Clasificación vía aprendizaje automático de conformaciones moleculares en estructuras teloméricas
Abstract
ABSTRACT
A great number of studies have been published on the area of small molecules bound to telomeric sequences that fold on a non-cannonical DNA structure known as G-quadruplex structure. The study of these structures has been driven for its potential as targets for drug development. Furthermore, G-quadruplex structures have been identified in other sequences of importance through all the genome. Within the different studies two main models have been used: the human telomeric sequence and the oxytricha, tetrahymena telomeric sequence. Both cases are used as models for computational studies and in vitro essays. Here we study the trajectory generated from an atomistic molecular dynamic simulation to obtain the amount of flexibility and mobility of these model structures. We used data mining and classification tools (i.e. k nearest neighbor method) to identify automatically subpopulations of structures within the simulation. We found the most populated conformations and we discuss the structural findings.
Keywords: DNA, molecular dynamic, G- Quadruplex, classification, data mining, k nearest neighbor.
RESUMEN
Un gran número de estudios han sido publicados en el área de pequeñas moléculas unidas a secuencias teloméricas que se pliegan en estructura no convencionales de AD denominadas estructuras G- Cuádruple. El estudio de estas estructuras ha sido motivado por su potencial utilidad como blancos para desarrollo de fármacos. Además, las estructuras G- Cuádruples han sido identificadas en otras secuencias de importancia a lo largo de todo el genoma. Dentro de los diversos estudios se han utilizado dos modelos: la secuencia telomérica humana y las secuencia telomérica el Oxytricha, Tetrahymena. Ambos han sido empleados como modelos para estudios computacionales y ensayos in vitro. Aquí estudiamos la trayectoria generada a partir de las simulaciones de dinámica molecular atomística para obtener la cantidad de flexibilidad y movilidad de estas estructuras modelos. Utilizamos herramientas de clasificación (es decir, método del vecino cercano k) y minería de datos. Encontramos las conformaciones más pobladas y discutimos los hallazgos estructurales.
Palabras clave: ADN, simulación molecular, G cuádruple, clasificación, minería de datos, vecino cercano k.
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