Berke Türkaydin
Phd Researcher Structural Chemistry & Computational Biophysics @Leibniz-Forschungsinstitut für Molekulare Pharmakologie (FMP)
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WORK HISTORY
Phd Researcher Structural Chemistry & Computational Biophysics @Leibniz-Forschungsinstitut für Molekulare Pharmakologie (FMP)
Berlin, DE
Research Group of Prof. Dr. Han Sun | Chemical Biology Investigated protein dynamics, allosteric regulation, and ligand binding in ion channels using large-scale molecular dynamics (MD), enhanced sampling, and free-energy calculations.Designed and performed high-throughput MD simulations, including advanced sampling and state-space exploration, to characterize functional conformational states and energetic landscapes.Developed Python analysis pipelines integrating molecular simulation data with machine-learning–driven methods (dimensionality reduction, clustering, feature extraction) to identify key structural transitions and predictive mechanistic signatures.Applied docking, structural modeling, and bioinformatics to support structure-based drug discovery and interpret protein–ligand interactions.Act as a scientific communicator by translating complex computational results into clear, actionable insights for experimental collaborators and interdisciplinary teams.Mentored and trained Master’s students and new PhD researchers, leading onboarding, project planning, and hands-on computational training in MD, PLUMED, Python, and analysis workflows.Published work based on research conducted during this period:\"Atomistic mechanism of coupling between cytosolic sensor domain and selectivity filter in TREK K2P channels\" Nature Communications 2024
EDUCATION
Technische Universität Berlin
Doctor of Philosophy - PhD, Chemistry
Istanbul Technical University
Bachelor's degree, Molecular Biology and Genetics
Freie Universität Berlin
Master's degree, Biochemistry
ABOUT BERKE TÜRKAYDIN
PhD candidate in Computational Biophysics and Structural Chemistry focused on protein dynamics, allosteric mechanisms, and protein–ligand interactions. My research explores how conformational landscapes govern molecular recognition and function, with applications in drug discovery, protein engineering, and protein design.I specialize in molecular dynamics simulations, structural modeling, docking, free-energy methods, and large-scale data analysis, primarily studying ion channels and complex membrane proteins. My work combines physics-based simulations with Python-driven data workflows to extract mechanistic insight and generate predictive models for ligand discovery and protein design.With an interdisciplinary background spanning computational chemistry, biophysics, and structural biology, I frequently work at the interface between experimental wet-lab biology and computational modeling. I enjoy translating experimental questions into computational hypotheses and using simulations to interpret, predict, and guide experiments.I am particularly interested in industry environments where molecular simulation, structural data, and machine learning can be integrated to accelerate therapeutic discovery and improve computational workflows.
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