Markov State Models Research Articles

Designing Reaction Coordinate for Ion-Induced Pore-Assisted Mechanism of Halide Ions Permeation through Lipid Bilayer by Umbrella Sampling.

Ion permeation mechanism through lipid membranes helps to understand cellular processes. We propose new reaction coordinates that allow ions to permeate according to their water affinity and interaction with the hydrophilic layer. Simulations were done for three different halides (F-, Cl-, and I-) in two different lipid bilayers, 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC) and 1,2-dinervonoyl-sn-glycero-3-phosphocholine (DNPC). It is found that the involvement of the water molecules decreases the free energy barrier. The ions were found to follow different pathways for permeation. Formation of proper pores required a collaboration effort of the hydration shell water molecules and the hydrophilic lipid layer, which was favored in the case of Cl- ions. The optimum charge density and good water affinity of Cl- with respect to F- and I- ions helped to form the pore. The effect was prominently seen in the case of DNPC membrane because of its higher hydrophobic thickness. The umbrella sampling results were compared with other methods such as the Markov state model (MSM) and well-tempered metadynamics (WT-metaD).

Revealing the conformational dynamics of UDP-GlcNAc recognition by O-GlcNAc transferase via Markov state model

The O-linked N-acetylglucosamine (O-GlcNAc) glycosylation is a critical post-translational modification and closely linked to various physiological and pathological conditions. The O-GlcNAc transferase (OGT) functions as the only glycosyltransferase of O-GlcNAc glycosylation by transferring GlcNAc from UDP-GlcNAc to serine or threonine residues on protein substrates. The interaction mode of UDP-GlcNAc against OGT has been preliminarily revealed by the crystal structures, yet an atomic-level comprehension for the conformational dynamics of the recognition process remains elusive. Here, we construct the Markov state model based on extensive all-atom molecular dynamics (MD) simulations with an aggregated simulation time of ∼9 μs, and reveal that the UDP-GlcNAc recognition process by OGT encompasses four key metastable states, occurring within an estimated timescale of ∼10 μs. During UDP-GlcNAc recognition process, we find the pyrophosphate moiety (P2O52−) initially anchors to the active pocket via salt bridge and hydrogen bonds, facilitating subsequent binding of the uridine and GlcNAc moieties. Furthermore, the functional roles of K842 involved in the salt bridge with P2O52− were evaluated through extra mutant MD simulations. Overall, our study provides valuable insights into the UDP-GlcNAc recognition mechanism by OGT, which could further aid in mechanistic studies of O-GlcNAc glycosylation and drug development targeting on OGT.

Disruption of the FMN-A524 interaction cascade and Glu513-induced collapse of the hydrophobic barrier promotes light-induced Jα-helix unfolding in AsLOV2

The C-terminal Jα-helix of the Avena sativa’s Light Oxygen and Voltage (AsLOV2) protein, unfolds on exposure to blue light. This characteristic seeks relevance in applications related to engineering novel biological photoswitches. Using molecular dynamics simulations and the Markov state modeling (MSM) approach we provide the mechanism that explains the stepwise unfolding of the Jα-helix. The unfolding was resolved into seven steps represented by the structurally distinguishable states distributed over the initiation and the post initiation phases. Whereas, the initiation phase occurs due to the collapse of the interaction cascade FMN-Q513-N492-L480-W491-Q479-V520-A524, the onset of the post initiation phase is marked by breaking of the hydrophobic interactions between the Jα-helix and the Iβ-strand. This study indicates that the displacement of N492 out of the FMN binding pocket, not necessarily requiring Q513, is essential for the initiation of the Jα-helix unfolding. Rather, the structural reorientation of Q513 activates the protein to cross the hydrophobic barrier and enter the post initiation phase. Similarly, the structural deviations in N482, rather than its integral role in unfolding, could enhance the unfolding rates. Furthermore, the MSM studies on the wild-type and the Q513 mutant, provide the spatiotemporal roadmap that lay out the possible pathways of structural transition between the dark and the light states of the protein. Overall, the study provides insights useful to enhance the performance of AsLOV2-based photoswitches.

