Biology subjects

Case, M.

Publications and source records attributed to Case, M..

3 recordsLinked to original sources

bioRxiv2023

Discovery of high affinity and specificity stapled peptide Bcl-xL inhibitors using bacterial surface display

Intracellular protein-protein interactions are involved in many different diseases, making them prime targets for therapeutic intervention. Several diseases are characterized by their overexpression of Bcl-xL, an anti-apoptotic B cell lymphoma 2 (Bcl-2) protein expressed on mitochondrial membranes. Bcl-xL overexpression inhibits apoptosis, and selective inhibition of Bcl-xL has the potential to increase cancer cell death while leaving healthy cells comparatively less affected. However, high homology between Bcl-xL and other Bcl-2 proteins has made it difficult to selectively inhibit this interaction by small molecule drugs. We engineered stapled peptides, a chemical modification that can improve cell penetration, protease stability, and conformational stability, towards the selective inhibition of Bcl-xL. To accomplish this task, we built a focused combinatorial mutagenesis library of peptide variants on the bacterial cell surface, used copper catalyzed click chemistry to form stapled peptides, and sorted the library for high binding to Bcl-xL and minimal binding towards other Bcl-2 proteins. We characterized the sequence and staple placement trends that governed specificity and identified molecules with [~]10 nM affinity to Bcl-xL and greater than 100-fold selectivity versus other Bcl-2 family members on and off the cell surface. We confirmed the mechanism of action of these peptides is consistent with apoptosis biology through mitochondrial outer membrane depolarization assays (MOMP). Overall, high affinity (10 nM Kd) and high specificity (100-fold selectivity) peptides were developed to target the Bcl-xL protein. These results demonstrate that stapled alpha helical peptides are promising candidates for the specific treatment of cancers driven by Bcl-2 dysregulation.

synthetic biology↗

bioRxiv2023

Microclimate buffering varies across forest types during an extreme heat event

Increasing temperatures and extreme heat episodes have become more common with climate change. While forests are known to be buffered from increasing temperatures compared to non-forested areas, whether this buffering is maintained under extreme temperature events, how such events influence forests, and how forest organisms respond to extreme heat is relatively unknown. Here we assess the effects of an extreme heat event (the Pacific Northwest (PNW) heatdome in June 2021) on forest microclimates, forests, and the organisms living within them. We first asked how the PNW heatdome affected microclimates in forests with differing canopy cover (including non-forests) and found that the buffering capacity of forests is greater under denser canopies, even under extreme heat events. We then combined this information with organismal temperature tolerance curves for 12 relevant species and found that canopy buffering can minimize the negative impacts of even extreme heat events on understory organisms, with greater canopy density providing greater microclimate moderation. Finally, we analyzed seasonal NDVI trends in recent years, and found signs of canopy stress following the extreme 2021 heat event. In all, this suggests that although forest canopies may buffer the negative effects of extreme heat events on understory organisms, a greater frequency of extreme heat events may threaten this capacity by damaging forest canopies.

ecology↗

bioRxiv2023

Machine Learning to Predict Continuous Protein Properties from Simple Binary Sorting and Deep Sequencing Data

Proteins are a diverse class of biomolecules responsible for wide-ranging cellular functions, from catalyzing reactions and recognizing pathogens to forming dynamic cellular structure. The ability to evolve proteins rapidly and inexpensively towards improved properties is a common objective for protein engineers. Powerful high-throughput methods like fluorescent activated cell sorting (FACS) and next-generation sequencing (NGS) have dramatically improved directed evolution experiments. However, it is unclear how to best leverage this data to characterize protein fitness landscapes more completely and identify lead candidates. In this work, we develop a simple yet powerful framework to improve protein optimization by predicting continuous protein properties from simple directed evolution experiments using interpretable machine learning. Evaluated across five diverse protein engineering tasks, continuous properties are consistently predicted from readily available deep sequencing data. To prospectively test the utility of this approach, we generated a library of stapled peptides and applied the framework to predict and optimize both affinity and specificity. We coupled integer linear programming with the interpretable machine learning model coefficients to identify new variants from experimentally unseen sequence space that have desired properties. This approach represents a versatile tool for improved analysis and identification of protein variants across many domains of protein engineering.

bioengineering↗