The development of useful synthetic tools to label amino acids within a peptide framework for the ultimate modification of proteins in a late-stage fashion is a challenging task of utmost importance within chemical biology. Herein, we report the first Pd-catalyzed C-H acylation of a collection of Tyr-containing peptides with aldehydes. This water-compatible tagging technique is distinguished by its site-specificity, scalability and full tolerance of sensitive functional groups. Remarkably, it provides straightforward access to a high number of oligopeptides with altered side-chain topology including mimetics of endomorphin-2 and neuromedin N, thus illustrating its promising perspectives toward the diversification of structurally complex peptides and chemical ligation.Siderophores play a vital role in the viability of fungi and are essential for the virulence of many pathogenic fungal species. NEM inhibitor Despite their importance in fungal physiology and pathogenesis, the programming rule of siderophore assembly by fungal nonribosomal peptide synthetases (NRPSs) remains unresolved. Here, we report the characterization of the bimodular fungal NRPS, SidD, responsible for construction of the extracellular siderophore fusarinine C. The use of intact protein mass spectrometry, together with in vitro biochemical assays of native and dissected enzymes, provided snapshots of individual biosynthetic steps during NPRS catalysis. The adenylation and condensation domain of SidD can iteratively load and condense the amino acid building block cis-AMHO, respectively, to synthesize fusarinine C. Our study showcases the iterative programming features of fungal siderophore-producing NRPSs.Supramolecular aggregates of synthetic dye molecules offer great perspectives to prepare biomimetic functional materials for light-harvesting and energy transport. The design is complicated by the fact that structure-property relationships are hard to establish, because the molecular packing results from a delicate balance of interactions and the excitonic properties that dictate the optics and excited state dynamics, in turn sensitively depend on this packing. Here we show how an iterative multiscale approach combining molecular dynamics and quantum mechanical exciton modeling can be used to obtain accurate insight into the packing of thousands of cyanine dye molecules in a complex double-walled tubular aggregate in close interaction with its solvent environment. Our approach allows us to answer open questions not only on the structure of these prototypical aggregates, but also about their molecular-scale structural and energetic heterogeneity, as well as on the microscopic origin of their photophysical properties. This opens the route to accurate predictions of energy transport and other functional properties.Herein, we report a hierarchical assembly strategy for constructing heterogeneous half-sandwich organometallic D-A (D = π-donor, A = π-acceptor) interlocked structures, and their application in near-infrared (NIR) photothermal conversion. Thienothiophene and diketopyrrolopyrrole groups were selected as the D and A units, leading to two homogeneous metalla[2]catenanes with D-D-D-D and A-A-A-A stacks, respectively. By the ordered secondary assembly of homogeneous metalla[2]catenanes, two unprecedented heterogeneous D-A metalla[2]catenanes comprising an unusual mixed D-A-D-D and unconventional D-A-A-A stacks were realized by the combination of multiple noncovalent interactions, as all demonstrated by a detailed X-ray crystallographic study. Benefiting from the mixed D-A stacking modes, NIR absorption of heterogeneous D-A metalla[2]catenanes is significantly enhanced in contrast to homogeneous metalla[2]catenanes. Thanks to the enhanced NIR absorption and the fluorescence quenching effect from half-sandwich organometallic fragments, heterogeneous D-A metalla[2]catenanes displayed high-performance NIR photothermal conversion properties (η = 27.3%).In-depth structural analysis of biorefined lignin is imperative to understand its physicochemical properties, essential for its efficient valorization to renewable materials and chemicals. Up to now, research on Reductive Catalytic Fractionation (RCF) of lignocellulose biomass, an emerging biorefinery technology, has strongly focused on the formation, separation and quantitative analysis of the abundant lignin-derived phenolic monomers. However, detailed structural information on the linkages in RCF lignin oligomers, constituting up to 50 wt% of RCF lignin, and their quantification, is currently lacking. This study discloses new detailed insights into the pine wood RCF lignin oil's molecular structure through the combination of fractionation and systematic analysis, resulting in the first assignment of the major RCF-derived structural units in the 1H-13C HSQC NMR spectrum of the RCF oligomers. Specifically, β-5 γ-OH, β-5 ethyl, β-1 γ-OH, β-1 ethyl, β-β 2x γ-OH, β-β THF, and 5-5 inter-unit linkages were assigned unambiguously, resulting in the quantification of over 80% of the lignin inter-unit linkages and end-units. Detailed inspection of the native lignin inter-unit linkages and their conversion reveals the occurring hydrogenolysis chemistry and the unambiguous proof of absence of lignin fragment condensation during proper RCF processing. Overall, the study offers an advanced analytical toolbox for future RCF lignin conversion and lignin structural analysis research, and valuable insights for lignin oil valorization purposes.A catalytic asymmetric conjugate addition/Schmidt-type rearrangement of vinyl azides and (E)-alkenyloxindoles was realized. It afforded a variety of optically active 3,2'-pyrrolinyl spirooxindoles with high yields (up to 98%), and excellent diastereo- and enantioselectivities (up to 98% ee, >19  1 dr), even at the gram-scale in the presence of a chiral N,N'-dioxide-nickel(ii) complex. In addition, a possible catalytic cycle and transition state model were proposed to rationalize the stereoselectivity.In the past few years, there has been considerable activity in both academic and industrial research to develop innovative machine learning approaches to locate novel, high-performing molecules in chemical space. Here we describe a new and fundamentally different type of approach that provides a holistic overview of how high-performing molecules are distributed throughout a search space. Based on an open-source, graph-based implementation [J. H. Jensen, Chem. Sci., 2019, 10, 3567-3572] of a traditional genetic algorithm for molecular optimisation, and influenced by state-of-the-art concepts from soft robot design [J. B. Mouret and J. Clune, Proceedings of the Artificial Life Conference, 2012, pp. 593-594], we provide an algorithm that (i) produces a large diversity of high-performing, yet qualitatively different molecules, (ii) illuminates the distribution of optimal solutions, and (iii) improves search efficiency compared to both machine learning and traditional genetic algorithm approaches.