Markov State Models Reconcile Conformational Plasticity of GTPase with Its Substrate Binding Event

Unveiling the Conformational Dynamics of the Histone Tails Using Markov State Modeling

Biomolecules predominantly exert their function by altering conformational dynamics. The nucleosome core particle (NCP) is the fundamental unit of chromatin. DNA with ∼146 base pairs wraps around the histone octamer to form a nucleosome. The histone octamer is composed of two copies of each histone protein (H3, H4, H2A, and H2B) with a globular core and disordered N-terminal tails. Epigenetic modifications of the histone N-terminal tails play a critical role in regulating the chromatin structure and biological processes such as transcription and DNA repair. Here, we report all-atom molecular dynamics (MD) simulations of the nucleosome at microsecond time scales to construct Markov state models (MSMs) to elucidate distinct conformations of the histone tails. We employ time-lagged independent component analysis (tICA) to capture their essential slow dynamics, with k-means clustering used to discretize the conformational space. MSMs unveil distinct states and transition probabilities to characterize the dynamics and kinetics of the tails. Next, we focus on the H2B tail, which is one of the least studied tails. We show that acetylation increases secondary structure formation with increased transition rates. These findings will aid in understanding the functional implications of tail conformations for nucleosome stability and gene regulation.

Luminescent Metal-Organic Framework with Negative Electrostatic Pores for Highly Selective GDP Sensing

Electrostatic potential (ESP) plays an essential role in studying interactions among molecules. Developing probe materials capable of selectively detecting analytes by aligning their molecular ESP with the electrostatic interaction of the host probe material is critically important for identifying analogous analytes; however, relevant research is extremely lacking. In this work, we synthesized a luminescent metal-organic framework (LMOF, Cd-DBDP) featuring negative electrostatic pore environments achieved by incorporating numerous electronegative oxygen atoms and N-containing aromatic rings from organic linkers. The molecular ESP distributions of Cd-DBDP and RNA-related nucleotides were calculated and employed to predict the sensing results. Fluorescence tests demonstrated that Cd-DBDP represents the first example of an MOF-based sensor for guanosine diphosphate (GDP) sensing, and the experimental observations were highly consistent with the theoretical prediction. The sensing mechanism for GDP was thoroughly studied through Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy/energy-dispersive spectroscopy (SEM/EDS), X-ray photoelectron spectroscopy (XPS), and theoretical calculations. These findings provide valuable insights into understanding the interplay between the molecular ESP distribution condition and the sensing results. This study offers a theoretical guide for future sensory research and provides effective means for the design and synthesis of highly efficient sensing MOFs, lending a solid groundwork for further exploration in this field.

Understanding the conformational dynamics of PI3Kα due to helical domain mutations: insights from Markov state model analysis

Phosphoinositide 3-kinases (PI3Ks) phosphorylate phosphoinositides on the membrane, which act as secondary signals for various cellular processes. PI3Kα, a heterodimer of the p110α catalytic subunit and the p85α regulatory subunit, is activated by growth factor receptors or mutations. Among these mutations, E545K present in the helical domain is strongly associated with cancer, and is known to disrupt interactions between the regulatory and catalytic subunits, leading to its constitutive activation. However, while the mutation's role in disrupting autoinhibition is well documented, the molecular mechanisms linking this mutation in the helical domain to the structural changes in the kinase domain remain poorly understood. This study aims to understand the conformational events triggered by the E545K mutation, elucidate how these changes propagate from the helical domain to the kinase domain, and identify crucial residues involved in the activation process. Molecular dynamics (MD) simulations combined with Markov state modeling (MSM) were employed to explore the conformational landscapes of both the wild-type and mutant systems. Structural and energetic analyses, including Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) calculations, revealed that the E545K mutation significantly reduces the binding affinity between the regulatory and catalytic subunits. The mutation was found to induce a sliding motion of the regulatory subunit along the catalytic subunit, leading to the disruption of key salt-bridges between these domains. This disruption releases the inhibitory effect of the regulatory subunit, resulting in increased domain motion, particularly in the adaptor-binding domain (ABD). Enhanced flexibility in the ABD, helical, and C2 domains facilitates the rearrangement of the two lobes of kinase domain, thereby promoting activation. Additionally, the mutation appears to enhance PI3Kα's membrane affinity via the Ras-binding domain (RBD). Network analysis helped to identify key residues that may involve in allosteric signaling pathways, providing insights into the communication between domains. Druggable pockets in the metastable states were predicted followed by its docking with a PI3K inhibitor library. Docking studies revealed the crucial residues that may be participating in inhibitor binding. The identification of residues and regions involved in activation mechanisms using MSM helped to reveal the conformational events and the knowledge on probable allosteric pockets, which may be helpful in designing better therapeutics.

