Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/16973
Title: Deep Autoencoders for Additional Insight into Protein Dynamics
Authors: Teletin, M
Czibula, G
Bocicor, M-I
Albert, S
Pandini, A
Keywords: Protein molecular dynamics;Autoencoders;Unsupervised learning
Issue Date: 2018
Publisher: Springer Verlag
Citation: Lecture Notes in Computer Science, 2018, 11140 pp. 79 - 89
Abstract: The study of protein dynamics through analysis of conformational transitions represents a significant stage in understanding protein function. Using molecular simulations, large samples of protein transitions can be recorded. However,extractingfunctionalmotionsfromthesesamplesisstillnotautomated andextremelytime-consuming.Inthispaperweinvestigatetheusefulnessofunsupervised machine learning methods for uncovering relevant information about protein functional dynamics. Autoencoders are being explored in order to highlight their ability to learn relevant biological patterns, such as structural characteristics. This study is aimed to provide a better comprehension of how protein conformational transitions are evolving in time, within the larger framework of automatically detecting functional motions.
URI: http://bura.brunel.ac.uk/handle/2438/16973
DOI: http://dx.doi.org/10.1007/978-3-030-01421-6_8
ISSN: 0302-9743
http://dx.doi.org/10.1007/978-3-030-01421-6_8
Appears in Collections:Dept of Computer Science Embargoed Research Papers

Files in This Item:
File Description SizeFormat 
Fulltext.pdf375.31 kBAdobe PDFView/Open    Request a copy


Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.