Tayhan, B. | bioRxiv Science

bioRxiv2026

Functional Calmodulin States are Selected from an Electrostatically Tuned Free Energy Landscape

Calmodulin (CaM) is a versatile calcium-binding protein whose structural flexibility enables regulation of diverse cellular processes. Capturing its full conformational landscape remains challenging due to high energy barriers between states. Here we employ well-tempered metadynamics simulations using key collective variables to explore CaM conformations under calcium-bound and calcium-free states at physiological and low salt concentrations. We identify four principal conformations that shift in population depending on calcium binding and ionic strength. Calcium binding favors compact states, while low salt conditions flatten the energy landscape, facilitating transitions, but also causing kinetic trapping due to salt-bridge interactions. Comparison with experimental CaM-protein complexes reveals that target binding stabilizes extended conformations distinct from minima accessible to free CaM. These findings elucidate how calcium and ionic environment orchestrate CaMs conformational dynamics, enhancing understanding of its functional adaptability in cellular calcium signaling.

biophysics↗

bioRxiv2024

High throughput mutational scanning of a protein via alchemistry on a high-performance computing resource

Antibiotic resistance presents a significant challenge to public health, as bacteria can develop resistance to antibiotics through random mutations during their life cycles, making the drugs ineffective. Understanding how these mutations contribute to drug resistance at the molecular level is crucial for designing new treatment approaches. Recent advancements in molecular biology tools have made it possible to conduct comprehensive analyses of protein mutations. Computational methods for assessing molecular fitness, such as binding energies, are not as precise as experimental techniques like deep mutational scanning. Although full atomistic alchemical free energy calculations offer the necessary precision, they are seldom used to assess high throughput data as they require significantly more computational resources. We generated a computational library using deep mutational scanning for dihydrofolate reductase (DHFR), a protein commonly studied in antibiotic resistance research. Due to resource limitations, we analyzed 33 out of 159 positions, identifying 16 single amino acid replacements. Calculations were conducted for DHFR in its drug-free state and in the presence of two different inhibitors. We demonstrate the feasibility of such calculations, made possible due to the enhancements in computational resources and their optimized use.

biophysics↗