Unbinding Events Research Articles

Regulating transport efficiency through the nuclear pore complex: The role of binding affinity with FG-Nups.

Macromolecules are transported through the nuclear pore complex (NPC) via a series of transient binding and unbinding events with FG-Nups, which are intrinsically disordered proteins anchored to the pore's inner wall. Prior studies suggest that the weak and transient nature of this binding is crucial for maintaining the transported molecules' diffusivity. In this study, we explored the relationship between binding kinetics and transport efficiency using Brownian dynamics simulations. Our results indicate that the duration of binding is a critical factor in regulating transport efficiency. Specifically, excessively short binding durations insufficiently facilitate transport, while overly long durations impede molecular movement. We calculated the optimal binding duration for efficient molecular transport and found that it aligns with other theoretical predictions. Additionally, the calculated value is comparable to experimental measurements of the association timescale between nuclear transport receptors and FG-Nups at a single binding site. Our study provides a quantitative framework that bridges local molecular interactions with overall transport dynamics through the NPC, offering valuable insights into the mechanisms governing selective molecular transport.

Computational screening of the effects of mutations on protein-protein off-rates and dissociation mechanisms by τRAMD

The dissociation rate, or its reciprocal, the residence time (τ), is a crucial parameter for understanding the duration and biological impact of biomolecular interactions. Accurate prediction of τ is essential for understanding protein-protein interactions (PPIs) and identifying potential drug targets or modulators for tackling diseases. Conventional molecular dynamics simulation techniques are inherently constrained by their limited timescales, making it challenging to estimate residence times, which typically range from minutes to hours. Building upon its successful application in protein-small molecule systems, τ-Random Acceleration Molecular Dynamics (τRAMD) is here investigated for estimating dissociation rates of protein-protein complexes. τRAMD enables the observation of unbinding events on the nanosecond timescale, facilitating rapid and efficient computation of relative residence times. We tested this methodology for three protein-protein complexes and their extensive mutant datasets, achieving good agreement between computed and experimental data. By combining τRAMD with MD-IFP (Interaction Fingerprint) analysis, dissociation mechanisms were characterized and their sensitivity to mutations investigated, enabling the identification of molecular hotspots for selective modulation of dissociation kinetics. In conclusion, our findings underscore the versatility of τRAMD as a simple and computationally efficient approach for computing relative protein-protein dissociation rates and investigating dissociation mechanisms, thereby aiding the design of PPI modulators.

A second life for the crystallographic structure of Berenil-dodecanucleotide complex: a computational revisitation thirty years after its publication

This study revisits the pioneering work of Professor Neidle, and co-workers, on the crystal structure of complexes formed between groove binders and DNA sequences. The original research revealed a DNA-ligand complex consisting of a dodecanucleotide bound with Berenil [1,3-bis(4′-amidinophenyl)-triazene] an anti-trypanocidal drug. This article aims to delve deeper into the structural dynamics of this system, showcasing the role played by water molecules in stabilizing the interaction between the ligand and the DNA. With this work, we reevaluate the findings from the original crystallographic study by employing modern molecular dynamics techniques, including Supervised Molecular Dynamics (SuMD) for generating binding trajectories, Thermal Titration Molecular Dynamics for assessing unbinding events, and AquaMMapS to identify regions occupied by stationary water molecules. The study addresses a minor and a major groove binding mode and assesses their strength and specificity using TTMD simulations, generating unbinding trajectories. This comprehensive approach integrates the understanding of the interaction of this DNA-ligand complex, which originated with the valuable work of Professor Neidle, resulting in an in-depth insight into the pivotal role of water molecules with this DNA, a behavior detected and extendable even to other nucleic acid complexes.

Dynamics and Evolutionary Conservation of B Complex Protein Recruitment During Spliceosome Activation.

Spliceosome assembly and catalytic site formation (called activation) involve dozens of protein and snRNA binding and unbinding events. The B-complex specific proteins Prp38, Snu23, and Spp381 have critical roles in stabilizing the spliceosome during conformational changes essential for activation. While these proteins are conserved, different mechanisms have been proposed for their recruitment to spliceosomes. To visualize recruitment directly, we used Colocalization Single Molecule Spectroscopy (CoSMoS) to study the dynamics of Prp38, Snu23, and Spp381 during splicing in real time. These proteins bind to and release from spliceosomes simultaneously and are likely associated with one another. We designate the complex of Prp38, Snu23, and Spp381 as the B Complex Protein (BCP) subcomplex. Under splicing conditions, the BCP associates with pre-mRNA after tri-snRNP binding. BCP release predominantly occurs after U4 snRNP dissociation and after NineTeen Complex (NTC) association. Under low concentrations of ATP, the BCP pre-associates with the tri-snRNP resulting in their simultaneous binding to pre-mRNA. Together, our results reveal that the BCP recruitment pathway to the spliceosome is conserved between S. cerevisiae and humans. Binding of the BCP to the tri-snRNP when ATP is limiting may result in formation of unproductive complexes that could be used to regulate splicing.