Unraveling the allosteric inhibition mechanism of PARP-1 CAT and the D766/770A mutation effects via Gaussian accelerated molecular dynamics and Markov state model

PARP-1 (Poly (ADP-ribose) polymerase 1) is a nuclear enzyme and plays a key role in many cellular functions, such as DNA repair, modulation of chromatin structure, and recombination. Developing the PARP-1 inhibitors has emerged as an effective therapeutic strategy for a growing list of cancers. The catalytic structural domain (CAT) of PARP-1 upon binding the inhibitor allosterically regulates the conformational changes of helix domain (HD), affecting its identification with the damaged DNA. The typical type I (EB47) and III (veliparib) inhibitors were able to lengthening or shortening the retention time of this enzyme on DNA damage and thus regulating the cytotoxicity. Nonetheless, the basis underlying allosteric inhibition is unclear, which limits the development of novel PARP-1 inhibitors. Here, to investigate the distinct allosteric changes of EB47 and veliparib against PARP-1 CAT, each complex was simulated via classical and Gaussian accelerated molecular dynamics (cMD and GaMD). To study the reverse allosteric basis and mutation effects, the complexes PARP-1 with UKTT15 and PARP-1 D766/770A mutant with EB47 were also simulated. Importantly, the markov state models were built to identify the transition pathways of crucial substates of allosteric communication and the induction basis of PARP-1 reverse allostery. The conformational change differences of PARP-1 CAT regulated by allosteric inhibitors were concerned with to their interaction at the active site. Energy calculations suggested the energy advantage of EB47 in inhibiting the wild-type PARP-1, compared with D766/770A PARP-1. Secondary structure results showed the change of two key loops (αB-αD and αE-αF) in different systems. This work reported the basis of PARP-1 allostery from both thermodynamic and kinetic views, providing the guidance for the discovery and design of more innovative PARP-1 allosteric inhibitors.

SciCSR infers B cell state transition and predicts class-switch recombination dynamics using single-cell transcriptomic data

Class-switch recombination (CSR) is an integral part of B cell maturation. Here we present sciCSR (pronounced ‘scissor’, single-cell inference of class-switch recombination), a computational pipeline that analyzes CSR events and dynamics of B cells from single-cell RNA sequencing (scRNA-seq) experiments. Validated on both simulated and real data, sciCSR re-analyzes scRNA-seq alignments to differentiate productive heavy-chain immunoglobulin transcripts from germline ‘sterile’ transcripts. From a snapshot of B cell scRNA-seq data, a Markov state model is built to infer the dynamics and direction of CSR. Applying sciCSR on severe acute respiratory syndrome coronavirus 2 vaccination time-course scRNA-seq data, we observe that sciCSR predicts, using data from an earlier time point in the collected time-course, the isotype distribution of B cell receptor repertoires of subsequent time points with high accuracy (cosine similarity ~0.9). Using processes specific to B cells, sciCSR identifies transitions that are often missed by conventional RNA velocity analyses and can reveal insights into the dynamics of B cell CSR during immune response.

SARS-CoV-2 RNA-Dependent RNA Polymerase Follows Asynchronous Translocation Pathway for Viral Transcription and Replication.

Translocation is one essential step for the SARS-CoV-2 RNA-dependent RNA polymerase (RdRp) to exert viral replication and transcription. Although cryo-EM structures of SARS-CoV-2 RdRp are available, the molecular mechanisms of dynamic translocation remain elusive. Herein, we constructed a Markov state model based on extensive molecular dynamics simulations to elucidate the translocation dynamics of the SARS-CoV-2 RdRp. We identified two intermediates that pinpoint the rate-limiting step of translocation and characterize the asynchronous movement of the template-primer duplex. The 3'-terminal nucleotide in the primer strand lags behind due to the uneven distribution of protein-RNA interactions, while the translocation of the template strand is delayed by the hurdle residue K500. Even so, the two strands share the same "ratchet" to stabilize the polymerase in the post-translocation state, suggesting a Brownian-ratchet model. Overall, our study provides intriguing insights into SARS-CoV-2 replication and transcription, which would open a new avenue for drug discoveries.

