Correction: Considerable slowdown of short DNA fragment translocation across a protein nanopore using pH-induced generation of enthalpic traps inside the permeation pathway.

Correction for 'Considerable slowdown of short DNA fragment translocation across a protein nanopore using pH-induced generation of enthalpic traps inside the permeation pathway' by Loredana Mereuta et al., Nanoscale, 2023, 15, 14754-14763, https://doi.org/10.1039/D3NR03344A.

Considerable slowdown of short DNA fragment translocation across a protein nanopore using pH-induced generation of enthalpic traps inside the permeation pathway.

A pressing challenge in the realm of nanopore-based sensing technologies for nucleic acid characterization has been the cheap and efficient control of analyte translocation. To address this, a plethora of methods were tested, including mutagenesis, molecular motors, enzymes, or the optimization of experimental conditions. Herein, we present a paradigm exploiting the manipulation of electrostatic interactions between 22-mer single-stranded DNAs (22_ssDNA) and low pH-induced charges in the alpha-hemolysin (α-HL) nanopore, to efficiently control the passage of captured molecules. We discovered that in electrolytes buffered at pH = 5 and pH = 4.5 where the nanopore's vestibule and lumen become oppositely charged as compared to that at neutral pH, the electrostatic anchoring at these regions of a 22_ssDNA fragment leads to a dramatic increase of the translocation time, orders of magnitude larger compared to that at neutral pH. This pH-dependent tethering effect is reversible, side invariant, and sensitive to the ionic strength and ssDNA contour length. In the long run, our discovery has the potential to provide a simple read-out of the sequence of bases pertaining to short nucleotide sequences, thus extending the efficacy of current nanopore-based sequencers.

A Suite of Tutorials for the WESTPA 2.0 Rare-Events Sampling Software [Article v2.0].

The weighted ensemble (WE) strategy has been demonstrated to be highly efficient in generating pathways and rate constants for rare events such as protein folding and protein binding using atomistic molecular dynamics simulations. Here we present two sets of tutorials instructing users in the best practices for preparing, carrying out, and analyzing WE simulations for various applications using the WESTPA software. The first set of more basic tutorials describes a range of simulation types, from a molecular association process in explicit solvent to more complex processes such as host-guest association, peptide conformational sampling, and protein folding. The second set ecompasses six advanced tutorials instructing users in the best practices of using key new features and plugins/extensions of the WESTPA 2.0 software package, which consists of major upgrades for larger systems and/or slower processes. The advanced tutorials demonstrate the use of the following key features: (i) a generalized resampler module for the creation of "binless" schemes, (ii) a minimal adaptive binning scheme for more efficient surmounting of free energy barriers, (iii) streamlined handling of large simulation datasets using an HDF5 framework, (iv) two different schemes for more efficient rate-constant estimation, (v) a Python API for simplified analysis of WE simulations, and (vi) plugins/extensions for Markovian Weighted Ensemble Milestoning and WE rule-based modeling for systems biology models. Applications of the advanced tutorials include atomistic and non-spatial models, and consist of complex processes such as protein folding and the membrane permeability of a drug-like molecule. Users are expected to already have significant experience with running conventional molecular dynamics or systems biology simulations.

Predicting residue cooperativity during protein folding: A combined, molecular dynamics and unsupervised learning approach.

Allostery in proteins involves, broadly speaking, ligand-induced conformational transitions that modulate function at active sites distal to where the ligand binds. In contrast, the concept of cooperativity (in the sense used in phase transition theory) is often invoked to understand protein folding and, therefore, function. The modern view on allostery is one based on dynamics and hinges on the time-dependent interactions between key residues in a complex network, interactions that determine the free-energy profile for the reaction at the distal site. Here, we merge allostery and cooperativity, and we discuss a joint model with features of both. In our model, the active-site reaction is replaced by the reaction pathway that leads to protein folding, and the presence or absence of the effector is replaced by mutant-vs-wild type changes in key residues. To this end, we employ our recently introduced time-lagged independent component analysis (tICA) correlation approach [Ray et al. Proc. Natl. Acad. Sci. 118(43) (2021), e2100943118] to identify the allosteric role of distant residues in the folded-state dynamics of a large protein. In this work, we apply the technique to identify key residues that have a significant role in the folding of a small, fast folding-protein, chignolin. Using extensive enhanced sampling simulations, we critically evaluate the accuracy of the predictions by mutating each residue one at a time and studying how the mutations change the underlying free energy landscape of the folding process. We observe that mutations in those residues whose associated backbone torsion angles have a high correlation score can indeed lead to loss of stability of the folded configuration. We also provide a rationale based on interaction energies between individual residues with the rest of the protein to explain this effect. From these observations, we conclude that the tICA correlation score metric is a useful tool for predicting the role of individual residues in the correlated dynamics of proteins and can find application to the problem of identifying regions of protein that are either most vulnerable to mutations or-mutatis mutandis-to binding events that affect their functionality.