Capturing the Effects of Single Atom Substitutions on the Inhibition Efficiency of Glycogen Synthase Kinase-3β Inhibitors via Markov State Modeling and Experiments.

Small modifications in the chemical structure of ligands are known to dramatically change their ability to inhibit the activity of a protein. Unraveling the mechanisms that govern these dramatic changes requires scrutinizing the dynamics of protein-ligand binding and unbinding at the atomic level. As an exemplary case, we have studied Glycogen Synthase Kinase-3β (GSK-3β), a multifunctional kinase that has been implicated in a host of pathological processes. As such, there is a keen interest in identifying ligands that inhibit GSK-3β activity. One family of compounds that are highly selective and potent inhibitors of GSK-3β is exemplified by a molecule termed COB-187. COB-187 consists of a five-member heterocyclic ring with a thione at C2, a pyridine substituted methyl at N3, and a hydroxyl and phenyl at C4. We have studied the inhibition of GSK-3β by COB-187-related ligands that differ in a single heavy atom from each other (either in the location of nitrogen in their pyridine ring, or with the pyridine ring replaced by a phenyl ring), or in the length of the alkyl group joining the pyridine and the N3. The inhibition experiments show a large range of half-maximal inhibitory concentration (IC50) values from 10 nM to 10 μM, implying that these ligands exhibit vastly different propensities to inhibit GSK-3β. To explain these differences, we perform Markov State Modeling (MSM) using fully atomistic simulations. Our MSM results are in excellent agreement with the experiments in that they accurately capture differences in the binding propensities of the ligands. The simulations show that the binding propensities are related to the ligands' ability to attain a compact conformation where their two aromatic rings are spatially close. We rationalize this result by sampling numerous binding and unbinding events via funnel metadynamics simulations, which show that indeed while approaching the bound state, the ligands prefer to be in their compact conformation. We find that the presence of nitrogen in the aromatic ring increases the probability of attaining the compact conformation. Protein-ligand binding is understood to be dictated by the energetics of interactions and entropic factors, like the release of bound water from the binding pockets. This work shows that changes in the conformational distribution of ligands due to atom-level modifications in the structure play an important role in protein-ligand binding.

Characterization of the Bottlenecks and Pathways for Inhibitor Dissociation from [NiFe] Hydrogenase.

[NiFe] hydrogenases can act as efficient catalysts for hydrogen oxidation and biofuel production. However, some [NiFe] hydrogenases are inhibited by gas molecules present in the environment, such as O2 and CO. One strategy to engineer [NiFe] hydrogenases and achieve O2- and CO-tolerant enzymes is by introducing point mutations to block the access of inhibitors to the catalytic site. In this work, we characterized the unbinding pathways of CO in the complex with the wild-type and 10 different mutants of [NiFe] hydrogenase from Desulfovibrio fructosovorans using τ-random accelerated molecular dynamics (τRAMD) to enhance the sampling of unbinding events. The ranking provided by the relative residence times computed with τRAMD is in agreement with experiments. Extensive data analysis of the simulations revealed that from the two bottlenecks proposed in previous studies for the transit of gas molecules (residues 74 and 122 and residues 74 and 476), only one of them (residues 74 and 122) effectively modulates diffusion and residence times for CO. We also computed pathway probabilities for the unbinding of CO, O2, and H2 from the wild-type [NiFe] hydrogenase, and we observed that while the most probable pathways are the same, the secondary pathways are different. We propose that introducing mutations to block the most probable paths, in combination with mutations to open the main secondary path used by H2, can be a feasible strategy to achieve CO and O2 resistance in the [NiFe] hydrogenase from Desulfovibrio fructosovorans.

The mutual and dynamic role of TSPO and ligands in their binding process: An example with PK-11195

Translocator protein (TSPO) is an 18 kDa transmembrane protein, localized primarily on the outer mitochondrial membrane. It has been found to be involved in various physiological processes and pathophysiological conditions. Though studies on its structure have been performed only recently, there is little information on the nature of dynamics and doubts about some structures referenced in the literature, especially the NMR structure of mouse TSPO. In the present work, we thoroughly study the dynamics of mouse TSPO protein by means of atomistic molecular dynamics simulations, in presence as well as in absence of the diagnostic ligand PKA. We considered two starting structures: the NMR structure and a homology model (HM) generated on the basis of X-ray structures from bacterial TSPO. We examine the conformational landscape in both the modes for both starting points, in presence and absence of the ligand, in order to measure its impact for both structures. The analysis highlights high flexibility of the protein globally, but NMR simulations show a surprisingly flexibility even in the presence of the ligand. Interestingly, this is not the case for HM calculations, to the point that the ligand seems not so stable as in the NMR system and an unbinding event process is partially sampled. All those results tend to show that the NMR structure of mTSPO seems not deficient but is just in another portion of the global conformation space of TSPO.

