Dirigent isoflavene-forming PsPTS2: 3D structure, stereochemical, and kinetic characterization comparison with pterocarpan-forming PsPTS1 homolog in pea.

Pea phytoalexins (-)-maackiain and (+)-pisatin have opposite C6a/C11a configurations, but biosynthetically how this occurs is unknown. Pea dirigent-protein (DP) PsPTS2 generates 7,2'-dihydroxy-4',5'-methylenedioxyisoflav-3-ene (DMDIF), and stereoselectivity toward four possible 7,2'-dihydroxy-4',5'-methylenedioxyisoflavan-4-ol (DMDI) stereoisomers was investigated. Stereoisomer configurations were determined using NMR spectroscopy, electronic circular dichroism, and molecular orbital analyses. PsPTS2 efficiently converted cis-(3R,4R)-DMDI into DMDIF 20-fold faster than the trans-(3R,4S)-isomer. The 4R-configured substrate's near ß-axial OH orientation significantly enhanced its leaving group abilities in generating A-ring mono-quinone methide (QM), whereas 4S-isomer's α-equatorial-OH was a poorer leaving group. Docking simulations indicated that the 4R-configured ß-axial OH was closest to Asp51, whereas 4S-isomer's α-equatorial OH was further away. Neither cis-(3S,4S)- nor trans-(3S,4R)-DMDIs were substrates, even with the former having C3/C4 stereochemistry as in (+)-pisatin. PsPTS2 used cis-(3R,4R)-7,2'-dihydroxy-4'-methoxyisoflavan-4-ol [cis-(3R,4R)-DMI] and C3/C4 stereoisomers to give 2',7-dihydroxy-4'-methoxyisoflav-3-ene (DMIF). DP homologs may exist in licorice (Glycyrrhiza pallidiflora) and tree legume Bolusanthus speciosus, as DMIF occurs in both species. PsPTS1 utilized cis-(3R,4R)-DMDI to give (-)-maackiain 2200-fold more efficiently than with cis-(3R,4R)-DMI to give (-)-medicarpin. PsPTS1 also slowly converted trans-(3S,4R)-DMDI into (+)-maackiain, reflecting the better 4R configured OH leaving group. PsPTS2 and PsPTS1 provisionally provide the means to enable differing C6a and C11a configurations in (+)-pisatin and (-)-maackiain, via identical DP-engendered mono-QM bound intermediate generation, which PsPTS2 either re-aromatizes to give DMDIF or PsPTS1 intramolecularly cyclizes to afford (-)-maackiain. Substrate docking simulations using PsPTS2 and PsPTS1 indicate cis-(3R,4R)-DMDI binds in the anti-configuration in PsPTS2 to afford DMDIF, and the syn-configuration in PsPTS1 to give maackiain.

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.