BioExcel’s webinar series continues with a special edition featuring student speakers who were awarded poster prizes at the BioExcel School 2022. Read along to find out more about our speakers and their research.
Date: 03 May, 2022 Time: 15:00 CEST
Cristina comes from Spain (Zaragoza), and she is in the first year of her PhD in the Frankfurt Institute for Advanced Studies and the Goethe University in Frankfurt am Main under the supervision of Dr. Sebastian Thallmair. Previously, she obtained her Master’s degree in “Theoretical Chemistry and Computational Modeling” in the Autonomous University of Madrid and her Bachelor’s in Chemistry in the University of Zaragoza. In addition, during the last year of the Bachelor she joined an Erasmus Internship in the University of Gothenburg (Sweden).
The focus of her research is on protein-ligand binding interactions for drug development applying molecular dynamics methods, mainly coarse-grained molecular dynamics (Martini 3.0). Currently, she is studying two different pharmaceuticals for the treatment of respiratory diseases and their target protein, a G protein-coupled receptor. Additionally, she also started to work on kinase inhibitors and photoswitchable molecules.
Coarse-Grained Modeling of Salbutamol and Salmeterol Binding to Beta2-Adrenergic Receptor
The beta2-adrenergic receptor (B2AR) belongs to the family of G protein-coupled receptors, one of the major drug targets. G protein-coupled receptors are integral membrane proteins that convert external signals into intracellular responses. Two already known drugs employed in the treatment of several respiratory diseases are salmeterol and salbutamol. They show a high affinity to B2AR, however, their binding pathways have not yet been fully characterized.
Along this project we will shed light on the binding process by means of coarse-grained molecular dynamics simulations using the Martini 3.0 force field. This methodology enables us to study the binding pathway of both drugs in an unbiased way.
First, we parametrized the new ligands and the target protein according to the Martini 3.0 model. The defined parameters were in good agreement with all-atom simulations and experimental properties. In addition, the analysis of the ligands’ behaviour within different membrane compositions provided fundamental details such as the high membrane affinity of salmeterol indicated by its longer residence time in the membrane compared to salbutamol. The placement of the ligands in their known binding site showed residence times expected for their high affinity to B2AR. Afterwards, a system composed of B2AR embedded in a membrane including multiple ligands in the water phase was simulated. Based on the binding events observed along the different simulation replicas, we will analyze the ligand hot spots on the B2AR surface as well as their binding pathways and affinities.
Helena Giramé Rizzo is currently a first year PhD student at the Institute of Computational Chemistry and Catalysis (IQCC) at the University of Girona. Her initial scientific experiences were as multidisciplinary as the BSc in Nanoscience and Nanotechnology she pursued (Autonomous University of Barcelona), during which she participated in projects within the areas of materials science, electronic and biomedical engineering.
With the Erasmus Mundus Joint Master in Theoretical Chemistry and Computational Modelling (KU Leuven and University of Groningen), her research interests shifted completely to the in silico approach. Her main project consisted in performing DFT calculations and employing diverse quantum chemistry tools to find and rationalize remarkable structures of Si/Ge-doped clusters.
As much as she liked it, her concerns and ambitions leaned towards the biological sciences. Subsequently, Helena took the opportunity from Dr. Marc Garcia-Borràs and Dr. Ferran Feixas to work with them in a PhD project to develop and apply new multi-scale computational protocols to study the molecular basis of photo-biochemical processes.
How the Assignment of Protonation States in Distal Residues May Alter Protein-Ligand Binding in Molecular Dynamics Simulations
Molecular dynamics (MD) and enhanced sampling simulations are attractive methodologies for the study of biochemical processes due to their ability to describe the structural and dynamic behaviour of biomolecules at atomic resolution. The starting point of these simulations is generally a crystal structure, which does not supply information on the hydrogen atoms and thus raises a challenge on the adequate selection of protonation states in titratable residues. Several methods and utilities have risen to resolve such ambiguity, yet they are far from infallible and therefore it is encouraged to manually revise the suggested states within the system’s region of interest.
Nonetheless, in this work we aim to put the spotlight outside that area of interest. The enzyme-inhibitor system trypsin-benzamidine is utilized to evaluate how the protonation states ofHis57,a residue located over 13 Å away from the binding site, can critically influence the computational characterization of protein-ligand binding.Performing spontaneous MD simulations foreach of the three possible protonation states, the number of encountered binding events was observed very different between the two neutral forms and the protonated one of His57. Further assessment with dimensionality reduction techniques demonstrated that the benzamidine binding pathway is dependent on the selected protonation state. A Constant-pH MD approach was additionally carried out, which supplied a more complete picture by accounting for the full equilibrium of the three forms of His57. The present study raises attention to the assignment of protonation states in distal residues in MD simulations, as they can be essential for the proper computational modelling of protein-ligand binding.
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I got my Bachelor’s degree in Physics at the University of Perugia (Italy). Then I moved to Padua (Italy) where I pursued my Master’s degree in Physics of Matter, with a special focus in the Statistical Mechanics area, Complex Systems, and computational methods. Currently, I am a first-year Ph.D. student at the Department of Physics and Astronomy in Padua under the supervision of Prof. A. Trovato. My research interests are related to the field of Statistical Mechanics and Computational Biology, indeed my current project is a continuation of my Master’s thesis in which I study a class of proteins characterized by a topological entangled structure in the native state. In particular, I use Molecular Dynamics to simulate the folding of these proteins within a coarse-grained representation. Another line of research related to the same topic involves capturing key mechanisms of folding through simple statistical mechanical models. During my Ph.D. I try to combine three areas that fascinate me: Biophysics, Computational Science, and Statistical Physics.
In the last couple of decades, growing attention was drawn to proteins having an entangled topology in their native ensemble. Their folding events are characterized by topological frustration and kinetic traps which pose challenges in their study. Nevertheless, some folding features can be inferred by simple topological descriptors. Baiesi and colleagues  introduced a linking number-inspired descriptor called Gaussian Entanglement.
Remarkably, up to 32% of proteins domains have a topologically entangled motif, therefore it is valuable to understand how these structures keep under control the entanglement during folding.
Using Molecular Dynamics to probe folding events of a test case protein having an entangled topology (the Type III Antifreeze Protein RD1), within a structure-based and Alpha-Carbon representation, we observed that native contacts related to the entanglement tend to form in the late stages of the folding.
These results are in agreement with the hypothesis that the protein evolved to postpone the formation of the so-called “entangled contacts”. As for future outlooks, a large-scale analysis of these entangled structures  suggests a possible key role of cotranslation in facilitating the protein to find its native ensemble.
 Baiesi, M.; Orlandini, E.; Trovato, A.; Seno, F. Scientific Reports 2016, 6, 33872
 Baiesi, M.; Orlandini, E.; Seno, F.; Trovato, A. Scientific Reports 2019, 9, 8426