Thesis Work, 30 Credits - Supporting Ligand-AI with High-Quality Protein Reagents and Next-Generation RP3Net Models

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About the role

Are you passionate about protein science and excited by the opportunities created by artificial intelligence in drug discovery? Join us for a hands-on master’s thesis where you will produce and quality-check recombinant proteins in E. coli, investigate expression conditions across 97 constructs, and help improve next-generation protein expression prediction models. About AstraZeneca: AstraZeneca is a global, science-led, patient-centred biopharmaceutical company focusing on discovering, developing, and commercialising prescription medicines for some of the world’s most serious diseases. But we’re more than a global leading pharmaceutical company. At AstraZeneca, we're dedicated to being a Great Place to Work and empowering employees to push the boundaries of science and fuel their entrepreneurial spirit. About the Opportunity: As a Thesis Worker at AstraZeneca, you’ll find an environment that’s full of unique opportunities and exciting challenges. Here, you’ll have the opportunity to pursue your areas of interest whilst equally developing a broad skillset and knowledge base to get the best out of your experience. You’ll be working on meaningful projects to make an impact and deliver real value for our patients and our business. Thesis work description: Artificial intelligence is increasingly being used to support protein production and accelerate drug discovery. However, the performance of AI models depends on access to high-quality experimental data and reliable protein reagents. In this thesis project, you will support the Ligand-AI project by producing recombinant proteins in Escherichia coli and generating data that can be used to evaluate and improve protein expression prediction models, including RP3Net. What you’ll do • You will work experimentally with recombinant protein production using E. coli as an expression system. The project will include expressing, purifying, and quality-checking proteins that will be screened to discover new small molecule ligands within the Ligand-AI project. • You will investigate how different codon optimization strategies and expression conditions affect protein production across 97 constructs. By systematically comparing experimental results, you will assess protein expression outcomes and identify conditions that support the production of high-quality proteins. • The project will also involve analyzing the experimental data in relation to predictions from RP3Net and other internal models. Based on your findings, you will explore how these models can be improved and how experimental data can contribute to more reliable predictions of recombinant protein expressions. Your impact: Your work will contribute to the generation of high-quality protein reagents for an AI-driven drug discovery project. By combining hands-on laboratory work with data analysis and model evaluation, you will help improve the understanding and prediction of recombinant protein production in E. coli. The results may support more efficient protein production workflows and contribute to the development of next-generation computational models. You can read more here about RP3Net and Ligand-AI: https://doi.org/10.1093/bioinformatics/btag003 https://ligand-ai.org/ Placement:   This is an on-site position at AstraZeneca Gothenburg. Please note, AstraZeneca does not support with accommodations for this role. Structure: • Duration: Spring 2027 • Credits: 30 Essential Requirements: • Enrolled in a Master’s programme within biochemistry, biotechnology, molecular biology, protein science, or a related field. • Previous lab-based experience with recombinant protein production using E. coli. • Expression of recombinant protein in E. coli cells and purification of recombinant proteins from E. coli cells, ideally usi

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