Unbiased Molecular Dynamics Simulations Research Articles

Statistical Mechanical Theories of Membrane Permeability.

A popular theoretical framework to compute the permeability coefficient of a molecule is provided by the classic Smoluchowski-Kramers treatment of the steady-state diffusive flux across a free-energy barrier. Within this framework, commonly termed "inhomogeneous solubility-diffusion" (ISD), the permeability, P, is expressed in closed form in terms of the potential of mean force and position-dependent diffusivity of the molecule of interest along the membrane normal. In principle, both quantities can be calculated from all-atom MD simulations. Although several methods exist for calculating the position-dependent diffusivity, each of these is at best an estimate. In addition, the ISD model does not account for memory effects along the chosen reaction coordinate. For these reasons, it is important to seek alternative theoretical formulations to determine the permeability coefficient that are able to account for the factors ignored by the ISD approximation. Using Green-Kubo linear response theory, we establish the familiar constitutive relation between the flux density across the membrane and the difference in the concentration of a permeant molecule, j = PΔC. On this basis, we derive a time-correlation function expression for the nonequilibrium flux across a membrane that is reminiscent of the transmission coefficient in the reactive flux formalism treatment of transition rates. An analysis based on the transition path theory framework is exploited to derive alternative expressions for the permeability coefficient. The different strategies are illustrated with stochastic simulations based on the generalized Langevin equation in addition to unbiased molecular dynamics simulations of water permeation of a lipid bilayer.

Exploring the constitutive activation mechanism of the class A orphan GPR20.

GPR20, an orphan G protein-coupled receptor (GPCR), shows significant expression in intestinal tissue and represents a potential therapeutic target to treat gastrointestinal stromal tumors. GPR20 performs high constitutive activity when coupling with Gi. Despite the pharmacological importance of GPCR constitutive activation, determining the mechanism has long remained unclear. In this study, we explored the constitutive activation mechanism of GPR20 through large-scale unbiased molecular dynamics simulations. Our results unveil the allosteric nature of constitutively activated GPCR signal transduction involving extracellular and intracellular domains. Moreover, the constitutively active state of the GPR20 requires both the N-terminal cap and Gi protein. The N-terminal cap of GPR20 functions like an agonist and mediates long-range activated conformational shift. Together with the previous study, this study enhances our knowledge of the self-activation mechanism of the orphan receptor, facilitates the drug discovery efforts that target GPR20.

AlphaFold-SFA: Accelerated sampling of cryptic pocket opening, protein-ligand binding and allostery by AlphaFold, slow feature analysis and metadynamics.

Sampling rare events in proteins is crucial for comprehending complex phenomena like cryptic pocket opening, where transient structural changes expose new binding sites. Understanding these rare events also sheds light on protein-ligand binding and allosteric communications, where distant site interactions influence protein function. Traditional unbiased molecular dynamics simulations often fail to sample such rare events, as the free energy barrier between metastable states is large relative to the thermal energy. This renders these events inaccessible on the timescales typically simulated by unbiased molecular dynamics, limiting our understanding of these critical processes. In this paper, we proposed a novel unsupervised learning approach termed as slow feature analysis (SFA) which aims to extract slowly varying features from high-dimensional temporal data. SFA trained on small unbiased molecular dynamics simulations launched from AlphaFold generated conformational ensembles manages to capture rare events governing cryptic pocket opening, protein-ligand binding, and allosteric communications in a kinase. Metadynamics simulations using SFA as collective variables manage to sample 'deep' cryptic pocket opening within a few hundreds of nanoseconds which was beyond the reach of microsecond long unbiased molecular dynamics simulations. SFA augmented metadynamics also managed to capture conformational plasticity of protein upon ligand binding/unbinding and provided novel insights into allosteric communication in receptor-interacting protein kinase 2 (RIPK2) which dictates protein-protein interaction. Taken together, our results show how SFA acts as a dimensionality reduction tool which bridges the gap between AlphaFold, molecular dynamics simulation and metadynamics in context of capturing rare events in biomolecules, extending the scope of structure-based drug discovery in the era of AlphaFold.

