During the past 30 yr an impasse has developed in the discovery and commercialization of synthetic herbicides with new molecular targets and novel chemistries. Similarly, there has been little success with bioherbicides, both microbial and chemical. These bioherbicides are needed to combat fast-growing herbicide resistance and to fulfill the need for more environmentally and toxicologically safe herbicides. In response to this substantial and growing opportunity, numerous start-up companies are utilizing novel approaches to provide new tools for weed management. These diverse new tools broaden the scope of discovery, encompassing advanced computational, bioinformatic, and imaging platforms; plant genome–editing and targeted protein degradation technologies; and machine learning and artificial intelligence (AI)-based strategies. This review contains summaries of the presentations of 10 such companies that took part in a symposium held at the WSSA annual meeting in 2024. Four of the companies are developing microbial bioherbicides or natural product–based herbicides, and the other six are using advanced technologies, such as AI, to accelerate the discovery of herbicides with novel molecular target sites or to develop non-GMO, herbicide-resistant crops.

Methionine is an essential amino acid in mammals and a precursor for vital metabolites required for the survival of all organisms. Consequently, its inclusion is required in diverse applications, such as food, feed, and pharmaceuticals. Although amino acids and other metabolites are commonly produced through microbial fermentation, high-yield biosynthesis of L-methionine remains a significant challenge due to the strict cellular regulation of the biosynthesis pathway. As a result, methionine is produced primarily synthetically, resulting in a racemic mixture of D,L-methionine. This study explores methionine bio-production in E. coli by replacing its native trans-sulfurylation pathway with the more common direct-sulfurylation pathway used by other bacteria. To this end, we generated a methionine auxotroph E. coli strain (MG1655) by simultaneously deleting metA and metB genes and complementing them with metX and metY from different bacteria. Complementation of the genetically modified E. coli with metX/metY from Cyclobacterium marinum or Deinococcus geothermalis, together with the deletion of the global repressor metJ and overexpression of the transporter yjeH, resulted in a substantial increase of up to 126 and 160-fold methionine relative to the wild-type strain, respectively, and accumulation of up to 700 mg/L using minimal MOPS medium and 2 ml culture. Our findings provide a method to study methionine biosynthesis and a chassis for enhancing L-methionine production by fermentation.

Protein-protein interactions (PPI) play a critical role in regulating many cellular processes. Finding novel PPI inhibitors that interfere with specific binding of two proteins is considered a great challenge, mainly due to the complexity involved in characterizing multi-molecular systems and limited understanding of the physical principles governing PPIs. Here we show that the combination of virtual screening techniques, which are capable of filtering a large library of potential small molecule inhibitors, and a unique secondary screening by isothermal titration calorimetry, a label-free method capable of observing direct interactions, is an efficient tool for finding such an inhibitor. In this study we applied this strategy in a search for a small molecule capable of interfering with the interaction of the tumor-suppressor p53 and the E3-ligase MDM2. We virtually screened a library of 15 million small molecules that were filtered to a final set of 80 virtual hits. Our in vitro experimental assay, designed to validate the activity of mixtures of compounds by isothermal titration calorimetry, was used to identify an active molecule against MDM2. At the end of the process the small molecule (4S,7R)-4-(4-chlorophenyl)-5-hydroxy-2,7-dimethyl-N-(6-methylpyridin-2-yl)-4,6,7,8 tetrahydrIoquinoline-3-carboxamide was found to bind MDM2 with a dissociation constant of ~2 µM. Following the identification of this single bioactive compound, spectroscopic measurements were used to further characterize the interaction of the small molecule with the target protein. 2D NMR spectroscopy was used to map the binding region of the small molecule, and fluorescence polarization measurement confirmed that it indeed competes with p53.

Collagen is the most abundant protein in various mammalian tissues and has an essential role in various cellular processes. Collagen is necessary for food-related biotechnological applications such as cultivated meat, medical engineering, and cosmetics. High-yield expression of natural collagen from mammalian cells is challenging and not cost-effective. Thus, external collagen is obtained primarily from animal tissues. Under cellular hypoxia, overactivation of the transcription factor hypoxia-inducible factor (HIF) was shown to correlate with enhanced accumulation of collagen. Herein, we showed that the small molecule ML228, a known molecular activator of HIF, enhances the accumulation of collagen type-I in human fibroblast cells. We report an increase in collagen levels by 2.33 ± 0.33 when fibroblasts were incubated with 5 μM of ML228. Our experimental results demonstrated, for the first time, that external modulation of the hypoxia biological pathway can boost collagen levels in mammalian cells. Our findings pave the way for enhancing natural collagen production in mammals by altering cellular signaling pathways.

Design of peptide binders is an attractive strategy for targeting “undruggable” protein-protein interfaces. Current design protocols rely on the extraction of an initial sequence from one known protein interactor of the target protein, followed by in-silico or in-vitro mutagenesis-based optimization of its binding affinity. Wet lab protocols can explore only a minor portion of the vast sequence space and cannot efficiently screen for other desirable properties such as high specificity and low toxicity, while in-silico design requires intensive computational resources and often relies on simplified binding models. Yet, for a multivalent protein target, dozens to hundreds of natural protein partners already exist in the cellular environment. Here, we describe a peptide design protocol that harnesses this diversity via a machine learning generative model. After identifying putative natural binding fragments by literature and homology search, a compositional Restricted Boltzmann Machine is trained and sampled to yield hundreds of diverse candidate peptides. The latter are further filtered via flexible molecular docking and an in-vitro microchip-based binding assay. We validate and test our protocol on calcineurin, a calcium-dependent protein phosphatase involved in various cellular pathways in health and disease. In a single screening round, we identified multiple 16-length peptides with up to six mutations from their closest natural sequence that successfully interfere with the binding of calcineurin to its substrates. In summary, integrating protein interaction and sequence databases, generative modeling, molecular docking and interaction assays enables the discovery of novel protein-protein interaction modulators.