Identifiability of equilibrium constants for receptors with two to five binding sites.

Ligand-gated ion channels (LGICs) are regularly oligomers containing between two and five binding sites for ligands. Neither in homomeric nor heteromeric LGICs the activation process evoked by the ligand binding is fully understood. Here, we show on theoretical grounds that for LGICs with two to five binding sites, the cooperativity upon channel activation can be determined in considerable detail. The main requirements for our strategy are a defined number of binding sites in a channel, which can be achieved by concatenation, a systematic mutation of all binding sites and a global fit of all concentration-activation relationships (CARs) with corresponding intimately coupled Markovian state models. We take advantage of translating these state models to cubes with dimensions 2, 3, 4, and 5. We show that the maximum possible number of CARs for these LGICs specify all 7, 13, 23, and 41 independent model parameters, respectively, which directly provide all equilibrium constants within the respective schemes. Moreover, a fit that uses stochastically varied scaled unitary start vectors enables the determination of all parameters, without any bias imposed by specific start vectors. A comparison of the outcome of the analyses for the models with 2 to 5 binding sites showed that the identifiability of the parameters is best for a case with 5 binding sites and 41 parameters. Our strategy can be used to analyze experimental data of other LGICs and may be applicable to voltage-gated ion channels and metabotropic receptors.

Top-Down Machine Learning of Coarse-Grained Protein Force Fields.

Developing accurate and efficient coarse-grained representations of proteins is crucial for understanding their folding, function, and interactions over extended time scales. Our methodology involves simulating proteins with molecular dynamics and utilizing the resulting trajectories to train a neural network potential through differentiable trajectory reweighting. Remarkably, this method requires only the native conformation of proteins, eliminating the need for labeled data derived from extensive simulations or memory-intensive end-to-end differentiable simulations. Once trained, the model can be employed to run parallel molecular dynamics simulations and sample folding events for proteins both within and beyond the training distribution, showcasing its extrapolation capabilities. By applying Markov state models, native-like conformations of the simulated proteins can be predicted from the coarse-grained simulations. Owing to its theoretical transferability and ability to use solely experimental static structures as training data, we anticipate that this approach will prove advantageous for developing new protein force fields and further advancing the study of protein dynamics, folding, and interactions.

Elucidating Interfacial Dynamics of Ti-Al Systems Using Molecular Dynamics Simulation and Markov State Modeling.

Due to their remarkable mechanical and chemical properties, Ti-Al-based materials are attracting considerable interest in numerous fields of engineering, such as automotive, aerospace, and defense. With their low density, high strength, and resistance to corrosion and oxidation, these intermetallic alloys and metal-compound composites have found diverse applications. However, additive manufacturing and heat treatment of Ti-Al alloys frequently lead to brittleness and severe formation of defects. The present study delves into the interfacial dynamics of these Ti-Al systems, particularly focusing on the behavior of Ti and Al atoms in the presence of TiAl3 grain boundaries under experimental heat treatment conditions. Using a combination of molecular dynamics and Markov state modeling, we scrutinize the kinetic processes involved in the formation of TiAl3. The molecular dynamics simulation indicates that at the early stage of heat treatment, the predominating process is the diffusion of Al atoms toward the Ti surface through the TiAl3 grain boundaries. Markov state modeling identifies three distinct dynamic states of Al atoms within the Ti/Al mixture that forms during the process, each exhibiting a unique spatial distribution. Using transition time scales as a qualitative measure of the rapidness of the dynamics, it is observed that the Al dynamics is significantly less rapid near the Ti surface compared to the Al surface. Put together, the results offer a comprehensive understanding of the interfacial dynamics and reveal a three-stage diffusion mechanism. The process initiates with the premelting of Al, proceeds with the prevalent diffusion of Al atoms toward the Ti surface, and eventually ceases as the Ti concentration within the mixture progressively increases. The insights gained from this study could contribute significantly to the control and optimization of manufacturing processes for these high-performing Ti-Al-based materials.