Unbiased MD simulations identify lipid binding sites in lipid transfer proteins.

The characterization of lipid binding to lipid transfer proteins (LTPs) is fundamental to understand their molecular mechanism. However, several structures of LTPs, and notably those proposed to act as bridges between membranes, do not provide the precise location of their endogenous lipid ligands. To address this limitation, computational approaches are a powerful alternative methodology, but they are often limited by the high flexibility of lipid substrates. Here, we develop a protocol based on unbiased coarse-grain molecular dynamics simulations in which lipids placed away from the protein can spontaneously bind to LTPs. This approach accurately determines binding pockets in LTPs and provides a working hypothesis for the lipid entry pathway. We apply this approach to characterize lipid binding to bridge LTPs of the Vps13-Atg2 family, for which the lipid localization inside the protein is currently unknown. Overall, our work paves the way to determine binding pockets and entry pathways for several LTPs in an inexpensive, fast, and accurate manner.

Proposed dual membrane contact with full-length Osh4

Membrane contacts sites (MCSs) play important roles in lipid trafficking across cellular compartments and maintain the widespread structural diversity of organelles. We have utilized microsecond long all-atom (AA) molecular dynamics (MD) simulations and enhanced sampling techniques to unravel the MCS structure targeting by yeast oxysterol binding protein (Osh4) in an environment that mimics the interface of membranes with an increased proportion of anionic lipids using CHARMM36m forcefield with additional CUFIX parameters for lipid-protein electrostatic interactions. In a dual-membrane environment, unbiased MD simulations show that Osh4 briefly interacts with both membranes, before aligning itself with a single membrane, adopting a β-crease-bound conformation similar to observations in a single-membrane scenario. Targeted molecular dynamics simulations followed by microsecond-long AA MD simulations have revealed a distinctive dual-membrane bound state of Osh4 at MCS, wherein the protein interacts with the lower membrane via the β-crease surface, featuring its PHE-239 residue positioned below the phosphate plane of membrane, while concurrently establishing contact with the opposite membrane through the extended α6-α7 region. Osh4 maintains these dual membrane contacts simultaneously over the course of microsecond-long MD simulations. Moreover, binding energy calculations highlighted the essential roles played by the phenylalanine loop and the α6 helix in dynamically stabilizing dual-membrane bound state of Osh4 at MCS. Our computational findings were corroborated through frequency of contact analysis, showcasing excellent agreement with past experimental cross-linking data. Our computational study reveals a dual-membrane bound conformation of Osh4, providing insights into protein-membrane interactions at membrane contact sites and their relevance to lipid transfer processes.

Coupling the role of lipids to the conformational dynamics of the ABC transporter P-glycoprotein

The ATP-binding cassette transporter P-glycoprotein (P-gp) is a multidrug efflux pump that is overexpressed in a variety of cancers and associated with the drug-resistance phenomenon. P-gp structures were previously determined in detergent and in nanodiscs, in which different transmembrane helix conformations were found, “straight” and “kinked,” respectively, indicating a possible role of the lipid environment on the P-gp structural ensemble. Here, we investigate the dynamic conformational ensembles and protein-lipid interactions of two human P-gp inward-open conformers, straight and kinked, employing all-atom molecular dynamics (MD) simulations in asymmetric multicomponent lipid bilayers that mimic the highly specialized hepatocyte membrane in which P-gp is expressed. The two conformers are found to differ in terms of the accessibility of the substrate cavity. The MD simulations show how cholesterol and different lipid species wedge, snorkel, and partially enter into the cavity of the straight P-gp conformer solved in detergent. However, access to the cavity of the kinked P-gp conformer solved in nanodiscs is restricted. Furthermore, the volume and dynamic fluctuations of the substrate cavity largely differ between the two P-gp conformers and are modulated by the presence (or absence) of cholesterol in the membrane and/or of ATP. From the mechanistic perspective, the findings indicate that the straight conformer likely precedes the kinked conformer in the functional working cycle of P-gp, with the latter conformation representing a post substrate-bound state. The inaccessibility of the main transmembrane cavity in the kinked conformer might be crucial in preventing substrate disengagement and transport withdrawal. Remarkably, in our unbiased MD simulations, one transmembrane helix (TM10) of the straight conformer underwent a spontaneous transition to a kinked conformation, underlining the relevance of both conformations in a native phospholipid environment and revealing structural descriptors defining the transition between the two P-gp conformers.

