Fast Epitope Mapping for the Anti‐MUC1 Monoclonal Antibody by Combining a One‐Bead‐One‐Glycopeptide Library and a Microarray Platform

Anti‐MUC1 monoclonal antibodies (mAbs) are powerful tools that can be used to recognize cancer‐related MUC1 molecules, the O‐glycosylation status of which is believed to affect binding affinity. We demonstrate the feasibility of using a rapid screening methodology to elucidate those effects. The approach involves i) “one‐bead‐one‐compound”‐based preparation of bilayer resins carrying glycopeptides on the shell and mass‐tag tripeptides coding O‐glycan patterns in the core, ii) on‐resin screening with an anti‐MUC1 mAb, iii) separating positive resins by utilizing secondary antibody conjugation with magnetic beads, and (iv) decoding the mass‐tag that is detached from the positive resins pool by using mass spectrometric analysis. We tested a small library consisting of 27 MUC1 glycopeptides with different O‐glycosylations against anti‐MUC1 mAb clone VU‐3C6. Qualitative mass‐tag analysis showed that increasing the number of glycans leads to an increase in the binding affinity. Six glycopeptides selected from the library were validated by using a microarray‐based assay. Our screening provides valuable information on O‐glycosylations of epitopes leading to high affinity with mAb.     

Raman and infrared spectra of the all-trans, 7-cis, 9-cis, 13-cis and 15-cis isomers of β-carotene: Key bands distinguishing stretched or terminal-bent configurations form central-bent configurations

Raman and infrared spectra in the region of 1800-150 cm⁻¹ were recorded for a set of cis-trans isomers of d̃-carotene, i.e. the all-trans, 7-cis, 9-cis, 13-cis and 15-cis isomers. Spectral comparison revealed Raman and infrared key bands which (1) distinguish stretched or terminal-bent configurations (all-trans, 7-cis and 9-cis) from central-bent configurations (13-cis and 15-cis), and (2) distinguish unmethylated 7-cis and 15-cis configuratios. Keybands (1) include Raman bands at 1160 and 1140 cm⁻¹ and infrared bands at 825 and 775 cm⁻¹ (the intensity varies with the position of the cis-bend) Key bands (2) include Raman bands at 1274 and 962 cm⁻¹ and an infrared band at 741 cm⁻¹ (characteristic of the 7-cis configuration), and also a Raman band at 1247 cm⁻¹ and an infrared band at 775 cm⁻¹ (characteristic of the 15-cis configuration). The normal modes for the key bands were determined by a set of normal coordinate calculations for the isomeric configurations of a simplified model of d̃-carotene. The key bands were mainly related to the C H in-plane bendings, coupled with the CC or C C stretching, or to the C H out-of-plane wagging vibrations, some of which coupled with the CC torsion.     

On the Nature of Functional Differentiation: The Role of Self-Organization with Constraints

The focus of this article is the self-organization of neural systems under constraints. In 2016, we proposed a theory for self-organization with constraints to clarify the neural mechanism of functional differentiation. As a typical application of the theory, we developed evolutionary reservoir computers that exhibit functional differentiation of neurons. Regarding the self-organized structure of neural systems, Warren McCulloch described the neural networks of the brain as being “heterarchical”, rather than hierarchical, in structure. Unlike the fixed boundary conditions in conventional self-organization theory, where stationary phenomena are the target for study, the neural networks of the brain change their functional structure via synaptic learning and neural differentiation to exhibit specific functions, thereby adapting to nonstationary environmental changes. Thus, the neural network structure is altered dynamically among possible network structures. We refer to such changes as a dynamic heterarchy. Through the dynamic changes of the network structure under constraints, such as physical, chemical, and informational factors, which act on the whole system, neural systems realize functional differentiation or functional parcellation. Based on the computation results of our model for functional differentiation, we propose hypotheses on the neuronal mechanism of functional differentiation. Finally, using the Kolmogorov–Arnold–Sprecher superposition theorem, which can be realized by a layered deep neural network, we propose a possible scenario of functional (including cell) differentiation.     

Further evidence for the intramolecular charge-transfer interaction between tropylium ion and remote benzene rings in 9,10-dihydro-9,10-(1,2-tropylio)anthracene tetrafluoroborate and its methyl derivatives

A series of 9,10-dihydro-9,10-(1,2-tropylio)anthracene tetrafluoroborates

has been prepared. The intramolecular charge-transfer interactions in

were confirmed by their UV spectra and pK_R+ values. As a model compound 1,2,3,4-tetrahydro-1,4-(1,2-tropylio)naphthalene tetrafluoroborate ($\text{5}$)_was also examined.     

Chiral norbornadienes as efficient ligands for the rhodium-catalyzed asymmetric 1,4-addition of arylboronic acids to fumaric and maleic compounds

[reaction: see text] A rhodium-catalyzed asymmetric 1,4-addition of arylboronic acids to fumaric and maleic compounds has been developed. While phosphorus-based chiral ligands fail to induce high stereoselectivity, chiral norbornadiene ligands have proved to be uniquely effective to achieve high enantioselectivity in these 1,4-addition reactions.