Biology subjects

Kack, H.

Publications and source records attributed to Kack, H..

3 recordsLinked to original sources

bioRxiv2026

Room-temperature fragment screening of soluble epoxide hydrolase by serial crystallography

Room temperature serial crystallography offers advantages over conventional cryo-crystallography, such as simplified crystal handling and the possibility to avoid potential artefacts associated with cryo-trapping. However, to be considered as an alternative for drug discovery, where compound availability may be limited and speed of structure delivery is a key factor, it suffers from several limitations. To address these challenges, we have optimized a serial crystallography workflow for ligand soaking, data collection and data processing, significantly reducing time and reagent consumption to make it a viable option for drug discovery applications, herein exemplified by crystallographic fragment screening. Our approach incorporates the use of dried-in fragment cocktails on fixed target supports, compatible with 96-well plates for crystal soaking, and an efficient data processing pipeline tailored for serial crystallography. To validate our workflow, we conducted an in-crystal fragment screen at room temperature on the protein soluble epoxide hydrolase. The screen comprised 384 compounds and resulted in identification of 40 fragment binders corresponding to a hit rate of 10.4 %. The resulting room-temperature structures are of high quality and reveal opportunities for specific interaction within the highly hydrophobic active site of soluble epoxide hydrolase. Finally, we discuss potential avenues for further workflow optimization, highlighting the future potential of this approach for drug discovery. SynopsisWe have developed a workflow that allowed us to efficiently conduct a fragment screen at room temperature using serial crystallography, of interest for future drug discovery campaigns.

biochemistry↗

bioRxiv2023

Exploring serial crystallography for drug discovery

Structure-based drug design is highly dependent on the availability of structures of the protein of interest in complex with lead compounds. Ideally, this information can be used to guide the chemical optimization of a compound into a pharmaceutical drug candidate. A limitation of the main structural method used today, rotational cryo-crystallography, is that it only provides structural information of the protein-complex in its frozen state. Serial crystallography is a relatively new approach that offers the possibility to study protein structures at room-temperature. Here, we explore the use of serial crystallography to determine structures of the pharmaceutical target soluble epoxide hydrolase. We introduce a new method to screen for optimal microcrystallization conditions suitable for use in serial crystallography and present a number of room-temperature ligand-bound structures of our target protein. From a comparison between the room-temperature structural data and previously published cryo-temperature structures, we describe an example of a temperature-dependent difference in ligand-binding mode and observe that flexible loops are better resolved at ambient temperature. Finally, we discuss current limitations and potential future advances of serial crystallography for use within pharmaceutical drug discovery.

biochemistry↗

bioRxiv2021

Protein structure dynamic prediction: a Machine Learning/Molecular Dynamic approach to investigate the protein conformational sampling

Proteins exist in several different conformations. These structural changes are often associated with fluctuations at the residue level. Recent findings show that co-evolutionary analysis coupled with machine- learning techniques improves the precision by providing quantitative distance predictions between pairs of residues. The predicted statistical distance distribution from Multi Sequence Analysis (MSA) reveals the presence of different local maxima suggesting the flexibility of key residue pairs. Here we investigate the ability of the residue-residue distance prediction to provide insights into the protein conformational ensemble. We combine deep learning approaches with mechanistic modeling to a set of proteins that experimentally showed conformational changes. The predicted protein models were filtered based on energy scores, RMSD clustering, and the centroids selected as the lowest energy structure per cluster. The models were compared to the experimental-Molecular Dynamics (MD) relaxed structure by analyzing the backbone residue torsional distribution and the sidechain orientations. Our pipeline not only allows us to retrieve the global experimental folding but also the experimental structural dynamics. We show the potential correlation between the experimental structure dynamics and the predicted model ensemble demonstrating the susceptibility of the current state-of-the-art methods in protein folding and dynamics prediction and pointing out the areas of improvement.

bioinformatics↗