Force-Field-Dependent DNA Breathing Dynamics: A Case Study of Hoogsteen Base Pairing in A6-DNA.

The Hoogsteen (HG) base pairing conformation, commonly observed in damaged and mutated DNA helices, facilitates DNA repair and DNA recognition. The free energy difference between HG and Watson-Crick (WC) base pairs has been computed in previous studies. However, the mechanism of the conformational transition is not well understood. A detailed understanding of the process of WC to HG base pair transition can provide a deeper understanding of DNA repair and recognition. In an earlier study, we explored the free energy landscape for this process using extensive computer simulation with the CHARMM36 force field. In this work, we study the impact of force field models in describing the WC to HG base pairing transition using meta-eABF enhanced sampling, quasi-harmonic entropy calculation, and nonbonded energy analysis. The secondary structures of both base pairing forms and the topology of the free energy landscapes were consistent over different force field models, although the relative free energy, entropy, and the interaction energies tend to vary. The relative stability of the WC and HG conformations is dictated by a delicate balance between the enthalpic stabilization and the reduced entropy of the structurally rigid HG structure. These findings highlight the impact that subtleties in force field models can have on accurately modeling DNA base pair dynamics and should stimulate further computational investigations into other dynamically important motions in DNA.

Point mutations in SARS-CoV-2 variants induce long-range dynamical perturbations in neutralizing antibodies.

Monoclonal antibodies are emerging as a viable treatment for the coronavirus disease 19 (COVID-19). However, newly evolved variants of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can reduce the efficacy of currently available antibodies and can diminish vaccine-induced immunity. Here, we demonstrate that the microscopic dynamics of neutralizing monoclonal antibodies can be profoundly modified by the mutations present in the spike proteins of the SARS-COV-2 variants currently circulating in the world population. The dynamical perturbations within the antibody structure, which alter the thermodynamics of antigen recognition, are diverse and can depend both on the nature of the antibody and on the spatial location of the spike mutation. The correlation between the motion of the antibody and that of the spike receptor binding domain (RBD) can also be changed, modulating binding affinity. Using protein-graph-connectivity networks, we delineated the mutant-induced modifications in the information-flow along allosteric pathway throughout the antibody. Changes in the collective dynamics were spatially distributed both locally and across long-range distances within the antibody. On the receptor side, we identified an anchor-like structural element that prevents the detachment of the antibodies; individual mutations there can significantly affect the antibody binding propensity. Our study provides insight into how virus neutralization by monoclonal antibodies can be impacted by local mutations in the epitope via a change in dynamics. This realization adds a new layer of sophistication to the efforts for rational design of monoclonal antibodies against new variants of SARS-CoV2, taking the allostery in the antibody into consideration.

Markovian Weighted Ensemble Milestoning (M-WEM): Long-Time Kinetics from Short Trajectories.

We introduce a rare-event sampling scheme, named Markovian Weighted Ensemble Milestoning (M-WEM), which inlays a weighted ensemble framework within a Markovian milestoning theory to efficiently calculate thermodynamic and kinetic properties of long-time-scale biomolecular processes from short atomistic molecular dynamics simulations. M-WEM is tested on the Müller-Brown potential model, the conformational switching in alanine dipeptide, and the millisecond time-scale protein-ligand unbinding in a trypsin-benzamidine complex. Not only can M-WEM predict the kinetics of these processes with quantitative accuracy but it also allows for a scheme to reconstruct a multidimensional free-energy landscape along additional degrees of freedom, which are not part of the milestoning progress coordinate. For the ligand-receptor system, the experimental residence time, association and dissociation kinetics, and binding free energy could be reproduced using M-WEM within a simulation time of a few hundreds of nanoseconds, which is a fraction of the computational cost of other currently available methods, and close to 4 orders of magnitude less than the experimental residence time. Due to the high accuracy and low computational cost, the M-WEM approach can find potential applications in kinetics and free-energy-based computational drug design.

Distant residues modulate conformational opening in SARS-CoV-2 spike protein.

