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Markov state models represent a popular means to interpret biomolecular processes in terms of memoryless transitions between metastable conformational states. To gain insight into the underlying mechanism, it is instructive to determine all relevant pathways between initial and final states of the process. Currently available methods, such as Markov chain Monte Carlo and transition path theory, are convenient for identifying the most frequented pathways. They are less suited to account for the typically huge amount of pathways with low probability which, though, may dominate the cumulative flux of the reaction. On the basis of a systematic construction of all possible pathways, the here proposed method MSMPathfinder is able to characterize the multitude of unique pathways (say, up to 10 10 ) in a complex system and to quantitatively calculate their correct weights and associated waiting times with predefined accuracy. Adopting the chiral transitions of a peptide helix and the folding of the villin headpiece as model problems, mechanisms and associated waiting times of these processes are discussed using a kinetic network representation. The analysis reveals that the waiting time distribution may yield only little insight into the diversity of pathways, because the measured folding times do typically not reflect the most probable path lengths but rather the cumulative effect of many different pathways.

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.

Coarse-graining of fully atomistic molecular dynamics simulations is a long-standing goal in order to allow the description of processes occurring on biologically relevant timescales. For example, the prediction of pathways, rates and rate-limiting steps in protein-ligand unbinding is crucial for modern drug discovery. To achieve the enhanced sampling, we perform dissipation-corrected targeted molecular dynamics simulations, which yield free energy and friction profiles of molecular processes under consideration. Subsequently, we use these fields to perform temperature-boosted Langevin simulations which account for the desired kinetics occurring on multisecond timescales and beyond. Adopting the dissociation of solvated sodium chloride, trypsin-benzamidine and Hsp90-inhibitor protein-ligand complexes as test problems, we reproduce rates from molecular dynamics simulation and experiments within a factor of 2-20, and dissociation constants within a factor of 1-4. Analysis of friction profiles reveals that binding and unbinding dynamics are mediated by changes of the surrounding hydration shells in all investigated systems.

Recent time-resolved experiments and accompanying molecular dynamics simulations allow us to monitor the flow of vibrational energy in biomolecules. As a simple means to describe these experimental and simulated data, Buchenberg et al. [J. Phys. Chem. Lett. 7, 25 (2016)] suggested a master equation model that accounts for the energy transport from an initially excited residue to some target residue. The transfer rates of the model were obtained from two scaling rules, which account for the energy transport through the backbone and via tertiary contacts, respectively, and were parameterized using simulation data of a small α-helical protein at low temperatures. To extend the applicability of the model to general proteins at room temperature, here a new parameterization is presented, which is based on extensive nonequilibrium molecular dynamics simulations of a number of model systems. With typical transfer times of 0.5-1 ps between adjacent residues, backbone transport represents the fastest channel of energy flow. It is well described by a diffusive-type scaling rule, which requires only an overall backbone diffusion coefficient and interatom distances as input. Contact transport, e.g., via hydrogen bonds, is considerably slower (6-30 ps) at room temperature. A new scaling rule depending on the inverse square contact distance is suggested, which is shown to successfully describe the energy transport in the allosteric protein PDZ3. Since both scaling rules require only the structure of the considered system, the model provides a simple and general means to predict energy transport in proteins. To identify the pathways of energy transport, Monte Carlo Markov chain simulations are performed, which highlight the competition between backbone and contact transport channels.

To facilitate the observation of biomolecular energy transport in real time and with single-residue resolution, recent experiments by Baumann et al. ( Angew. Chem. Int. Ed. 2019 , 58 , 2899 , DOI: 10.1002/anie.201812995 ) have used unnatural amino acids β-(1-azulenyl)alanine (Azu) and azidohomoalanine (Aha) to site-specifically inject and probe vibrational energy in proteins. To aid the interpretation of such experiments, non-equilibrium molecular dynamics simulations of the anisotropic energy flow in proteins TrpZip2 and PDZ3 domains are presented. On this account, an efficient simulation protocol is established that accurately mimics the excitation and probing steps of Azu and Aha. The simulations quantitatively reproduce the experimentally found cooling times of the solvated proteins at room temperature and predict that the cooling slows by a factor 2 below the glass temperature of water. In PDZ3, vibrational energy is shown to travel from the initially excited peptide ligand via a complex network of inter-residue contacts and backbone transport to distal regions of the protein. The supposed connection of these energy transport pathways with pathways of allosteric communication is discussed.

An azobenzene-derived photoswitch has been covalently cross-linked to two sites of the S-peptide in the RNase S complex in a manner that the α-helical content of the S-peptide reduces upon cis -to- trans isomerization of the photoswitch. Three complementary experimental techniques have been employed, isothermal titration calorimetry, circular dichroism spectroscopy and intrinsic tyrosine fluorescence quenching, to determine the binding affinity of the S-peptide to the S-protein in the two states of the photoswitch. Five mutants with the photoswitch attached to different sites of the S-peptide have been explored, with the goal to maximize the change in binding affinity upon photoswitching, and to identify the mechanisms that determine the binding affinity. With regard to the first goal, one mutant has been identified, which binds with reasonable affinity in the one state of the photoswitch, while specific binding is completely switched off in the other state. With regard to the second goal, accompanying molecular dynamics simulations combined with a quantitative structure activity relationship revealed that the α-helicity of the S-peptide in the binding pocket correlates surprisingly well with measured dissociation constants. Moreover, the simulations show that both configurations of all S-peptides exhibit quite well-defined structures, even in apparently disordered states.

