Development and validation of transferable amide I vibrational frequency maps for peptides

AI protocol for retrieving protein dynamic structures from two-dimensional infrared spectra

Understanding the dynamic evolution of protein structures is crucial for uncovering their biological functions. Yet, real-time prediction of these dynamic structures remains a significant challenge. Two-dimensional infrared (2DIR) spectroscopy is a powerful tool for analyzing protein dynamics. However, translating its complex, low-dimensional signals into detailed three-dimensional structures is a daunting task. In this study, we introduce a machine learning-based approach that accurately predicts dynamic three-dimensional protein structures from 2DIR descriptors. Our method establishes a robust "spectrum-structure" relationship, enabling the recovery of three-dimensional structures across a wide variety of proteins. It demonstrates broad applicability in predicting dynamic structures along different protein folding trajectories, spanning timescales from microseconds to milliseconds. This approach also shows promise in identifying the structures of previously uncharacterized proteins based solely on their spectral descriptors. The integration of AI with 2DIR spectroscopy offers insights and represents a significant advancement in the real-time analysis of dynamic protein structures.

Do selectivity filter carbonyls in K+ channels flip away from the pore? Two-dimensional infrared spectroscopy study

Molecular dynamics simulations revealed that the carbonyls of the Val residue in the conserved selectivity filter sequence TVGTG of potassium ion channels can flip away from the pore to form hydrogen bonds with the network of water molecules residing behind the selectivity filter. Such a configuration has been proposed to be relevant for C-type inactivation. Experimentally, X-ray crystallography of the KcsA channel admits the possibility that the Val carbonyls can flip, but it cannot decisively confirm the existence of such a configuration. In this study, we combined molecular dynamics simulations and line shape theory to design two-dimensional infrared spectroscopy experiments that can corroborate the existence of the selectivity filter configuration with flipped Val carbonyls. This ability to distinguish between flipped and unflipped carbonyls is based on the varying strength of the electric field inside and outside the pore, which is directly linked to carbonyl stretching frequencies that can be resolved using infrared spectroscopy.

Estimation of vibrational spectra of Trp-cage protein from nonequilibrium metadynamics simulations

The development of methods that allow a structural interpretation of linear and nonlinear vibrational spectra is of great importance, both for spectroscopy and for optimizing force field quality. The experimentally measured signals are ensemble averages over all accessible configurations, which complicates spectral calculations. To account for this, we present a recipe for calculating vibrational amide-I spectra of proteins based on metadynamics molecular dynamics simulations. For each frame, a one-exciton Hamiltonian is set up for the backbone amide groups, in which the couplings are estimated with the transition-charge coupling model for nonnearest neighbors, and with a parametrized map of ab initio calculations that give the coupling as a function of the dihedral angles for nearest neighbors. The local-mode frequency variations due to environmental factors such as hydrogen bonds are modeled by exploiting the linear relationship between the amide C-O bond length and the amide-I frequency. The spectra are subsequently calculated while taking into account the equilibrium statistical weights of the frames that are determined using a previously published reweighting procedure. By implementing all these steps in an efficient Fortran code, the spectra can be averaged over very large amounts of structures, thereby extensively covering the phase space of proteins. Using this recipe, the spectral responses of 2.5 million frames of a metadynamics simulation of the miniprotein Trp-cage are averaged to reproduce the experimental temperature-dependent IR spectra very well. The spectral calculations provide new insight into the origin of the various spectral signatures (which are typically challenging to disentangle in the congested amide-I region), and allow for a direct structural interpretation of the experimental spectra and for validation of the molecular dynamics simulations of ensembles.

Salivary Molecular Spectroscopy with Machine Learning Algorithms for a Diagnostic Triage for Amelogenesis Imperfecta

Amelogenesis imperfecta (AI) is a genetic disease characterized by poor formation of tooth enamel. AI occurs due to mutations, especially in AMEL, ENAM, KLK4, MMP20, and FAM83H, associated with changes in matrix proteins, matrix proteases, cell-matrix adhesion proteins, and transport proteins of enamel. Due to the wide variety of phenotypes, the diagnosis of AI is complex, requiring a genetic test to characterize it better. Thus, there is a demand for developing low-cost, noninvasive, and accurate platforms for AI diagnostics. This case-control pilot study aimed to test salivary vibrational modes obtained in attenuated total reflection fourier-transformed infrared (ATR-FTIR) together with machine learning algorithms: linear discriminant analysis (LDA), random forest, and support vector machine (SVM) could be used to discriminate AI from control subjects due to changes in salivary components. The best-performing SVM algorithm discriminates AI better than matched-control subjects with a sensitivity of 100%, specificity of 79%, and accuracy of 88%. The five main vibrational modes with higher feature importance in the Shapley Additive Explanations (SHAP) were 1010 cm-1, 1013 cm-1, 1002 cm-1, 1004 cm-1, and 1011 cm-1 in these best-performing SVM algorithms, suggesting these vibrational modes as a pre-validated salivary infrared spectral area as a potential biomarker for AI screening. In summary, ATR-FTIR spectroscopy and machine learning algorithms can be used on saliva samples to discriminate AI and are further explored as a screening tool.

A new computational methodology for the characterization of complex molecular environments using IR spectroscopy: bridging the gap between experiments and computations

The molecular interactions and dynamics of complex liquid solutions are now routinely measured using IR and 2DIR spectroscopy. In particular, the use of the latter allows the determination of the frequency fluctuation correlation function (FFCF), while the former provides us with the average frequency. In turn, the FFCF can be used to quantify the vibrational dynamics of a molecule in a solution, and the center frequency provides details about the chemical environment, solvatochromism, of the vibrational mode. In simple solutions, the IR methodology can be used to unambiguously assign the interactions and dynamics observed by a molecule in solution. However, in complex environments with molecular heterogeneities, this assignment is not simple. Therefore, a method that allows for such an assignment is essential. Here, a parametrization free method, called Instantaneous Frequencies of Molecules or IFM, is presented. The IFM method, when coupled to classical molecular simulations, can predict the FFCF of a molecule in solutions. Here, N-methylacetamide (NMA) in seven different chemical environments, both simple and complex, is used to test this new method. The results show good agreement with experiments for the NMA solvatochromism and FFCF dynamics, including characteristic times and amplitudes of fluctuations. In addition, the new method shows equivalent or improved results when compared to conventional frequency maps. Overall, the use of the new method in conjunction with molecular dynamics simulations allows unlocking the full potential of IR spectroscopy to generate molecular maps from vibrational observables, capable of describing the interaction landscape of complex molecular systems.

