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PAPAMOKOS GEORGIOS

PAPAMOKOS GEORGIOS
PAPAMOKOS GEORGIOS
Dr. George V. Papamokos is a computational chemist and biophysicist, currently serving as a Senior Scientist at FORTH-IACM. Dr. Papamokos entered the Chemistry Department achieving the fifth best score, and during his studies, he was awarded an Erasmus Scholarship to study at Trinity College Dublin. With BSc in Chemistry and a Ph.D. in Computational Chemistry from the University of Ioannina, Dr. Papamokos has a solid foundation in theoretical and physical chemistry, which he has continuously built upon through a career that spans multiple international research institutions, including the École Polytechnique Fédérale de Lausanne, German Research School for Computational Biophysics, Jülich-Germany, School of Engineering and Applied Sciences, Harvard University, and the International School for Advanced Studies (SISSA). Over the years, he has held positions that reflect his versatility and leadership in computational science, from Research Scientist roles to invited lecturer positions, highlighting his commitment to advancing research and education in the field.

Dr. Papamokos’s research interests lie at the convergence of quantum chemistry, molecular dynamics, high-performance computing, artificial intelligence, and computational biophysics. His work uniquely integrates computational and theoretical methods to explore the behavior of biomolecules under complex environmental conditions.

Throughout his career, Dr. Papamokos has been instrumental in projects exploring the structural aspects of biomolecules and their functional implications in various physiological and stress-induced environments. His publications include high-impact papers on the molecular recognition of unsaturated fatty acids in serum albumin, the dynamic behavior of polybutadiene under nanoconfinement. Notably, his contributions in computational epigenetics have been recognized as pioneering, particularly his studies on the structural role of combinatorially modified histone tails in chromatin interactions.

Dr. Papamokos has participated in EU-sponsored projects under the ESPA Thalis and Pythagoras II programs. His collaboration with leading experts across academia and industry, such as his partnership with Illy café at the SISSA, underscores his adaptability and dedication to interdisciplinary research.

As an educator, Dr. Papamokos has mentored numerous students in both undergraduate and graduate programs, guiding them through the complexities of computational biochemistry, high-performance computing, and molecular modeling. He has held several adjunct and visiting lecturer positions, where he developed and taught advanced courses in biophysics, structural biology, and computational methods, including courses tailored for the German Research School for Simulation Sciences. He has been a Review Editor for Frontiers in Chemistry and a reviewer for journals including ACS Macromolecules, PhysChemChemPhys, and Current Medicinal Chemistry.

PAPAMOKOS

GEORGIOS

gpapamokos@iacm.forth.gr

curriculum-vitae.pdf

IACM-FORTH, Ν. Plastira 100, Vassilika Vouton, GR70013 Heraklion, Crete, Greece

+30 2810 391805

G. Papamokos and E. Kaxiras. “How to evict HP1 from H3: Using a double salt bridge.” Biophysical Chemistry, 2023, 123, 27α.

T. Venianakis, M. Siskos, G. Papamokos, I. P. Gerothanassis. “NMR and DFT studies of unsaturated free fatty acids in the liquid state reveal a unique atomic-level structural model of DHA.” Journal of Molecular Liquids, 2023, 376, 121459.

G. V. Papamokos. “The nature of biological material and the irreproducibility problem in biomedical research.” EMBO Journal, 2019, e101011 (commentary – reviewed by the board of directors).

G. V. Papamokos, G. Tziatzos, D. G. Papageorgiou, S. D. Georgatos, E. Kaxiras, A. Politou. “Progressive Phosphorylation Dictates the Self-Association of a Variably Modified Histone H3 Peptide.” Frontiers in Molecular Biosciences, 2021, 8, Article no. 698182.

Vlachopanos A., Soupsana E., Politou A., G. V. Papamokos. “Potamos Mass Spectrometry Calculator: Computer-Aided Mass Spectrometry to the Post-Translational Modifications of Proteins. A Focus on Histones.” Computers in Biology and Medicine, 2014, 55, 36-41.

G. V. Papamokos, G. Tziatzos, D. G. Papageorgiou, S. D. Georgatos, A. S. Politou, E. Kaxiras. “Structural role of RKS motifs in chromatin interactions: an MD study of HP1 bound to a variably modified histone tail.” Biophysical Journal, 2012, 102, 1926-1933.

T. Venianakis, A. Primikyri, T. Opatz, S. Petry, G. Papamokos, I. P. Gerothanassis. “NMR and Docking Calculations Reveal Novel Atomistic Selectivity of a Synthetic High-Affinity Free Fatty Acid vs. Free Fatty Acids in Sudlow’s Drug Binding Sites in Human Serum Albumin.” Molecules, 2023, 28, 7991.

G. V. Papamokos, I. Silins. “Combining QSAR Modeling and Text-Mining Techniques to Link Chemical Structures and Carcinogenic Modes of Action.” Frontiers in Pharmacology, 2016, 7, 284.

E. Alexandri, A. Primikyri, G. Papamokos, I. P. Gerothanassis. “NMR and computational studies reveal atomic-level molecular recognition of unsaturated fatty acids with non-labeled albumin.” FEBS Journal, 2022, 289 (18), 5617-5636.

Papamokos G. V., I. G. Tsoulos, I. N. Demetropoulos, E. Glavas. “Location of amide I mode of vibration in computed data utilizing constructed neural networks.” Expert Systems with Applications, 2009, 36, 12210-12213.

– Epigenetic Regulation and Chromatin Dynamics:

Investigating liquid-liquid phase separation (LLPS) and post-translational modifications (PTMs) in chromatin structure and gene regulation.

– Molecular Dynamics and High-Performance Computing:

Large-scale simulations of biomolecular interactions under various environmental conditions, focusing on computational efficiency and accuracy.

– Quantum Chemistry and Quantum Mechanistic Simulations:

Applying quantum mechanical methods to study molecular structure, reactivity, and energy landscapes, particularly in biologically relevant systems.

– AI and Machine Learning in Molecular Modeling:

Using AI to enhance molecular docking, predictive modeling, and structure-function analysis in computational chemistry and biochemistry.

– Computational Toxicology and QSAR Modeling:

Predictive modeling of chemical interactions and carcinogenicity through machine learning and quantitative structure-activity relationships (QSAR).

– Protein-Ligand and Protein-Protein Interactions:

Structural and energetic analysis of interactions in chromatin-associated proteins, emphasizing binding affinities and regulatory mechanisms.