Abstract PO3-26-09: A novel class of RNR inhibitors that induces genomic instability and breast cancer cell death without inducing overt toxicities

Abstract Despite the development of screening techniques and therapies, nearly 30% of breast cancers progress to Stage IV metastatic disease. These advanced cancers are highly proliferative, engaging in ongoing DNA synthesis that requires ribonucleotide reductase (RNR), the rate-limiting enzyme for dNTP synthesis. RRM2, the small subunit of RNR, activates the enzyme, increases deoxynucleotide pools, and is overexpressed in many cancers, including breast cancer. Thus, RNR is an established therapeutic target in advanced breast cancer. Currently, gemcitabine is the only FDA-approved RNR inhibitor used to treat Stage IV disease. As a nucleoside analog, gemcitabine also causes severe dose-limiting toxicities (i.e. myelosuppression and hepatotoxicity) due to off-target DNA replication termination. We hypothesize that a novel, non-nucleoside, RNR inhibitor may overcome these limitations. To address this, we developed TMU27a, a derivative of naphthyl salicylic acyl hydrazone (NSAH), which was identified in an in silico screen for its high-affinity binding to the RNR catalytic site. To measure direct interactions between TMU27a and purified RNR protein, we used surface plasmon resonance, revealing on-target, tight binding (Kd = 150 nM). Unbiased docking and molecular dynamics simulations (using lowest energy calculations) predicted the binding sight of TMU27a to be the catalytic site of RNR, where it is stabilized through extensive interactions formed with conserved residues. Preliminary docking predictions also indicate multiple clash sites between TMU27a and the DNA polymerase lambda nucleotide pocket even in the lowest energy pose (unlike gemcitabine), suggesting that TMU27a inhibits RNR activity through a novel mechanism without significant off-target binding to other nucleotide binding proteins that contribute to toxicity. Demonstrating on-target inhibition, TMU27a reduces dATP levels and induces S and G2/M-phase stalling, leading to significant DNA damage and chromosomal instability. This excessive genomic damage causes growth repression, apoptosis, and senescence in several triple negative breast cancer (TNBC) cell lines. In xenograft mouse models of TNBC, TMU27a fully suppressed tumor growth without impacting mouse weight, suggesting strong drug efficacy in the absence of overt toxicity. Moreover, while short-term exposure to gemcitabine suppresses red and white blood cell populations, TMU27a does not. Together, these data indicate that TMU27a and its potential analogs are novel, non-toxic RNR inhibitors that overcome the limitations of gemcitabine for treating breast cancer. We are currently investigating how and where TMU27a binds human RNR using cryo-electron microscopy with the goal of generating a novel class of RNR inhibitors through structure-based drug design, improving binding affinity and selectivity. Citation Format: Taylor Baker, Kristen Weber-Bonk, Wei Huang, Derek Taylor, Ruth Keri. A novel class of RNR inhibitors that induces genomic instability and breast cancer cell death without inducing overt toxicities [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-26-09.

Extracting binding energies and binding modes from biomolecular simulations of fragment binding to endothiapepsin.

