"Design of peptide inhibitors"

The Drew Lab studies macromolecular assemblies in cells. These assemblies, made up of mostly proteins and nucleic acids, are responsible for most biological functions. We are specifically interested in learning how these assemblies function mechanistically and how they relate to human disease. We investigate assemblies using high throughput proteomics approaches to determine their components, biophysical/biochemical characteristics, and understand their structure. We also design tools to disrupt assemblies to understand their role in disease and develop pathways to therapeutics.

“Antibody engineering with deep learning”

Antibodies are critical molecules in the immune system and as pharmaceuticals. But many deep learning methods perform weakly on them because of their great diversity and reliance on loop structures, which have fewer data available. In this project you will create or apply new approaches such as language models, diffusion or flow models, toward predicting and designing better antibodies.

"Designing peptides for drug discovery"

Description Coming Soon

"De novo design of protein assemblies to interface with biological systems"

Current computational methods are able of designing novel protein assemblies with atomic-level accuracy, yet several aspects of current methods limit the structural and functional space that can be explored. We aim to expand the plethora of available protein assemblies for application by introducing and controlling new structural properties (e.g. flexibility, assembly stability, structural switches) and functional moiteties (e.g. environemntal responsiveness, catalysis, surface binding). In this project, students will explore one of these design axis using the latest AI-based algorithms for protein modelling and design, in the beautiful LMU campus in the south of Munich.

“Rosetta-informed enzyme engineering”

Enzyme engineering is an enabling technlogy to deliver biocatalytic solutions to pharmaceutical manufacturing. The project will involved computer-aided techniques to develop novel proteins and enzyme-catalyzed reactions to enable access to biologically active compounds and chemical reactions to the synthesis of drug-like molecules.

“Predictive modeling and design of cyclic peptides”

Cyclic peptides are an emerging therapeutic modality with growing clinical validation and the potential to combine biologic-like target engagement with small-molecule-like delivery, enabling access to challenging targets beyond the reach of many traditional drugs. In this project, you will develop computational approaches for cyclic peptide structure prediction, ADMET prediction, and generative design, integrating molecular modeling and machine learning to discover and optimize next-generation therapeutics.

“Active learning for antibody optimization”

Antibody CDR mutagenesis data provide a valuable resource for learning sequence-function relationships and guiding the design of improved variants. In this project you will apply computational and machine learning methods to train on single-mutant data and predict the effects of mutation combinations on properties relevant to antibody therapeutics. The project will explore active learning strategies to prioritize the most promising variants for follow-up testing, with the goal of more efficiently identifying combinations that improve multiple properties. You will be embedded in the biologics discovery department at the Merck South San Francisco and will additionally gain experience within the biopharmaceutical industry focused on inventing novel medicines for patients.

"Accurate modeling of adaptive immune recognition"

Accurate modeling of the complexes of antibodies and T cell receptors (TCRs) with their targets is critical for understanding immunity and design of biotherapeutics. Deep learning tools like AlphaFold have dramatically improved structural modeling accuracy for monomeric proteins and protein complexes, yet antibody and TCR complexes have limited success and remain a major challenge. This project will assess the latest tools against updated benchmarks of antibody-protein, antibody-peptide, and TCR-peptide-MHC complexes, identify what distinguishes correct predictions from failures, and use those insights to improve success through development of optimized scoring functions or modeling parameters.

"Stabilization of a coronavirus glycoprotein for pandemic preparedness"

Although coronaviruses were intesively studied during and after the COVID-19 pandemic, many poorly characterized coronaviruses remain, which pose a significant health thread. Being able to produce and characterize the major vaccine target, the spike protein, enables vaccine research. In the Schoeder lab, we build and constantly develope vaccine design pipelines. During this project, you will use and optimze a module in this pipeline and subsequently work on one of our target coronavirus glycoproteins, e.g. CCoV or TGEV. These proteins will be produced and characterized.

"Design of ion channel modulators"

The overreaching goals of my research are: 1) to rationally design novel subtype-specific ion channel modulators for the treatment of pain, epilepsy, and cardiovascular diseases; 2) to study molecular determinants of ion channel modulation and gating. While we have a solid basis for understanding of the physiological role of ion channel function, the molecular mechanisms of ion channel modulation and gating remain elusive. To gain novel insights into atomic scale structural details of ion channel gating, my group is using computational biology approaches to generate experimentally testable structural hypotheses. My group is leading an effort to develop structural models of ion channels with atomic accuracy using computational modeling approaches and use this knowledge for the rational design of novel ion channel modulators that can be used for the treatment of chronic pain, epilepsy, and cardiac arrhythmias. Related recent papers:

https://pubmed.ncbi.nlm.nih.gov/39463944/

https://pubmed.ncbi.nlm.nih.gov/39189871/

https://pubmed.ncbi.nlm.nih.gov/38445990/