Direct Spectroscopic Detection of the Orientation of Free OH Groups in Methyl Lactate–(Water)1,2 Clusters: Hydration of a Chiral Hydroxy Ester

Hydration of chiral molecules is a subject of significant current interest in light of recent experimental observations of chirality transfer from chiral solutes to water in solution and the important roles which water plays in biological events. Using a broadband chirped pulse and a cavity based microwave spectrometer, we detected spectroscopic signatures of the mono‐ and dihydrates of methyl lactate, a chiral hydroxy ester. Surprisingly, these small hydration clusters show highly specific binding preferences. Not only do they strongly prefer the insertion H‐bonding topology, but they also favor specific pointing direction(s) for their non‐H‐bonded hydroxy group(s). We observed that the particular dihydrate conformer identified is not the most stable one predicted. This work highlights the superior capability of high‐resolution spectroscopy to identify specific water binding topologies, and provides quantitative data to test state‐of‐the‐art theory.     

Multicomponent Self‐Assembly with a Shape‐Persistent N‐Heterotriangulene Macrocycle on Au(111)

Multicomponent network formation by using a shape‐persistent macrocycle ( MC6 ) at the interface between an organic liquid and Au(111) surface is demonstrated. MC6 serves as a versatile building block that can be coadsorbed with a variety of organic molecules based on different types of noncovalent interactions at the liquid–solid interface. Scanning tunneling microscopy (STM) reveals the formation of crystalline bicomponent networks upon codeposition of MC6 with aromatic molecules, such as fullerene (C₆₀) and coronene. Tetracyanoquinodimethane, on the other hand, was found to induce disorder into the MC6 networks by adsorbing on the rim of the macrocycle. Immobilization of MC6 itself was studied in two different noncovalently assembled host networks. MC6 assumed a rather passive role as a guest and simply occupied the host cavities in one network, whereas it induced a structural transition in the other. Finally, the central cavity of MC6 was used to capture C₆₀ in a complex three‐component system. Precise immobilization of organic molecules at discrete locations within multicomponent networks, as demonstrated here, constitutes an important step towards bottom‐up fabrication of functional surface‐based nanostructures.     

The influence of Au-nanoparticles presence in PDMS on microstructures creation by ion beam lithography

3D microstructures in pure poly(dimethylsiloxane) (PDMS) and PDMS with embedded Au nanoparticles were prepared by ion beam lithography without any further etching. Two mega-electron volts helium and 10 MeV oxygen ions were used for ion microstructuring. Parallel lines of 1 mm in length and 10 μm in thickness were fabricated for investigation of the effect of the nanoparticles presence in the polymer on the surface morphology of the created microstructures. The created microstructures were checked by optical microscope. Infrared (IR) spectrometry was used to study the effect of the ions type and fluence on the chemical changes of the material. Atomic force microscopy was used for the fine detail study as well as for checking the microstructure quality. Analysis revealed an increased radiation resistance of the nanocomposite compared to the pure PDMS. Shrinkage is proportional to the fluence, but the maximum value for both materials is limited by saturation. 3D microstructure in modified PDMS obtained at the same irradiation condition as pure PDMS is characterized by its smaller height. Obtaining the microstructure in nanocomposite of the same height as in pure PDMS by increasing the fluence can be impossible due to saturation of shrinkage and/or radiation-induced heating of the material.     

Neural networks convergence using physicochemical data

An investigation of the neural network convergence and prediction based on three optimization algorithms, namely, Levenberg-Marquardt, conjugate gradient, and delta rule, is described. Several simulated neural networks built using the above three algorithms indicated that the Levenberg-Marquardt optimizer implemented as a back-propagation neural network converged faster than the other two algorithms and provides in most of the cases better prediction. These conclusions are based on eight physicochemical data sets, each with a significant number of compounds comparable to that usually used in the QSAR/QSPR modeling. The superiority of the Levenberg-Marquardt algorithm is revealed in terms of functional dependence of the change of the neural network weights with respect to the gradient of the error propagation as well as distribution of the weight values. The prediction of the models is assessed by the error of the validation sets not used in the training process.     

Automated linkage of proteins and payloads producing monodisperse conjugates

Controlled protein functionalization holds great promise for a wide variety of applications. However, despite intensive research, the stoichiometry of the functionalization reaction remains difficult to control due to the inherent stochasticity of the conjugation process. Classical approaches that exploit peculiar structural features of specific protein substrates, or introduce reactive handles via mutagenesis, are by essence limited in scope or require substantial protein reengineering. We herein present equimolar native chemical tagging (ENACT), which precisely controls the stoichiometry of inherently random conjugation reactions by combining iterative low-conversion chemical modification, process automation, and bioorthogonal trans-tagging. We discuss the broad applicability of this conjugation process to a variety of protein substrates and payloads.

