|16:00 CEST (Berlin)|
The discovery that proteins and/or protein families of interest can be labelled selectively with chemical reagents resulted in an active research field that aims to profile the amount and activity of (a subset of) proteins in a relevant setting. For this purpose, a diverse set of so-called activity- and affinity-based probes (ABPs) have been developed nowadays. The molecular structure of these probes generally consists of a recognition element, which generates selectivity for the protein of interest, a reactive group or warhead, which facilitates covalent binding of the probe to the protein, and a reporter group, which is used for read-out purposes. The activity-based protein profiling field has matured over the past decades and these research tools are nowadays employed in both fundamental and applied settings.
During this webinar, I will introduce the basics of activity- and affinity-based protein profiling. I will describe the general design approaches for ABPs, as well as their applications. In the second part of the webinar, I will discuss novel combinatorial approaches to prepare probes and I will demonstrate how chemical probes can be combined with nanoLC-MS/MS and docking studies to map the binding sites of small molecules.
The development of a new drug currently takes 10-12 years, with costs of around €2bn. The main reasons for failures comprise a lack of efficacy and unforeseen toxicity.
In this webinar, Professor Ecker from Vienna University and 'Master Mind' of his recently founded company "Phenaris" will outline computational approaches to minimize the risk of failures due to toxicity. These comprise classical machine learning models to predict certain toxicity endpoints such as cholestasis, as well as deep learning approaches to overcome insufficient size and imbalance of toxicity datasets.
Integration of structure-based methods for the prediction of molecular initiating events with machine learning and pharmacophore modeling is outlined for the use case of mitochondrial toxicity. Leveraging complex data analysis pursued with KNIME workflows allows to create compound-pathway interaction fingerprints and link them to hepatotoxicity and cardiotoxicity. Finally, ToxPHACTS, a data-driven tool for toxicological read-across will be presented.
Applying dynamic combinatorial chemistry (DCC) to medicinal chemistry projects can be a helpful strategy for finding starting points in the drug-discovery process. Especially, when DCC is combined with structure-based or fragment-based drug design approaches, potent compounds can be obtained.
As relevant drug target, 14-3-3 proteins play a role in several diseases and many biological processes. Proteins of this family engage in protein-protein interactions (PPIs), and can do so with numerous different binding partners. By forming these PPIs, these proteins regulate their binding partner's activity. The activity can be both up- or down-regulated. Finding modulators of PPIs is very challenging via traditional screening approaches.
In this webinar we will briefly introduce DCC, followed by the first application of DCC to a PPI, where we found small-molecule modulators of the PPI of 14-3-3/ synaptopodin. These hit compounds were then evaluated for their biochemical properties via surface plasmon resonance (SPR) and fluorescence polarization (FP) assay.
There has typically been a divide between tools for three-dimensional (3D) structure-based design and those for analysis of structure-activity relationships (SAR) based on two-dimensional (2D) compound structures. Seamless integration between these approaches would enable all the available structural knowledge to be used to guide the efficient design of high quality, active compounds.
We have been working together with BioSolveIT to seamlessly integrate their unique SeeSAR™ technology, including pose generation, HYDE scoring and torsion angle analysis within our StarDrop™ platform. In this webinar, we will illustrate how 2D analyses of SAR, such as activity cliff detection and matched molecular pair analyses, can be seamlessly linked in a highly visual way with related 3D structural information to understand and rationalize this SAR. Furthermore, information from 2D models of key physicochemical and absorption, distribution, metabolism, elimination and toxicity (ADMET) properties can be combined with 3D docked poses and affinity estimates. The influence of each atom or functional group on these properties can be highlighted and combined with visualization of the atomistic contributions to binding affinity, enabling development of optimization strategies that balance potency with the ADMET properties required in a safe and efficacious drug.
These combined capabilities enable the efficient design of compounds with improved target affinity in a truly multi-parameter optimization environment.