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Curr Opin Struct Biol. 2015 Aug;33:146-60. doi: 10.1016/j.sbi.2015.09.001.

Deep sequencing in library selection projects: what insight does it bring?

Author information

1
Program in Computational and Systems Immunology, Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA, USA.
2
University of New Mexico Comprehensive Cancer Center, and Division of Molecular Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA.
3
ETH Zurich, Department of Biosystems Science and Engineering, Basel, Switzerland.
4
Bioscience division, Los Alamos National Laboratory, Los Alamos, NM, USA.
5
Bioscience division, Los Alamos National Laboratory, Los Alamos, NM, USA. Electronic address: amb@lanl.gov.

Abstract

High throughput sequencing is poised to change all aspects of the way antibodies and other binders are discovered and engineered. Millions of available sequence reads provide an unprecedented sampling depth able to guide the design and construction of effective, high quality naïve libraries containing tens of billions of unique molecules. Furthermore, during selections, high throughput sequencing enables quantitative tracing of enriched clones and position-specific guidance to amino acid variation under positive selection during antibody engineering. Successful application of the technologies relies on specific PCR reagent design, correct sequencing platform selection, and effective use of computational tools and statistical measures to remove error, identify antibodies, estimate diversity, and extract signatures of selection from the clone down to individual structural positions. Here we review these considerations and discuss some of the remaining challenges to the widespread adoption of the technology.

PMID:
26451649
PMCID:
PMC4648538
DOI:
10.1016/j.sbi.2015.09.001
[Indexed for MEDLINE]
Free PMC Article

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