A Rational Approach Towards a New Ferrocenyl Pyrrolidine for Stereoselective Enamine Catalysis

n/

TAL Effectors Specificity Stems from Negative Discrimination

Transcription Activator-Like (TAL) effectors are DNA-binding proteins secreted by phytopathogenic bacteria that interfere with native cellular functions by binding to plant DNA promoters. The key element of their architecture is a domain of tandem-repeats with almost identical sequences. Most of the polymorphism is located at two consecutive amino acids termed Repeat Variable Diresidue (RVD). The discovery of a direct link between the RVD composition and the targeted nucleotide allowed the design of TAL-derived DNA-binding tools with programmable specificities that revolutionized the field of genome engineering. Despite structural data, the molecular origins of this specificity as well as the recognition mechanism have remained unclear. Molecular simulations of the recent crystal structures suggest that most of the protein-DNA binding energy originates from non-specific interactions between the DNA backbone and non-variable residues, while RVDs contributions are negligible. Based on dynamical and energetic considerations we postulate that, while the first RVD residue promotes helix breaks - allowing folding of TAL as a DNA-wrapping super-helix - the second provides specificity through a negative discrimination of matches. Furthermore, we propose a simple pharmacophore-like model for the rationalization of RVD-DNA interactions and the interpretation of experimental findings concerning shared affinities and binding efficiencies. The explanatory paradigm presented herein provides a better comprehension of this elegant architecture and we hope will allow for improved designs of TAL-derived biotechnological tools

The Lipopolysaccharide from Capnocytophaga canimorsus Reveals an Unexpected Role of the Core-Oligosaccharide in MD-2 Binding

Capnocytophaga canimorsus is a usual member of dog's mouths flora that causes rare but dramatic human infections after dog bites. We determined the structure of C. canimorsus lipid A. The main features are that it is penta-acylated and composed of a “hybrid backbone” lacking the 4′ phosphate and having a 1 phosphoethanolamine (P-Etn) at 2-amino-2-deoxy-d-glucose (GlcN). C. canimorsus LPS was 100 fold less endotoxic than Escherichia coli LPS. Surprisingly, C. canimorsus lipid A was 20,000 fold less endotoxic than the C. canimorsus lipid A-core. This represents the first example in which the core-oligosaccharide dramatically increases endotoxicity of a low endotoxic lipid A. The binding to human myeloid differentiation factor 2 (MD-2) was dramatically increased upon presence of the LPS core on the lipid A, explaining the difference in endotoxicity. Interaction of MD-2, cluster of differentiation antigen 14 (CD14) or LPS-binding protein (LBP) with the negative charge in the 3-deoxy-d-manno-oct-2-ulosonic acid (Kdo) of the core might be needed to form the MD-2 – lipid A complex in case the 4′ phosphate is not present

LinChemIn: Route Arithmetic — Operations on Digital Synthetic Routes

Computational tools are revolutionizing our understanding and prediction of chemical reactivity by combining traditional data analysis techniques with new predictive models. These tools extract additional value from the reaction data corpus, but to effectively convert this value into actionable knowledge, domain specialists need to interact easily with the computer-generated output. In this application note, we demonstrate the capabilities of the open-source Python toolkit LinChemIn, which simplifies the manipulation of reaction networks and provides advanced functionality for working with synthetic routes. LinChemIn ensures chemical consistency when merging, editing, mining, and analyzing reaction networks. Its flexible input interface can process routes from various sources, including predictive models and expert input. The toolkit also efficiently extracts individual routes from the combined synthetic tree, identifying alternative paths and reaction combinations. By reducing the operational barrier to accessing and analyzing synthetic routes from multiple sources, LinChemIn facilitates a constructive interplay between Artificial Intelligence and human expertise

LinChemIn: SynGraph. A data model and a toolkit to analyze and compare synthetic routes

Background. The increasing amount of chemical reaction data makes traditional ways to navigate its corpus less effective, while the demand for novel approaches and instruments is rising. Recent data science and machine learning techniques support the development of new ways to extract value from the available reaction data. On the one side, Computer-Aided Synthesis Planning tools can predict synthetic routes in a model-driven approach; on the other side, experimental routes can be extracted from the Network of Organic Chemistry, in which reaction data are linked in a network. In this context, the need to combine, compare and analyze synthetic routes generated by different sources arises naturally. Results. Here we present LinChemIn, a python toolkit that allows chemoinformatics operations on synthetic routes and reaction networks. Wrapping some third-party packages for handling graph arithmetic and chemoinformatics and implementing new data models and functionalities, LinChemIn allows the interconversion between data formats and data models and enables route-level analysis and operations, including route comparison and descriptors calculation. Object-Oriented Design principles inspire the software architecture, and the modules are structured to maximize code reusability and support code testing and refactoring. The code structure should facilitate external contributions, thus encouraging open and collaborative software development. Conclusions. The current version of LinChemIn allows users to combine synthetic routes generated from various tools and analyze them, and constitutes an open and extensible framework capable of incorporating contributions from the community and fostering scientific discussion. Our roadmap envisages the development of sophisticated metrics for routes evaluation, a multi-parameter scoring system, and the implementation of an entire "ecosystem" of functionalities operating on synthetic routes. LinChemIn is freely available at https://github.com/syngenta/linchemin

Rhodopsin and GFP Chromophores: QM/MM Absorption Spectra in Solvent and Protein

This work presents a QM/MM investigation of the spectral properties of the 11\u2010cis\u2010retinal and the GFP chromophore in polar solvents and protein. The results of the computations are in surprising agreement with the experimental values indicating the accuracy of our computational approach. In addition it has been demonstrated the key role of the solvent to create a \u201cvirtual conter\u2010ion\u201d. This effect is due to the reorientation (i.e. polarization) of the polar solvent close to the chromophore: the polarized permanent dipoles of the solvent act similarly to the counter ion, stabilizing the ground state respect to the charge transfer excited state