Kack, H. | bioRxiv Science

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↗