Amphiphile self-assembly is an essential bottom-up approach of fabricating advanced functional materials. Self-assembled materials with desired structures are often obtained through thermodynamic control. Here, we demonstrate that the selection of kinetic pathways can lead to drastically different self-assembled structures, underlining the significance of kinetic control in self-assembly. By constructing kinetic network models from large-scale molecular dynamics simulations, we show that two largely similar amphiphiles, 1-[11-oxo-11-(pyren-1-ylmethoxy)-undecyl]pyridinium bromide (PYR) and 1-(11-((5a1,8a-dihydropyren-1-yl)methylamino)-11-oxoundecyl)pyridinium bromide (PYN), prefer distinct kinetic assembly pathways. While PYR prefers an incremental growth mechanism and forms a nanotube, PYN favors a hopping growth pathway leading to a vesicle. Such preference was found to originate from the subtle difference in the distributions of hydrophobic and hydrophilic groups in their chemical structures, which leads to different rates of the adhesion process among the aggregating micelles. Our results are in good agreement with experimental results, and accentuate the role of kinetics in the rational design of amphiphile self-assembly.

Due to their enhanced stability and cell permeability, cyclic cell-penetrating peptides have been widely used as delivery vectors for transporting cell-impermeable cargos into cells. In this study, we synthesized a panel of conformationally constrained peptides with either α-helix or β-hairpin conformations. We tuned the amphiphilicity of these constrained peptides with different distributions of charged or hydrophobic residues and compared their cellular uptake efficiencies in different cell lines. We found that the amphipathicity of these conformationally constrained peptides correlates well with their cellular uptake efficiency. We proposed that peptides with larger hydrophobic moments (HMs) have stronger binding affinities with the cell membrane which further accelerates the endocytosis process. This finding should provide an approach towards the design of more potent conformationally constrained cell-penetrating peptides for biomedical applications.

Maltose transporter MalFGK2 is a type-I importer in the ATP-binding cassette (ABC) transporter superfamily. Upon the binding of its periplasmic binding protein, MalE, the ATPase activity of MalFGK2 can be greatly enhanced. Crystal structures of the MalFGK2–MalE–maltose complex in a so-called “pretranslocation” (“pre-T”) state with a partially closed conformation suggest that the formation of this MalE-stabilized intermediate state is a key step leading to the outward-facing catalytic state. On the contrary, crosslinking and fluorescence studies suggest that ATP binding alone is sufficient to promote the outward-facing catalytic state, thereby doubting the role of MalE binding. To clarify the role of MalE binding and to gain deeper understanding of the molecular mechanisms of MalFGK2, we calculated the free energy surfaces (FESs) related to the lateral motion in the presence and absence of MalE using atomistic metadynamics simulations. The results showed that, in the absence of MalE, laterally closing motion was energetically forbidden but, upon MalE binding, more closed conformations similar to the pre-T state become more stable. The significant effect of MalE binding on the free energy landscapes was in agreement with crystallographic studies and confirmed the important role of MalE in stabilizing the pre-T state. Our simulations also revealed that the allosteric effect of MalE stimulation originates from the MalE-binding-promoted vertical motion between MalF and MalG cores, which was further supported by MD simulation of the MalE-independent mutant MalF500.

