Combinatorial interactions among natural structural variants of Brassica SOC1 promoters and SVP depict conservation of binding affinity despite molecular diversity

Abstract

Abstract Genes constituting floral regulatory network can be targeted to generate climate resilient, early flowering crops. SOC1, a central integrator of flowering, is downregulated by SVP. In highly duplicated, amphidiploid genome of Brassica juncea, flowering is plausibly mediated by combinatorial interactions among natural variants of multiple SOC1 promoters and SVP. Although fluctuating temperatures can influence energetics of molecular interactions, a mechanistic view on how these impact phenotypes remains unexplored. Analysis of binding patterns of biomolecules thus underpin new paradigms for precision trait engineering. Herein, we characterize 9 natural variants (homeologs and isoforms) of B. juncea SVP differing in MIKC domains. Generation and characterization of refined models of 15 SVP proteins (natural and hypothetical) and 3 SOC1 promoter fragments revealed extensive structural diversity. Despite this, binding affinity of 48 docked complexes were comparable except in cases where truncated proteins were involved. Investigation of 27 docked complexes for distribution and type of molecular contacts (π-π stacking, hydrophobic interactions, Van-der-Waals forces, Hydrogen bonds); shared or unique interacting patterns, revealed substantial variation suggesting involvement of compensatory mutations for preserving binding. Yeast one-hybrid assays validated binding potential predicted in docked complexes. Conserved amino-acid residues and nucleotides involved in non-covalent interactions were identified. Computational alanine substitution, established the cruciality of amino-acid hotspots conferring stability to docked complexes. Our study is relevant from an application standpoint. Identification of conserved amino-acid hotspots is essential for rational protein design since targeted mutagenesis of these can modify natural binding spectrum of regulatory proteins, and is a way forward for trait engineering.