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The carboxyl (COOH) side chain groups of amino acids, such as aspartic acid, play an important role in biochemical processes, including enzymatic proton transport. In many theoretical studies, it was found that the (bio)chemical reactivity of the carboxyl group strongly depends on the conformation of this group. Interestingly, up to now there has been no experimental investigation of the geometry and the stability of different COOH conformers under biorelevant conditions. Here, we investigate the conformational isomerism of the side chain COOH group of N-acetyl aspartic acid amide using polarization-resolved two-dimensional infrared spectroscopy. We find that the carboxyl group shows two distinct near-planar conformers (syn and anti) when dissolved in water at room temperature. Both conformers are significantly populated in aqueous solution (75 +/- 10% and 25 +/- 10% for syn and anti, respectively). Molecular dynamics simulations show that the anti conformer interacts more strongly with water molecules than the syn conformer, explaining why this conformer is significantly present in aqueous solution.

DNA predominantly contains Watson–Crick (WC) base pairs, but a non-negligible fraction of base pairs are in the Hoogsteen (HG) hydrogen bonding motif at any time. In HG, the purine is rotated ∼180° relative to the WC motif. The transitions between WC and HG may play a role in recognition and replication, but are difficult to investigate experimentally because they occur quickly, but only rarely. To gain insight into the mechanisms for this process, we performed transition path sampling simulations on a model nucleotide sequence in which an AT pair changes from WC to HG. This transition can occur in two ways, both starting with loss of hydrogen bonds in the base pair, followed by rotation around the glycosidic bond. In one route the adenine base converts from WC to HG geometry while remaining entirely within the double helix. The other route involves the adenine leaving the confines of the double helix and interacting with water. Our results indicate that this outside route is more probable. We used transition interface sampling to compute rate constants and relative free energies for the transitions between WC and HG. Our results agree with experiments, and provide highly detailed insights into the mechanisms of this important process.

With the continual improvement of computing hardware and algorithms, simulations have become a powerful tool for understanding all sorts of (bio)molecular processes. To handle the large simulation data sets and to accelerate slow, activated transitions, a condensed set of descriptors, or collective variables (CVs), is needed to discern the relevant dynamics that describes the molecular process of interest. However, proposing an adequate set of CVs that can capture the intrinsic reaction coordinate of the molecular transition is often extremely difficult. Here, we present a framework to find an optimal set of CVs from a pool of candidates using a combination of artificial neural networks and genetic algorithms. The approach effectively replaces the encoder of an autoencoder network with genes to represent the latent space, i.e., the CVs. Given a selection of CVs as input, the network is trained to recover the atom coordinates underlying the CV values at points along the transition. The network performance is used as an estimator of the fitness of the input CVs. Two genetic algorithms optimize the CV selection and the neural network architecture. The successful retrieval of optimal CVs by this framework is illustrated at the hand of two case studies: the well-known conformational change in the alanine dipeptide molecule and the more intricate transition of a base pair in B-DNA from the classic Watson-Crick pairing to the alternative Hoogsteen pairing. Key advantages of our framework include the following: optimal interpretable CVs, avoiding costly calculation of committor or time-correlation functions, and automatic hyperparameter optimization. In addition, we show that applying a time-delay between the network input and output allows for enhanced selection of slow variables. Moreover, the network can also be used to generate molecular configurations of unexplored microstates, for example, for augmentation of the simulation data.

This is the peer reviewed version of the following article: Mroginski, Adam, Amoyal, Barnoy, Bondar, ... Schapiro. (2020). Frontiers in Multiscale Modeling of Photoreceptor Proteins. Photochemistry and Photobiology, 97(2):243-269, which has been published in final form at https://doi.org/10.1111/php.13372. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited. This perspective article highlights the challenges in the theoretical description of photoreceptor proteins using multiscale modeling, as discussed at the CECAM workshop in Tel Aviv, Israel. The participants have identified grand challenges and discussed the development of new tools to address them. Recent progress in understanding representative proteins such as green fluorescent protein, photoactive yellow protein, phytochrome, and rhodopsin is presented, along with methodological developments.