As standard unbiased molecular dynamics (MD) simulations become impractical for sampling rare events, "targeted MD" employs a moving distance constraint to enforce rare transitions along some reaction coordinate x. To calculate free energy profiles from these nonequilibrium simulations via Δ G( x) = W( x) - W diss ( x), apart from the (readily obtained) work W( x) performed on the system, the dissipated work W diss ( x) is also required. By employing a second-order cumulant expansion of Jarzynski's equality combined with an analysis within Langevin theory, the dissipated work can be expressed via a nonequilibrium friction coefficient Γ NEQ ( x) that may be calculated on-the-fly from constraint force fluctuations. Adopting the ion dissociation of NaCl in water as a test system, this friction correction is shown to result in accurate free energy profiles, even for a modest number of simulations and at high constraint velocities. As a bonus, the analysis of Γ NEQ ( x) may yield valuable insight into the microscopic mechanism of friction.

The statistical analysis of molecular dynamics simulations requires dimensionality reduction techniques, which yield a low-dimensional set of collective variables (CVs) { x i } = x that in some sense describe the essential dynamics of the system. Considering the distribution P ( x ) of the CVs, the primal goal of a statistical analysis is to detect the characteristic features of P ( x ), in particular, its maxima and their connection paths. This is because these features characterize the low-energy regions and the energy barriers of the corresponding free energy landscape Δ G ( x ) = - k B T ln P ( x ), and therefore amount to the metastable states and transition regions of the system. In this perspective, we outline a systematic strategy to identify CVs and metastable states, which subsequently can be employed to construct a Langevin or a Markov state model of the dynamics. In particular, we account for the still limited sampling typically achieved by molecular dynamics simulations, which in practice seriously limits the applicability of theories (e.g., assuming ergodicity) and black-box software tools (e.g., using redundant input coordinates). We show that it is essential to use internal (rather than Cartesian) input coordinates, employ dimensionality reduction methods that avoid rescaling errors (such as principal component analysis), and perform density based (rather than k -means-type) clustering. Finally, we briefly discuss a machine learning approach to dimensionality reduction, which highlights the essential internal coordinates of a system and may reveal hidden reaction mechanisms.

While the theory of protein folding is well developed, including concepts such as rugged energy landscape, folding funnel, etc., the same degree of understanding has not been reached for the description of the dynamics of allosteric transitions in proteins. This is not only due to the small size of the structural change upon ligand binding to an allosteric site, but also due to challenges in designing experiments that directly observe such an allosteric transition. On the basis of recent pump-probe-type experiments (Buchli et al. 2013 Proc. Natl Acad. Sci. USA 110 , 11 725-11 730. (doi:10.1073/pnas.1306323110)) and non-equilibrium molecular dynamics simulations (Buchenberg et al. 2017 Proc. Natl Acad. Sci. USA 114 , E6804-E6811. (doi:10.1073/pnas.1707694114)) studying an photoswitchable PDZ2 domain as model for an allosteric transition, we outline in this perspective how such a description of allosteric communication might look. That is, calculating the dynamical content of both experiment and simulation (which agree remarkably well with each other), we find that allosteric communication shares some properties with downhill folding, except that it is an 'order-order' transition. Discussing the multiscale and hierarchical features of the dynamics, the validity of linear response theory as well as the meaning of 'allosteric pathways', we conclude that non-equilibrium experiments and simulations are a promising way to study dynamical aspects of allostery.This article is part of a discussion meeting issue 'Allostery and molecular machines'., (© 2018 The Author(s).)

The molecular actions of proteins occur along reaction coordinates. Current computer methods have limited ability to explore them. We describe a fast protocol called MELD-path that (1) efficiently samples relevant conformational states via MELD, an accelerator of Molecular Dynamics (MD), (2) seeds multiple short MD trajectories from MELD states, and then (3) constructs Markov State Models (MSM) that give the routes and kinetics. We tested the method against extensive (multi μs) MD simulations of the right-handed- to left-handed-helix transition of a 9-mer peptide of AIB, the symmetry of which allows us to establish convergence. MELD-path finds all the metastable states, their correct relative populations, and the full ensemble of routes, not just a single assumed route. For this transition, we find a very broad route structure. MELD-path is highly parallelizable and efficient, yielding the full route map in a few days of computation. We believe MELD-path could be a general and rapid way to explore mechanistic processes in biomolecules on the computer.

Computational studies of vibrational energy flow in biomolecules have to date mapped out transport pathways on a case-by-case basis. To provide a more general approach, we derive scaling rules for vibrational energy transport in a globular protein, which are identified from extensive nonequilibrium molecular dynamics simulations of vibrational energy flow in the villin headpiece subdomain HP36. We parametrize a master equation based on inter-residue, residue-solvent, and heater-residue energy-transfer rates, which closely reproduces the results of the all-atom simulations. From that fit, two scaling rules emerge, one for energy transport along the protein backbone which relies on a diffusion model and another for energy transport between tertiary contacts, which is based on a harmonic model. Requiring only the calculation of mean and variance of relatively few atomic distances, the approach holds the potential to predict the pathways and time scales of vibrational energy flow in large proteins.