Deciphering allosteric mechanisms in KRAS activation: insights from GTP-induced conformational dynamics and interaction network reorganization

The conformational dynamics and activation mechanisms of KRAS proteins are of great importance for targeted cancer therapy. However, the detailed molecular mechanics of KRAS activation induced by GTP binding remains unclear. In this study, we systematically investigated how GTP/GDP exchange affects the thermodynamic and kinetic properties of KRAS and explored the activation mechanism using molecular dynamics (MD) simulations, Markov state models (MSMs), and neural relational inference (NRI) models. Our MD simulation results show that GTP binding significantly enhances the conformational flexibility of KRAS, and thus promotes its transition to an active conformation with more open switch I and II regions. MSMs analyses show that KRAS in the GTP-bound state can be transitioned to the active state more efficiently during the simulation than in the GDP-bound state. In addition, NRI model calculations showed that GTP binding enhanced residue-residue interactions within the KRAS protein, especially when the long-range interactions were significantly enhanced. Furthermore, the allosteric signaling pathways from the P-loop to switch I and II as well as the key amino acid sites along the pathways were obtained using a graph-based shortest path analysis. Our results can contribute to a deeper understanding of the mechanism of KRAS allosteric activation and provide a foundation for the development of targeted therapeutic drugs to regulate KRAS activity.

Molecular Modeling of Glycosylated Catalytic Domain of Human Carbonic Anhydrase IX

Glycans exhibit significant structural diversity due to the flexibility of glycosidic bonds linking their constituent monosaccharides and the formation of numerous hydrogen bonds. The present work searches a simulated ensemble of glycan chain conformations attached to the catalytic domain of N-glycosylated human carbonic anhydrase IX (HCA IX-c) to identify conformations pointed away or back-folded toward the protein surface guided by different amino acid residues. A series of classical molecular dynamics (MD) simulation studies for a total of 30 μs followed by accelerated MD simulations for a total of 2 μs have been performed using two different force fields to capture varying degrees of fluctuations of both glycan chain and HCA IX. From the underlying free energy profile and kinetics derived using hidden Markov state model, several stable glycan orientations are identified that extend away from the protein surface and convert among each other with rate constants of the order 107-1010 S-1. Most importantly, we have identified a rare glycan conformation which reaches close to a catalytically important amino acid residue, Glu-106. We further enlist the protein residues that couple such less frequent event of the glycan chain back-folding toward the surface of the protein.

Optimized Collective Variable for Collapse Transition in Linear Hydrophobic Polymers: Importance of Hydration Water and End-to-End Distance

Choosing an appropriate collective variable (CV) for any biomolecular process is a challenging task. Researchers are developing methods to solve this issue using a variety of methodologies, most recently using machine learning (ML) methods. In this work, we investigate the mechanism of collapse transition across various lengths of polymer systems through adaptively sampled multiple short trajectories utilizing the Time Lagged Independent Component Analysis (TICA) framework. From TICA analysis, it is revealed that the radius of gyration (Rg) and end-to-end distance serve as good order parameters (OPs) for these systems describing overall energy landscapes. Markov state model (MSM) and mean first passage time (MFPT) analysis suggest that hydration water (Nw) plays a determining role in dictating the time scale and barrier for the collapsed transition for the C40 system. P-fold analysis on identifying transition state ensembles (TSE) identified by committor analysis also strengthens the role of Nw in such a transition. TICA, MSM, and committor analyses on the collapse transition for C45 reveal similarities with C40 systems in different aspects. Furthermore, we propose a pipeline integrating XGBoost regression along with an interpretable ML model, Shapley Additive exPlanation (SHAP) to precisely elucidate the contribution of each OP locally at the TSE. Through this approach, we observe that the collapse transition is primarily driven by Nw for both polymer systems. A carefully designed protocol for the collapsed transition of C60 systems indirectly reiterates the above result. Overall, our results suggest that while the end-to-end distance should be considered for better resolution of metastable states in the landscape, Nw is the crucial coordinate to be used in enhanced sampling for the exploration of actual collapse transitions for linear hydrophobic polymer systems. The Python code for analyzing the contribution of different OPs in the TSE using an ML-aided protocol is available on GitHub (https://github.com/saikat-ai/linear_polymer_project).