Membrane-Mediated Cooperative Interactions of CD47 and SIRPα.

The specific binding of the ubiquitous 'marker of self' protein CD47 to the SIRPα protein anchored in the macrophage plasma membrane results in the inhibition of the engulfment of 'self' cells by macrophages and thus constitutes a key checkpoint of our innate immune system. Consequently, the CD47-SIRPα protein complex has been recognized as a potential therapeutic target in cancer and inflammation. Here, we introduce a lattice-based mesoscale model for the biomimetic system studied recently in fluorescence microscopy experiments where GFP-tagged CD47 proteins on giant plasma membrane vesicles bind to SIRPα proteins immobilized on a surface. Computer simulations of the lattice-based mesoscale model allow us to study the biomimetic system on multiple length scales, ranging from single nanometers to several micrometers and simultaneously keep track of single CD47-SIRPα binding and unbinding events. Our simulations not only reproduce data from the fluorescence microscopy experiments but also are consistent with results of several other experiments, which validates our numerical approach. In addition, our simulations yield quantitative predictions on the magnitude and range of effective, membrane-mediated attraction between CD47-SIRPα complexes. Such detailed information on CD47-SIRPα interactions cannot be obtained currently from experiments alone. Our simulation results thus extend the present understanding of cooperative effects in CD47-SIRPα interactions and may have an influence on the advancement of new cancer treatments.

Accelerating Molecular Dynamics Simulations for Drug Discovery.

Accurate prediction of ligand binding thermodynamics and kinetics is crucial in drug design. However, it remains challenging for conventional molecular dynamics (MD) simulations due to sampling issues. Gaussian accelerated MD (GaMD) is an enhanced sampling method that adds a harmonic boost to overcome energy barriers, which has demonstrated significant benefits in exploring protein-ligand interactions. Especially, the ligand GaMD (LiGaMD) applies a selective boost potential to the ligand nonbonded potential energy, significantly improving sampling for ligand binding and dissociation. Furthermore, a selective boost potential is applied to the potential of both ligand and protein residues around binding pocket in LiGaMD2 to further increase the sampling of protein-ligand interaction. LiGaMD and LiGaMD2 simulations could capture repetitive ligand binding and unbinding events within microsecond simulations, allowing to simultaneously characterize ligand binding thermodynamics and kinetics, which is expected to greatly facilitate drug design. In this chapter, we provide a brief review of the status of LiGaMD in drug discovery and outline its usage.

Reconstruction of the unbinding pathways of new inhibitors of the SARS-CoV-2 papain-like protease using molecular dynamics simulation

Developing novel antiviral drugs against the SARS-CoV-2 virus and COVID-19 disease is imperative as the vaccines may not offer absolute protection. PLpro plays a crucial role in the viral life cycle, making it an attractive target for drug development. Several PLpro inhibitors have been developed, and their 3D structures in complex with PLpro are available. In this work, we employed Supervised Molecular Dynamics (SuMD), a specific Unbiased Molecular Dynamics (UMD) method, to investigate unbinding pathways of the novel inhibitors of PLpro (PDB IDs: 7LBR, 7RZC, 7SDR and 7E35) and GRL0617 (PDB ID: 7JRN) as a reference. We conducted three simulations for each ligand and achieved unbinding events in the nanosecond timescale in all simulations. We found that unbinding events are commonly affected by altering the conformation of the BL2 loop, which is caused by the natural fluctuations of the loop that are required to trap the substrate and throw out the product. BL2 loop is crucial for keeping the ligand and unbinding and acts as a double-edged sword. Any inhibitor designed to be effective must prevent the loop’s natural fluctuations. We perceived that increasing ligands interactions with the binding pocket interior and the BL2 loop will help prevent natural fluctuation of the BL2 loop, Although the interactions with the binding pocket’s inner side are more critical than the BL2 loop. These findings may be helpful in developing more potent inhibitors against SARS-CoV-2. Communicated by Ramaswamy H. Sarma

Diffusion-controlled reactions with non-Markovian binding/unbinding kinetics.