Fragment-based drug discovery (FBDD) aims to discover a set of small binding fragments that may be subsequently linked together. Therefore, in-depth knowledge of the individual fragments' structural and energetic binding properties is essential. In addition to experimental techniques, the direct simulation of fragment binding by molecular dynamics (MD) simulations became popular to characterize fragment binding. However, former studies showed that long simulation times and high computational demands per fragment are needed, which limits applicability in FBDD. Here, we performed short, unbiased MD simulations of direct fragment binding to endothiapepsin, a well-characterized model system of pepsin-like aspartic proteases. To evaluate the strengths and limitations of short MD simulations for the structural and energetic characterization of fragment binding, we predicted the fragments' absolute free energies and binding poses based on the direct simulations of fragment binding and compared the predictions to experimental data. The predicted absolute free energies are in fair agreement with the experiment. Combining the MD data with binding mode predictions from molecular docking approaches helped to correctly identify the most promising fragments for further chemical optimization. Importantly, all computations and predictions were done within 5 days, suggesting that MD simulations may become a viable tool in FBDD projects.

Unlocking the specificity of antimicrobial peptide interactions for membrane-targeted therapies

Antimicrobial peptides (AMPs) are increasingly recognized as potent therapeutic agents, with their selective affinity for pathological membranes, low toxicity profile, and minimal resistance development making them particularly attractive in the pharmaceutical landscape. This study offers a comprehensive analysis of the interaction between specific AMPs, including magainin-2, pleurocidin, CM15, LL37, and clavanin, with lipid bilayer models of very different compositions that have been ordinarily used as biological membrane models of healthy mammal, cancerous, and bacterial cells. Employing unbiased molecular dynamics simulations and metadynamics techniques, we have deciphered the intricate mechanisms by which these peptides recognize pathogenic and pathologic lipid patterns and integrate into lipid assemblies. Our findings reveal that the transverse component of the peptide's hydrophobic dipole moment is critical for membrane interaction, decisively influencing the molecule's orientation and expected therapeutic efficacy. Our approach also provides insight on the kinetic and dynamic dependence on the peptide orientation in the axial and azimuthal angles when coming close to the membrane. The aim is to establish a robust framework for the rational design of peptide-based, membrane-targeted therapies, as well as effective quantitative descriptors that can facilitate the automated design of novel AMPs for these therapies using machine learning methods.

Mechanistic insights into G-protein activation via phosphorylation mediated non-canonical pathway

Activation of heterotrimeric G-proteins (Gαβγ) downstream to receptor tyrosine kinases (RTKs) is a well-established crosstalk between the signaling pathways mediated by G-protein coupled receptors (GPCRs) and RTKs. While GPCR serves as a guanine exchange factor (GEF) in the canonical activation of Gα that facilitates the exchange of GDP for GTP, the mechanism through which RTK phosphorylations induce Gα activation remains unclear. Recent experimental studies revealed that the epidermal growth factor receptor (EGFR), a well-known RTK, phosphorylates the helical domain tyrosine residues Y154 and Y155 and accelerates the GDP release from the Gαi3, a subtype of Gα-protein. Using well-tempered metadynamics and extensive unbiased molecular dynamics simulations, we captured the GDP release event and identified the intermediates between bound and unbound states through Markov state models. In addition to weakened salt bridges at the domain interface, phosphorylations induced the unfolding of helix αF, which contributed to increased flexibility near the hinge region, facilitating a greater distance between domains in the phosphorylated Gαi3. Although the larger domain separation in the phosphorylated system provided an unobstructed path for the nucleotide, the accelerated release of GDP was attributed to increased fluctuations in several conserved regions like P-loop, switch 1, and switch 2. Overall, this study provides atomistic insights into the activation of G-proteins induced by RTK phosphorylations and identifies the specific structural motifs involved in the process. The knowledge gained from the study could establish a foundation for targeting non-canonical signaling pathways and developing therapeutic strategies against the ailments associated with dysregulated G-protein signaling.

