Curriculum Vitae

Leiden University Medical Center (LUMC), The Netherlands

Project goal: Rationally engineer catalytically active serine protease coagulation Factor X variants with reduced binding of small-molecule and natural inhibitors for therapeutic purposes

• • Sole computational scientist in a predominantly experimental group, translating complex simulation results for clinicians and experimentalists.
• • Analyzed experimental Factor X-small molecule inhibitor structures from the Protein Data Bank; generated additional complexes using molecular docking (HADDOCK, Schrödinger Glide) where structures were unavailable.
• • Prepared using Schrödinger Suite or AmberTools and simulated protein-ligand systems using AMBER, and/or Desmond.
• • Performed extensive molecular dynamics trajectory analyses, including RMSD/RMSF, protein–ligand interaction profiling, and binding free-energy calculations (MM/PBSA, MM/GBSA).
• • Performed per-residue free-energy decomposition to identify mutations that selectively weakened inhibitor binding, followed by targeted simulations of prioritized variants.
• • Modeled Factor X–natural inhibitor complexes using AlphaFold-Multimer, generating and refining multiple binding configurations via molecular dynamics simulations.
• • Identified interaction hotspots governing natural inhibitor binding; provided new detailed insight of their interactions and guided the design of inhibitor-resistant variants.
• • Developed a computational pipeline to assess the enzymatic competence of engineered variants despite overlapping substrate and inhibitor binding sites (currently under revision at the Journal of Chemical Information and Modeling).
• • Conducted all data analysis using Python, R, and GraphPad Prism; managed large-scale simulations via Bash scripting and Slurm job scheduling on HPC systems.
• • Generated appealing visual representations using PyMol and VMD.
• • Initiated and maintained external collaborations with researchers at Vrije Universiteit Amsterdam and the University of Basel.
• • Delivered regular presentations, posters, and oral talks, contributing to award-winning interdisciplinary conference presentations and funded grants.
• • PhD representative for the Dutch Association of Thrombosis and Haemostasis and LUMC.
• • Designed a project and supervised MSc student; taught and mentored MSc Biomedical Sciences students.

Utrecht University

GPA: 7.33/10

• • Minor internship: "Engineering Factor Xa variants with lower Apixaban binding"
• • Major internship: "Resistance of Streptococcus suis against antimicrobial peptides"
• • Thesis: "Current modelling approaches to generate 3D structures of protein-oligosaccharide complexes"
• • Courses: Introduction to Python and R for Life Sciences, Design of anti-infective drug (through molecular modelling)

Machine Learning Training & Project Experience

• • Completed Introduction to Machine Learning in Coursera.
• • Developed predictive models for protein abundance in mouse kidney tissue using curated multi-species datasets at Roche PMDA Summer School 2025.
• • Trained and evaluated multiple models (multilayer perceptrons, XGBoost, ridge regression, and random forest) to outperform linear regression.