles, and reducing the number of experiments. To obtain a predictive and reliable QSAR model, scientists use methods from various fields such as molecular modeling, pattern recognition, machine learning or artificial intelligence. QSAR modeling relies on three main steps: molecular structure codification into molecular descriptors, selection of relevant variables in the context of the analyzed activity, and search of the optimal mathematical model that correlates the molecular descriptors with a specific activity. Since a variety of techniques from statistics and artificial intelligence can aid variable selection and model building steps, this review focuses on the evolutionary computation methods supporting these tasks. Thus, this review explains the basic of the genetic algorithms and genetic programming as evolutionary computation approaches, the selection methods for high-dimensional data in QSAR, the methods to build QSAR models, the current evolutionary feature selection methods and applications in QSAR and the future trend on the joint or multi-task feature selection methods.Instituto de Salud Carlos III, PIO52048Instituto de Salud Carlos III, RD07/0067/0005Ministerio de Industria, Comercio y Turismo; TSI-020110-2009-53)Galicia. Consellería de Economía e Industria; 10SIN105004P
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