Infection by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) involves the attachment of the receptor-binding domain (RBD) of its spike proteins to the ACE2 receptors on the peripheral membrane of host cells. Binding is initiated by a down-to-up conformational change in the spike protein, the change that presents the RBD to the receptor. To date, computational and experimental studies that search for therapeutics have concentrated, for good reason, on the RBD. However, the RBD region is highly prone to mutations, and is therefore a hotspot for drug resistance. In contrast, we here focus on the correlations between the RBD and residues distant to it in the spike protein. This allows for a deeper understanding of the underlying molecular recognition events and prediction of the highest-effect key mutations in distant, allosteric sites, with implications for therapeutics. Also, these sites can appear in emerging mutants with possibly higher transmissibility and virulence, and preidentifying them can give clues for designing pan-coronavirus vaccines against future outbreaks. Our model, based on time-lagged independent component analysis (tICA) and protein graph connectivity network, is able to identify multiple residues that exhibit long-distance coupling with the RBD opening. Residues involved in the most ubiquitous D614G mutation and the A570D mutation of the highly contagious UK SARS-CoV-2 variant are predicted ab initio from our model. Conversely, broad-spectrum therapeutics like drugs and monoclonal antibodies can target these key distant-but-conserved regions of the spike protein.

Kinetics and free energy of ligand dissociation using weighted ensemble milestoning.

We consider the recently developed weighted ensemble milestoning (WEM) scheme [D. Ray and I. Andricioaei, J. Chem. Phys. 152, 234114 (2020)] and test its capability of simulating ligand-receptor dissociation dynamics. We performed WEM simulations on the following host-guest systems: Na+/Cl- ion pair and 4-hydroxy-2-butanone ligand with FK506 binding protein. As a proof of principle, we show that the WEM formalism reproduces the Na+/Cl- ion pair dissociation timescale and the free energy profile obtained from long conventional MD simulation. To increase the accuracy of WEM calculations applied to kinetics and thermodynamics in protein-ligand binding, we introduced a modified WEM scheme called weighted ensemble milestoning with restraint release (WEM-RR), which can increase the number of starting points per milestone without adding additional computational cost. WEM-RR calculations obtained a ligand residence time and binding free energy in agreement with experimental and previous computational results. Moreover, using the milestoning framework, the binding time and rate constants, dissociation constants, and committor probabilities could also be calculated at a low computational cost. We also present an analytical approach for estimating the association rate constant (kon) when binding is primarily diffusion driven. We show that the WEM method can efficiently calculate multiple experimental observables describing ligand-receptor binding/unbinding and is a promising candidate for computer-aided inhibitor design.

Free Energy Landscape and Conformational Kinetics of Hoogsteen Base Pairing in DNA vs. RNA.

Genetic information is encoded in the DNA double helix, which, in its physiological milieu, is characterized by the iconical Watson-Crick nucleo-base pairing. Recent NMR relaxation experiments revealed the transient presence of an alternative, Hoogsteen (HG) base pairing pattern in naked DNA duplexes, and estimated its relative stability and lifetime. In contrast with DNA, such structures were not observed in RNA duplexes. Understanding HG base pairing is important because the underlying "breathing" motion between the two conformations can significantly modulate protein binding. However, a detailed mechanistic insight into the transition pathways and kinetics is still missing. We performed enhanced sampling simulation (with combined metadynamics and adaptive force-bias method) and Markov state modeling to obtain accurate free energy, kinetics, and the intermediates in the transition pathway between Watson-Crick and HG base pairs for both naked B-DNA and A-RNA duplexes. The Markov state model constructed from our unbiased MD simulation data revealed previously unknown complex extrahelical intermediates in the seemingly simple process of base flipping in B-DNA. Extending our calculation to A-RNA, for which HG base pairing is not observed experimentally, resulted in relatively unstable, single-hydrogen-bonded, distorted Hoogsteen-like bases. Unlike B-DNA, the transition pathway primarily involved base paired and intrahelical intermediates with transition timescales much longer than that of B-DNA. The seemingly obvious flip-over reaction coordinate (i.e., the glycosidic torsion angle) is unable to resolve the intermediates. Instead, a multidimensional picture involving backbone dihedral angles and distance between hydrogen bond donor and acceptor atoms is required to gain insight into the molecular mechanism.

Weighted ensemble milestoning (WEM): A combined approach for rare event simulations.

