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Functional Motif Discovery in Signaling Biology Using a Deep Sequence‐to‐Structure‐to‐Function Analysis
Author(s) -
Prokop Jeremy W
Publication year - 2016
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.30.1_supplement.969.31
Subject(s) - biology , computational biology , gene , structural motif , genetics , sequence analysis , biochemistry
Signaling proteins have been heavily studied; however, new functional regions are actively discovered. With genomic sequencing growing, gene annotation for more than 150 species is now available, but this information is rarely used to understand functional regions within signaling genes. We provide a systematic pipeline, known as the deep Sequence‐to‐Structure‐to‐Function Analysis, applied to the evolution of 16 genes ( AGT, REN, ATP6AP2, ACE, ENPEP, CMA1, AGTR1, AGTRAP, AGTR2, MME, ACE2, ERAP1, PRCP, MAS1, MRGPRD, LNPEP ) involved in the renin‐angiotensin system. Just under one million codons were analyzed with an average depth of 111 species for 59,710 codons for each gene. This allowed for a profile of deeply selected DNA codons that can be used to identify biologically important regions and motifs. This represents the largest analysis for the evolution of an endocrine system, providing the research community with alignments and structural models to analyze renin‐angiotensin signaling in a broad range of species. Using the evolutionary information combined with protein modeling, conserved regions were identified such as electrostatic surface interaction sites and the top three functional motifs in each of the 16 members. Many of these motifs represent known interaction sites, such as AGT‐REN interaction, but some elucidated novel posttranscriptional regulation and protein‐protein interaction sites that warrant further investigation. The pipeline has additionally been applied to signalling pathways such as leptin, melanocortin, and ephrin. This deep sequence‐to‐structure‐to‐function analysis is a promising pipeline to study additional endocrine signaling proteins in the future, seeking community input of additional signaling pathways to analyze. Support or Funding Information This work is supported by the National Institutes of Health, Office of the Director Big Data to Knowledge (BD2K) grant K01‐ES025435. Deep Sequence‐to‐Structure‐to‐Function Analysis (dSSFA) pipelineEach step is shown using AGT data as an example.