Identification of Aggregation Mechanism of Acetylated PHF6* and PHF6 Tau Peptides Based on Molecular Dynamics Simulations and Markov State Modeling.

The microtubule-associated protein tau (MAPT) has a critical role in the development and preservation of the nervous system. However, tau's dysfunction and accumulation in the human brain can lead to several neurodegenerative diseases, such as Alzheimer's disease, Down's syndrome, and frontotemporal dementia. The microtubule binding (MTB) domain plays a significant, important role in determining the tau's pathophysiology, as the core of paired helical filaments PHF6* (275VQIINK280) and PHF6 (306VQIVYK311) of R2 and R3 repeat units, respectively, are formed in this region, which promotes tau aggregation. Post-translational modifications, and in particular lysine acetylation at K280 of PHF6* and K311 of PHF6, have been previously established to promote tau misfolding and aggregation. However, the exact aggregation mechanism is not known. In this study, we established an atomic-level nucleation-extension mechanism of the separated aggregation of acetylated PHF6* and PHF6 hexapeptides, respectively, of tau. We show that the acetylation of the lysine residues promotes the formation of β-sheet enriched high-ordered oligomers. The Markov state model analysis of ac-PHF6* and ac-PHF6 aggregation revealed the formation of an antiparallel dimer nucleus which could be extended from both sides in a parallel manner to form mixed-oriented and high-ordered oligomers. Our study describes the detailed mechanism for acetylation-driven tau aggregation, which provides valuable insights into the effect of post-translation modification in altering the pathophysiology of tau hexapeptides.

Comparative Analysis of Conformational Dynamics and Systematic Characterization of Cryptic Pockets in the SARS-CoV-2 Omicron BA.2, BA.2.75 and XBB.1 Spike Complexes with the ACE2 Host Receptor: Confluence of Binding and Structural Plasticity in Mediating Networks of Conserved Allosteric Sites.

In the current study, we explore coarse-grained simulations and atomistic molecular dynamics together with binding energetics scanning and cryptic pocket detection in a comparative examination of conformational landscapes and systematic characterization of allosteric binding sites in the SARS-CoV-2 Omicron BA.2, BA.2.75 and XBB.1 spike full-length trimer complexes with the host receptor ACE2. Microsecond simulations, Markov state models and mutational scanning of binding energies of the SARS-CoV-2 BA.2 and BA.2.75 receptor binding domain complexes revealed the increased thermodynamic stabilization of the BA.2.75 variant and significant dynamic differences between these Omicron variants. Molecular simulations of the SARS-CoV-2 Omicron spike full-length trimer complexes with the ACE2 receptor complemented atomistic studies and enabled an in-depth analysis of mutational and binding effects on conformational dynamic and functional adaptability of the Omicron variants. Despite considerable structural similarities, Omicron variants BA.2, BA.2.75 and XBB.1 can induce unique conformational dynamic signatures and specific distributions of the conformational states. Using conformational ensembles of the SARS-CoV-2 Omicron spike trimer complexes with ACE2, we conducted a comprehensive cryptic pocket screening to examine the role of Omicron mutations and ACE2 binding on the distribution and functional mechanisms of the emerging allosteric binding sites. This analysis captured all experimentally known allosteric sites and discovered networks of inter-connected and functionally relevant allosteric sites that are governed by variant-sensitive conformational adaptability of the SARS-CoV-2 spike structures. The results detailed how ACE2 binding and Omicron mutations in the BA.2, BA.2.75 and XBB.1 spike complexes modulate the distribution of conserved and druggable allosteric pockets harboring functionally important regions. The results are significant for understanding the functional roles of druggable cryptic pockets that can be used for allostery-mediated therapeutic intervention targeting conformational states of the Omicron variants.