The secondary structure of diatom silaffin peptide R5 determined by two-dimensional infrared spectroscopy

Diatoms, unicellular marine organisms, harness short peptide repeats of the protein silaffin to transform silicic acid into biosilica nanoparticles. This process has been a white whale for material scientists due to its potential in biomimetic applications, ranging from medical to microelectronic fields. Replicating diatom biosilicification will depend on a thorough understanding of the silaffin peptide structure during the reaction, yet existing models in the literature offer conflicting views on peptide folding during silicification. In our study, we employed two-dimensional infrared spectroscopy (2DIR) within the amide I region to determine the secondary structure of the silaffin repeat unit 5 (R5), both pre- and post-interaction with silica. The 2DIR experiments are complemented by molecular dynamics (MD) simulations of pure R5 reacting with silicate. Subsequently, theoretical 2DIR spectra calculated from these MD trajectories allowed us to compare calculated spectra with experimental data, and to determine the diverse structural poses of R5. Our findings indicate that unbound R5 predominantly forms β-strand structures alongside various atypical secondary structures. Post-silicification, there's a noticeable shift: a decrease in β-strands coupled with an increase in turn-type and bend-type configurations. We theorize that this structural transformation stems from silicate embedding within R5's hydrogen-bond network, prompting the peptide backbone to contract and adapt around the biosilica precursors.

Unraveling dynamic protein structures by two-dimensional infrared spectra with a pretrained machine learning model

Dynamic protein structures are crucial for deciphering their diverse biological functions. Two-dimensional infrared (2DIR) spectroscopy stands as an ideal tool for tracing rapid conformational evolutions in proteins. However, linking spectral characteristics to dynamic structures poses a formidable challenge. Here, we present a pretrained machine learning model based on 2DIR spectra analysis. This model has learned signal features from approximately 204,300 spectra to establish a "spectrum-structure" correlation, thereby tracing the dynamic conformations of proteins. It excels in accurately predicting the dynamic content changes of various secondary structures and demonstrates universal transferability on real folding trajectories spanning timescales from microseconds to milliseconds. Beyond exceptional predictive performance, the model offers attention-based spectral explanations of dynamic conformational changes. Our 2DIR-based pretrained model is anticipated to provide unique insights into the dynamic structural information of proteins in their native environments.

Artificial Intelligence-based Amide-II Infrared Spectroscopy Simulation for Monitoring Protein Hydrogen Bonding Dynamics

The structurally sensitive amide II infrared (IR) bands of proteins provide valuable information about the hydrogen bonding of protein secondary structures, which is crucial for understanding protein dynamics and associated functions. However, deciphering protein structures from experimental amide II spectra relies on time-consuming quantum chemical calculations on tens of thousands of representative configurations in solvent water. Currently, the accurate simulation of amide II spectra for whole proteins remains a challenge. Here, we present a machine learning (ML)-based protocol designed to efficiently simulate the amide II IR spectra of various proteins with an accuracy comparable to experimental results. This protocol stands out as a cost-effective and efficient alternative for studying protein dynamics, including the identification of secondary structures and monitoring the dynamics of protein hydrogen bonding under different pH conditions and during protein folding process. Our method provides a valuable tool in the field of protein research, focusing on the study of dynamic properties of proteins, especially those related to hydrogen bonding, using amide II IR spectroscopy.

Water inside the Selectivity Filter of a K+ Ion Channel: Structural Heterogeneity, Picosecond Dynamics, and Hydrogen Bonding

Water inside biological ion channels regulates the key properties of these proteins, such as selectivity, ion conductance, and gating. In this article, we measure the picosecond spectral diffusion of amide I vibrations of an isotope-labeled KcsA potassium channel using two-dimensional infrared (2D IR) spectroscopy. By combining waiting time (100-2000 fs) 2D IR measurements of the KcsA channel including 13C18O isotope-labeled Val76 and Gly77 residues with molecular dynamics simulations, we elucidated the site-specific dynamics of water and K+ ions inside the selectivity filter of KcsA. We observe inhomogeneous 2D line shapes with extremely slow spectral diffusion. Our simulations quantitatively reproduce the experiments and show that water is the only component with any appreciable dynamics, whereas K+ ions and the protein are essentially static on a picosecond timescale. By analyzing simulated and experimental vibrational frequencies, we find that water in the selectivity filter can be oriented to form hydrogen bonds with adjacent or nonadjacent carbonyl groups with the reorientation timescales being three times slower and comparable to that of water molecules in liquid, respectively. Water molecules can reside in the cavity sufficiently far from carbonyls and behave essentially like "free" gas-phase-like water with fast reorientation times. Remarkably, no interconversion between these configurations was observed on a picosecond timescale. These dynamics are in stark contrast with liquid water, which remains highly dynamic even in the presence of ions at high concentrations.

Refining protein amide I spectrum simulations with simple yet effective electrostatic models for local wavenumbers and dipole derivative magnitudes

Analysis of the amide I band of proteins is probably the most wide-spread application of bioanalytical infrared spectroscopy. Although highly desirable for a more detailed structural interpretation, a quantitative description of this absorption band is still difficult. This work optimized several electrostatic models with the aim to reproduce the effect of the protein environment on the intrinsic wavenumber of a local amide I oscillator. We considered the main secondary structures - α-helices, parallel and antiparallel β-sheets - with a maximum of 21 amide groups. The models were based on the electric potential and/or the electric field component along the CO bond at up to four atoms in an amide group. They were bench-marked by comparison to Hessian matrices reconstructed from density functional theory calculations at the BPW91, 6-31G** level. The performance of the electrostatic models depended on the charge set used to calculate the electric field and potential. Gromos and DSSP charge sets, used in common force fields, were not optimal for the better performing models. A good compromise between performance and the stability of model parameters was achieved by a model that considered the electric field at the positions of the oxygen, nitrogen, and hydrogen atoms of the considered amide group. The model describes also some aspects of the local conformation effect and performs similar on its own as in combination with an explicit implementation of the local conformation effect. It is better than a combination of a local hydrogen bonding model with the local conformation effect. Even though the short-range hydrogen bonding model performs worse, it captures important aspects of the local wavenumber sensitivity to the molecular surroundings. We improved also the description of the coupling between local amide I oscillators by developing an electrostatic model for the dependency of the dipole derivative magnitude on the protein environment.