Molecular Mechanisms Underlying Medium-Chain Free Fatty Acid-Regulated Activity of the Phospholipase PlaF from Pseudomonas aeruginosa.

PlaF is a membrane-bound phospholipase A1 from Pseudomonas aeruginosa that is involved in remodeling membrane glycerophospholipids (GPLs) and modulating virulence-associated signaling and metabolic pathways. Previously, we identified the role of medium-chain free fatty acids (FFAs) in inhibiting PlaF activity and promoting homodimerization, yet the underlying molecular mechanism remained elusive. Here, we used unbiased and biased molecular dynamics simulations and free energy computations to assess how PlaF interacts with FFAs localized in the water milieu surrounding the bilayer or within the bilayer and how these interactions regulate PlaF activity. Medium-chain FFAs localized in the upper bilayer leaflet can stabilize inactive dimeric PlaF, likely through interactions with charged surface residues, as has been experimentally validated. Potential of mean force (PMF) computations indicate that membrane-bound FFAs may facilitate the activation of monomeric PlaF by lowering the activation barrier for changing into a tilted, active configuration. We estimated that the coupled equilibria of PlaF monomerization-dimerization and tilting at the physiological concentration of PlaF lead to the majority of PlaF forming inactive dimers when in a cell membrane loaded with decanoic acid (C10). This is in agreement with a suggested in vivo product feedback loop and gas chromatography-mass spectrometry profiling results, indicating that PlaF catalyzes the release of C10 from P. aeruginosa membranes. Additionally, we found that C10 in the water milieu can access the catalytic site of active monomeric PlaF, contributing to the competitive component of C10-mediated PlaF inhibition. Our study provides mechanistic insights into how medium-chain FFAs may regulate the activity of PlaF, a potential bacterial drug target.

Tepotinib and tivantinib as potential inhibitors for the serine/threonine kinase of the mpox virus: insights from structural bioinformatics analysis

The serine/threonine kinase (STK) plays a central role as the primary kinase in poxviruses, directing phosphoryl transfer reactions. Such reactions are pivotal for the activation of certain proteins during viral replication, assembly, and maturation. Therefore, targeting this key protein is anticipated to impede virus replication. In this work, a structural bioinformatics approach was employed to evaluate the potential of drug-like kinase inhibitors in binding to the ATP-binding pocket on the STK of the Mpox virus. Virtual screening of known kinase inhibitors revealed that the top 10 inhibitors exhibited binding affinities ranging from −8.59 to −12.05 kcal/mol. The rescoring of compounds using the deep-learning default model in GNINA was performed to predict accurate binding poses. Subsequently, the top three inhibitors underwent unbiased molecular dynamics (MD) simulations for 100 ns. Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) analysis and Principal Component Analysis (PCA) suggested tepotinib as a competitive inhibitor for Mpox virus STK as evidenced by its binding free energy and the induction of similar conformational behavior of the enzyme. Nevertheless, it is sensible to experimentally test all top 10 compounds, as scoring functions and energy calculations may not consistently align with experimental findings. These insights are poised to provide an attempt to identify an effective inhibitor for the Mpox virus. Communicated by Ramaswamy H. Sarma

Free energy profile of the substrate-induced occlusion of the human serotonin transporter.

The serotonin transporter (SERT) is a member of the Solute Carrier 6 (SLC6) family and is responsible for maintaining the appropriate level of serotonin in the brain. Dysfunction of SERT has been linked to several neuropsychiatric disorders, including depression, anxiety and obsessive-compulsive disorder. Therefore, an in-depth understanding of the mechanism on an atomistic level, coupled with a quantification of transporter dynamics and the associated free energies is required. Here, we constructed Markov state models (MSMs) from extensive unbiased molecular dynamics simulations to quantify the free energy profile of serotonin (5HT) triggered SERT occlusion and explored the driving forces of the mechanism of occlusion. Our results reveal that SERT occludes via multiple intermediate conformations and show that the motion of occlusion is energetically downhill for the 5HT-bound transporter. Force distribution analyses show that the interactions of 5HT with the bundle domain are crucial. During occlusion, attractive forces steadily increase and pull on the bundle domain, which leads to SERT occlusion. Some interactions become repulsive upon full occlusion, suggesting that SERT creates pressure on 5HT to promote its movement towards the cytosol.

