Haplosaurus computes protein haplotypes for use in precision drug design

William Spooner, William McLaren, Timothy Slidel, Donna K. Finch, Robin Butler, Jamie Campbell, Laura Eghobamien, David Rider (), Christine Mione Kiefer, Matthew J. Robinson, Colin Hardman, Fiona Cunningham, Tristan Vaughan, Paul Flicek and Catherine Chaillan Huntington ()
Additional contact information
William Spooner: Wellcome Genome Campus, Hinxton
William McLaren: Wellcome Genome Campus, Hinxton
Timothy Slidel: MedImmune Ltd., Granta Park
Donna K. Finch: MedImmune Ltd., Granta Park
Robin Butler: MedImmune Ltd., Granta Park
Jamie Campbell: MedImmune Ltd., Granta Park
Laura Eghobamien: MedImmune Ltd., Granta Park
David Rider: MedImmune Ltd., Granta Park
Christine Mione Kiefer: MedImmune
Matthew J. Robinson: MedImmune Ltd., Granta Park
Colin Hardman: MedImmune Ltd., Granta Park
Fiona Cunningham: Wellcome Genome Campus, Hinxton
Tristan Vaughan: MedImmune Ltd., Granta Park
Paul Flicek: Wellcome Genome Campus, Hinxton
Catherine Chaillan Huntington: MedImmune Ltd., Granta Park

Nature Communications, 2018, vol. 9, issue 1, 1-12

Abstract: Abstract Selecting the most appropriate protein sequences is critical for precision drug design. Here we describe Haplosaurus, a bioinformatic tool for computation of protein haplotypes. Haplosaurus computes protein haplotypes from pre-existing chromosomally-phased genomic variation data. Integration into the Ensembl resource provides rapid and detailed protein haplotypes retrieval. Using Haplosaurus, we build a database of unique protein haplotypes from the 1000 Genomes dataset reflecting real-world protein sequence variability and their prevalence. For one in seven genes, their most common protein haplotype differs from the reference sequence and a similar number differs on their most common haplotype between human populations. Three case studies show how knowledge of the range of commonly encountered protein forms predicted in populations leads to insights into therapeutic efficacy. Haplosaurus and its associated database is expected to find broad applications in many disciplines using protein sequences and particularly impactful for therapeutics design.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-06542-1

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DOI: 10.1038/s41467-018-06542-1

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