Water inside the selectivity filter of a K+ ion channel: structural heterogeneity, picosecond dynamics, and hydrogen-bonding

Water inside biological ion channels regulates the key properties of these proteins such as selectivity, ion conductance, and gating. In this Article we measure the picosecond spectral diffusion of amide I vibrations of an isotope labeled KcsA potassium channel using two-dimensional infrared (2D IR) spectroscopy. By combining waiting time (100 - 2000 fs) 2D IR measurements of the KcsA channel including 13C18O isotope labeled Val76 and Gly77 residues with molecular dynamics simulations, we elucidated the site-specific dynamics of water and K+ ions inside the selectivity filter of KcsA. We observe inhomogeneous 2D lineshapes with extremely slow spectral diffusion. Our simulations quantitatively reproduce the experiments and show that water is the only component with any appreciable dynamics, whereas K+ ions and the protein are essentially static on a picosecond timescale. By analyzing simulated and experimental vibrational frequencies, we find that water in the selectivity filter can be oriented to form hydrogen bonds with adjacent, or non-adjacent carbonyl groups with the reorientation timescales being three times slower and comparable to that of water molecules in liquid, respectively. Water molecules can reside in the cavity sufficiently far from carbonyls and behave essentially like "free" gas-phase-like water with fast reorientation times. Remarkably, no interconversion between these configurations were observed on a picosecond timescale. These dynamics are in stark contrast with liquid water that remains highly dynamic even in the presence of ions at high concentrations.

Structural Dependence of Extended Amide III Vibrations in Two-Dimensional Infrared Spectra

Two-dimensional infrared (2D-IR) spectroscopy is a powerful experimental method for probing the structure and dynamics of proteins in aqueous solution. So far, most experimental studies have focused on the amide I vibrations, for which empirical vibrational exciton models provide a means of interpreting such experiments. However, such models are largely lacking for other regions of the vibrational spectrum. To close this gap, we employ an efficient quantum-chemical methodology for the calculation of 2D-IR spectra, which is based on anharmonic theoretical vibrational spectroscopy with localized modes. We apply this approach to explore the potential of 2D-IR spectroscopy in the extended amide III region. Using calculations for a dipeptide model as well as alanine polypeptides, we show that distinct 2D-IR cross-peaks in the extended amide III region can potentially be used to distinguish α-helix and β-strand structures. We propose that the extended amide III region could be a promising target for future 2D-IR experiments.

Universal machine learning for the response of atomistic systems to external fields

Machine learned interatomic interaction potentials have enabled efficient and accurate molecular simulations of closed systems. However, external fields, which can greatly change the chemical structure and/or reactivity, have been seldom included in current machine learning models. This work proposes a universal field-induced recursively embedded atom neural network (FIREANN) model, which integrates a pseudo field vector-dependent feature into atomic descriptors to represent system-field interactions with rigorous rotational equivariance. This "all-in-one" approach correlates various response properties like dipole moment and polarizability with the field-dependent potential energy in a single model, very suitable for spectroscopic and dynamics simulations in molecular and periodic systems in the presence of electric fields. Especially for periodic systems, we find that FIREANN can overcome the intrinsic multiple-value issue of the polarization by training atomic forces only. These results validate the universality and capability of the FIREANN method for efficient first-principles modeling of complicated systems in strong external fields.

Probing Ion Configurations in the KcsA Selectivity Filter with Single-Isotope Labels and 2D IR Spectroscopy

The potassium ion (K+) configurations of the selectivity filter of the KcsA ion channel protein are investigated with two-dimensional infrared (2D IR) spectroscopy of amide I vibrations. Single 13C-18O isotope labels are used, for the first time, to selectively probe the S1/S2 or S2/S3 binding sites in the selectivity filter. These binding sites have the largest differences in ion occupancy in two competing K+ transport mechanisms: soft-knock and hard-knock. According to the former, water molecules alternate between K+ ions in the selectivity filter while the latter assumes that K+ ions occupy the adjacent sites. Molecular dynamics simulations and computational spectroscopy are employed to interpret experimental 2D IR spectra. We find that in the closed conductive state of the KcsA channel, K+ ions do not occupy adjacent binding sites. The experimental data is consistent with simulated 2D IR spectra of soft-knock ion configurations. In contrast, the simulated spectra for the hard-knock ion configurations do not reproduce the experimental results. 2D IR spectra of the hard-knock mechanism have lower frequencies, homogeneous 2D lineshapes, and multiple peaks. In contrast, ion configurations of the soft-knock model produce 2D IR spectra with a single peak at a higher frequency and inhomogeneous lineshape. We conclude that under equilibrium conditions, in the absence of transmembrane voltage, both water and K+ ions occupy the selectivity filter of the KcsA channel in the closed conductive state. The ion configuration is central to the mechanism of ion transport through potassium channels.

Asymmetry of the Electrostatic Environment as the Origin of the Symmetry Breaking Effect of the Nitrate Ion in Aqueous Solution

Elucidating the mechanism of how vibrational modes are affected by intermolecular interactions is important for a better understanding of the nature of the former as probes of the latter. Here, such an analysis is carried out for the N-O stretching modes of the nitrate ion interacting with water, with an emphasis on the symmetry breaking effect. On the basis of theoretical calculations on the structural, vibrational, and electrostatic properties of molecular clusters and spectral simulations for an aqueous solution, a transparent view is demonstrated on the mechanism that modulations of spatially local electrostatic environment give rise to structural and spectroscopic symmetry breaking effect. The electrostatic interaction model constructed here is a seven-parameter model; the use of a single electrostatic parameter, such as the electric field on a single atomic site, is found to be insufficient for quantitative evaluation. It is also shown that the frequency modulations of the N-O stretching modes in aqueous solution occur on a time scale much shorter than 0.1 ps.

Unpolarized laser method for infrared spectrum calculation of amide I CO bonds in proteins using molecular dynamics simulation

The investigation of the strong infrared (IR)-active amide I modes of peptides and proteins has received considerable attention because a wealth of detailed information on hydrogen bonding, dipole-dipole interactions, and the conformations of the peptide backbone can be derived from the amide I bands. The interpretation of experimental spectra typically requires substantial theoretical support, such as direct ab-initio molecular dynamics simulation or mixed quantum-classical description. However, considering the difficulties associated with these theoretical methods and their applications are limited in small peptides, it is highly desirable to develop a simple yet efficient approach for simulating the amide I modes of any large proteins in solution. In this work, we proposed a comprehensive computational method that extends the well-established molecular dynamics (MD) simulation method to include an unpolarized IR laser for exciting the CO bonds of proteins. We showed the amide I frequency corresponding to the frequency of the laser pulse which resonated with the CO bond vibration. At this frequency, the protein energy and the CO bond length fluctuation were maximized. Overall, the amide I bands of various single proteins and amyloids agreed well with experimental data. The method has been implemented into the AMBER simulation package, making it widely available to the scientific community. Additionally, the application of the method to simulate the transient amide I bands of amyloid fibrils during the IR laser-induced disassembly process was discussed in details.