We develop a theory of reversible diffusion-controlled reactions with generalized binding/unbinding kinetics. In this framework, a diffusing particle can bind to the reactive substrate after a random number of arrivals onto it, with a given threshold distribution. The particle remains bound to the substrate for a random waiting time drawn from another given distribution and then resumes its bulk diffusion until the next binding and so on. When both distributions are exponential, one retrieves the conventional first-order forward and backward reactions whose reversible kinetics is described by generalized Collins-Kimball's (or back-reaction) boundary condition. In turn, if either of distributions is not exponential, one deals with generalized (non-Markovian) binding or unbinding kinetics (or both). Combining renewal technique with the encounter-based approach, we derive spectral expansions for the propagator, the concentration of particles, and the diffusive flux on the substrate. We study their long-time behavior and reveal how anomalous rarity of binding or unbinding events due to heavy tails of the threshold and waiting time distributions may affect such reversible diffusion-controlled reactions. Distinctions between time-dependent reactivity, encounter-dependent reactivity, and a convolution-type Robin boundary condition with a memory kernel are elucidated.

Evolution of large Aβ16-22 aggregates at atomic details and potential of mean force associated to peptide unbinding and fragmentation events.

Atomic characterization of large nonfibrillar aggregates of amyloid polypeptides cannot be determined by experimental means. Starting from β-rich aggregates of Y and elongated topologies predicted by coarse-grained simulations and consisting of more than 100 Aβ16-22 peptides, we performed atomistic molecular dynamics (MD), replica exchange with solute scaling (REST2), and umbrella sampling simulations using the CHARMM36m force field in explicit solvent. Here, we explored the dynamics within 3 μs, the free energy landscape, and the potential of mean force associated with either the unbinding of one single peptide in different configurations within the aggregate or fragmentation events of a large number of peptides. Within the time scale of MD and REST2, we find that the aggregates experience slow global conformational plasticity, and remain essentially random coil though we observe slow beta-strand structuring with a dominance of antiparallel beta-sheets over parallel beta-sheets. Enhanced REST2 simulation is able to capture fragmentation events, and the free energy of fragmentation of a large block of peptides is found to be similar to the free energy associated with fibril depolymerization by one chain for longer Aβ sequences.

Single-molecule studies of RNA aptamers binding to SARS-CoV-2 spike proteins expressed on virus-like particles.

The SARS-CoV-2 spike protein is the responsible agent for the first step of viral entry into human cells. We present single-molecule total internal reflection fluorescence studies of fluorescently tagged RNA aptamers designed to bind to the receptor binding domain (RBD) of the spike protein, neutralizing its ability to bind to the ACE-2 receptor. Our results show individual binding and unbinding events of aptamers, which are recorded as a function of time. An analysis of these single-molecule trajectories indicates that multiple aptamers can bind to the spike trimer with high affinity, and that aptamers are able to bind spike proteins on virus-like particles. This single-molecule approach allows new insights into ligand binding to multimeric proteins and viruses. We show that multivalent interactions increase the affinity of RNA aptamers to the spike protein in both the wild-type and Omicron SARS-CoV-2 variants.

Quality over quantity: Sampling high probability rare events with the weighted ensemble algorithm.

The prediction of (un)binding rates and free energies is of great significance to the drug design process. Although many enhanced sampling algorithms and approaches have been developed, there is not yet a reliable workflow to predict these quantities. Previously we have shown that free energies and transition rates can be calculated by directly simulating the binding and unbinding processes with our variant of the WE algorithm "Resampling of Ensembles by Variation Optimization", or "REVO". Here, we calculate binding free energies retrospectively for three SAMPL6 host-guest systems and prospectively for a SAMPL9 system to test a modification of REVO that restricts its cloning behavior in quasi-unbound states. Specifically, trajectories cannot clone if they meet a physical requirement that represents a high likelihood of unbinding, which in the case of this work is a center-of-mass to center-of-mass distance. The overall effect of this change was difficult to predict, as it results in fewer unbinding events each of which with a much higher statistical weight. For all four systems tested, this new strategy produced either more accurate unbinding free energies or more consistent results between simulations than the standard REVO algorithm. This approach is highly flexible, and any feature of interest for a system can be used to determine cloning eligibility. These findings thus constitute an important improvement in the calculation of transition rates and binding free energies with the weighted ensemble method.

Reversible Immunosensor for the Continuous Monitoring of Cortisol in Blood Plasma Sampled with Microdialysis.

