Activation Mechanism of Strigolactone Receptors and Its Impact on Ligand Selectivity between Host and Parasitic Plants

Contribution of Signaling Partner Association to Strigolactone Receptor Selectivity

The parasitic plant witchweed, Striga hermonthica, results in agricultural losses of billions of dollars per year. It perceives its host via plant hormones called strigolactones, which act as germination stimulants for witchweed. Strigolactone signaling involves substrate binding to the strigolactone receptor, followed by substrate hydrolysis and a conformational change from an inactive, or open state, to an active, or closed state. In the active state, the receptor associates with a signaling partner, MAX2. Recently, it was shown that this MAX2 association process acts as a strong contributor to the uniquely high signaling activity observed in ShHTL7; however, it is unknown why ShHTL7 has enhanced MAX2 association affinity. Using an umbrella sampling molecular dynamics approach, we characterized the association processes of AtD14, ShHTL7, a mutant of ShHTL7, and ShHTL6 with MAX2 homologue OsD3. From these results, we show that ShHTL7 has an enhanced standard binding free energy of OsD3 compared to those of the other receptors. Additionally, our results suggest that the overall topology of the T2/T3 helix region is likely an important modulator of MAX2 binding. Thus, differences in MAX2 association, modulated by differences in the T2/T3 helix region, are a contributor to differences in signaling activity between different strigolactone receptors.

Effect of histidine covalent modification on strigolactone receptor activation and selectivity

The parasitic weed Striga has led to billions of dollars' worth of agricultural productivity loss worldwide. Striga detects host plants using compounds of the strigolactone class of phytohormones. Early steps in the strigolactone signaling pathway involve substrate binding and hydrolysis followed by a conformational change to an "active" or "closed" state, after which it associates with a MAX2-family downstream signaling partner. The structures of the inactive and active states of strigolactone receptors are known through X-ray crystallography, and the transition pathway from the inactive to active state in apo receptors has previously been characterized using molecular dynamics simulations. However, it also has been suggested that a covalent butenolide modification of the receptor on the catalytic histidine through substrate hydrolysis promotes formation of the active state. Using molecular dynamics simulations, we show that the presence of the covalent butenolide enhances activation in both AtD14, a receptor found in Arabidopsis, and ShHTL7, a receptor found in Striga, but the enhancement is ∼50 times greater in ShHTL7. We also show that several conserved interactions with the covalent butenolide modification promote transition to the active state in both AtD14 (non-parasite) and ShHTL7 (parasite). Finally, we demonstrate that the enhanced activation of ShHTL7 likely results from disruption of ShHTL7-specific histidine interactions that inhibited activation in the apo case. These results provide a possible explanation for difference in strigolactone sensitivity seen between different strigolactone-sensitive proteins and can be used to aid the design of selective modulators to control Striga parasites.

Karrikin perception and signalling

Karrikins (KARs) are a class of butenolide compounds found in smoke that were first identified as seed germination stimulants for fire-following species. Early studies of KARs classified the germination and postgermination responses of many plant species and investigated crosstalk with plant hormones that regulate germination. The discovery that Arabidopsis thaliana responds to KARs laid the foundation for identifying mutants with altered KAR responses. Genetic analysis of KAR signalling revealed an unexpected link to strigolactones (SLs), a class of carotenoid-derived plant hormones. Substantial progress has since been made towards understanding how KARs are perceived and regulate plant growth, in no small part due to advances in understanding SL perception. KAR and SL signalling systems are evolutionarily related and retain a high degree of similarity. There is strong evidence that KARs are natural analogues of an endogenous signal(s), KAI2 ligand (KL), which remains unknown. KAR/KL signalling regulates many developmental processes in plants including germination, seedling photomorphogenesis, and root and root hair growth. KAR/KL signalling also affects abiotic stress responses and arbuscular mycorrhizal symbiosis. Here, we summarise the current knowledge of KAR/KL signalling and discuss current controversies and unanswered questions in this field.

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.

Expansion of the Strigolactone Profluorescent Probes Repertory: The Right Probe for the Right Application

Strigolactones (SLs) are intriguing phytohormones that not only regulate plant development and architecture but also interact with other organisms in the rhizosphere as root parasitic plants (Striga, Orobanche, and Phelipanche) and arbuscular mycorrhizal fungi. Starting with a pioneering work in 2003 for the isolation and identification of the SL receptor in parasitic weeds, fluorescence labeling of analogs has proven a major strategy to gain knowledge in SL perception and signaling. Here, we present novel chemical tools for understanding the SL perception based on the enzymatic properties of SL receptors. We designed different profluorescent SL Guillaume Clavé (GC) probes and performed structure-activity relationship studies on pea, Arabidopsis thaliana, and Physcomitrium (formerly Physcomitrella) patens. The binding of the GC probes to PsD14/RMS3, AtD14, and OsD14 proteins was tested. We demonstrated that coumarin-based profluorescent probes were highly bioactive and well-adapted to dissect the enzymatic properties of SL receptors in pea and a resorufin profluorescent probe in moss, contrary to the commercially available fluorescein profluorescent probe, Yoshimulactone Green (YLG). These probes offer novel opportunities for the studies of SL in various plants.

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.