Profiling sorghum-microbe interactions with a specialized photoaffinity probe identifies key sorgoleone binders in Acinetobacter pittii.

Interactions between plants and soil microbial communities that benefit plant growth and enhance nutrient acquisition are driven by the selective release of metabolites from plant roots, or root exudation. To investigate these plant-microbe interactions, we developed a photoaffinity probe based on sorgoleone (sorgoleone diazirine alkyne for photoaffinity labeling, SoDA-PAL), a hydrophobic secondary metabolite and allelochemical produced in Sorghum bicolor root exudates. We applied SoDA-PAL to the identification of sorgoleone-binding proteins in Acinetobacter pittii SO1, a potential plant growth-promoting microbe isolated from sorghum rhizosphere soil. Competitive photoaffinity labeling of A. pittii whole cell lysates with SoDA-PAL identified 137 statistically enriched proteins, including putative transporters, transcriptional regulators, and a subset of proteins with predicted enzymatic functions. We performed computational protein modeling and docking with sorgoleone to prioritize candidates for experimental validation and then confirmed binding of sorgoleone to four of these proteins in vitro: the α/ß fold hydrolase SrgB (OH685_09420), a fumarylacetoacetase (OH685_02300), a lysophospholipase (OH685_14215), and an unannotated hypothetical protein (OH685_18625). Our application of this specialized sorgoleone-based probe coupled with structural bioinformatics streamlines the identification of microbial proteins involved in metabolite recognition, metabolism, and toxicity, widening our understanding of the range of cellular pathways that can be affected by a plant secondary metabolite.IMPORTANCEHere, we demonstrate that a photoaffinity-based chemical probe modeled after sorgoleone, an important secondary metabolite released by sorghum roots, can be used to identify microbial proteins that directly interact with sorgoleone. We applied this probe to the sorghum-associated bacterium Acinetobacter pittii and showed that probe labeling is dose-dependent and sensitive to competition with purified sorgoleone. Coupling the probe with proteomics and computational analysis facilitated the identification of putative sorgoleone binders, including a protein implicated in a conserved pathway essential for sorgoleone catabolism. We anticipate that discoveries seeded by this workflow will expand our understanding of the molecular mechanisms by which specific metabolites in root exudates shape the sorghum rhizosphere microbiome.

Leveraging Artificial Intelligence to Expedite Antibody Design and Enhance Antibody-Antigen Interactions.

This perspective sheds light on the transformative impact of recent computational advancements in the field of protein therapeutics, with a particular focus on the design and development of antibodies. Cutting-edge computational methods have revolutionized our understanding of protein-protein interactions (PPIs), enhancing the efficacy of protein therapeutics in preclinical and clinical settings. Central to these advancements is the application of machine learning and deep learning, which offers unprecedented insights into the intricate mechanisms of PPIs and facilitates precise control over protein functions. Despite these advancements, the complex structural nuances of antibodies pose ongoing challenges in their design and optimization. Our review provides a comprehensive exploration of the latest deep learning approaches, including language models and diffusion techniques, and their role in surmounting these challenges. We also present a critical analysis of these methods, offering insights to drive further progress in this rapidly evolving field. The paper includes practical recommendations for the application of these computational techniques, supplemented with independent benchmark studies. These studies focus on key performance metrics such as accuracy and the ease of program execution, providing a valuable resource for researchers engaged in antibody design and development. Through this detailed perspective, we aim to contribute to the advancement of antibody design, equipping researchers with the tools and knowledge to navigate the complexities of this field.

Machine Learning Models for Predicting Molecular UV-Vis Spectra with Quantum Mechanical Properties.

Accurate understanding of ultraviolet-visible (UV-vis) spectra is critical for the high-throughput synthesis of compounds for drug discovery. Experimentally determining UV-vis spectra can become expensive when dealing with a large quantity of novel compounds. This provides us an opportunity to drive computational advances in molecular property predictions using quantum mechanics and machine learning methods. In this work, we use both quantum mechanically (QM) predicted and experimentally measured UV-vis spectra as input to devise four different machine learning architectures, UVvis-SchNet, UVvis-DTNN, UVvis-Transformer, and UVvis-MPNN, and assess the performance of each method. We find that the UVvis-MPNN model outperforms the other models when using optimized 3D coordinates and QM predicted spectra as input features. This model has the highest performance for predicting UV-vis spectra with a training RMSE of 0.06 and validation RMSE of 0.08. Most importantly, our model can be used for the challenging task of predicting differences in the UV-vis spectral signatures of regioisomers.

Bayesian Inference for Integrating Yarrowia lipolytica Multiomics Datasets with Metabolic Modeling.

Optimizing the metabolism of microbial cell factories for yields and titers is a critical step for economically viable production of bioproducts and biofuels. In this process, tuning the expression of individual enzymes to obtain the desired pathway flux is a challenging step, in which data from separate multiomics techniques must be integrated with existing biological knowledge to determine where changes should be made. Following a design-build-test-learn strategy, building on recent advances in Bayesian metabolic control analysis, we identify key enzymes in the oleaginous yeast Yarrowia lipolytica that correlate with the production of itaconate by integrating a metabolic model with multiomics measurements. To this extent, we quantify the uncertainty for a variety of key parameters, known as flux control coefficients (FCCs), needed to improve the bioproduction of target metabolites and statistically obtain key correlations between the measured enzymes and boundary flux. Based on the top five significant FCCs and five correlated enzymes, our results show phosphoglycerate mutase, acetyl-CoA synthetase (ACSm), carbonic anhydrase (HCO3E), pyrophosphatase (PPAm), and homoserine dehydrogenase (HSDxi) enzymes in rate-limiting reactions that can lead to increased itaconic acid production.