To directly simulate rare events using atomistic molecular dynamics is a significant challenge in computational biophysics. Well-established enhanced-sampling techniques do exist to obtain the thermodynamic functions for such systems. However, developing methods for obtaining the kinetics of long timescale processes from simulation at atomic detail is comparatively less developed an area. Milestoning and the weighted ensemble (WE) method are two different stratification strategies; both have shown promise for computing long timescales of complex biomolecular processes. Nevertheless, both require a significant investment of computational resources. We have combined WE and milestoning to calculate observables in orders-of-magnitude less central processing unit and wall-clock time. Our weighted ensemble milestoning method (WEM) uses WE simulation to converge the transition probability and first passage times between milestones, followed by the utilization of the theoretical framework of milestoning to extract thermodynamic and kinetic properties of the entire process. We tested our method for a simple one-dimensional double-well potential, for an eleven-dimensional potential energy surface with energy barrier, and on the biomolecular model system alanine dipeptide. We were able to recover the free energy profiles, time correlation functions, and mean first passage times for barrier crossing events at a significantly small computational cost. WEM promises to extend the applicability of molecular dynamics simulation to slow dynamics of large systems that are well beyond the scope of present day brute-force computations.

How the phage T4 injection machinery works including energetics, forces, and dynamic pathway.

The virus bacteriophage T4, from the family Myoviridae, employs an intriguing contractile injection machine to inject its genome into the bacterium Escherichia coli Although the atomic structure of phage T4 is largely understood, the dynamics of its injection machinery remains unknown. This study contributes a system-level model describing the nonlinear dynamics of the phage T4 injection machinery interacting with a host cell. The model employs a continuum representation of the contractile sheath using elastic constants inferred from atomistic molecular-dynamics (MD) simulations. Importantly, the sheath model is coupled to component models representing the remaining structures of the virus and the host cell. The resulting system-level model captures virus-cell interactions as well as competing energetic mechanisms that release and dissipate energy during the injection process. Simulations reveal the dynamical pathway of the injection process as a "contraction wave" that propagates along the sheath, the energy that powers the injection machinery, the forces responsible for piercing the host cell membrane, and the energy dissipation that controls the timescale of the injection process. These results from the model compare favorably with the available (but limited) experimental measurements.

Advances in milestoning. II. Calculating time-correlation functions from milestoning using stochastic path integrals.

In the milestoning framework, and more generally in related transition interface sampling schemes, one significantly enhances the calculation of relaxation rates for complex equilibrium kinetics from molecular dynamics simulations between the milestones or interfaces. The goal of the present paper is to advance milestoning applications into the realm of non-equilibrium statistical mechanics, in particular, to calculate entire time correlation functions. In order to accomplish this, we introduce a novel methodology for obtaining the flux through a given milestone configuration as a function of both time and initial configuration and build upon it with a novel formalism describing autocorrelation for Langevin motion in a discrete configuration space. The method is then applied to three different test systems: a harmonic oscillator, which we solve analytically, a two-well potential, which is solved numerically, and an atomistic molecular dynamics simulation of alanine dipeptide.

Advances in milestoning. I. Enhanced sampling via wind-assisted reweighted milestoning (WARM).

The milestoning algorithm of Elber and co-workers creates a framework for computing the time scale of processes that are too long and too complex to be studied using simply brute force simulations. The fundamental objects involved in the milestoning algorithm are the first passage time distributions KAB (τ) between adjacent conformational milestones A and B. The method proposed herein aims to further enhance milestoning (or other interface based sampling methods) by employing an artificially applied force, akin to a wind that blows the trajectories from their initial to their final states, and by subsequently applying corrective weights to the trajectories to yield the true first passage time distributions KAB (τ) in a fraction of the computation time required for unassisted calculations. The re-weighting method is rooted in the formalism of stochastic path integrals. The theoretical basis for the technique and numerical examples are presented.

Modulation of Hoogsteen dynamics on DNA recognition.

In naked duplex DNA, G-C and A-T Watson-Crick base pairs exist in dynamic equilibrium with their Hoogsteen counterparts. Here, we used nuclear magnetic resonance (NMR) relaxation dispersion and molecular dynamics (MD) simulations to examine how Watson-Crick/Hoogsteen dynamics are modulated upon recognition of duplex DNA by the bisintercalator echinomycin and monointercalator actinomycin D. In both cases, DNA recognition results in the quenching of Hoogsteen dynamics at base pairs involved in intermolecular base-specific hydrogen bonds. In the case of echinomycin, the Hoogsteen population increased 10-fold for base pairs flanking the chromophore most likely due to intermolecular stacking interactions, whereas actinomycin D minimally affected Hoogsteen dynamics at other sites. Modulation of Hoogsteen dynamics at binding interfaces may be a general phenomenon with important implications for DNA-ligand and DNA-protein recognition.

Automated placement of interfaces in conformational kinetics calculations using machine learning.