Unleashing the Power of Evolution in Xylanase Engineering: Investigating the Role of Distal Mutation Regulation

The drive to enhance enzyme performance in industrial applications frequently clashes with the practical limitations of exhaustive experimental screening, underscoring the urgency for more refined and strategic methodologies in enzyme engineering. In this study, xylanase Xyl-1 was used as the model, coupling evolutionary insights with energy functions to obtain theoretical potential mutants, which were subsequently validated experimentally. We observed that mutations in the nonloop region primarily aimed at enhancing stability and also encountered selective pressure for activity. Notably, mutations in this region simultaneously boosted the Xyl-1 stability and activity, achieving a 65% success rate. Using a greedy strategy, mutant M4 was developed, achieving a 12 °C higher melting temperature and doubled activity. By integration of spectroscopy, crystallography, and quantum mechanics/molecular mechanics molecular dynamics, the mechanism behind the enhanced thermal stability of M4 was elucidated. It was determined that the activity differences between M4 and the wild type were primarily driven by dynamic factors influenced by distal mutations. In conclusion, the study emphasizes the pivotal role of evolution-based approaches in augmenting the stability and activity of the enzymes. It sheds light on the unique adaptive mechanisms employed by various structural regions of proteins and expands our understanding of the intricate relationship between distant mutations and enzyme dynamics.

Modulation of the conformational landscape of the PDZ3 domain by perturbation on a distal non-canonical α3 helix: decoding the microscopic mechanism of allostery in the PDZ3 domain

While allosteric signal transduction is crucial for protein signaling and regulation, the dynamic process of allosteric communication remains poorly understood. The third PDZ domain (PDZ stands for the common structural domain shared by the postsynaptic density protein (PSD95), Drosophila disc large tumor suppressor (DlgA), and zonula occludens-1 protein (ZO-1)) serves as a classic example of a single-domain allosteric protein, demonstrating a long-range coupling between the C-terminal α helix (known as the α3 helix) and ligand binding. A molecular level understanding of how the α3 helix modulates the ligand binding affinity of the PDZ3 domain is still lacking. In this study, extensive molecular dynamics simulations corroborated with principal component analysis (PCA), ligand binding free energy calculations, energetic frustration analysis and Markov state model analysis are employed to uncover such molecular details. We demonstrate the definite presence of a binding competent closed-like state in the conformational landscape of wild-type PDZ3. The population modulations of this closed state and other binding incompetent states in the landscape due to α3-truncation/mutation of PDZ3 are explored. A correlation between the closed state population and calculated binding free energy is established, which supports the conformation selection mechanism. Covariance analysis identified the presence of correlated motion between two distant loops (β1-β2 and β2-β3) in the wild-type PDZ3 system, which weakened due to truncation/mutation in the distant α3 helix. It has also been observed that whenever the α3 helix was perturbed, the β2-β3 loop got further away from the binding groove and it is found to be correlated with the binding free energy values. Energetic frustration analysis of the PDZ3 domain also showed that the β2-β3 loop is highly frustrated. Finally, MSM analysis revealed a relevant timescale (closed to open state transition), which is similar to the observed experimental signal transduction timescale for the system. These observations led to the conclusion that the distantly located α3 helix plays a pivotal role in regulating the conformational landscape of the PDZ3 domain, determining the ligand binding affinity and resulting in allosteric behavior of the domain.

Incorporating Prior Knowledge in the Seeds of Adaptive Sampling Molecular Dynamics Simulations of Ligand Transport in Enzymes with Buried Active Sites

Because most proteins have buried active sites, protein tunnels or channels play a crucial role in the transport of small molecules into buried cavities for enzymatic catalysis. Tunnels can critically modulate the biological process of protein-ligand recognition. Various molecular dynamics methods have been developed for exploring and exploiting the protein-ligand conformational space to extract high-resolution details of the binding processes, a recent example being energetically unbiased high-throughput adaptive sampling simulations. The current study systematically contrasted the role of integrating prior knowledge while generating useful initial protein-ligand configurations, called seeds, for these simulations. Using a nontrivial system of a haloalkane dehalogenase mutant with multiple transport tunnels leading to a deeply buried active site, simulations were employed to derive kinetic models describing the process of association and dissociation of the substrate molecule. The most knowledge-based seed generation enabled high-throughput simulations that could more consistently capture the entire transport process, explore the complex network of transport tunnels, and predict equilibrium dissociation constants, koff/kon, on the same order of magnitude as experimental measurements. Overall, the infusion of more knowledge into the initial seeds of adaptive sampling simulations could render analyses of transport mechanisms in enzymes more consistent even for very complex biomolecular systems, thereby promoting drug development efforts and the rational design of enzymes with buried active sites.