RNA polymerase II (Pol II) is an essential enzyme that catalyzes transcription with high efficiency and fidelity in eukaryotic cells. During transcription elongation, Pol II catalyzes the nucleotide addition cycle (NAC) to synthesize mRNA using DNA as the template. The transitions between the states of the NAC require conformational changes of both the protein and nucleotides. Although X-ray structures are available for most of these states, the dynamics of the transitions between states are largely unknown. Molecular dynamics (MD) simulations can predict structure-based molecular details and shed light on the mechanisms of these dynamic transitions. However, the employment of MD simulations on a macromolecule (tens to hundreds of nanoseconds) such as Pol II is challenging due to the difficulty of reaching biologically relevant timescales (tens of microseconds or even longer). For this challenge to be overcome, kinetic network models (KNMs), such as Markov State Models (MSMs), have become a popular approach to access long-timescale conformational changes using many short MD simulations.We describe here our application of KNMs to characterize the molecular mechanisms of the NAC of Pol II. First, we introduce the general background of MSMs and further explain procedures for the construction and validation of MSMs by providing some technical details. Next, we review our previous studies in which we applied MSMs to investigate the individual steps of the NAC, including translocation and pyrophosphate ion release. In particular, we describe in detail how we prepared the initial conformations of Pol II elongation complex, performed MD simulations, extracted MD conformations to construct MSMs, and further validated them. We also summarize our major findings on molecular mechanisms of Pol II elongation based on these MSMs. In addition, we have included discussions regarding various key points and challenges for applications of MSMs to systems as large as the Pol II elongation complex. Finally, to study the overall NAC, we combine the individual steps of the NAC into a five-state KNM based on a nonbranched Brownian ratchet scheme to explain the single-molecule optical tweezers experimental data. The studies complement experimental observations and provide molecular mechanisms for the transcription elongation cycle. In the long term, incorporation of sequence-dependent kinetic parameters into KNMs has great potential for identifying error-prone sequences and predicting transcription dynamics in genome-wide transcriptomes.

Blood purification systems, in which the adsorbent removes exogenous and endogenous toxins from the blood, are widely used in clinical practice. To improve the protein resistance of and detoxification by the adsorbent, researchers can modify the adsorbent with functional molecules, such as polyvinylpyrrolidone (PVP). However, achieving precise control of the functional molecular density, which is crucial to the activity of the adsorbent, remains a significant challenge. In the present study, we prepared a model system for blood purification adsorbents in which we controlled the integration density of PVP molecules of different molecular weights on an Au substrate by quartz crystal microbalance with dissipation (QCM-D). We characterized the samples with atomic force microscopy, X-ray photoelectron spectroscopy, and QCM-D and found that the molecular density and the chain length of the PVP molecules played important roles in determining the properties of the sample. At the optimal condition, the modified sample demonstrated strong resistance to plasma proteins, decreasing the adsorption of human serum albumin (HSA) and fibrinogen (Fg) by 92.5% and 79.2%, respectively. In addition, the modified sample exhibited excellent detoxification, and the adsorption of bilirubin increased 2.6-fold. Interestingly, subsequent atomistic molecular dynamics simulations indicated that the favorable interactions between PVP and bilirubin were dominated by hydrophobic interactions. An in vitro platelet adhesion assay showed that the adhesion of platelets on the sample decreased and that the platelets were maintained in an inactivated state. The CCK-8 assay indicated that the modified sample exhibited negligible cytotoxicity to L929 cells. These results demonstrated that our method holds great potential for the modification of adsorbents in blood purification systems.

Argonaute proteins (Ago) are core components of the RNA Induced Silencing Complex (RISC) that load and utilize small guide nucleic acids to silence mRNAs or cleave foreign DNAs. Despite the essential role of Ago in gene regulation and defense against virus, the molecular mechanism of guide-strand loading into Ago remains unclear. We explore such a mechanism in the bacterium Thermus thermophilus Ago (TtAgo), via a computational approach combining molecular dynamics, bias-exchange metadynamics, and protein–DNA docking. We show that apo TtAgo adopts multiple closed states that are unable to accommodate guide-DNA. Conformations able to accommodate the guide are beyond the reach of thermal fluctuations from the closed states. These results suggest an induced-fit dominant mechanism for guide-strand loading in TtAgo, drastically different from the two-step mechanism for human Ago 2 (hAgo2) identified in our previous study. Such a difference between TtAgo and hAgo2 is found to mainly originate from the distinct rigidity of their L1-PAZ hinge. Further comparison among known Ago structures from various species indicates that the L1-PAZ hinge may be flexible in general for prokaryotic Ago’s but rigid for eukaryotic Ago’s.