Controlled protein functionalization holds great promise for a wide variety of applications.

Applications of protein conjugates are limitless, including imaging, diagnostics, drug delivery, and sensing.^1–4 In many of these applications, it is crucial that the conjugates are homogeneous.⁵ The site-selectivity of the conjugation process and the number of functional labels per biomolecule, known as the degree of conjugation (DoC), are crucial parameters that define the composition of the obtained products and are often the limiting factors to achieving adequate performance of the conjugates. For instance, immuno-PCR, an extremely sensitive detection technique, requires rigorous control of the average number of oligonucleotide labels per biomolecule (its DoC) in order to achieve high sensitivity.⁶ In optical imaging, the performance of many super-resolution microscopy techniques is directly defined by the DoC of fluorescent tags.⁷ For therapeutics, an even more striking example is provided by antibody–drug conjugates, which are prescribed for the treatment of an increasing range of cancer indications.⁸ A growing body of evidence from clinical trials indicates that bioconjugation parameters, DoC and DoC distribution, directly influence the therapeutic index of these targeted agents and hence must be tightly controlled.⁹Standard bioconjugation techniques, which rely on nucleophile–electrophile reactions, result in a broad distribution of different DoC species (Fig. 1a), which have different biophysical parameters, and consequently different functional properties.¹⁰Open in a separate windowFig. 1Schematic representation of the types of protein conjugates.To address this key issue and achieve better DoC selectivity, a number of site-specific conjugation approaches have been developed (Fig. 1b). These techniques rely on protein engineering for the introduction of specific motifs (e.g., free cysteines,¹¹ selenocysteines,¹² non-natural amino acids,^13,14 peptide tags recognized by specific enzymes^15,16) with distinct reactivity compared to the reactivity of the amino acids present in the native protein. These motifs are used to simultaneously control the DoC (via chemo-selective reactions) and the site of payload attachment. Both parameters are known to influence the biological and biophysical parameters of the conjugates,¹¹ but so far there has been no way of evaluating their impact separately.The influence of DoC is more straightforward, with a lower DoC allowing the minimization of the influence of payload conjugation on the properties of the protein substrate. The lowest DoC that can be achieved for an individual conjugate is 1 (corresponding to one payload attached per biomolecule). It is noteworthy that DoC 1 is often difficult to achieve through site-specific conjugation techniques due to the symmetry of many protein substrates (e.g., antibodies). Site selection is a more intricate process, which usually relies on a systematic screening of conjugation sites for some specific criteria, such as stability or reactivity.¹⁷Herein, we introduce a method of accessing an entirely new class of protein conjugates with multiple conjugation sites but strictly homogenous DoCs (Fig. 1c). To achieve this, we combined (a) iterative low conversion chemical modification, (b) process automation, and (c) bioorthogonal trans-tagging in one workflow.The method has been exemplified for protein substrates, but it is applicable to virtually any native bio-macromolecule and payload. Importantly, this method allows for the first time the disentangling of the effects of homogeneous DoC and site-specificity on conjugate properties, which is especially intriguing in the light of recent publications revealing the complexity of the interplay between payload conjugation sites and DoC for in vivo efficacy of therapeutic bioconjugates.¹⁸ Finally, it is noteworthy that this method can be readily combined with an emerging class of site-selective bioconjugation reagents to produce site-specific DoC 1 conjugates, thus further expanding their potential for biotechnology applications.¹⁹     

Reactions of ((i)PrO)3M≡M(O(i)Pr)3 (M = Mo, W) with low-coordinate phosphorus compounds. Formation of the first four-membered planar metallacycles, containing an M≡M triple bond

The low-coordinate phosphorus compounds (Me(3)Si)(2)N-P=NSiMe(3), (Me(3)Si)(2)N-P(=S)=N(t)Bu and (Me(3)Si)(2)N-P(=NSiMe(3))(2) react with ((i)PrO)(3)M≡M(O(i)Pr)(3) (M = Mo, W) to form four- and five-membered metallacycles with intact endocyclic or exocyclic M≡M triple bonds. The first four-membered planar metallacycles, containing an M≡M triple bond were obtained in reaction with (Me(3)Si)(2)N-P=NSiMe(3).