Several recent implementations of algorithms for sampling reaction pathways employ a strategy for placing interfaces or milestones across the reaction coordinate manifold. Interfaces can be introduced such that the full feature space describing the dynamics of a macromolecule is divided into Voronoi (or other) cells, and the global kinetics of the molecular motions can be calculated from the set of fluxes through the interfaces between the cells. Although some methods of this type are exact for an arbitrary set of cells, in practice, the calculations will converge fastest when the interfaces are placed in regions where they can best capture transitions between configurations corresponding to local minima. The aim of this paper is to introduce a fully automated machine-learning algorithm for defining a set of cells for use in kinetic sampling methodologies based on subdividing the dynamical feature space; the algorithm requires no intuition about the system or input from the user and scales to high-dimensional systems.

Dynamic Model Exposes the Energetics and Dynamics of the Injection Machinery for Bacteriophage T4.

Bacteriophage T4 infects the bacterial host (Escherichia coli) using an efficient genomic delivery machine that is driven by elastic energy stored in a contractile tail sheath. Although the atomic structure of T4 is largely known, the dynamics of its fascinating injection machinery is not understood. This article contributes, to our knowledge, the first predictions of the energetics and dynamics of the T4 injection machinery using a novel dynamic model. The model employs an atomistic (molecular dynamics) representation of a fraction of the sheath structure to generate a continuum model of the entire sheath that also couples to a model of the viral capsid and tail tube. The resulting model of the entire injection machine reveals estimates for the energetics, timescale, and pathway of the T4 injection process as well as the force available for cell rupture. It also reveals the large and highly nonlinear conformational changes of the sheath whose elastic energy drives the injection process.

Electric-Field-Induced Protein Translocation via a Conformational Transition in SecDF: An MD Study.

SecDF is an important component of the Sec protein translocation machinery embedded in the bacterial membrane, which is associated with many functions, such as stabilizing other Sec translocon components within the membrane, maintaining the transmembrane (TM) potential, and facilitating the ATP-independent stage of the translocation mechanism. Related studies suggest that SecDF undergoes functionally important conformational changes that involve mainly its P1-head domain and that these changes are coupled with the proton motive force (Δp). However, there still is not a clear understanding of how SecDF functions, its exact role in the translocation machinery, and how its function is related to Δp. Here, using all-atom molecular dynamics simulations combined with umbrella sampling, we study the P1-head conformational change and how it is coupled to the proton motive force. We report potentials of mean force along a root-mean-square-distance-based reaction coordinate obtained in the presence and absence of the TM electrical potential. Our results show that the interaction of the P1 domain dipole moment with the TM electrical field considerably lowers the free-energy barrier in the direction of F-form to I-form transition.

m(1)A and m(1)G disrupt A-RNA structure through the intrinsic instability of Hoogsteen base pairs.

The B-DNA double helix can dynamically accommodate G-C and A-T base pairs in either Watson-Crick or Hoogsteen configurations. Here, we show that G-C(+) (in which + indicates protonation) and A-U Hoogsteen base pairs are strongly disfavored in A-RNA. As a result,N(1)-methyladenosine and N(1)-methylguanosine, which occur in DNA as a form of alkylation damage and in RNA as post-transcriptional modifications, have dramatically different consequences. Whereas they create G-C(+) and A-T Hoogsteen base pairs in duplex DNA, thereby maintaining the structural integrity of the double helix, they block base-pairing and induce local duplex melting in RNA. These observations provide a mechanism for disrupting RNA structure through post-transcriptional modifications. The different propensities to form Hoogsteen base pairs in B-DNA and A-RNA may help cells meet the opposing requirements of maintaining genome stability, on the one hand, and of dynamically modulating the structure of the epitranscriptome, on the other.

Reaction Coordinate-Free Approach to Recovering Kinetics from Potential-Scaled Simulations: Application of Kramers' Rate Theory.

Enhanced sampling techniques are used to increase the frequency of "rare events" during computer simulations of complex molecules. Although methods exist that allow accurate thermodynamics to be recovered from enhanced simulations, recovering kinetics proves to be more challenging. Here we present an extrapolation approach that allows reliable kinetics to be recovered from potential-scaled MD simulations. The approach, based on Kramers' rate theory, is simple and computationally efficient, and allows kinetics to be recovered without defining reaction coordinates. To test our approach, we use it to determine the kinetics of barrier crossing between two metastable states on the 2D-Müller potential and the C7eq to αR transition in alanine dipeptide. The mean first passage time estimates obtained are in excellent agreement with reference values obtained from direct simulations on the unscaled potentials performed over times that are orders of magnitude longer.