The conformational dynamics of multibody systems plays crucial roles in many important problems. Markov state models (MSMs) are powerful kinetic network models that can predict long-time-scale dynamics using many short molecular dynamics simulations. Although MSMs have been successfully applied to conformational changes of individual proteins, the analysis of multibody systems is still a challenge because of the complexity of the dynamics that occur on a mixture of drastically different time scales. In this work, we have developed a new algorithm, automatic state partitioning for multibody systems (APM), for constructing MSMs to elucidate the conformational dynamics of multibody systems. The APM algorithm effectively addresses different time scales in the multibody systems by directly incorporating dynamics into geometric clustering when identifying the metastable conformational states. We have applied the APM algorithm to a 2D potential that can mimic a protein–ligand binding system and the aggregation of two hydrophobic particles in water and have shown that it can yield tremendous enhancements in the computational efficiency of MSM construction and the accuracy of the models.

Transcription is a central step in gene expression, in which the DNA template is processively read by RNA polymerase II (Pol II), synthesizing a complementary messenger RNA transcript. At each cycle, Pol II moves exactly one register along the DNA, a process known as translocation. Although X-ray crystal structures have greatly enhanced our understanding of the transcription process, the underlying molecular mechanisms of translocation remain unclear. Here we use sophisticated simulation techniques to observe Pol II translocation on a millisecond timescale and at atomistic resolution. We observe multiple cycles of forward and backward translocation and identify two previously unidentified intermediate states. We show that the bridge helix (BH) plays a key role accelerating the translocation of both the RNA:DNA hybrid and transition nucleotide by directly interacting with them. The conserved BH residues, Thr831 and Tyr836, mediate these interactions. To date, this study delivers the most detailed picture of the mechanism of Pol II translocation at atomic level.

Amyloid fibril deposits of the intrinsically disordered hIAPP peptide are found in 95% of type II diabetes patients, and the aggregation of this peptide is suggested to induce apoptotic cell-death in insulin-producing β-cells. Understanding the structure and dynamics of the hIAPP monomer in solution is thus important for understanding the nucleation of aggregation and the formation of oligomers. In this study, we identify the metastable conformational states of the hIAPP monomer and the dynamics of transitioning between them using Markov state models constructed from extensive molecular dynamics simulations. We show that the overall structure of the hIAPP peptide is random coil-like and lacks a dominant folded structure. Despite this fact, our model reveals a large number of reasonably well-populated metastable conformational states (or local free energy minima) having populations of a few percent or less. The time scales for transitioning between these states range from several microseconds to milliseconds. In contrast to folded proteins, there is no kinetic hub. More strikingly, a few states contain significant amounts of β-hairpin secondary structure and extended hydrophobic surfaces that are exposed to the solvent. We propose that these states may facilitate the nucleation of hIAPP aggregation through a significant component of the conformational selection mechanism, because they may increase their populations upon aggregation by promoting hydrophobic interactions and at the same time provide a flat geometry to seed the ordered β-strand packing of the fibrils.

Amyloid fibrillation of proteins is associated with a great variety of pathologic conditions. Development of new molecules that can monitor amyloidosis kinetics and inhibit fibril formation is of great diagnostic and therapeutic value. In this work, we have developed a biocompatible molecule that functions as an ex situ monitor and an in situ inhibitor for protein fibrillation, using insulin as a model protein. 1,2-Bis[4-(3-sulfonatopropoxyl)phenyl]-1,2-diphenylethene salt (BSPOTPE) is nonemissive when it is dissolved with native insulin in an incubation buffer but starts to fluoresce when it is mixed with preformed insulin fibril, enabling ex situ monitoring of amyloidogenesis kinetics and high-contrast fluorescence imaging of protein fibrils. Premixing BSPOTPE with insulin, on the other hand, inhibits the nucleation process and impedes the protofibril formation. Increasing the dose of BSPOTPE boosts its inhibitory potency. Theoretical modeling using molecular dynamics simulations and docking reveals that BSPOTPE is prone to binding to partially unfolded insulin through hydrophobic interaction of the phenyl rings of BSPOTPE with the exposed hydrophobic residues of insulin. Such binding is assumed to have stabilized the partially unfolded insulin and obstructed the formation of the critical oligomeric species in the protein fibrillogenesis process.