Integrative generalized master equation: A method to study long-timescale biomolecular dynamics via the integrals of memory kernels.

The generalized master equation (GME) provides a powerful approach to study biomolecular dynamics via non-Markovian dynamic models built from molecular dynamics (MD) simulations. Previously, we have implemented the GME, namely the quasi Markov State Model (qMSM), where we explicitly calculate the memory kernel and propagate dynamics using a discretized GME. qMSM can be constructed with much shorter MD trajectories than the MSM. However, since qMSM needs to explicitly compute the time-dependent memory kernels, it is heavily affected by the numerical fluctuations of simulation data when applied to study biomolecular conformational changes. This can lead to numerical instability of predicted long-time dynamics, greatly limiting the applicability of qMSM in complicated biomolecules. We present a new method, the Integrative GME (IGME), in which we analytically solve the GME under the condition when the memory kernels have decayed to zero. Our IGME overcomes the challenges of the qMSM by using the time integrations of memory kernels, thereby avoiding the numerical instability caused by explicit computation of time-dependent memory kernels. Using our solutions of the GME, we have developed a new approach to compute long-time dynamics based on MD simulations in a numerically stable, accurate and efficient way. To demonstrate its effectiveness, we have applied the IGME in three biomolecules: the alanine dipeptide, FIP35 WW-domain, and Taq RNA polymerase. In each system, the IGME achieves significantly smaller fluctuations for both memory kernels and long-time dynamics compared to the qMSM. We anticipate that the IGME can be widely applied to investigate biomolecular conformational changes.

Engineered β-glycosidase from Hyperthermophilic Sulfolobus solfataricus with Improved Rd-hydrolyzing Activity for Ginsenoside Compound K Production.

Hyperthermophilic Sulfolobus solfataricus β-glycosidase (SS-βGly), with higher stability and activity than mesophilic enzymes, has potential for industrial ginsenosides biotransformation. However, its relatively low ginsenoside Rd-hydrolyzing activity limits the production of pharmaceutically active minor ginsenoside compound K (CK). In this study, first, we used molecular docking to predict the key enzyme residues that may hypothetically interact with ginsenoside Rd. Then, based on sequence alignment and alanine scanning mutagenesis approach, key variant sites were identified that might improve the enzyme catalytic efficiency. The enzyme catalytic efficiency (kcat/Km) and substrate affinity (Km) of the N264D variant enzyme for ginsenoside Rd increased by 60% and decreased by 17.9% compared with WT enzyme, respectively, which may be due to a decrease in the binding free energy (∆G) between the variant enzyme and substrate Rd. In addition, Markov state models (MSM) analysis during the whole 1000-ns MD simulations indicated that altering N264 to D made the variant enzyme achieve a more stable SS-βGly conformational state than the wild-type (WT) enzyme and corresponding Rd complex. Under identical conditions, the relative activities and the CK conversion rates of the N264D enzyme were 1.7 and 1.9 folds higher than those of the WT enzyme. This study identified an excellent hyperthermophilic β-glycosidase candidate for industrial biotransformation of ginsenosides.

Functionalized Fullerene Potentially Inhibits SARS-CoV-2 Infection by Modulating Spike Protein Conformational Changes.

The disease of SARS-CoV-2 has caused considerable morbidity and mortality globally. Spike proteins on the surface of SARS-CoV-2 allow it to bind with human cells, leading to infection. Fullerenes and their derivatives are promising SARS-CoV-2 inhibitors and drug-delivery vehicles. In this study, Gaussian accelerated molecular dynamics simulations and the Markov state model were employed to delve into the inhibitory mechanism of Fullerene-linear-polyglycerol-b-amine sulfate (F-LGPS) on spike proteins. During the study, it was discovered that fullerene derivatives can operate at the interface of the receptor-binding domain (RBD) and the N-terminal domain (NTD), keeping structural domains in a downward conformation. It was also observed that F-LGPS demonstrated superior inhibitory effects on the XBB variant in comparison to the wild-type variant. This study yielded invaluable insights for the potential development of efficient therapeutics targeting the spike protein of SARS-CoV-2.