Design of an Electrostatic Frequency Map for the NH Stretch of the Protein Backbone and Application to Chiral Sum Frequency Generation Spectroscopy

We develop an electrostatic map for the vibrational NH stretch (amide A) of the protein backbone with a focus on vibrational chiral sum frequency generation spectroscopy (chiral SFG). Chiral SFG has been used to characterize protein secondary structure at interfaces using the NH stretch and to investigate chiral water superstructures around proteins using the OH stretch. Interpretation of spectra has been complicated because the NH stretch and OH stretch overlap spectrally. Although an electrostatic map for water OH developed by Skinner and co-workers was used previously to calculate the chiral SFG response of water structures around proteins, a map for protein NH that is directly responsive to biological complexity has yet to be developed. Here, we develop such a map, linking the local electric field to vibrational frequencies and transition dipoles. We apply the map to two protein systems and achieve much better agreement with experiment than was possible in our previous studies. We show that couplings between NH and OH vibrations are crucial to the line shape, which informs the interpretation of chiral SFG spectra, and that the chiral NH stretch response is sensitive to small differences in structure. This work increases the utility of the NH stretch in biomolecular spectroscopy.

Simulation of two-dimensional infrared Raman spectroscopy with application to proteins

Two-dimensional infrared Raman spectroscopy is a powerful technique for studying the structure and interaction in molecular and biological systems. Here, we present a new implementation of the simulation of the two-dimensional infrared Raman signals. The implementation builds on the numerical integration of the Schrödinger equation approach. It combines the prediction of dynamics from molecular dynamics with a map-based approach for obtaining Hamiltonian trajectories and response function calculations. The new implementation is tested on the amide-I region for two proteins, where one is dominated by α-helices and the other by β-sheets. We find that the predicted spectra agree well with experimental observations. We further find that the two-dimensional infrared Raman spectra at least of the studied proteins are much less sensitive to the laser polarization used compared to conventional two-dimensional infrared experiments. The present implementation and findings pave the way for future applications for the interpretation of two-dimensional infrared Raman spectra.

Quantum-chemical calculation of two-dimensional infrared spectra using localized-mode VSCF/VCI

Computational protocols for the simulation of two-dimensional infrared (2D IR) spectroscopy usually rely on vibrational exciton models which require an empirical parameterization. Here, we present an efficient quantum-chemical protocol for predicting static 2D IR spectra that does not require any empirical parameters. For the calculation of anharmonic vibrational energy levels and transition dipole moments, we employ the localized-mode vibrational self-consistent field (L-VSCF)/vibrational configuration interaction (L-VCI) approach previously established for (linear) anharmonic theoretical vibrational spectroscopy [P. T. Panek and C. R. Jacob, ChemPhysChem 15, 3365-3377 (2014)]. We demonstrate that with an efficient expansion of the potential energy surface using anharmonic one-mode potentials and harmonic two-mode potentials, 2D IR spectra of metal carbonyl complexes and dipeptides can be predicted reliably. We further show how the close connection between L-VCI and vibrational exciton models can be exploited to extract the parameters of such models from those calculations. This provides a novel route to the fully quantum-chemical parameterization of vibrational exciton models for predicting 2D IR spectra.

Effect of the N-Alkyl Side Chain on the Amide-Water Interactions

Amide-water interactions influence the structure and functions of amide-based systems, such as proteins and homopolymers. In particular, the N-alkylation of the amide unit appears to play a critical role in defining the interactions of the amide group. Previous studies have linked the thermal behavior of amide-based polymers to the nature of their N-alkyl side chain. However, the connection between the chemical structure of the N-alkyl and the hydration of the amide remains elusive. In this study, the solvation structure and dynamics of amides, having differing N-alkyl groups, are investigated using a combination of linear and nonlinear infrared spectroscopies and computational methods. Interestingly, the dynamics of the amide local environment do not slow down as the N-alkyl side chain becomes bulkier, but rather speeds up. Computational calculations confirm the hydration dynamics and assign the effect to smaller amplitude and faster rotations of the bulkier group. It is also observed experimentally that the hydrogen-bond making and breaking between water and the amide carbonyl do not directly relate to the size of the N-alkyl side chain. The bulkier N-isopropyl substituent presents significantly slower chemical exchange dynamics than smaller chains (ethyl and methyl), but the two small groups do not present a major difference. The hydrogen-bond making and breaking disparities and similarities among groups are well modeled by the theory demonstrating that the N-alkyl group affects the amide hydration structure and dynamics via a steric effect. In summary, the results presented here show that the size of the N-substituted alkyl group significantly influences the hydration dynamics of amides and stress the importance of considering this effect on much larger systems, such as polymers.

AIM: A Mapping Program for Infrared Spectroscopy of Proteins

Here, we present a new analysis program, AIM, that allows extracting the vibrational amide-I Hamiltonian using molecular dynamics trajectories for protein infrared spectroscopy modeling. The constructed Hamiltonians can be used as input for spectral calculations allowing the calculation of infrared absorption spectra, vibrational circular dichroism, and two-dimensional infrared spectra. These spectroscopies allow the study of the structure and dynamics of proteins. We will explain the essence of how AIM works and give examples of the information and spectra that can be obtained with the program using the Trypsin Inhibitor as an example. AIM is freely available from GitHub, and the package contains a demonstration allowing easy introduction to the use of the program.

Atomistic Simulations for Reactions and Vibrational Spectroscopy in the Era of Machine Learning─Quo Vadis?

Atomistic simulations using accurate energy functions can provide molecular-level insight into functional motions of molecules in the gas and in the condensed phase. This Perspective delineates the present status of the field from the efforts of others and some of our own work and discusses open questions and future prospects. The combination of physics-based long-range representations using multipolar charge distributions and kernel representations for the bonded interactions is shown to provide realistic models for the exploration of the infrared spectroscopy of molecules in solution. For reactions, empirical models connecting dedicated energy functions for the reactant and product states allow statistically meaningful sampling of conformational space whereas machine-learned energy functions are superior in accuracy. The future combination of physics-based models with machine-learning techniques and integration into all-purpose molecular simulation software provides a unique opportunity to bring such dynamics simulations closer to reality.