Pyrophosphate ion (PPi) release after nucleotide incorporation is a necessary step for RNA polymerase II (pol II) to enter the next nucleotide addition cycle during transcription elongation. However, the role of pol II residues in PPi release and the mechanistic relationship between PPi release and the conformational change of the trigger loop remain unclear. In this study, we constructed a Markov state model (MSM) from extensive all-atom molecular dynamics (MD) simulations in the explicit solvent to simulate the PPi release process along the pol II secondary channel. Our results show that the trigger loop has significantly larger intrinsic motion after catalysis and formation of PPi, which in turn aids PPi release mainly through the hydrogen bonding between the trigger loop residue H1085 and the (Mg–PPi)2– group. Once PPi leaves the active site, it adopts a hopping model through several highly conserved positively charged residues such as K752 and K619 to release from the pol II pore region of the secondary channel. These positive hopping sites form favorable interactions with PPi and generate four kinetically metastable states as identified by our MSM. Furthermore, our single-mutant simulations suggest that H1085 and K752 aid PPi exit from the active site after catalysis, whereas K619 facilitates its passage through the secondary channel. Finally, we suggest that PPi release could help the opening motion of the trigger loop, even though PPi release precedes full opening of the trigger loop due to faster PPi dynamics. Our simulations provide predictions to guide future experimental tests.

We proposed a computational protocol of simulating the T-jump peptide unfolding experiments and the related transient IR and two-dimensional IR (2DIR) spectra based on the Markov state model (MSM) and nonlinear exciton propagation (NEP) methods. MSMs partition the conformation space into a set of nonoverlapping metastable states, and we can calculate spectra signal for each of these states using the NEP method. Thus the overall spectroscopic observable for a given system is simply the sum of spectra of different metastable states weighted by their populations. We show that results from MSMs constructed from a large number of simulations have a much better agreement with the equilibrium experimental 2DIR spectra compared to that generated from straightforward MD simulations starting from the folded state. This indicates that a sufficient sampling of important relevant conformational states is critical for calculating the accurate spectroscopic observables. MSMs are also capable of simulating the unfolding relaxation dynamics upon the temperature jump. The agreement of the simulation using MSMs and NEP with the experiment not only provides a justification for our protocol, but also provides the physical insight of the underlying spectroscopic observables. The protocol we developed has the potential to be extended to simulate a wide range of fast triggering plus optical detection experiments for biomolecules.

A structurally conserved element, the trigger loop, has been suggested to play a key role in substrate selection and catalysis of RNA polymerase II (pol II) transcription elongation. Recently resolved X-ray structures showed that the trigger loop forms direct interactions with the β-phosphate and base of the matched nucleotide triphosphate (NTP) through residues His1085 and Leu1081, respectively. In order to understand the role of these two critical residues in stabilizing active site conformation in the dynamic complex, we performed all-atom molecular dynamics simulations of the wild-type pol II elongation complex and its mutants in explicit solvent. In the wild-type complex, we found that the trigger loop is stabilized in the “closed” conformation, and His1085 forms a stable interaction with the NTP. Simulations of point mutations of His1085 are shown to affect this interaction; simulations of alternative protonation states, which are inaccessible through experiment, indicate that only the protonated form is able to stabilize the His1085-NTP interaction. Another trigger loop residue, Leu1081, stabilizes the incoming nucleotide position through interaction with the nucleotide base. Our simulations of this Leu mutant suggest a three-component mechanism for correctly positioning the incoming NTP in which (i) hydrophobic contact through Leu1081, (ii) base stacking, and (iii) base pairing work together to minimize the motion of the incoming NTP base. These results complement experimental observations and provide insight into the role of the trigger loop on transcription fidelity.

Constructing Markov State Models (MSMs) based on short molecular dynamics simulations is a powerful computational technique to complement experiments in predicting long-time kinetics of biomolecular processes at atomic resolution. Even though the MSM approach has been widely applied to study one-body processes such as protein folding and enzyme conformational changes, the majority of biological processes, e.g. protein–ligand recognition, signal transduction, and protein aggregation, essentially involve multiple entities. Here we review the attempts at constructing MSMs for multi-body systems, point out the challenges therein and discuss recent algorithmic progresses that alleviate these challenges. In particular, we describe an automatic kinetics based partitioning method that achieves optimal definition of the conformational states in a multi-body system, and discuss a novel maximum-likelihood approach that efficiently estimates the slow uphill kinetics utilizing pre-computed equilibrium populations of all states. We expect that these new algorithms and their combinations may boost investigations of important multi-body biological processes via the efficient construction of MSMs.