Characterization of Common Genetic Variants in P2RX7 and Their Contribution to Chronic Pain Conditions

The adenosine triphosphate (ATP)-gated channel P2X7 is encoded by a gene enriched for common nonsynonymous variants. Many of these variants have functional cellular effects, and some have been implicated in chronic pain. In this study, we first systematically characterized all 17 common nonsynonymous variants using whole-cell patch clamp electrophysiology. Then, we analyzed these variants for statistical association with chronic pain phenotypes using both individual P2RX7 variants as predictors and cumulative allele counts of same-direction cellular effect in univariate models. Association and validation analyses were conducted in the Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA) cohort (N = 3260) and in the Complex Persistent Pain Conditions (CPPC) cohort (N = 900), respectively. Our results showed an association between allele A of rs7958311 and an increased risk of chronic pelvic pain, with convergent evidence for contribution to fibromyalgia and irritable bowel syndrome, confirmed in a meta-analysis. This allelic variant produced a unique cellular phenotype: a gain-of-function in channel opening, and a loss-of-function in pore opening. A computational study using a 12-state Markov model of ATP binding to the P2X7 receptor suggested that this cellular phenotype arises from an increased ATP binding affinity and an increased open channel conductance combined with a loss of sensitization. Cumulative allele count analysis did not provide additional insights. In conclusion, our results go beyond reproducing association for rs7958311 with chronic pain and suggest that its unique combination of gain-of-function in channel and loss-of-function in pore activity may explain why it is likely the only common P2RX7 variant with contribution to chronic pain. PerspectiveThis study characterizes all common P2RX7 variants using cellular assays and statistical association analyses with chronic pain, with Markov state modeling of the most robustly associated variant.

Exploring the Barriers in the Aggregation of a Hexadecameric Human Prion Peptide through the Markov State Model.

The prefibrillar aggregation kinetics of prion peptides are still an enigma. In this perspective, we employ atomistic molecular dynamics (MD) simulations of the shortest human prion peptide (HPP) (127GYMLGS132) at various temperatures and peptide concentrations and apply the Markov state model to determine the various intermediates and lag phases. Our results reveal that the natural mechanism of prion peptide self-assembly in the aqueous phase is impeded by two significant kinetic barriers with oligomer sizes of 6-9 and 12-13 peptides, respectively. The first one is the aggregation of unstructured lower-order oligomers, and the second is fibril nucleation, which impedes the further growth of prion aggregates. Among these two activation barriers, the second one is found to be dominant irrespective of the increase in temperature and peptide concentration. These lag phases are captured in all three different force-field parameters, namely, GROMOS-54a7, AMBER-99SB-ILDN, and CHARMMS 36m, at different concentrations. The GROMOS-54a7 and AMBER-99SB-ILDN force fields showed a comparatively higher percentage of β-sheet formation in the metastable aggregate that evolved during the aggregation process. In contrast, the CHARMM-36m force field showed mostly coil or turn conformations. The addition of a novel catecholamine derivative (naphthoquinone dopamine (NQDA)) arrests the aggregation process between the lag phases by increasing the activation barrier for the Lag1 and Lag2 phases in all of the force fields, which further validates the existence of these lag phases. The preferential binding of NQDA with the peptides increases the hydration of peptides and eventually disrupts the organized morphology of prefibrillar aggregates. It reduces the dimer dissociation energy by -24.34 kJ/mol.

Pathways of hLL-3717-29 Aggregation Give Insight into the Mechanism of α-Amyloid Formation.

