Dual Role of Strigolactone Receptor Signaling Partner in Inhibiting Substrate Hydrolysis

Mechanistic basis for enhanced strigolactone sensitivity in KAI2 triple mutant

Striga hermonthica is a parasitic weed that destroys billions of dollars' worth of staple crops every year. Its rapid proliferation stems from an enhanced ability to metabolize strigolactones (SLs), plant hormones that direct root branching and shoot growth. Striga's SL receptor, ShHTL7, bears more similarity to the staple crop karrikin receptor karrikin insensitive 2 (KAI2) than to SL receptor D14, though KAI2 variants in plants like Arabidopsis thaliana show minimal SL sensitivity. Recently, studies have indicated that a small number of point mutations to HTL7 residues can confer SL sensitivity to AtKAI2. Here, we analyze both wild-type AtKAI2 and SL-sensitive mutant Var64 through all-atom, long-timescale molecular dynamics simulations to determine the effects of these mutations on receptor function at a molecular level. We demonstrate that the mutations stabilize SL binding by about 2 kcal/mol. They also result in a doubling of the average pocket volume and eliminate the dependence of binding on certain pocket conformational arrangements. Although the probability of certain nonbinding SL-receptor interactions increases in the mutant compared with the wild-type, the rate of binding also increases by a factor of 10. All these changes account for the increased SL sensitivity in mutant KAI2 and suggest mechanisms for increasing the functionality of host crop SL receptors.

Mechanistic Basis for Enhanced Strigolactone Sensitivity in KAI2 Triple Mutant

Striga hermonthica is a parasitic weed that destroys billions of dollars' worth of staple crops every year. Its rapid proliferation stems from an enhanced ability to metabolize strigolactones (SLs), plant hormones that direct root branching and shoot growth. Striga's SL receptor, ShHTL7, bears more similarity to the staple crop karrikin receptor KAI2 than to SL receptor D14, though KAI2 variants in plants like Arabidopsis thaliana show minimal SL sensitivity. Recently, studies have indicated that a small number of point mutations to HTL7 residues can confer SL sensitivity to AtKAI2. Here, we analyze both wild-type AtKAI2 and SL-sensitive mutant Var64 through all-atom, long-timescale molecular dynamics simulations to determine the effects of these mutations on receptor function at a molecular level. We demonstrate that the mutations stabilize SL binding by about 2 kcal/mol. They also result in a doubling of the average pocket volume, and eliminate the dependence of binding on certain pocket conformational arrangements. While the probability of certain non-binding SL-receptor interactions increases in the mutant compared with the wild-type, the rate of binding also increases by a factor of ten. All these changes account for the increased SL sensitivity in mutant KAI2, and suggest mechanisms for increasing functionality of host crop SL receptors.

Perception of strigolactones and the coordinated phytohormonal regulation on rice (Oryza sativa) tillering is affected by endogenous ascorbic acid

Phytohormones play a key role in regulating tiller number. Ascorbic acid (Asc)-phytohormone interaction plays a pivotal role in the regulation of senescence. We analysed the relationship between Asc and the enzyme concentrations and gene transcript abundances related to the signal perception of strigolactones (SLs), the contents of four phytohormones (abscisic acid, ABA; jasmonic acid, JA; indole acetic acid, IAA; cytokinin, CTK), the enzyme concentrations and gene transcript abundances related to the synthesis or transportation of these four phytohormones. Our results showed that Asc deficiency leads to the upregulation of enzyme concentrations, gene transcript abundances related to the SL signal perception, ABA synthesis and IAA transport. The altered level of Asc also leads to a change in the contents of ABA, JA, IAA and CTK. These findings support the conclusion that Asc or Asc/DHA play an important role in the signal perception and transduction of SLs, and Asc may affect the coordinated regulation of SL, IAA and CTK on rice (Oryza sativa ) tillering.

Strigolactones: diversity, perception, and hydrolysis

Strigolactones (SLs) are a unique and novel class of phytohormones that regulate numerous processes of growth and development in plants. Besides their endogenous functions as hormones, SLs are exuded by plant roots to stimulate critical interactions with symbiotic fungi but can also be exploited by parasitic plants to trigger their seed germination. In the past decade, since their discovery as phytohormones, rapid progress has been made in understanding the SL biosynthesis and signaling pathway. Of particular interest are the diversification of natural SLs and their exact mode of perception, selectivity, and hydrolysis by their dedicated receptors in plants. Here we provide an overview of the emerging field of SL perception with a focus on the diversity of canonical, non-canonical, and synthetic SL probes. Moreover, this review offers useful structural insights into SL perception, the precise molecular adaptations that define receptor-ligand specificities, and the mechanisms of SL hydrolysis and its attenuation by downstream signaling components.

Integration of machine learning with computational structural biology of plants

Computational structural biology of proteins has developed rapidly in recent decades with the development of new computational tools and the advancement of computing hardware. However, while these techniques have widely been used to make advancements in human medicine, these methods have seen less utilization in the plant sciences. In the last several years, machine learning methods have gained popularity in computational structural biology. These methods have enabled the development of new tools which are able to address the major challenges that have hampered the wide adoption of the computational structural biology of plants. This perspective examines the remaining challenges in computational structural biology and how the development of machine learning techniques enables more in-depth computational structural biology of plants.