Quantum vibration perturbation approach with polyatomic probe in simulating infrared spectra

The quantitative prediction of vibrational spectra of chromophore molecules in solution is challenging and numerous methods have been developed. In this work, we present a quantum vibration perturbation (QVP) approach, which is a procedure that combines molecular quantum vibration and molecular dynamics with perturbation theory. In this framework, an initial Newtonian molecular dynamics simulation is performed, followed by a substitution process to embed molecular quantum vibrational wave functions into the trajectory. The instantaneous vibrational frequency shift at each time step is calculated using the Rayleigh-Schrödinger perturbation theory, where the perturbation operator is the difference in the vibrational potential between the reference chromophore and the perturbed chromophore in the environment. Semi-classical statistical mechanics is employed to obtain the spectral lineshape function. We validated our method using HCOOH·nH₂O (n = 1-2) clusters and HCOOH aqueous solution as examples. The QVP method can be employed for rapid prediction of the vibrational spectrum of a specific mode in solution.

Distinct electrostatic frequency tuning rates for amide I and amide I' vibrations

Amide I spectroscopy probes the backbone C=O stretch vibrations of peptides and proteins. Amide I spectra are often collected in deuterated water (D₂O) since this provides a cleaner background in the amide I frequency range; such data are often referred to as amide I' spectra since deuteration induces changes in the mode structure, including a roughly ∼10 cm^-1 redshift. For biological samples, however, deuteration is often not possible. As amide I frequency maps are increasingly applied to quantitative protein structural analysis, this raises the interesting challenge of drawing direct connections between amide I and amide I' data. We here analyze amide I and amide I' peak frequencies for a series of dipeptides and related compounds. Changes in protonation state induce large electrostatic shifts in the peak frequencies, allowing us to amass a sizable library of data points for direct amide I/amide I' comparison. While we find an excellent linear correlation between amide I and amide I' peak frequencies, the deuteration-induced shift is smaller for more red-shifted vibrations, indicating different electrostatic tuning rates in the two solvents. H₂O/D₂O shifts were negligible for proline-containing dipeptides that lack exchangeable amide hydrogens, indicating that the intrinsic properties of the solvent do not strongly influence the H/D shift. These findings indicate that the distinct tuning rates observed for the two vibrations arise from modifications to the intrinsic properties of the amide bond and provide (at least for solvated dipeptides) a simple, linear "map" for translating between amide I and amide I' frequencies.

Multipolar Force Fields for Amide-I Spectroscopy from Conformational Dynamics of the Alanine Trimer

The dynamics and spectroscopy of N-methyl-acetamide (NMA) and trialanine in solution are characterized from molecular dynamics simulations using different energy functions, including a conventional point charge (PC)-based force field, one based on a multipolar (MTP) representation of the electrostatics, and a semiempirical DFT method. For the 1D infrared spectra, the frequency splitting between the two amide-I groups is 10 cm^-1 from the PC, 13 cm^-1 from the MTP, and 47 cm^-1 from self-consistent charge density functional tight-binding (SCC-DFTB) simulations, compared with 25 cm^-1 from experiment. The frequency trajectory required for the frequency fluctuation correlation function (FFCF) is determined from individual normal mode (INM) and full normal mode (FNM) analyses of the amide-I vibrations. The spectroscopy, time-zero magnitude of the FFCF C(t = 0), and the static component Δ₀² from simulations using MTP and analysis based on FNM are all consistent with experiments for (Ala)₃. Contrary to this, for the analysis excluding mode-mode coupling (INM), the FFCF decays to zero too rapidly and for simulations with a PC-based force field, the Δ₀² is too small by a factor of two compared with experiments. Simulations with SCC-DFTB agree better with experiment for these observables than those from PC-based simulations. The conformational ensemble sampled from simulations using PCs is consistent with the literature (including P_II, β, α_R, and α_L), whereas that covered by the MTP-based simulations is dominated by P_II with some contributions from β and α_R. This agrees with and confirms recently reported Bayesian-refined populations based on 1D infrared experiments. FNM analysis together with a MTP representation provides a meaningful model to correctly describe the dynamics of hydrated trialanine.

Structural dynamics and vibrational feature of N-Acetyl-d-glucosamine in aqueous solution

Molecular dynamics simulations and DFT calculations were performed for the demonstration of the structural dynamics and vibrational feature of N-Acetyl-d-glucosamine (NAG) in solution phase. The interactions between NAG and solvent molecules were evaluated through spatial distribution function and radial distribution function, and the preferred conformations of NAG in aqueous solution were revealed by cluster analysis. Results from normal mode analysis show that the solvent induced structural fluctuation of NAG could be reflected in the vibrational feature of specific chromophores, thus we can evaluate the molecular structure with the help of its vibrational signature based on the built correlation between molecular structure and vibrational frequencies of specific groups.

Pump Slice Amplitudes: A Simple and Robust Method for Connecting Two-Dimensional Infrared and Fourier Transform Infrared Spectra

Ultrafast two-dimensional infrared (2D IR) spectroscopy and Fourier transform infrared (FTIR) spectroscopy are often performed in tandem, with FTIR typically used to interpret and provide hypotheses for 2D IR experiments. Comparisons between 2D IR and FTIR spectra can also be used to examine the structure and orientation in systems of coupled vibrational chromophores. The most common method for comparing 2D IR and FTIR lineshapes, the diagonal slice method, contains significant artifacts when applied to oscillators with low anharmonicities. Here, we introduce a new technique, the pump slice amplitude (PSA) method, for relating 2D IR lineshapes to FTIR lineshapes and compare PSAs against diagonal slices using theoretical and experimental spectra. We find that PSAs are significantly more similar to FTIR lineshapes than diagonal slices in systems with low anharmonicity.

AI-based spectroscopic monitoring of real-time interactions between SARS-CoV-2 and human ACE2

The COVID-19 caused by SARS-CoV-2 virus has posed a tremendous threat to human health. The interactions between human angiotensin-converting enzyme 2 and the spike glycoprotein of SARS-CoV-2 hold the key to understanding the molecular mechanism to develop treatment and vaccines. However, the simulation of these interactions in fluctuating surroundings is challenging because it requires many electronic structure calculations at the quantum mechanics level for a large number of representative configurations. We report a machine learning protocol that can efficiently predict the IR spectra of SARS-CoV-2 with high efficiency and characterize fine changes in IR spectra associated with variations of protein secondary structures. Machine learning provides a cost-effective tool for monitoring of real-time interactions between the SARS-CoV-2 and human ACE2.

The novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), invades a human cell via human angiotensin-converting enzyme 2 (hACE2) as the entry, causing the severe coronavirus disease (COVID-19). The interactions between hACE2 and the spike glycoprotein (S protein) of SARS-CoV-2 hold the key to understanding the molecular mechanism to develop treatment and vaccines, yet the dynamic nature of these interactions in fluctuating surroundings is very challenging to probe by those structure determination techniques requiring the structures of samples to be fixed. Here we demonstrate, by a proof-of-concept simulation of infrared (IR) spectra of S protein and hACE2, that time-resolved spectroscopy may monitor the real-time structural information of the protein−protein complexes of interest, with the help of machine learning. Our machine learning protocol is able to identify fine changes in IR spectra associated with variation of the secondary structures of S protein of the coronavirus. Further, it is three to four orders of magnitude faster than conventional quantum chemistry calculations. We expect our machine learning protocol would accelerate the development of real-time spectroscopy study of protein dynamics.

A computational insight into the relationship between side chain IR line shapes and local environment in fibril-like structures

Infrared spectroscopy is a widely used technique to characterize protein structures and protein mediated processes. While the amide I band provides information on proteins' secondary structure, amino acid side chains are used as infrared probes for the investigation of protein reactions and local properties. In this paper, we use a hybrid quantum mechanical/classical molecular dynamical approach based on the perturbed matrix method to compute the infrared band due to the C=O stretching mode of amide-containing side chains. We calculate, at first, the infrared band of zwitterionic glutamine in water and obtain results in very good agreement with the experimental data. Then, we compute the signal arising from glutamine side chains in a microcrystal of the yeast prion Sup35-derived peptide, GNNQQNY, with a fibrillar structure. The infrared bands obtained by selective isotopic labeling of the two glutamine residues, Q4 and Q5, of each peptide were experimentally used to investigate the local hydration in the fibrillar microcrystal. The experimental spectra of the two glutamine residues, which experience different hydration environments, feature different spectral signals that are well reproduced by the corresponding calculated spectra. In addition, the analysis of the simulated spectra clarifies the molecular origin of the experimentally observed spectroscopic differences that arise from the different local electric field experienced by the two glutamine residues, which is, in turn, determined by a different hydrogen bonding pattern.

Vibrational exciton delocalization precludes the use of infrared intensities as proxies for surfactant accumulation on aqueous surfaces

Surface-sensitive vibrational spectroscopy is a common tool for measuring molecular organization and intermolecular interactions at interfaces. Peak intensity ratios are typically used to extract molecular information from one-dimensional spectra but vibrational coupling between surfactant molecules can manifest as signal depletion in one-dimensional spectra. Through a combination of experiment and theory, we demonstrate the emergence of vibrational exciton delocalization in infrared reflection–absorption spectra of soluble and insoluble surfactants at the air/water interface. Vibrational coupling causes a significant decrease in peak intensities corresponding to C–F vibrational modes of perfluorooctanoic acid molecules. Vibrational excitons also form between arachidic acid surfactants within a compressed monolayer, manifesting as signal reduction of C–H stretching modes. Ionic composition of the aqueous phase impacts surfactant intermolecular distance, thereby modulating vibrational coupling strength between surfactants. Our results serve as a cautionary tale against employing alkyl and fluoroalkyl vibrational peak intensities as proxies for concentration, although such analysis is ubiquitous in interface science.

Coupling between surfactant molecules at the air/water interface bleeds intensity into a diffuse background, such that single-wavelength vibrational intensity is effectively depleted at high surface coverage.

Computing the frequency fluctuation dynamics of highly coupled vibrational transitions using neural networks

The description of frequency fluctuations for highly coupled vibrational transitions has been a challenging problem in physical chemistry. In particular, the complexity of their vibrational Hamiltonian does not allow us to directly derive the time evolution of vibrational frequencies for these systems. In this paper, we present a new approach to this problem by exploiting the artificial neural network to describe the vibrational frequencies without relying on the deconstruction of the vibrational Hamiltonian. To this end, we first explored the use of the methodology to predict the frequency fluctuations of the amide I mode of N-methylacetamide in water. The results show good performance compared with the previous experimental and theoretical results. In the second part, the neural network approach is used to investigate the frequency fluctuations of the highly coupled carbonyl stretch modes for the organic carbonates in the solvation shell of the lithium ion. In this case, the frequency fluctuation predicted by the neural networks shows a good agreement with the experimental results, which suggests that this model can be used to describe the dynamics of the frequency in highly coupled transitions.

Computational IR Spectroscopy of Insulin Dimer Structure and Conformational Heterogeneity

We have investigated the structure and conformational dynamics of insulin dimer using a Markov state model (MSM) built from extensive unbiased atomistic molecular dynamics simulations and performed infrared spectral simulations of the insulin MSM to describe how structural variation within the dimer can be experimentally resolved. Our model reveals two significant conformations to the dimer: a dominant native state consistent with other experimental structures of the dimer and a twisted state with a structure that appears to reflect a ∼55° clockwise rotation of the native dimer interface. The twisted state primarily influences the contacts involving the C-terminus of insulin's B chain, shifting the registry of its intermolecular hydrogen bonds and reorganizing its side-chain packing. The MSM kinetics predict that these configurations exchange on a 14 μs time scale, largely passing through two Markov states with a solvated dimer interface. Computational amide I spectroscopy of site-specifically ¹³C¹⁸O labeled amides indicates that the native and twisted conformation can be distinguished through a series of single and dual labels involving the B24F, B25F, and B26Y residues. Additional structural heterogeneity and disorder is observed within the native and twisted states, and amide I spectroscopy can also be used to gain insight into this variation. This study will provide important interpretive tools for IR spectroscopic investigations of insulin structure and transient IR kinetics experiments studying the conformational dynamics of insulin dimer.

Site-selective dynamics of azidolysozyme

The spectroscopic response of and structural dynamics around all azido-modified alanine residues (AlaN₃) in lysozyme are characterized. It is found that AlaN₃ is a positionally sensitive probe for the local dynamics, covering a frequency range of ∼15 cm^-1 for the center frequency of the line shape. This is consistent with findings from selective replacements of amino acids in PDZ2, which reported a frequency span of ∼10 cm^-1 for replacements of Val, Ala, or Glu by azidohomoalanine. For the frequency fluctuation correlation functions, the long-time decay constants τ₂ range from ∼1 to ∼10 ps, which compares with experimentally measured correlation times of 3 ps. Attaching azide to alanine residues can yield dynamics that decays to zero on the few ps time scale (i.e., static component Δ₀ ∼ 0 ps^-1) or to a remaining, static contribution of ∼0.5 ps^-1 (corresponding to 2.5 cm^-1), depending on the local environment on the 10 ps time scale. The magnitude of the static component correlates qualitatively with the degree of hydration of the spectroscopic probe. Although attaching azide to alanine residues is found to be structurally minimally invasive with respect to the overall protein structure, analysis of the local hydrophobicity indicates that the hydration around the modification site differs for modified and unmodified alanine residues, respectively.

