Development of a small molecule that corrects misfolding and increases secretion of Z α1 -antitrypsin.

Severe α1 -antitrypsin deficiency results from the Z allele (Glu342Lys) that causes the accumulation of homopolymers of mutant α1 -antitrypsin within the endoplasmic reticulum of hepatocytes in association with liver disease. We have used a DNA-encoded chemical library to undertake a high-throughput screen to identify small molecules that bind to, and stabilise Z α1 -antitrypsin. The lead compound blocks Z α1 -antitrypsin polymerisation in vitro, reduces intracellular polymerisation and increases the secretion of Z α1 -antitrypsin threefold in an iPSC model of disease. Crystallographic and biophysical analyses demonstrate that GSK716 and related molecules bind to a cryptic binding pocket, negate the local effects of the Z mutation and stabilise the bound state against progression along the polymerisation pathway. Oral dosing of transgenic mice at 100 mg/kg three times a day for 20 days increased the secretion of Z α1 -antitrypsin into the plasma by sevenfold. There was no observable clearance of hepatic inclusions with respect to controls over the same time period. This study provides proof of principle that "mutation ameliorating" small molecules can block the aberrant polymerisation that underlies Z α1 -antitrypsin deficiency

Prioritizing multiple therapeutic targets in parallel using automated DNA-encoded library screening

AbstractThe identification and prioritization of chemically tractable therapeutic targets is a significant challenge in the discovery of new medicines. We have developed a novel method that rapidly screens multiple proteins in parallel using DNA-encoded library technology (ELT). Initial efforts were focused on the efficient discovery of antibacterial leads against 119 targets from Acinetobacter baumannii and Staphylococcus aureus. The success of this effort led to the hypothesis that the relative number of ELT binders alone could be used to assess the ligandability of large sets of proteins. This concept was further explored by screening 42 targets from Mycobacterium tuberculosis. Active chemical series for six targets from our initial effort as well as three chemotypes for DHFR from M. tuberculosis are reported. The findings demonstrate that parallel ELT selections can be used to assess ligandability and highlight opportunities for successful lead and tool discovery.</jats:p

THE SELFCLONAL VARIABILITY BY THE SIGN OF SALT STABILITY IN THE CULTURE OF THE RICE CELLS

The salt-stable clones can be obtained in the different selective systems. The obtained clones of rice are resistant to PZG and ethionine. From the stable clones, the plants with the high salt resistance, have been regenerated. The offered selective systems can be recommended for receiving the saltresistant cellular line and the regernats of other species of plantsAvailable from VNTIC / VNTIC - Scientific & Technical Information Centre of RussiaSIGLERURussian Federatio

Approaches for Modifying Oxide-Semiconductor Materials to Increase the Efficiency of Photocatalytic Water Splitting

The constant increase in the amount of energy consumed and environmental problems associated with the use of fossil fuels determine the relevance of the search for alternative and renewable energy sources. One of these is hydrogen gas, which can be produced by sunlight-driven photocatalytic water splitting. The decisive role in the efficiency of the process is played by the properties of the photocatalyst. Oxide materials are widely used as photocatalysts due to their appropriate band structure, high-enough photochemical stability and corrosion resistance. However, the bandgap, crystallinity and the surface morphology of oxide materials are subject to improvement. Apart from the properties of the photocatalyst, the parameters of the process influence the hydrogen-production efficiency. This paper outlines the key ways to improve the characteristics of oxide-semiconductor photocatalysts with the optimum parameters of photocatalytic water splitting

Building Block-Based Binding Predictions for DNA-Encoded Libraries

DNA-encoded libraries (DELs) provide the means to make and screen millions of diverse compounds against a target of interest in a single experiment. However, despite producing large volumes of binding data at a relatively low cost, the DEL selection process is susceptible to noise, necessitating computational follow-up to increase signal-to-noise ratios. In this work, we present a set of informatics tools to analyze DEL selection data so that subsequent DEL screens probe productive regions of chemical space. Our approach segments DEL data at the individual building block level to identify productive building blocks in a library. We show how similar building blocks have a similar probability of binding, which we then employ to predict the behavior of untested building blocks. Lastly, we build a model from the inference that the combined behavior of individual building blocks is predictive of the activity of an overall compound. We report a performance of more than an order of magnitude greater than random guessing on a holdout set, demonstrating that our model can serve as a baseline for comparison against other machine learning models on DEL data

Building Block-Based Binding Predictions for DNA-Encoded Libraries.

DNA-encoded libraries (DELs) provide the means to make and screen millions of diverse compounds against a target of interest in a single experiment. However, despite producing large volumes of binding data at a relatively low cost, the DEL selection process is susceptible to noise, necessitating computational follow-up to increase signal-to-noise ratios. In this work, we present a set of informatics tools to employ data from prior DEL screen(s) to gain information about which building blocks are most likely to be productive when designing new DELs for the same target. We demonstrate that similar building blocks have similar probabilities of forming compounds that bind. We then build a model from the inference that the combined behavior of individual building blocks is predictive of whether an overall compound binds. We illustrate our approach on a set of three-cycle OpenDEL libraries screened against soluble epoxide hydrolase (sEH) and report performance of more than an order of magnitude greater than random guessing on a holdout set, demonstrating that our model can serve as a baseline for comparison against other machine learning models on DEL data. Lastly, we provide a discussion on how we believe this informatics workflow could be applied to benefit researchers in their specific DEL campaigns

Predicting Electrophoretic Mobility of Protein–Ligand Complexes for Ligands from DNA-Encoded Libraries of Small Molecules

Selection of target-binding ligands from DNA-encoded libraries of small molecules (DELSMs) is a rapidly developing approach in drug-lead discovery. Methods of kinetic capillary electrophoresis (KCE) may facilitate highly efficient homogeneous selection of ligands from DELSMs. However, KCE methods require accurate prediction of electrophoretic mobilities of protein–ligand complexes. Such prediction, in turn, requires a theory that would be applicable to DNA tags of different structures used in different DELSMs. Here we present such a theory. It utilizes a model of a globular protein connected, through a single point (small molecule), to a linear DNA tag containing a combination of alternating double-stranded and single-stranded DNA (dsDNA and ssDNA) regions of varying lengths. The theory links the unknown electrophoretic mobility of protein–DNA complex with experimentally determined electrophoretic mobilities of the protein and DNA. Mobility prediction was initially tested by using a protein interacting with 18 ligands of various combinations of dsDNA and ssDNA regions, which mimicked different DELSMs. For all studied ligands, deviation of the predicted mobility from the experimentally determined value was within 11%. Finally, the prediction was tested for two proteins and two ligands with a DNA tag identical to those of DELSM manufactured by GlaxoSmithKline. Deviation between the predicted and experimentally determined mobilities did not exceed 5%. These results confirm the accuracy and robustness of our model, which makes KCE methods one step closer to their practical use in selection of drug leads, and diagnostic probes from DELSMs