Date Published: May 7, 2019
Publisher: Public Library of Science
Author(s): Yan Li, Michael D. Netherland, Chaoyang Zhang, Huixiao Hong, Ping Gong, Wenfei Li.
Mutations that confer herbicide resistance are a primary concern for herbicide-based chemical control of invasive plants and are often under-characterized structurally and functionally. As the outcome of selection pressure, resistance mutations usually result from repeated long-term applications of herbicides with the same mode of action and are discovered through extensive field trials. Here we used acetohydroxyacid synthase (AHAS) of Kochia scoparia (KsAHAS) as an example to demonstrate that, given the sequence of a target protein, the impact of genetic mutations on ligand binding could be evaluated and resistance mutations could be identified using a biophysics-based computational approach. Briefly, the 3D structures of wild-type (WT) and mutated KsAHAS-herbicide complexes were constructed by homology modeling, docking and molecular dynamics simulation. The resistance profile of two AHAS-inhibiting herbicides, tribenuron methyl and thifensulfuron methyl, was obtained by estimating their binding affinity with 29 KsAHAS (1 WT and 28 mutated) using 6 molecular mechanical (MM) and 18 hybrid quantum mechanical/molecular mechanical (QM/MM) methods in combination with three structure sampling strategies. By comparing predicted resistance with experimentally determined resistance in the 29 biotypes of K. scoparia field populations, we identified the best method (i.e., MM-PBSA with single structure) out of all tested methods for the herbicide-KsAHAS system, which exhibited the highest accuracy (up to 100%) in discerning mutations conferring resistance or susceptibility to the two AHAS inhibitors. Our results suggest that the in silico approach has the potential to be widely adopted for assessing mutation-endowed herbicide resistance on a case-by-case basis.
Acetohydroxyacid synthase (AHAS, also known as acetolactate synthase or ALS) is a group of biosynthetic enzymes found in all plants, fungi, and bacteria (but absent in animals and humans). AHAS is a key enzyme that catalyzes the formation of acetolactate and acetohydroxybutyrate from pyruvate and 2-ketobutyrate [1, 2]. This is the first step in biosynthesis of the essential branched-chain amino acids (valine, leucine, and isoleucine), which are critical for all forms of life. AHAS has long been an attractive target in the development of herbicides, fungicides, and antimicrobials because its inhibitors have a low toxicity to mammals while still being highly selective and very potent . AHAS-inhibiting herbicides are the largest site-of-action group on the market, with more than 50 chemicals belonging to five classes (sulfonylaminocarbonyltriazolinones, triazolopyrimidines, pyrimidinyl(thio)benzoate, sulfonylureas, and imidazolinones) and sulfonylureas being the majority . However, persistent use of herbicides has exerted intense selection pressure on a great variety of weed species and resulted in the evolution of resistance . In the most common mechanism, resistance is conferred by alteration of amino acids in the target site that attenuates the sensitivity to target-specific herbicides [6, 7]. The magnitude of herbicide resistance depends on weed species, structural change induced by mutation, and the type of herbicide. For a specific herbicide, a given mutation may endow moderate to high resistance [7, 8] or, in rare instances, an increase in sensitivity to the herbicide in different species . In the current practice of weed control, resistance mutations may be discovered only after repeated failure of herbicide application. Therefore, there is a strong and urgent demand for a reliable and systematic approach for determining resistance profiles of different herbicides that are in use or have been newly developed before commencing weed treatment. Compared to wet lab-based experiments and techniques, computational approaches provide a rapid and cost-effective solution to screen and detect resistance mutations.
It is noteworthy that there was a large variation in the discerning ability of the tested methods on the basis of single structure (Fig 4A and 4B). MM-PBSA combined with single structure was the frontrunner among all approaches, but some QM/MM GBSA methods with the same sampling strategy led to the worst performance in this work. By contrast, the discriminating power remained stable across different approaches based on an ensemble of structures from either classical or QM/MM MD simulations. It was also observed that the ability of QM/MM-GBSA to distinguish resistance mutations depended on the GB model and SQM correction. Here we further discuss how sampling techniques (i.e., single structure, classical MD, and QM/MM MD), GB models (GBOBC and GBn), and SQM corrections (D and DH) affected identification of resistance mutations using different methods for binding affinity calculation.
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