Structure Changes of a Membrane Polypeptide under an Applied Voltage Observed with Surface-Enhanced 2D IR Spectroscopy

The structures of many membrane-bound proteins and polypeptides depend on the membrane potential. However, spectroscopically studying their structures under an applied field is challenging, because a potential is difficult to generate across more than a few bilayers. We study the voltage-dependent structures of the membrane-bound polypeptide, alamethicin, using a spectroelectrochemical cell coated with a rough, gold film to create surface plasmons. The plasmons sufficiently enhance the 2D IR signal to measure a single bilayer. The film is also thick enough to conduct current and thereby apply a potential. The 2D IR spectra resolve features from both 3₁₀- and α-helical structures and cross-peaks connecting the two. We observe changes in the peak intensity, not their frequencies, upon applying a voltage. A similar change occurs with pH, which is known to alter the angle of alamethicin relative to the surface normal. The spectra are modeled using a vibrational exciton Hamiltonian, and the voltage-dependent spectra are consistent with a change in angle of the 3₁₀- and α-helices in the membrane from 55 to 44°and from 31 to 60°, respectively. The 3₁₀- and α-helices are coupled by approximately 10 cm^-1. These experiments provide new structural information about alamethicin under a potential difference and demonstrate a technique that might be applied to voltage-gated membrane proteins and compared to molecular dynamics structures.

Dynamics and Infrared Spectrocopy of Monomeric and Dimeric Wild Type and Mutant Insulin

The infrared spectroscopy and dynamics of -CO labels in wild type and mutant insulin monomer and dimer are characterized from molecular dynamics simulations using validated force fields. It is found that the spectroscopy of monomeric and dimeric forms in the region of the amide-I vibration differs for residues B24-B26 and D24-D26, which are involved in dimerization of the hormone. Also, the spectroscopic signatures change for mutations at position B24 from phenylalanine, which is conserved in many organisms and is known to play a central role in insulin aggregation, to alanine or glycine. Using three different methods to determine the frequency trajectories (solving the nuclear Schrödinger equation on an effective 1-dimensional potential energy curve, using instantaneous normal modes, and using parametrized frequency maps) leads to the same overall conclusions. The spectroscopic response of monomeric WT and mutant insulin differs from that of their respective dimers, and the spectroscopy of the two monomers in the dimer is also not identical. For the WT and F24A and F24G monomers, spectroscopic shifts are found to be ∼20 cm^-1 for residues (B24-B26) located at the dimerization interface. Although the crystal structure of the dimer is that of a symmetric homodimer, dynamically the two monomers are not equivalent on the nanosecond time scale. Together with earlier work on the thermodynamic stability of the WT and the same mutants, it is concluded that combining computational and experimental infrared spectroscopy provides a potentially powerful way to characterize the aggregation state and dimerization energy of modified insulins.

A Machine Learning Protocol for Predicting Protein Infrared Spectra

Infrared (IR) absorption provides important chemical fingerprints of biomolecules. Protein secondary structure determination from IR spectra is tedious since its theoretical interpretation requires repeated expensive quantum-mechanical calculations in a fluctuating environment. Herein we present a novel machine learning protocol that uses a few key structural descriptors to rapidly predict amide I IR spectra of various proteins and agrees well with experiment. Its transferability enabled us to distinguish protein secondary structures, probe atomic structure variations with temperature, and monitor protein folding. This approach offers a cost-effective tool to model the relationship between protein spectra and their biological/chemical properties.

Vibrational Spectroscopic Map, Vibrational Spectroscopy, and Intermolecular Interaction

Vibrational spectroscopy is an essential tool in chemical analyses, biological assays, and studies of functional materials. Over the past decade, various coherent nonlinear vibrational spectroscopic techniques have been developed and enabled researchers to study time-correlations of the fluctuating frequencies that are directly related to solute-solvent dynamics, dynamical changes in molecular conformations and local electrostatic environments, chemical and biochemical reactions, protein structural dynamics and functions, characteristic processes of functional materials, and so on. In order to gain incisive and quantitative information on the local electrostatic environment, molecular conformation, protein structure and interprotein contacts, ligand binding kinetics, and electric and optical properties of functional materials, a variety of vibrational probes have been developed and site-specifically incorporated into molecular, biological, and material systems for time-resolved vibrational spectroscopic investigation. However, still, an all-encompassing theory that describes the vibrational solvatochromism, electrochromism, and dynamic fluctuation of vibrational frequencies has not been completely established mainly due to the intrinsic complexity of intermolecular interactions in condensed phases. In particular, the amount of data obtained from the linear and nonlinear vibrational spectroscopic experiments has been rapidly increasing, but the lack of a quantitative method to interpret these measurements has been one major obstacle in broadening the applications of these methods. Among various theoretical models, one of the most successful approaches is a semiempirical model generally referred to as the vibrational spectroscopic map that is based on a rigorous theory of intermolecular interactions. Recently, genetic algorithm, neural network, and machine learning approaches have been applied to the development of vibrational solvatochromism theory. In this review, we provide comprehensive descriptions of the theoretical foundation and various examples showing its extraordinary successes in the interpretations of experimental observations. In addition, a brief introduction to a newly created repository Web site (http://frequencymap.org) for vibrational spectroscopic maps is presented. We anticipate that a combination of the vibrational frequency map approach and state-of-the-art multidimensional vibrational spectroscopy will be one of the most fruitful ways to study the structure and dynamics of chemical, biological, and functional molecular systems in the future.

Efficient and Accurate Simulations of Vibrational and Electronic Spectra with Symmetry-Preserving Neural Network Models for Tensorial Properties

Machine learning has revolutionized the high-dimensional representations for molecular properties such as potential energy. However, there are scarce machine learning models targeting tensorial properties, which are rotationally covariant. Here, we propose tensorial neural network (NN) models to learn both tensorial response and transition properties in which atomic coordinate vectors are multiplied with scalar NN outputs or their derivatives to preserve the rotationally covariant symmetry. This strategy keeps structural descriptors symmetry invariant so that the resulting tensorial NN models are as efficient as their scalar counterparts. We validate the performance and universality of this approach by learning response properties of water oligomers and liquid water and transition dipole moment of a model structural unit of proteins. Machine-learned tensorial models have enabled efficient simulations of vibrational spectra of liquid water and ultraviolet spectra of realistic proteins, promising feasible and accurate spectroscopic simulations for biomolecules and materials.

