Simulations predict preferred Mg2+ coordination in a nonenzymatic primer-extension reaction center.

The mechanism by which genetic information was copied prior to the evolution of ribozymes is of great interest because of its importance to the origin of life. The most effective known process for the nonenzymatic copying of an RNA template is primer extension by a two-step pathway in which 2-aminoimidazole-activated nucleotides first react with each other to form an imidazolium-bridged intermediate that subsequently reacts with the primer. Reaction kinetics, structure-activity relationships, and X-ray crystallography have provided insight into the overall reaction mechanism, but many puzzles remain. In particular, high concentrations of Mg2+ are required for efficient primer extension, but the mechanism by which Mg2+ accelerates primer extension remains unknown. By analogy with the mechanism of DNA and RNA polymerases, a role for Mg2+ in facilitating the deprotonation of the primer 3'-hydroxyl is often assumed, but no catalytic metal ion is seen in crystal structures of the primer-extension complex. To explore the potential effects of Mg2+ binding in the reaction center, we performed atomistic molecular dynamics simulations of a series of modeled complexes in which a Mg2+ ion was placed in the reaction center with inner-sphere coordination with different sets of functional groups. Our simulations suggest that coordination of a Mg2+ ion with both O3' of the terminal primer nucleotide and the pro-Sp nonbridging oxygen of the reactive phosphate of an imidazolium-bridged dinucleotide would help to pre-organize the structure of the primer/template substrate complex to favor the primer-extension reaction. Our results suggest that the catalytic metal ion may play an important role in overcoming electrostatic repulsion between a deprotonated O3' and the reactive phosphate of the bridged dinucleotide and lead to testable predictions of the mode of Mg2+ binding that is most relevant to catalysis of primer extension.

Experimental Tests of the Virtual Circular Genome Model for Nonenzymatic RNA Replication.

The virtual circular genome (VCG) model was proposed as a means of going beyond template copying to indefinite cycles of nonenzymatic RNA replication during the origin of life. In the VCG model, the protocellular genome is a collection of short oligonucleotides that map to both strands of a virtual circular sequence. Replication is driven by templated nonenzymatic primer extensions on a subset of kinetically trapped partially base-paired configurations, followed by the shuffling of these configurations to enable continued oligonucleotide elongation. Here, we describe initial experimental studies of the feasibility of the VCG model for replication. We designed a small 12-nucleotide model VCG and synthesized all 247 oligonucleotides of lengths 2 to 12 corresponding to this genome. We experimentally monitored the fate of individual labeled primers in the pool of VCG oligonucleotides following the addition of activated nucleotides and investigated the effect of factors such as oligonucleotide length, concentration, composition, and temperature on the extent of primer extension. We observe a surprisingly prolonged equilibration process in the VCG system that enables a considerable extent of reaction. We find that environmental fluctuations would be essential for continuous templated extension of the entire VCG system since the shortest oligonucleotides can only bind to templates at low temperatures, while the longest oligonucleotides require high-temperature spikes to escape from inactive configurations. Finally, we demonstrate that primer extension is significantly enhanced when the mix of VCG oligonucleotides is preactivated. We discuss the necessity of ongoing in situ activation chemistry for continuous and accurate VCG replication.

Reconciling membrane protein simulations with experimental DEER spectroscopy data.

Spectroscopy experiments are crucial to study membrane proteins for which traditional structure determination methods still prove challenging. Double electron-electron resonance (DEER) spectroscopy experiments provide protein residue-pair distance distributions that are indicative of their conformational heterogeneity. Atomistic molecular dynamics (MD) simulations are another tool that have been proven to be vital to study the structural dynamics of membrane proteins such as to identify inward-open, occluded, and outward-open conformations of transporter membrane proteins, among other partially open or closed states of the protein. Yet, studies have reported that there is no direct consensus between the distributional data from DEER experiments and MD simulations, which has challenged validation of structures obtained from long-timescale simulations and using simulations to design experiments. Current coping strategies for comparisons rely on heuristics, such as mapping the nearest matching peaks between two ensembles or biased simulations. Here we examine the differences in residue-pair distance distributions arising due to the choice of membranes around the protein and covalent modification of a pair of residues to nitroxide spin labels in DEER experiments. Through comparing MD simulations of two proteins, PepTSo and LeuT-both of which have been characterized using DEER experiments previously-we show that the proteins' dynamics are similar despite the choice of the detergent micelle as a membrane mimetic in DEER experiments. On the other hand, covalently modified residues show slight local differences in their dynamics and a huge divergence when the oxygen atom pair distances between spin labeled residues are measured rather than protein backbone distances. Given the computational expense associated with pairwise MTSSL labeled MD simulations, we examine the use of biased simulations to explore the conformational dynamics of the spin labels only to reveal that such simulations alter the underlying protein dynamics. Our study identifies the main cause for the mismatch between DEER experiments and MD simulations and will accelerate the development of potential mitigation strategies to improve the match.

