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Array-based evolution of DNA aptamers allows modeling of an explicit sequence-fitness landscape

Oxford University Press
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  • Biology
  • Computer Science


gkn899 1..10 Published online 23 November 2008 Nucleic Acids Research, 2009, Vol. 37, No. 1 e6 doi:10.1093/nar/gkn899 Array-based evolution of DNA aptamers allows modelling of an explicit sequence-fitness landscape Christopher G. Knight1,2,3,*, Mark Platt1,2, William Rowe1,2, David C. Wedge1,2, Farid Khan1,2, Philip J. R. Day1,4, Andy McShea5, Joshua Knowles1,6 and Douglas B. Kell1,2 1Manchester Interdisciplinary Biocentre, The University of Manchester, 131 Princess Street, Manchester M1 7DN, UK, 2School of Chemistry, The University of Manchester, Oxford Road, Manchester, M13 9PL, UK, 3Faculty of Life Sciences, The University of Manchester, Simon Building, Brunswick Street, Manchester M13 9PL, UK, 4School of Medicine, The University of Manchester, Oxford Road, Manchester M13 9PT, UK, 5Combimatrix Corporation, 6500 Harbor Heights Parkway, Suite #303, Mukilteo, WA 98275, USA and 6School of Computer Science, The University of Manchester, Kilburn Building, Oxford Road, Manchester, M13 9PL, UK Received September 20, 2008; Revised October 20, 2008; Accepted October 23, 2008 ABSTRACT Mapping the landscape of possible macromolecular polymer sequences to their fitness in performing biological functions is a challenge across the bio- sciences. A paradigm is the case of aptamers, nucleic acids that can be selected to bind particular target molecules. We have characterized the sequence-fitness landscape for aptamers binding allophycocyanin (APC) protein via a novel Closed Loop Aptameric Directed Evolution (CLADE) approach. In contrast to the conventional SELEX methodology, selection and mutation of aptamer sequences was carried out in silico, with explicit fit- ness assays for 44 131 aptamers of known sequence using DNA microarrays in vitro. We capture the landscape using a predictive machine learning model linking sequence features and function and validate this model using 5500 entirely separate test sequences, which give a very high observed versus predicted correlation of 0.87. This appro

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