Fragment-based quantum mechanical approach to biomolecules, molecular clusters, molecular crystals and liquids

To study large molecular systems beyond the system size that the current state-of-the-art ab initio electronic structure methods could handle, fragment-based quantum mechanical (QM) approaches have been developed over the past years, and proved to be efficient in dealing with large molecular systems at various ab initio levels. According to the fragmentation approach, a large molecular system can be divided into subsystems (fragments), and subsequently the property of the whole system can be approximately obtained by taking a proper combination of the corresponding terms of individual fragments. Therefore, the standard QM calculation of a large system could be circumvented by carrying out a series of calculations on small fragments, which significantly promotes computational efficiency. The electrostatically embedded generalized molecular fractionation with conjugate caps (EE-GMFCC) method is one of the fragment-based QM approaches which has been developed by our research group in recent years. This Perspective presents the theoretical framework of this fragmentation method and its applications in biomolecules, molecular clusters, molecular crystals and liquids, including total energy calculation, protein-ligand/protein binding affinity prediction, geometry optimization, vibrational spectrum simulation, ab initio molecular dynamics simulation, and prediction of excited-state properties.

Infrared Difference Spectroscopy of Proteins: From Bands to Bonds

Infrared difference spectroscopy probes vibrational changes of proteins upon their perturbation. Compared with other spectroscopic methods, it stands out by its sensitivity to the protonation state, H-bonding, and the conformation of different groups in proteins, including the peptide backbone, amino acid side chains, internal water molecules, or cofactors. In particular, the detection of protonation and H-bonding changes in a time-resolved manner, not easily obtained by other techniques, is one of the most successful applications of IR difference spectroscopy. The present review deals with the use of perturbations designed to specifically change the protein between two (or more) functionally relevant states, a strategy often referred to as reaction-induced IR difference spectroscopy. In the first half of this contribution, I review the technique of reaction-induced IR difference spectroscopy of proteins, with special emphasis given to the preparation of suitable samples and their characterization, strategies for the perturbation of proteins, and methodologies for time-resolved measurements (from nanoseconds to minutes). The second half of this contribution focuses on the spectral interpretation. It starts by reviewing how changes in H-bonding, medium polarity, and vibrational coupling affect vibrational frequencies, intensities, and bandwidths. It is followed by band assignments, a crucial aspect mostly performed with the help of isotopic labeling and site-directed mutagenesis, and complemented by integration and interpretation of the results in the context of the studied protein, an aspect increasingly supported by spectral calculations. Selected examples from the literature, predominately but not exclusively from retinal proteins, are used to illustrate the topics covered in this review.

Application of a transparent window vibrational probe (azido probe) to the structural dynamics of model dipeptides and amyloid β-peptide

The azido asymmetric stretching motion is widely used for the elucidation of the intrinsic conformational preference and folding mechanism of protein since it has strong vibrational absorbance in the spectral transparent windows. However, the possible secondary structural disturbance induced by the insertion of azido group in the side chain of polypeptides should be carefully evaluated. Here, DFT calculation and enhanced sampling method were employed for model dipeptides with or without azido substitution, and the outcome results show that the lower potential energy basins of isolated model dipeptides are consistent with the preferred structural distributions of model dipeptides in aqueous solution. The azido asymmetric stretching frequency shows its sensitivity to the backbone configurations just like amide-I vibration does, and the azido vibration exhibits great potential as a structural reporter in the transparent window. For the evaluation of the application of azido group in biologically related system, the structural dynamics of Aβ_37-42 and N₃-Aβ_37-42 fragments and the self-assemble process of their protofiliments in aqueous solution were demonstrated. The outcome results show that the structural fluctuations of Aβ_37-42 and its protofilament in aqueous solution are quite similar with or without azido substitution, and the dewetting transitions of Aβ_37-42 and N₃-Aβ_37-42 β-sheet layers are both complete within 30 ns and assemble into stable protofilaments. Therefore, the azido asymmetric vibrational motion is a minimally invasive structural probe and would not introduce much disturbance to the structural dynamics of polypeptides.

Modeling amino-acid side chain infrared spectra: the case of carboxylic residues

Infrared (IR) spectroscopy is commonly utilized for the investigation of protein structures and protein-mediated processes.

Empirical S=O stretch vibrational frequency map

Dimethyl sulfoxide (DMSO) water mixtures have been widely studied due to their unique concentration-dependent bulk properties. Here, we present an empirical spectroscopic map for the sulfinyl (S=O) stretching mode. The model can be used to interpret infrared (IR) absorption and ultrafast two-dimensional infrared (2D IR) spectra and quantify hydrogen bond populations and lifetimes by directly connecting spectroscopic measurements with structures and dynamics from molecular dynamics simulations. The electrostatic map is directly parameterized against experimental absorption spectra in the S=O stretching region (980-1100 cm^-1) of dilute DMSO in water. A comparison of center peak frequencies shows that the map performs well across the entire DMSO concentration range, accurately reproducing the ∼10 cm^-1 red-shift per hydrogen bond observed in the experiments. We further benchmark the map by comparing experimental and simulated 2D IR spectra generated by direct numerical integration of the Schrödinger equation. We expect that this empirical frequency map will provide a quantitative platform for investigating intermolecular interactions, microscopic heterogeneity, and ultrafast dynamics in complex liquid mixtures containing DMSO.

IR Spectroscopy Can Reveal the Mechanism of K+ Transport in Ion Channels

Ion channels like KcsA enable ions to move across cell membranes at near diffusion-limited rates and with very high selectivity. Various mechanisms have been proposed to explain this phenomenon. Broadly, there is disagreement among the proposed mechanisms about whether ions occupy adjacent sites in the channel during the transport process. Here, using a mixed quantum-classical approach to calculate theoretical infrared spectra, we propose a set of infrared spectroscopy experiments that can discriminate between mechanisms with and without adjacent ions. These experiments differ from previous ones in that they independently probe specific ion binding sites within the selectivity filter. When ions occupy adjacent sites in the selectivity filter, the predicted spectra are significantly redshifted relative to when ions do not occupy adjacent sites. Comparisons between theoretical and experimental peak frequencies will therefore discriminate the mechanisms.