α-amyloids present a novel self-assembly principle that can be utilized to prepare functional biomaterials. Evidence of α-amyloid formation in the active core of the human LL-37 protein (comprising residues 17 to 29) was associated with this peptide's membranolytic property. Though mechanistic pathways of β-amyloid formation are known, such studies are scarce in α-amyloids. Modern computational techniques allow such mechanistic studies in molecular detail. Here, we propose aggregation pathways in hLL-3717-29 through molecular dynamics simulations. We first identified oligomers among peptides based on a distance criterion. The distribution of oligomers was then used to build Markov state models from which pathways were obtained using the framework of transition path theory. We checked the structural stability of the peptides during oligomerization, which is crucial from their functional point of view. We also investigated the key residues that participate in oligomer formation, the interactions between them, and the effect of residue mutations on the binding free energy of the peptides. Our findings suggest that larger oligomers are produced from the association of smaller and intermediate oligomers. The peptides retain their helical structure during aggregation with transient occurrences of 3-10 helix and turns. Hydrophobic interactions are vital in the aggregation of these peptides with Ile24 playing a crucial role. Mutation of this residue to alanine decreases the peptides' binding free energy, resulting in reduced aggregation tendency.

Nonmonotonic Modulation of the Protein-Ligand Recognition Event by Inert Crowders.

The ubiquitous event of a protein recognizing small molecules or ligands at its native binding site is crucial for initiating major biological processes. However, how a crowded environment, as is typically represented by a cellular interior, would modulate the protein-ligand search process is largely debated. Excluded volume-based theory suggests that the presence of an inert crowder would reinforce a steady stabilization and enhancement of the protein-ligand recognition process. Here, we counter this long-held perspective via the molecular dynamics simulation and Markov state model of the protein-ligand recognition event in the presence of inert crowders. Specifically, we demonstrate that, depending on concentration, even purely inert crowders can exert a nonmonotonic effect via either stabilizing or destabilizing the protein-ligand binding event. Analysis of the kinetic network of binding pathways reveals that the crowders would either modulate precedent non-native on-pathway intermediates or would devise additional ones in a multistate recognition event across a wide range of concentrations. As an important insight, crowders gradually shift the relative transitional preference of these intermediates toward a native-bound state, with ligand residence time at the binding pocket dictating the trend of nonmonotonic concentration dependence by simple inert crowders.

Methane hydrate nucleation path and structure evolution revealed by Markov modelling

A deep understanding of hydrate formation path is currently one of the most important thresholds for the energy, environmental and safety applications of hydrate. Methane hydrate, a kind of clean energy and the most widespread hydrate clathrates, is selected as the research focus in this study. The trajectories of the hydrate growth are generated by Molecular Dynamics (MD) simulations. Then, these trajectories are analyzed by applying the Markov State Model for the first time, from which the evolution path and structure conversion are extracted. The results show that nine independent meta-stable states can be identified. The findings reveal the existence of nine distinct metastable states during the hydrate formation process. Transitions between these states occur at nanosecond timescales, considerably slower than conventional MD simulations. Moreover, based on the state transitions, a single-track path traversing the metastable states leads to the final state, while two bidirectional paths arise due to the infiltration or escape of methane molecules. These results facilitate the sampling and observation of the hydrate formation process in laboratory conditions and inspire strategies for controlling hydrate evolution.

Energetic and Kinetic Origins of CALB Interfacial Activation Revealed by PaCS-MD/MSM.

The conformational dynamics of Candida antarctica lipase B (CALB) was investigated by molecular dynamics (MD) simulation, parallel cascade selection MD (PaCS-MD), and the Markov state model (MSM) and mainly focused on the lid-opening motion closely related to substrate binding. All-atom MD simulation of CALB was conducted in water and on the interface of water and tricaprylin. CALB initially situated in water and separated by layers of water from the interface is spontaneously adsorbed onto the tricaprylin surface during MD simulation. The opening and closing motions of the lid are simulated by PaCS-MD, and subsequent MSM analysis provided the free-energy landscape and time scale of the conformational transitions among the closed, semiopen, and open states. The closed state is the most stable in the water system, but the stable conformation in the interface system shifts to the semiopen state. These effects could explain the energetics and kinetics origin of the previously reported interfacial activation of CALB. These findings could help expand the application of CALB toward a wide variety of substrates.