• Implemented prime editing and base editing prediction methods.
• Validated algorithm performance using real-world data.
• Collaborated with cross-functional teams to integrate algorithms into Biomod AI’s platform.

Outcome: Integrated prime design and base editing tools into the platform.

Key skills: Python, GitHub, teamwork, communication, work under pressure in a fast-paced environment

Biologics engineering

• Applied protein engineering strategies for vaccine design.
• Performed structural modelling and characterisation of engineered constructs.
• Designed de novo peptides.
• Conducted mutagenesis studies for functional analysis.

Outcome: Designed a multivalent vaccine concept against Lyme disease (patent pending), conducted functional study of protein mutants for vaccine applications (patent pending).

Computational biophysics

• Conducted molecular dynamics studies of de novo peptides.
• Characterised peptides’ physicochemical properties.
• Computed metrics for peptides, e.g., folding stability, hydrophobicity, electrostatics, self-association.

Outcome: Developed a computational workflow to prioritise peptides based on biophysical properties and potential for chemical conjugation with lipid vesicles.

Additional duties

• Collaborated with experimentalists.
• Managed multiple projects independently.
• Prepared internal reports and patent applications.
• Presented results in follow-up meetings.

Key skills: protein design and engineering (AlphaFold2 Modeller, ESM), molecular dynamics (GROMACS, Plumed), enhanced sampling (OPES metadynamics), self-directed work, teamwork, communication, Python, Bash

Supervisor: Prof. Francesco Luigi Gervasio

Mechanical biochemistry

• Modelled class A and F GPCRs based on CryoEM data and prediction tools.
• Conducted molecular dynamics studies on class A adrenergic receptors (beta 1 and beta 2) and class F GPCRs collaborating with experimentalists.
• Developed collective variables to study the GPCR activation via enhanced sampling.

Outcome: published 3 papers in ACS, published 1 paper in Science.

Method development

• Defined a machine learning potential for long-range interactions in biological systems with explicit water simulations.

Outcome: Proposal submissions: H2020-MSCA-IF-2020 nr. 101028255, HORIZON-MSCA-2021-PF-01 nr. 101062489, SNSF Swiss Postdoctoral Fellowships 2021 nr. TMPFP2_210127/1.

Additional duties

• Authored postdoctoral proposals and secured computing time.
• Managed multiple projects independently.
• Supervised and mentored PhD students.

Key skills: molecular dynamics (GROMACS, Plumed), enhanced sampling (metadynamics), grant writing, scientific writing, mentoring, self-directed work, teamwork, communication, Python, Bash, HPC

• Developed and applied computational methods for drug discovery (protein-ligand binding/unbinding kinetics, binding free energy prediction, small molecule solubility prediction).
• Authored scientific papers for international journals.
• Managed multiple projects.
• Mentored junior researchers.

Outcome: Published 6 papers published in peer-reviewed journals.

Key skills: molecular dynamics (GROMACS, Plumed), statistical mechanics, data analysis and visualisation, scientific writing, mentoring, self-directed work, teamwork, communication, Python, Bash, HPC

Conducted experimental and computational studies of Axl kinase-ligand binding kinetics by surface plasmon resonance measurements and enhanced molecular dynamics simulations

Supervisors: Dr. Gerhard Hessler, Dr. Matthias Dreyer

Key skills: surface plasmon resonance (SPR), molecular dynamics (GROMACS),Bash, communication, self-directed work, teamwork, communication