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We aim to automatize the identification of collective variables to simplify and speed up enhanced sampling simulations of conformational dynamics in biomolecules. We focus on anharmonic low-frequency vibrations that exhibit fluctuations on timescales faster than conformational transitions but describe a path of least resistance towards structural change. A key challenge is that harmonic approximations are ill-suited to characterize these vibrations, which are observed at far-infrared frequencies and are easily excited by thermal collisions at room temperature. Here, we approached this problem with a frequency-selective anharmonic (FRESEAN) mode analysis that does not rely on harmonic approximations and successfully isolates anharmonic low-frequency vibrations from short molecular dynamics simulation trajectories. We applied FRESEAN mode analysis to simulations of alanine dipeptide, a common test system for enhanced sampling simulation protocols, and compare the performance of isolated low-frequency vibrations to conventional user-defined collective variables (here backbone dihedral angles) in enhanced sampling simulations. The comparison shows that enhanced sampling along anharmonic low-frequency vibrations not only reproduces known conformational dynamics but can even further improve sampling of slow transitions compared to user-defined collective variables. Notably, free energy surfaces spanned by low-frequency anharmonic vibrational modes exhibit lower barriers associated with conformational transitions relative to representations in backbone dihedral space. We thus conclude that anharmonic low-frequency vibrations provide a promising path for highly effective and fully automated enhanced sampling simulations of conformational dynamics in biomolecules. 

Peptides that bind to inorganic materials can be used to functionalize surfaces, control crystallization, or assist ininterfacial self-assembly. In the past, inorganic-binding peptides have been found predominantly through peptide library screening. While this method has successfully identified peptides that bind to a variety of materials, an alternative design approach that can intelligently search for peptides and provide physical insight for peptide affinity would be desirable. In this work, we develop a computational, physics-based approach to design inorganic-binding peptides, focusing on peptides that bind to the common plastics polyethylene, polypropylene, polystyrene, and poly(ethylene terephthalate). The PepBD algorithm, a Monte Carlo method that samples peptide sequence and conformational space, was modified to include simulated annealing, relax hydration constraints, and an ensemble of conformations to initiate design. These modifications led to the discovery of peptides with significantly better scores compared to those obtained using the original PepBD. PepBD scores were found to improve with increasing van der Waals interactions, although strengthening the intermolecular van der Waals interactions comes at the cost of introducing unfavorable electrostatic interactions. The best designs are enriched in amino acids with bulky side chains and possess hydrophobic and hydrophilic patches whose location depends on the adsorbed conformation. Future work will evaluate the top peptide designs in molecular dynamics simulations and experiment, enabling their application in microplastic pollution remediation and plastic-based biosensors. 

Proton transfer reactions are ubiquitous in chemistry, especially in aqueous solutions. We investigate photo-induced proton transfer between the photoacid 8-hydroxypyrene-1,3,6-trisulfonate (HPTS) and water using fast fluorescence spectroscopy and ab initio molecular dynamics simulations. Photo-excitation causes rapid proton release from the HPTS hydroxyl. Previous experiments on HPTS/water described the progress from photoexcitation to proton diffusion using kinetic equations with two time constants. The shortest time constant has been interpreted as protonated and photoexcited HPTS evolving into an “associated” state, where the proton is “shared” between the HPTS hydroxyl and an originally hydrogen bonded water. The longer time constant has been interpreted as indicating evolution to a “solvent separated” state where the shared proton undergoes long distance diffusion. In this work, we refine the previous experimental results using very pure HPTS. We then use excited state ab initio molecular dynamics to elucidate the detailed molecular mechanism of aqueous excited state proton transfer in HPTS. We find that the initial excitation results in rapid rearrangement of water, forming a strong hydrogen bonded network (a “water wire”) around HPTS. HPTS then deprotonates in ≤3 ps, resulting in a proton that migrates back and forth along the wire before localizing on a single water molecule. We find a near linear relationship between emission wavelength and proton-HPTS distance over the simulations’ time scale, suggesting that emission wavelength can be used as a ruler for proton distance. Our simulations reveal that the “associated” state corresponds to a water wire with a mobile proton and that the diffusion of the proton away from this water wire (to a generalized “solvent-separated” state) corresponds to the longest experimental time constant.