Cortisol is a steroid hormone involved in a wide range of medical conditions. The level of the hormone fluctuates over time, but with traditional laboratory-based assays, such dynamics cannot be monitored in real time. Here, a reversible cortisol sensor is reported that allows continuous monitoring of cortisol in blood plasma using sampling by microdialysis. The sensor is based on measuring single-molecule binding and unbinding events of tethered particles. The particles are functionalized with antibodies and the substrate with cortisol-analogues, causing binding and unbinding events to occur between particles and substrate. The frequency of binding events is reduced when cortisol is present in the solution as it blocks the binding sites of the antibodies. The sensor responds to cortisol in the high nanomolar to low micromolar range and can monitor cortisol concentrations over multiple hours. Results are shown for cortisol monitoring in filtered and in microdialysis-sampled human blood plasma.

Enhanced-Sampling Simulations for the Estimation of Ligand Binding Kinetics: Current Status and Perspective.

The dissociation rate (k off) associated with ligand unbinding events from proteins is a parameter of fundamental importance in drug design. Here we review recent major advancements in molecular simulation methodologies for the prediction of k off. Next, we discuss the impact of the potential energy function models on the accuracy of calculated k off values. Finally, we provide a perspective from high-performance computing and machine learning which might help improve such predictions.

Detailed balance for particle models of reversible reactions in bounded domains.

In particle-based stochastic reaction-diffusion models, reaction rates and placement kernels are used to decide the probability per time a reaction can occur between reactant particles and to decide where product particles should be placed. When choosing kernels to use in reversible reactions, a key constraint is to ensure that detailed balance of spatial reaction fluxes holds at all points at equilibrium. In this work, we formulate a general partial-integral differential equation model that encompasses several of the commonly used contact reactivity (e.g., Smoluchowski-Collins-Kimball) and volume reactivity (e.g., Doi) particle models. From these equations, we derive a detailed balance condition for the reversible A + B ⇆ C reaction. In bounded domains with no-flux boundary conditions, when choosing unbinding kernels consistent with several commonly used binding kernels, we show that preserving detailed balance of spatial reaction fluxes at all points requires spatially varying unbinding rate functions near the domain boundary. Brownian dynamics simulation algorithms can realize such varying rates through ignoring domain boundaries during unbinding and rejecting unbinding events that result in product particles being placed outside the domain.

Reaction-path statistical mechanics of enzymatic kinetics.

We introduce a reaction-path statistical mechanics formalism based on the principle of large deviations to quantify the kinetics of single-molecule enzymatic reaction processes under the Michaelis-Menten mechanism, which exemplifies an out-of-equilibrium process in the living system. Our theoretical approach begins with the principle of equal a priori probabilities and defines the reaction path entropy to construct a new nonequilibrium ensemble as a collection of possible chemical reaction paths. As a result, we evaluate a variety of path-based partition functions and free energies by using the formalism of statistical mechanics. They allow us to calculate the timescales of a given enzymatic reaction, even in the absence of an explicit boundary condition that is necessary for the equilibrium ensemble. We also consider the large deviation theory under a closed-boundary condition of the fixed observation time to quantify the enzyme-substrate unbinding rates. The result demonstrates the presence of a phase-separation-like, bimodal behavior in unbinding events at a finite timescale, and the behavior vanishes as its rate function converges to a single phase in the long-time limit.

Probing Allosteric Modulation of Membrane Receptor in the Native State by Data Mining-Integrated Tracking Microscopy

Probing Allosteric Modulation of Membrane Receptor in the Native State by Data Mining-Integrated Tracking Microscopy

Complete reconstruction of dasatinib unbinding pathway from c-Src kinase by supervised molecular dynamics simulation method; assessing efficiency and trustworthiness of the method

Over the past years, rational drug design has gained lots of attention since employing it gave the world targeted therapy and more effective treatment solutions. Structure-based drug design (SBDD) is an excellent tool in rational drug design that takes advantage of accurate methods such as unbiased molecular dynamics (UMD) simulation for designing and optimizing molecular entities by understanding the binding and unbinding pathways of the binders. Supervised molecular dynamics (SuMD) simulation is a branch of UMD in which long-duration simulations are turned into short simulations, called replica, and a specific parameter is monitored throughout the simulation. In this work, we utilized this strategy to reconstruct the unbinding pathway of the anticancer drug dasatinib from its target protein, the c-Src kinase. Several unbinding events with valuable details were achieved. Then, to assess the efficiency and trustworthiness of the SuMD method, the unbinding pathway was also reconstructed by conventional UMD simulation, which uncovered some of the limitations of this method, such as limited sampling of the active site and finding the metastable states in the unbinding pathway. Furthermore, in times like these, when the world is desperate to find treatments for the Covid-19 disease, we think these methods are of exceptional value. Communicated by Ramaswamy H. Sarma