Biology subjects

Smiesko, M.

Publications and source records attributed to Smiesko, M..

3 recordsLinked to original sources

bioRxiv2023

PanScreen: A Comprehensive Approach to Off-Target Liability Assessment

Drug development projects are getting increasingly more expensive while their success rate is stagnating. Safety issues attributed to off-target binding represent a major reason for the failure of new drugs. Besides desired on-target binding, small molecules may interact with off-targets, triggering adverse effects. Therefore, the development of novel methods for early recognition of such issues that are resource-efficient and cost-effective becomes vital. Here, we introduce PanScreen, an online platform for the automated assessment of off-target liabilities. PanScreen combines structure-based modeling techniques with state-of-the-art deep learning methods to not only predict accurate binding affinities but also give insight into potential modes of action. We show that the predictions are approaching experimental accuracy found in public datasets and that the same technology can also be used for other research areas, such as drug repurposing. Such fast and inexpensive methods allow researchers to test not only drug candidates, but all small molecules that might come into contact with a human organism for potential safety concerns very early in the development process. PanScreen is publicly available at www.panscreen.ch.

bioinformatics↗

bioRxiv2023

Quality Matters: Deep Learning-Based Analysis of Protein-Ligand Interactions with Focus on Avoiding Bias

The efficient and accurate prediction of protein-ligand binding affinities is an extremely appealing yet still unresolved goal in computational pharmacy. In recent years, many scientists have taken advantage of the remarkable progress of deep learning and applied it to address this issue. Despite all the advances in this field, there is increasing evidence that the typically applied validation of these methods is not suitable for medicinal chemistry applications. This work assesses the importance of dataset quality and proper dataset splitting techniques demonstrated on the example of the PDBbind dataset. We also introduce a new tool for the analysis of protein-ligand complexes, called po-sco. Po-sco allows the extraction of interaction information with much higher detail and comprehensibility than the tools available to date. We trained a transformer-based deep learning model to generate protein-ligand interaction fingerprints that can be utilized for downstream predictions, such as binding affinity. When using po-sco, this model generated predictions that were superior to those based on commonly used PLIP and ProLIF tools. We also demonstrate that the quality of the dataset is more important than the number of data points and that suboptimal dataset splitting can lead to a significant overestimation of model performance.

bioinformatics↗

bioRxiv2023

The action of physiological and synthetic steroids on the calcium channel CatSper in human sperm

The sperm-specific channel CatSper (cation channel of sperm) controls the intracellular Ca2+ concentration ([Ca2+]i) and plays an essential role in sperm function. It is mainly activated by the steroid progesterone (P4) but is also promiscuously activated by a wide range of synthetic and physiological compounds. These compounds include diverse steroids whose action on the channel is so far still controversial. To investigate the effect of these compounds on CatSper and sperm function, we developed a high-throughput-screening (HTS) assay to measure changes in [Ca2+]i in human sperm and screened 1,280 approved and off-patent drugs including 90 steroids from the Prestwick chemical library. More than half of the steroids tested (53%) induced an increase in [Ca2+]i and reduced the P4-induced Ca2+ influx in human sperm in a dose-dependent manner. Ten of the most potent steroids (activating and inhibiting) were selected for a detailed analysis of their action on CatSper and their ability to act on sperm motility, acrosomal exocytosis (AR), and penetration in viscous media. We found that these steroids show an inhibitory effect on P4 but not on prostaglandin E1-induced CatSper activation, suggesting that they compete for the same binding site as P4. Pregnenolone, dydrogesterone, epiandrosterone, nandrolone, and dehydroepiandrosterone acetate (DHEA) were found to activate CatSper at physiological concentrations. Stanozolol, epiandrosterone, and pregnenolone induced AR similarly to P4, whereas stanozolol and estropipate induced an increase in sperm penetration into viscous medium. Furthermore, using a hybrid approach integrating pharmacophore analysis and statistical modelling, we were able to screen in silico for steroids that can activate the channel and define the physicochemical and structural properties required for a steroid to exhibit agonist activity against CatSper. Overall, our results indicate that not only physiological but also synthetic steroids can modulate the activity of CatSper with varying potency and affect human sperm functions in vitro.

cell biology↗