Role of substrate recognition in modulating strigolactone receptor selectivity in witchweed

2022

Preprint

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The parasitic plant witchweed, or Striga hermonthica, results in agricultural losses of billions of dollars per year. It perceives its host via plant hormones called strigolactones, which acts as a germination stimulant 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. While 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 homolog OsD3. From these results, we show that ShHTL7 has an enhanced standard binding free energy of OsD3 compared to 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.

Section: Discussionmentioning

confidence: 60%

Contribution of Signaling Partner Association to Strigolactone Receptor Selectivity

Chen

2022

Preprint

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“…In light of these disagreements over details of the strigolactone hydrolysis mechanism, this work aims to (i) determine whether the reaction predominantly occurs via the canonical model in which hydrolysis is initiated via nucleophilic attack on the D-ring (acyl substitution) or the alternatively proposed Michael addition mechanism, and (ii) determine the dominant hydrolysis-induced covalent modification to the receptor that acts as a promoter of activation and downstream signaling. In our previous work, we have used long-timescale molecular dynamics simulations to characterize substrate recognition and conformational dynamics of strigolactone receptors 11,15 . However, a limitation to classical molecular dynamics is that they are unable to capture changes in covalent bonds such as those involved in chemical reactions.…”

Section: Introductionmentioning

confidence: 99%

“…In our previous work, we have used long-timescale molecular dynamics simulations to characterize substrate recognition and conformational dynamics of strigolactone receptors 11,15 . However, a limitation to classical molecular dynamics is that they are unable to capture changes in covalent bonds such as those involved in chemical reactions.…”

Section: Introductionmentioning

confidence: 99%

Multiple modes of substrate hydrolysis-induced covalent modification of strigolactone receptors

Chen

2022

Preprint

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The strigolactone signaling pathway in plants is unconventional among plant hormone signaling pathways in that the receptor also acts as an enzyme that hydrolyzes the strigolactone substrate. While the canonical view of strigolactone hydrolysis is that it occurs via a nucleophilic attack on the butenolide ring of strigolactone, an alternative Michael addition mechanism in which hydrolysis occurs via a nucleophilic attack on the enol-ether bridge has been proposed. Furthermore, while it is known that a hydrolysis-induced covalent modification to the receptor promotes strigolactone receptor activation, the nature of this covalent modification has been disputed. Here, we employ QM/MM string method simulations to determine the favored pathway of strigolactone hydrolysis and the nature of the covalent modification that acts as a promoter of strigolactone receptor activation. Our simulations show that strigolactone hydrolysis occurs via an acyl substitution pathway beginning with nucleophilic attack on the butenolide ring, which is well corroborated by previous experimental literature. Additionally, we show that multiple possible modes of covalent modifications to the catalytic residues by the butenolide ring are able to form and interconvert, reconciling several seemingly conflicting views on the hydrolysis-induced covalent modification to strigolactone receptors.

Section: Introductionmentioning

confidence: 99%

“…However, cellular processes of interest occur at slower time scales, such as protein folding, 3 activation of signalling proteins, 4,5 transport across transmembrane proteins, [6][7][8] and ligand binding. 9,10 To alleviate this problem, numerous enhanced sampling methods have been proposed. 11 The shared goal of these techniques is to reduce the required run time to obtain adequate sampling of the system at hand.…”

Section: Introductionmentioning

confidence: 99%

Multi-Agent Reinforcement Learning-based Adaptive Sampling for Conformational Sampling of Proteins

Kleiman

2022

Preprint

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Machine Learning is increasingly applied to improve the efficiency and accuracy of Molecular Dynamics (MD) simulations. Although the growth of distributed computer clusters has allowed researchers to obtain higher amounts of data, unbiased MD simulations have difficulty sampling rare states, even under massively parallel adaptive sampling schemes. To address this issue, several algorithms inspired by reinforcement learning (RL) have arisen to promote exploration of the slow collective variables (CVs) of complex systems. Nonetheless, most of these algorithms are not well-suited to leverage the information gained by simultaneously sampling a system from different initial states (e.g., a protein in different conformations associated with distinct functional states). To fill this gap, we propose two algorithms inspired by multi-agent RL that extend the functionality of closely-related techniques (REAP and TSLC) to situations where the sampling can be accelerated by learning from different regions of the energy landscape through coordinated agents. Essentially, the algorithms work by remembering which agent discovered each conformation and sharing this information with others at the action-space discretization step. A stakes function is introduced to modulate how different agents sense rewards from discovered states of the system. The consequences are threefold: (i) agents learn to prioritize CVs using only relevant data, (ii) redundant exploration is reduced, and (iii) agents that obtain higher stakes are assigned more actions. We compare our algorithm with other adaptive sampling techniques (Least Counts, REAP, TSLC, and AdaptiveBandit) to show and rationalize the gain in performance.