Disruptive Mutations Elucidated by Centrality of Residues and Redundancies in Inter-Residue Communication Pathways
Tandac Fürkan GUCLU
In our study, we investigated how mutations in the C2 fragment of Streptococcal protein G (GB1) influence its binding to the Fc domain of human IgG (IgG-Fc) through a combination of in silico mutational scanning and molecular dynamics (MD) simulations. We performed mutational scanning to analyze all possible single mutations of GB1, and conducted MD simulations of the wild-type GB1 in both its unbound form and when bound to IgG-Fc. These simulations allowed us to identify key hydrogen bonds critical for binding, which were further assessed using a method called Mutation and Minimization (MuMi) to predict the fitness landscape and stability of different mutations. We also examined the relative solvent accessibility of residues and the likelihood of residues being located at the binding interface, providing insights into the dynamics of binding. Our results demonstrate MuMi's reliability and efficiency as a predictive tool for protein fitness landscapes, offering significant advantages over traditional experimental approaches. This research lays the groundwork for improved predictive tools in protein stability and interaction studies, which are essential for advancing drug design and synthetic biology.
Dynamics of the Homotrimeric TOLC Transmembrane Protein
Isik KANTARCIOGLU
We previously utilized advanced computational methods to capture intrinsic movements of TolC [1]. These insights into the structural shifts that optimize TolC for efflux were pivotal. Additionally, our whole-gene saturation mutagenesis assay identified specific mutations in TolC that influence the efficiency of drug efflux. Understanding these mutations is essential for guiding the design of new antibiotics capable of overcoming bacterial resistance.
Mapping drug-TolC interactions. Carbenicillin (blue; top row), piperacillin (green; middle row), oxacillin (yellow; bottom row). Vertical stripes use the color code of the regions of TolC. The work curves from SMD simulationswere calculated by binning the reaction coordinate vs. work data. The data were collected at the intervals of 200 fs. (Error bars were calculated by block averaging the data in each bin as well as bootstrapping. Errors indicated by shades were less than 10%). B. The average number of hydrogen bonds between drug and TolC along the reaction coordinate (colored; left y-axis) and the first derivative of the work (black; right y-axis). C. Residues that have more than an average of two hydrogen bonds (above dashed line in D) plotted on the three-dimensional representation of the protein; charged residues red, polars purple, and hydrophobics cyan. D. Number of hydrogen bonds formed between a given residue and the drug averaged over 45 SMD simulations (hydrogen bond recording timestep is 100 psfor 30 ns long SMD simulations, N=45 for A-D).
Building on these findings, we expanded our research to include the adaptor protein and inner membrane protein of efflux complexes involved in antibiotic expulsion. Through this research, we aim to enhance our understanding of antibiotic resistance mechanisms and pave the way for developing more effective treatments against multidrug-resistant bacteria.
Genetically Encoded Fluorescent Biosensors
Melike BERKSOZ
Schematic representation of conformationally induced fluorescence.
Genetically encoded fluorescent biosensors (GEFB) are molecular tools that couple ligand-induced conformational changes to a fluorescence output. Structural determinants of successful biosensors are difficult to predict which makes the development of new biosensors a long trial-and-error process. We model high performance biosensors that are reported in the literature and perform molecular dynamics to reveal the structural fingerprints of high fluorescence output. By extracting information about hydrogen bond dynamics, we try to predict the protonation state of the chromophore in ligand bound and unbound sensors.
Hydrogen bond network around chromophore of GFP.
Resistance Mechanisms of E. coli on the Inhibition of DHFR
Ebru CETIN
Antibiotic resistance is a common health problem that requires urgent and creative solutions. According to the Centers for Disease Control and Prevention, USA, globally more than 150,000 deaths per year occurs due to antibiotic resistance. [1] Thus, current medical practices seek for a thorough understanding on the purpose of developing proper approaches to attack this problem. Dihydrofolate reductase (DHFR) is an enzyme that catalyzes the hydride transfer from the cofactor NADPH to 7,8-dihydrofolate (DHF) to facilitate the formation of 5,6,7,8-tetrahydrofolate (THF). THF is a critical carbon carrier in the biosynthesis of nucleotides which makes DHFR an important player on cell growth and proliferation [2]. Trimethoprim (antibiotic) and methotrexate (antineoplastic agent) are known inhibitors of DHFR which are used in therapeutics. Trimethoprim was synthesized in 1962 and is the last original antibiotic produced; all other antibiotics produced are some variation of older antibiotics. [3] Trimethoprim inhibits the function of DHFR by competitively binding the enzyme, displacing dihydrofolate. We are trying to understand the physical rationale behind the resistant DHFR binding mechanisms and selectivity of the enzyme between DHF and Trimethoprim. In pursuit of this aim, we use atomistic level Molecular Dynamics simulations and utilize the Free Energy Perturbation Method to understand energetic effects of resistance, to trace the roots of catalytic efficiency and selectivity towards the substrate.
Testing the Applicability of PRS on Molecular Energy Landscapes
Kurt ARICANLI
While protein dynamical studies can be carried out in solution and molecular dynamics (MD) simulations can be used to probe dynamics of folding intermediates, computationally expensive in silico studies cannot attempt to statistically elucidate entire protein landscapes. Illustrated in the figure below, representative two-dimensional energy landscapes of model molecules such as pentane ‐ based on the essential degrees of freedom of the two torsional angles (φ) ‐ are readily obtained using MD simulations on a millisecond timescale. These can be subsequently compared with energy landscapes constructed using the steered molecular dynamics (SMD) approach, by which a conformational change is induced by applying an appropriate, site-specific external force. Potential positions and directions of external forces are obtained using a computational tool known as perturbation response scanning (PRS) [1], whose implementation is illustrated in the figure. Our research aims to study the relationship between SMD and pure MD energy landscapes of toy models, and to test the applicability of the PRS-SMD approach in elucidating molecular energy landscapes.
Energy landscape of pentane with respect to its two dihedral angles (φ) (left); applied force vectors for conformational changes of pentane obtained using PRS computational tool (right).
Mapping Conformational Dynamics of CaM Across Environmental Shifts
Busra Tayhan
In our research, we investigate how the conformational landscape of calmodulin (CaM) shifts under changing environmental conditions. This calcium-binding protein involved in diverse cellular processes, exhibiting multiple conformations observed under different experimental conditions. Its conformational space is well-represented by two simplified degrees of freedom (DoF): one describing the rotational motion between its two lobes and another representing the overall compactness of CaM. While MD simulations offer atomic-level insights into complex molecular processes, they are limited by high-energy barriers, restricting complete sampling of conformational space due to time scale constraints. To overcome this challenge, we performed well-tempered metadynamics simulations by defining the same DoFs as collective variables (CVs) and explored four distinct conditions representing calcium-bound/unbound CaM at physiological and low ionic strength.