Maltose transporter MalFGK2 is a type-I importer in the ATP-binding cassette (ABC) transporter superfamily. Upon the binding of its periplasmic binding protein, MalE, the ATPase activity of MalFGK2 can be greatly enhanced. Crystal structures of the MalFGK2–MalE–maltose complex in a so-called “pretranslocation” (“pre-T”) state with a partially closed conformation suggest that the formation of this MalE-stabilized intermediate state is a key step leading to the outward-facing catalytic state. On the contrary, crosslinking and fluorescence studies suggest that ATP binding alone is sufficient to promote the outward-facing catalytic state, thereby doubting the role of MalE binding. To clarify the role of MalE binding and to gain deeper understanding of the molecular mechanisms of MalFGK2, we calculated the free energy surfaces (FESs) related to the lateral motion in the presence and absence of MalE using atomistic metadynamics simulations. The results showed that, in the absence of MalE, laterally closing motion was energetically forbidden but, upon MalE binding, more closed conformations similar to the pre-T state become more stable. The significant effect of MalE binding on the free energy landscapes was in agreement with crystallographic studies and confirmed the important role of MalE in stabilizing the pre-T state. Our simulations also revealed that the allosteric effect of MalE stimulation originates from the MalE-binding-promoted vertical motion between MalF and MalG cores, which was further supported by MD simulation of the MalE-independent mutant MalF500.

Amyloid deposits of human islet amyloid polypeptide (hIAPP) are identified in 95% of type II diabetes patients. The oligomers during the early stage of hIAPP aggregation are believed to be more cytotoxic than the mature fibrils. However, the structural details during the initial stage of hIAPP aggregation are still under debate experimentally. To understand its initial nucleation mechanism, we investigate the thermodynamics and kinetics of hIAPP(11–25) dimerization, which is the first manifestation of the interplay between intra- and inter-molecular interactions, via the construction of Markov state models from extensive molecular dynamics simulations. We identified a largely populated metastable dimer state with the antiparallel cross-β structure, although tangled coil states are also observed. The dimerization process consists of two stages kinetically: the initial collision of separate monomers followed by structural rearrangements. During the collapsing stage, hydrophobic interactions are the main driving force, although electrostatic interactions also play a role. In the subsequent structural rearrangement step, there exist heterogeneous pathways from the initial collapsed complexes to the antiparallel cross-β structure, with the transition time-scales around hundreds of microseconds. Our replica-exchange molecular dynamics simulations demonstrate that this antiparallel cross-β state is negligible in the dimer ensemble of the fibril-free S20P mutant, indicating that it is an on-pathway intermediate for hIAPP(11–25) fibrillation. These results, together with those from our previous study of the monomer, prompt us to propose a generalized model with the combination of the induced-fit and conformational-selection mechanisms for this dimerization process. These findings shed light on the understanding of hIAPP(11–25) aggregation mechanisms.

Self-assembly processes play a key role in the fabrication of functional nano-structures with widespread application in drug delivery and micro-reactors. In addition to the thermodynamics, the kinetics of the self-assembled nano-structures also play an important role in determining the formed structures. However, as the self-assembly process is often highly heterogeneous, systematic elucidation of the dominant kinetic pathways of self-assembly is challenging. Here, based on mass flow, we developed a new method for the construction of kinetic network models and applied it to identify the dominant kinetic pathways for the self-assembly of star-like block copolymers. We found that the dominant pathways are controlled by two competing kinetic parameters: the encounter time Te, characterizing the frequency of collision and the transition time Tt for the aggregate morphology change from rod to sphere. Interestingly, two distinct self-assembly mechanisms, diffusion of an individual copolymer into the aggregate core and membrane closure, both appear at different stages (with different values of Tt) of a single self-assembly process. In particular, the diffusion mechanism dominates the middle-sized semi-vesicle formation stage (with large Tt), while the membrane closure mechanism dominates the large-sized vesicle formation stage (with small Tt). Through the rational design of the hydrophibicity of the copolymer, we successfully tuned the transition time Tt and altered the dominant self-assembly pathways.