Structure-Activity Relationships in Nonenzymatic Template-Directed RNA Synthesis.

The template-directed synthesis of RNA played an important role in the transition from prebiotic chemistry to the beginnings of RNA based life, but the mechanism of RNA copying chemistry is incompletely understood. We measured the kinetics of template copying with a set of primers with modified 3'-nucleotides and determined the crystal structures of these modified nucleotides in the context of a primer/template/substrate-analog complex. pH-rate profiles and solvent isotope effects show that deprotonation of the primer 3'-hydroxyl occurs prior to the rate limiting step, the attack of the alkoxide on the activated phosphate of the incoming nucleotide. The analogs with a 3 E ribose conformation show the fastest formation of 3'-5' phosphodiester bonds. Among those derivatives, the reaction rate is strongly correlated with the electronegativity of the 2'-substituent. We interpret our results in terms of differences in steric bulk and charge distribution in the ground vs. transition states.

Dodine as a Kosmo-Chaotropic Agent.

Denaturants such as the guanidinium cation unfold proteins at molar concentrations, which interferes with ultraviolet- and infrared-based spectroscopy measurements. Dodine denatures some proteins cooperatively at a thousand-fold lower concentration, allowing for spectroscopy measurements. Nonetheless, dodine's microscopic mechanism of interaction with proteins is not understood. We probe the effect of dodine on α-helices and tertiary structure by investigating the stability of the small helical protein B. Experiments show that dodine promotes formation of helical structure (a kosmotropic effect), while inducing the loss of tertiary structure (a chaotropic effect). Although dodine destabilizes native protein structure, it does not lower the thermal denaturation midpoint temperature of protein B. All-atom simulations reveal the cause for both observations: The denaturant action of dodine's guanidyl headgroup is counteracted by its aliphatic tail, which stabilizes amphipathic helices and associates with an expanded protein core. The Janus-like behavior of headgroup and tail make dodine a simultaneous stabilizer-destabilizer or "kosmo-chaotrope".

Free Energy Landscape of the Complete Transport Cycle in a Key Bacterial Transporter.

PepTSo is a proton-coupled bacterial symporter, from the major facilitator superfamily (MFS), which transports di-/tripeptide molecules. The recently obtained crystal structure of PepTSo provides an unprecedented opportunity to gain an understanding of functional insights of the substrate transport mechanism. Binding of the proton and peptide molecule induces conformational changes into occluded (OC) and outward-facing (OF) states, which we are able to characterize using molecular dynamics (MD) simulations. The structural knowledge of the OC and OF state is important to fully understand the major energy barrier associated with the transport cycle. In order to gain functional insight into the interstate dynamics, we performed extensive all atom MD simulations. The Markov state model was constructed to identify the free energy barriers between the states, and kinetic information on intermediate pathways was obtained using the transition pathway theory (TPT). TPT shows that the OF state is obtained by the movement of TM1 and TM7 at the extracellular side approximately 12-16 Å away from each other, and the inward movement of TM4 and TM10 at the intracellular halves to 3-4 Å characterizes the OC state. Helix distance distributions obtained from MD simulations were compared with experimental double electron-electron resonance spectroscopy and were found to be in excellent agreement with previous studies. We also predicted the optimal positions for placement of methane thiosulfonate spin label probes to capture the slowest protein dynamics. Our finding sheds light on the conformational cycle of this key membrane transporter and the functional relationships between the multiple intermediate states.

Maximizing Kinetic Information Gain of Markov State Models for Optimal Design of Spectroscopy Experiments.

