Bifunctional Rhodium Intercalator Conjugates as Mismatch-Directing DNA Alkylating Agents

A conjugate of a DNA mismatch-specific rhodium intercalator, containing the bulky chrysenediimine ligand, and an aniline mustard has been prepared, and targeting of mismatches in DNA by this conjugate has been examined. The preferential alkylation of mismatched over fully matched DNA is found by a mobility shift assay at concentrations where untethered organic mustards show little reaction. The binding site of the Rh intercalator was determined by DNA photocleavage, and the position of covalent modification was established on the basis of the enhanced depurination associated with N-alkylation. The site-selective alkylation at mismatched DNA renders these conjugates useful tools for the covalent tagging of DNA base pair mismatches and new chemotherapeutic design

HPLC analysis of the organometallic modification of synthetic peptides: An evaluation of the indole and iClick conjugatins

Two different peptides were successfully synthesized, the sequences TFSDL and VLAKVAA via solid phase synthesis using in the first case a Fmoc-Leu-Wang resin and in the second case the attachment of the first amino acid to the Wang resin was done ourselves. The characterisation of both was done through mass spectrometry and HPLC, were good results were obtained. The first peptide obtained was coupled to a diene (2-carboxylic-3-trifluoromethyl-oxanorbornadiene) trying with 4 different coupling reactants which lead to the COMU/DIPEA ((1-cyano-2-ethoxy-2-oxoethylidenaminooxy)dimethylamino-morpholino-carbenium hexafluorophosphate / Diisoprophylethylamine) as the chosen reagents for this reaction. In addition purification was needed, which was done by separating the different peaks that appeared in the HPLC measurement. A mass spectrum was obtained from each peak to characterise the product. The peptide with the masked alkyne attached onto it was used for iClick reaction with a ruthenium complex, (Ru(N3)(bpym)(p-cym)), which was measured for 6 days by HPLC, and a positive result from the coupling was obtained. It was performed two times because the first method could not separate the peaks of the ruthenium and the peptide. The second peptide synthesized (VLAKVAA) was used for the indole conjugation where all the peaks obtained in the mass spectrometer are related to the product, therefore it was a good method for the conjugation. Using the same procedure a metal indole conjugation was performed with the cymantrene (N-Cymantrenylmethyl-6-aminomethylindole) both at 50ºC and room temperature, but unfortunately the product was not formed because the cymantrene was not reactive enough. Another try was done with another metal indole (Mn(bpg6 aminomethyl)indol-k3N)(CO3)) but also did not work because it was too reactive that it formed a dimer with itself. In conclusion the goal of this research was archived, which is to obtain a metal peptide, although the metal indole conjugation did not work with both the metal indole used

Data format standards in analytical chemistry

Research data is an essential part of research and almost every publication in chemistry. The data itself can be valuable for reuse if sustainably deposited, annotated and archived. Thus, it is important to publish data following the FAIR principles, to make it findable, accessible, interoperable and reusable not only for humans but also in machine-readable form. This also improves transparency and reproducibility of research findings and fosters analytical work with scientific data to generate new insights, being only accessible with manifold and diverse datasets. Research data requires complete and informative metadata and use of open data formats to obtain interoperable data. Generic data formats like AnIML and JCAMP-DX have been used for many applications. Special formats for some analytical methods are already accepted, like mzML for mass spectrometry or nmrML and NMReDATA forNMRspectroscopy data. Other methods still lack common standards for data. Only a joint effort of chemists, instrument and software vendors, publishers and infrastructure maintainers can make sure that the analytical data will be of value in the future. In this review, we describe existing data formats in analytical chemistry and introduce guidelines for the development and use of standardized and open data formats

A manganese photosensitive tricarbonyl molecule [Mn(CO)3(tpa-κ(3)N)]Br enhances antibiotic efficacy in a multi-drug-resistant Escherichia coli

Carbon monoxide-releasing molecules (CORMs) are a promising class of new antimicrobials, with multiple modes of action that are distinct from those of standard antibiotics. The relentless increase in antimicrobial resistance, exacerbated by a lack of new antibiotics, necessitates a better understanding of how such novel agents act and might be used synergistically with established antibiotics. This work aimed to understand the mechanism(s) underlying synergy between a manganese-based photoactivated carbon monoxide-releasing molecule (PhotoCORM), [Mn(CO)3(tpa-κ(3)N)]Br [tpa=tris(2-pyridylmethyl)amine], and various classes of antibiotics in their activities towards Escherichia coli EC958, a multi-drug-resistant uropathogen. The title compound acts synergistically with polymyxins [polymyxin B and colistin (polymyxin E)] by damaging the bacterial cytoplasmic membrane. [Mn(CO)3(tpa-κ(3)N)]Br also potentiates the action of doxycycline, resulting in reduced expression of tetA, which encodes a tetracycline efflux pump. We show that, like tetracyclines, the breakdown products of [Mn(CO)3(tpa-κ(3)N)]Br activation chelate iron and trigger an iron starvation response, which we propose to be a further basis for the synergies observed. Conversely, media supplemented with excess iron abrogated the inhibition of growth by doxycycline and the title compound. In conclusion, multiple factors contribute to the ability of this PhotoCORM to increase the efficacy of antibiotics in the polymyxin and tetracycline families. We propose that light-activated carbon monoxide release is not the sole basis of the antimicrobial activities of [Mn(CO)3(tpa-κ(3)N)]Br

SELFIES and the future of molecular string representations

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, SMILES, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, SMILES has several shortcomings -- most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100\% robustness: SELFIES (SELF-referencIng Embedded Strings). SELFIES has since simplified and enabled numerous new applications in chemistry. In this manuscript, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete Future Projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science.Comment: 34 pages, 15 figures, comments and suggestions for additional references are welcome

SELFIES and the future of molecular string representations

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, Smiles, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, Smiles has several shortcomings—most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100% robustness: SELF-referencing embedded string (Selfies). Selfies has since simplified and enabled numerous new applications in chemistry. In this perspective, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete future projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages, and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science

SELFIES and the future of molecular string representations

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, SMILES, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, SMILES has several shortcomings -- most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100\% robustness: SELFIES (SELF-referencIng Embedded Strings). SELFIES has since simplified and enabled numerous new applications in chemistry. In this manuscript, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete Future Projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science

Übergangsmetallkomplexe mit Nitronyl Nitroxid-Radikalen - Bausteine für molekulare magnetische Materialien

Die immer weitergehende Miniaturisierung elektronischer Bauelemente bis an ihre ultimative atomare Grenze im Rahmen der molekularen Elektronik ist von entscheidender Bedeutung für weitere Fortschritte im Rahmen der Informationstechnologie. Die vorliegende Dissertation beschäftigt sich mit der Synthese sowie strukturellen, elektronischen und magnetischen Charakterisierung von Übergangsmetallkomplexen mit Nitronyl Nitroxid-Liganden als Zugang zu molekularen magnetischen Materialien im Rahmen des Metall-Radikal-Ansatzes. Nitronyl Nitroxide mit Donorgruppen wie Carboxylat, Phenolat und Phosphonat wurden synthetisiert und an Übergangsmetalle wie Kobalt(II), Nickel(II) und Kupfer(II) koordiniert. Die aus Suszeptibilitätsmessungen und EPR-spektroskopischen Untersuchungen ermittelten Spingrundzustände der synthetisierten Verbindungen konnte durch theoretische Berechnungen im Rahmen der Dichtefunktionaltheorie bestätigt werden