Biology subjects

Tyrchan, C.

Publications and source records attributed to Tyrchan, C..

4 recordsLinked to original sources

bioRxiv2024

Efficient Sampling of PROTAC-Induced Ternary Complexes

Proteolysis targeting chimeras (PROTACs) are bifunctional small molecules that recruit an E3 ligase to a target protein, leading to ubiquitin transfer and subsequent proteasomal degradation. The formation of ternary complexes is a crucial step in PROTAC-induced protein degradation, and gaining structural insights is essential for rational PROTAC design. In this study, we present a novel approach for efficiently sampling PROTAC-induced ternary complexes, which has been validated using 40 co-crystallized ternary complex structures. In comparison to protein-protein docking-based integrative approaches, our method achieved an impressive success rate of 97% and 50% retrospectively, measured by C-RMSD to the crystal structure within 10 and 4 [A], respectively, with an average CPU time of 4 hours. Notably, utilizing unbound protein structures, the C-RMSD values between the predicted and experimental structures were consistently within 7 [A] across six WDR5-PROTAC-VHL ternary structures. Our open-source software enables the modeling of ternary structures in a single step and holds promise for enhancing PROTAC design efforts. TOC O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=141 SRC="FIGDIR/small/619573v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@c380e9org.highwire.dtl.DTLVardef@14478c8org.highwire.dtl.DTLVardef@559e67org.highwire.dtl.DTLVardef@1128491_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2024

AI-Augmented R-Group Exploration in Medicinal Chemistry

Efficient R-group exploration in the vast chemical space, enabled by increasingly available building blocks or generative AI, remains an open challenge. Here, we developed an enhanced Free-Wilson QSAR model embedding R-groups by atom-centric pharmacophoric features. Regioisomers of R-groups can be distinguished by explicitly accounting for the atomic positions. Good predictivity is observed consistently across 12 public datasets. Integrated into an open-source program, we showcase its application in performing classic Free-Wilson analysis as well as R-group exploration in uncharted chemical space.

bioinformatics↗

bioRxiv2024

Enhanced Thompson Sampling by Roulette Wheel Selection for Screening Ultra-Large Combinatorial Libraries

Chemical space exploration has gained significant interest with the increase in available building blocks, which enables the creation of ultra-large virtual libraries containing billions or even trillions of compounds. However, the challenge of selecting most suitable compounds for synthesis arises, and one such challenge is hit expansion. Recently, Thompson sampling, a probabilistic search approach, has been proposed by Walters et al. to achieve efficiency gains by operating in the reagent space rather than the product space. Here, we aim to address some of its shortcomings and propose optimizations. We introduce a warmup routine to ensure that initial probabilities are set for all reagents with a minimum number of molecules evaluated. Additionally, a roulette wheel selection is proposed with adapted stop criteria to improve sampling efficiency, and belief distributions of reagents are only updated when they appear in new molecules. We demonstrate that a 100% recovery rate can be achieved by sampling 0.1% of the fully enumerated library, showcasing the effectiveness of our proposed optimizations.

bioinformatics↗

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↗