The biological functions of natural polyelectrolytes are strongly influenced by the presence of ions, which bind to the polymer chains and thereby modify their properties. Although the biological impact of such modifications is well recognized, a detailed molecular picture of the binding process and of the mechanisms that drive the subsequent structural changes in the polymer is lacking. Here, we study the molecular mechanism of the condensation of calcium, a divalent cation, on hyaluronan, a ubiquitous polymer in human tissues. By combining two-dimensional infrared spectroscopy experiments with molecular dynamics simulations, we find that calcium specifically binds to hyaluronan at millimolar concentrations. Because of its large size and charge, the calcium cation can bind simultaneously to the negatively charged carboxylate group and the amide group of adjacent saccharide units. Molecular dynamics simulations and single-chain force spectroscopy measurements provide evidence that the binding of the calcium ions weakens the intramolecular hydrogen-bond network of hyaluronan, increasing the flexibility of the polymer chain. We also observe that the binding of calcium to hyaluronan saturates at a maximum binding fraction of similar to 10-15 mol %. This saturation indicates that the binding of Ca2+ strongly reduces the probability of subsequent binding of Ca2+ at neighboring binding sites, possibly as a result of enhanced conformational fluctuations and/or electrostatic repulsion effects. Our findings provide a detailed molecular picture of ion condensation and reveal the severe effect of a few, selective and localized electrostatic interactions on the rigidity of a polyelectrolyte chain.

Advances in the atomistic understanding of biomolecular structure and function come with wide applications in medicine, pharmacology and biomaterials, as well as with fundamental answers about the nature of living things. Such insight can in principle be brought by molecular dynamics (MD) simulations. However, even with nowadays vast computational resources, the timescales that are usually accessible in standard MD simulations cannot reliably sample many relevant biological processes, which occur either too slowly or too infrequently. We refer to this as the "rare event" problem. To overcome it, the MD community has devised an arsenal of enhanced sampling techniques, which deliver insight in the form of free-energy landscapes, projected on key descriptive molecular degrees of freedom, i.e. collective variables (CVs). Nonetheless, the computational cost scales exponentially with the number of CVs; meaning that complex transitions are typically out of grasp. In this thesis, we introduce novel path-based enhanced sampling strategies, and also combine them with other powerful simulation techniques; thus, advancing the capabilities to navigate complex molecular transitions. Our framework enables the study of intricate conformational and chemical changes in a variety of biomolecules; including oligopeptides, sensor proteins, DNA and polysaccharides. From each system, we extract fine mechanistic details, free energies and biological insights. We hope for these advancements to contribute to the long-term goals of biomolecular simulation; enabling the atomistic understanding of ever larger, more complex and realistic living systems.

The biological functions of natural polyelectrolytes are strongly influenced by the presence of ions, which bind to the polymer chains and thereby modify their properties. Although the biological impact of such modifications is well-recognized, a detailed molecular picture of the binding process and of the mechanisms that drive the subsequent structural changes in the polymer is lacking. Here, we study the molecular mechanism of the condensation of calcium, a divalent cation, on hyaluronan, a ubiquitous polymer in human tissues. By combining two-dimensional infrared spectroscopy experiments with molecular dynamics simulations, we find that calcium specifically binds to hyaluronan at millimolar concentrations. Because of its large size and charge, the calcium cation can bind simultaneously to the negatively charged carboxylate group and the amide group of adjacent saccharide units. Molecular dynamics simulations and single-chain force spectroscopy measurements provide evidence that the binding of the calcium ions weakens the intra-molecular hydrogen-bond network of hyaluronan, increasing the flexibility of the polymer chain. We also observe that the binding of calcium to hyaluronan saturates at a maximum binding fraction of ~10-15 mol %. This saturation indicates that the binding of Ca2+ strongly reduces the probability of subsequent binding of Ca2+ at neighboring binding sites, possibly as a result of enhanced conformational fluctuations and/or electrostatic repulsion effects. Our findings provide a detailed molecular picture of ion condensation, and reveal the severe effect of a few, selective and localized electrostatic interactions on the rigidity of a polyelectrolyte chain.TOC