Based on extensive ab initio calculations and the time-propagation of the nuclear Schrödinger equation, we study the vibrational relaxation dynamics and resulting spectral signatures of the OH stretch vibration of a hydrogen-bonded complex, HCO2 (-)⋅H2O. Despite their smallness, it has been shown experimentally by Johnson and coworkers that the gas-phase infrared spectra of these types of complexes exhibit much of the complexity commonly observed for hydrogen-bonded systems. That is, the OH stretch band exhibits a significant red shift together with an extreme broadening and a pronounced substructure, which reflects its very strong anharmonicity. Employing an adiabatic separation of time scales between the three intramolecular high-frequency modes of the water molecule and the three most important intermolecular low-frequency modes of the complex, we calculate potential energy surfaces (PESs) of the ground and the first excited states of the high-frequency modes and identify a vibrational conical intersection between the PESs of the OH stretch fundamental and the HOH bend overtone. By performing a time-dependent propagation of the resulting system, we show that the conical intersection affects a coherent population transfer between the two states, the first step of which being ultrafast (60 fs) and irreversible. The subsequent relaxation of vibrational energy into the HOH bend and ground state occurs incoherently but also quite fast (1 ps), although the corresponding PESs are well separated in energy. Owing to the smaller effective mass difference between light and heavy degrees of freedom, the adiabatic ansatz is consequently less significant for vibrations than in the electronic case. Based on the model, we consider several approximations to calculate the measured Ar-tag action spectrum of HCO2 (-)⋅H2O and achieve semiquantitative agreement with the experiment.

Classical replica-exchange molecular dynamics simulations are performed to study structure, dynamics and thermostability of the 14-mer RNA hairpins uCACGg and cUUCGg. Despite of the different sequence and closing base pair of the two systems, recent NMR studies have shown that the tetraloop CACG is strikingly similar in overall geometry and hydrogen bonding to the canonical UUCG tetraloop. On the other hand, the two systems differ significantly in their functionality and thermostability. The simulations confirm the structural similarities of the two RNA hairpins at room temperature but also reveal that the UUCG loop is more flexible than the CACG loop. Concerning the functionality, the CACG loop shows a stronger attitude to donate hydrogens than the UUCG loop, although their global solvent accessible surface is quite similar. The simulations qualitatively reproduce the experimentally found difference in melting temperatures (20 K). In the case of the uCACGg hairpin, the thermal unfolding occurs cooperatively in an all-or-none fashion, while the cUUCGg hairpin shows less cooperativity but exhibits intermediate states during the unfolding process.

We investigate energy transport through an alpha-aminoisobutyric acid-based 3(10)-helix dissolved in chloroform in a combined experimental-theoretical approach. Vibrational energy is locally deposited at the N terminus of the helix by ultrafast internal conversion of a covalently attached, electronically excited, azobenzene moiety. Heat flow through the helix is detected with subpicosecond time resolution by employing vibrational probes as local thermo meters at various distances from the heat source. The experiment is supplemented by detailed nonequilibrium molecular dynamics (MD) simulations of the process, revealing good qualitative agreement with experiment: Both theory and experiment exhibit an almost instantaneous temperature jump of the reporter units next to the heater which is attributed to the direct impact of the isomerizing azobenzene moiety. After this impact event, helix and azobenzene moiety appear to be thermally decoupled. The energy deposited in the helix thermalizes on a subpicosecond timescale and propagates along the helix in a diffusive-like process, accompanied by a significant loss into the solvent. However, in terms of quantitative numbers, theory and experiment differ. In particular, the MD simulation seems to overestimate the heat diffusion constant (2 A(2) ps(-1) from the experiment) by a factor of five.

The free energy landscape and the folding mechanism of the C-terminal beta-hairpin of protein G is studied by extensive replica exchange molecular dynamics simulations (40 replicas and 340 ns total simulation time), using the GROMOS96 force field and the SPC explicit water solvent. The study reveals that the system preferentially adopts a beta-hairpin structure at biologically important temperatures, and that the helix content is low at all temperatures studied. Representing the free energy landscape as a function of several types of reaction coordinates, four local minima corresponding to the folded, partially folded, molten globule, and unfolded states are identified. The findings suggest that the folding of the beta-hairpin occurs as the sequence: collapse of hydrophobic core --> formation of H-bond --> formation of the turn. Identifying the folded and molten globule states as the main conformations, the free energy landscape of the beta-hairpin is consistent with a two-state behavior with a broad transition state. The temperature dependence of the folding-unfolding transition is investigated in some detail. The enthalpy and entropy jumps at the folding transition temperature are found to be about three times lower than the experimental estimates, indicating that the folding-unfolding transition in silico is less cooperative than its in vitro counterpart., (Proteins 2005. 2005 Wiley-Liss, Inc.)