Spectroscopic techniques such as Trp-Tyr quenching, luminescence resonance energy transfer, and triplet-triplet energy transfer are widely used for understanding the dynamic behavior of proteins. These experiments measure the relaxation of a particular labeled set of residue pairs, and the choice of residue pairs requires careful thought. As a result, experimentalists must pick residue pairs from a large pool of possibilities. In the current work, we show that molecular simulation datasets of protein dynamics can be used to systematically select an optimal set of residue positions to place probes for conducting spectroscopic experiments. The method described in this work, called Optimal Probes, can be used to rank trial sets of residue pairs in terms of their ability to capture the conformational dynamics of the protein. Optimal probes ensures two conditions: residue pairs capture the slow dynamics of the protein and their dynamics is not correlated for maximum information gain to score each trial set. Eventually, the highest scored set can be used for biophysical experiments to study the kinetics of the protein. The scoring methodology is based on kinetic network models of protein dynamics and a variational principle for molecular kinetics to optimize the hyperparameters used for the model. We also discuss that the scoring strategy used by Optimal Probes is the best possible way to ensure the ideal choice of residue pairs for experiments. We predict the best experimental probe positions for proteins λ-repressor, ß2-adrenergic receptor, and villin headpiece domain. These proteins have been well-studied and allow for a rigorous comparison of Optimal Probes predictions with already available experiments. Additionally, we also illustrate that our method can be used to predict the best choice for experiments by including any previous experiment choices available from other studies on the same protein. We consistently find that the best choice cannot be based on intuition or structural information such as distance difference between few known stable structures of the protein. Therefore, we show that incorporating protein dynamics could be used to maximize the information gain from experiments.

Predicting Optimal DEER Label Positions to Study Protein Conformational Heterogeneity.

Double electron-electron resonance (DEER) spectroscopy is a powerful experimental technique for understanding the conformational heterogeneity of proteins. It involves attaching nitroxide spin labels to two residues in the protein to obtain a distance distribution between them. However, the choice of residue pairs to label in the protein requires careful thought, as experimentalists must pick label positions from a large set of all possible residue-pair combinations in the protein. In this article, we address the problem of the choice of DEER spin-label positions in a protein. For this purpose, we utilize all-atom molecular dynamics simulations of protein dynamics, to rank the sets of labeled residue pairs in terms of their ability to capture the conformational dynamics of the protein. Our design methodology is based on the following two criteria: (1) An ideal set of DEER spin-label positions should capture the slowest conformational-change processes observed in the protein dynamics, and (2) any two sets of residue pairs should describe orthogonal conformational-change processes to maximize the overall information gain and reduce the number of labeled residue pairs. We utilize Markov state models of protein dynamics to identify slow dynamical processes and a genetic-algorithm-based approach to predict the optimal choices of residue pairs with limited computational time requirements. We predict the optimal residue pairs for DEER spectroscopy in ß2 adrenergic receptor, the C-terminal domain of calmodulin, and peptide transporter PepTSo. We find that our choices were ranked higher than those used to perform DEER experiments on the proteins investigated in this study. Hence, the predicted choices of DEER residue pairs determined by our method provide maximum insight into the conformational heterogeneity of the protein while using the minimum number of labeled residues.

RNABP COGEST: a resource for investigating functional RNAs.

Structural bioinformatics of RNA has evolved mainly in response to the rapidly accumulating evidence that non-(protein)-coding RNAs (ncRNAs) play critical roles in gene regulation and development. The structures and functions of most ncRNAs are however still unknown. Most of the available RNA structural databases rely heavily on known 3D structures, and contextually correlate base pairing geometry with actual 3D RNA structures. None of the databases provide any direct information about stabilization energies. However, the intrinsic interaction energies of constituent base pairs can provide significant insights into their roles in the overall dynamics of RNA motifs and structures. Quantum mechanical (QM) computations provide the only approach toward their accurate quantification and characterization. 'RNA Base Pair Count, Geometry and Stability' (http://bioinf.iiit.ac.in/RNABPCOGEST) brings together information, extracted from literature data, regarding occurrence frequency, experimental and quantum chemically optimized geometries, and computed interaction energies, for non-canonical base pairs observed in a non-redundant dataset of functional RNA structures. The database is designed to enable the QM community, on the one hand, to identify appropriate biologically relevant model systems and also enable the biology community to easily sift through diverse computational results to gain theoretical insights which could promote hypothesis driven biological research.