In the past decade, great progress has been made in the development of enhanced sampling methods, aimed at overcoming the time-scale limitations of molecular dynamics (MD) simulations. Many sampling schemes rely on adding an external bias to favor the sampling of transitions and to estimate the underlying free energy landscape. Nevertheless, sampling molecular processes described by many order parameters, or collective variables (CVs), such as complex biomolecular transitions, remains often very challenging. The computational cost has a prohibitive scaling with the dimensionality of the CV-space. Inspiration can be taken from methods that focus on localizing transition pathways: the CV-space can be projected onto a path-CV that connects two stable states, and a bias can be exerted onto a one-dimensional parameter that captures the progress of the transition along the path-CV. In principle, such a sampling scheme can handle an arbitrarily large number of CVs. A standard enhanced sampling technique combined with an adaptive path-CV can then locate the mean transition pathway and obtain the free energy profile along the path. In this chapter, we discuss the adaptive path-CV formalism and its numerical implementation. We apply the path-CV with several enhanced sampling methods-steered MD, metadynamics, and umbrella sampling-to a biologically relevant process: the Watson-Crick to Hoogsteen base-pairing transition in double-stranded DNA. A practical guide is provided on how to recognize and circumvent possible pitfalls during the calculation of a free energy landscape that contains multiple pathways. Examples are presented on how to perform enhanced sampling simulations using PLUMED, a versatile plugin that can work with many popular MD engines.

In the past decade, great progress has been made in the development of enhanced sampling methods, aimed at overcoming the time-scale limitations of molecular dynamics (MD) simulations. Many sampling schemes rely on adding an external bias to favor the sampling of transitions and to estimate the underlying free energy landscape. Nevertheless, sampling molecular processes described by many order parameters, or collective variables (CVs), such as complex biomolecular transitions, remains often very challenging. The computational cost has a prohibitive scaling with the dimensionality of the CV-space. Inspiration can be taken from methods that focus on localizing transition pathways: the CV-space can be projected onto a path-CV that connects two stable states, and a bias can be exerted onto a one-dimensional parameter that captures the progress of the transition along the path-CV. In principle, such a sampling scheme can handle an arbitrarily large number of CVs. A standard enhanced sampling technique combined with an adaptive path-CV can then locate the mean transition pathway and obtain the free energy profile along the path. In this chapter, we discuss the adaptive path-CV formalism and its numerical implementation. We apply the path-CV with several enhanced sampling methods-steered MD, metadynamics, and umbrella sampling-to a biologically relevant process: the Watson-Crick to Hoogsteen base-pairing transition in double-stranded DNA. A practical guide is provided on how to recognize and circumvent possible pitfalls during the calculation of a free energy landscape that contains multiple pathways. Examples are presented on how to perform enhanced sampling simulations using PLUMED, a versatile plugin that can work with many popular MD engines.

Study of complex activated molecular transitions by molecular dynamics (MD) simulation can be a daunting task, especially when little knowledge is available on the reaction coordinate describing the mechanism of the process. Here, we assess the path-metadynamics enhanced sampling approach in combination with force field and ab initio [density functional theory (DFT)] MD simulations of conformational and chemical transitions that require three or more collective variables (CVs) to describe the processes. We show that the method efficiently localizes the average transition path of each process and simultaneously obtains the free energy profile along the path. The new multiple-walker implementation greatly speeds-up the calculation, with an almost trivial scaling of the number of parallel replicas. Increasing the dimensionality by expanding the set of CVs leads to a less than linear increase in the computational cost, as shown by applying the method to a conformational change in increasingly longer polyproline peptides. Combined with DFT-MD to model acid (de-)protonation in explicit water solvent, the transition path and associated free energy profile were obtained in less than 100 ps of simulation. A final application to hydrogen fuel production catalyzed by a hydrogenase enzyme showcases the unique mechanistic insight and chemical understanding that can be obtained from the average transition path. Published by AIP Publishing.