Therapeutic Peptides Are Preferentially Solubilized in Specific Microenvironments within PEG-PLGA Polymer Nanoparticles.

Polymeric nanoparticles are a highly promising drug delivery formulation. However, a lack of understanding of the molecular mechanisms that underlie their drug solubilization and controlled release capabilities has hindered the efficient clinical translation of such technologies. Polyethylene glycol-poly(lactic-co-glycolic) acid (PEG-PLGA) nanoparticles have been widely studied as cancer drug delivery vehicles. In this letter, we use unbiased coarse-grained molecular dynamics simulations to model the self-assembly of a PEG-PLGA nanoparticle and its solubulization of the anticancer peptide, EEK, with good agreement with previously reported experimental structural data. We applied unsupervised machine learning techniques to quantify the conformations that polymers adopt at various locations within the nanoparticle. We find that the local microenvironments formed by the various polymer conformations promote preferential EEK solubilization within specific regions of the NP. This demonstrates that these microenvironments are key in controlling drug storage locations within nanoparticles, supporting the rational design of nanoparticles for therapeutic applications.

The density-threshold affinity:Calculating lipid binding affinities from unbiased coarse-grained molecular dynamics simulations.

Many membrane proteins are sensitive to their local lipid environment. As structural methods for membrane proteins have improved, there is growing evidence of direct, specific binding of lipids to protein surfaces. Unfortunately the workhorse of understanding protein-small molecule interactions, the binding affinity for a given site, is experimentally inaccessible for these systems. Coarse-grained molecular dynamics simulations can be used to bridge this gap, and are relatively straightforward to learn. Such simulations allow users to observe spontaneous binding of lipids to membrane proteins and quantify localized densities of individual lipids or lipid fragments. In this chapter we outline a protocol for extracting binding affinities from these localized distributions, known as the "density threshold affinity." The density threshold affinity uses an adaptive and flexible definition of site occupancy that alleviates the need to distinguish between "bound'' lipids and bulk lipids that are simply diffusing through the site. Furthermore, the method allows "bead-level" resolution that is suitable for the case where lipids share binding sites, and circumvents ambiguities about a relevant reference state. This approach provides a convenient and straightforward method for comparing affinities of a single lipid species for multiple sites, multiple lipids for a single site, and/or a single lipid species modeled using multiple forcefields.

Transfer learning of hyperparameters for fast construction of anisotropic GPR models: design and application to the machine-learned force field FFLUX.

The polarisable machine-learned force field FFLUX requires pre-trained anisotropic Gaussian process regression (GPR) models of atomic energies and multipole moments to propagate unbiased molecular dynamics simulations. The outcome of FFLUX simulations is highly dependent on the predictive accuracy of the underlying models whose training entails determining the optimal set of model hyperparameters. Unfortunately, traditional direct learning (DL) procedures do not scale well on this task, especially when the hyperparameter search is initiated from a (set of) random guess solution(s). Additionally, the complexity of the hyperparameter space (HS) increases with the number of geometrical input features, at least for anisotropic kernels, making the optimization of hyperparameters even more challenging. In this study, we propose a transfer learning (TL) protocol that accelerates the training process of anisotropic GPR models by facilitating access to promising regions of the HS. The protocol is based on a seeding-relaxation mechanism in which an excellent guess solution is identified by rapidly building one or several small source models over a subset of the target training set before readjusting the previous guess over the entire set. We demonstrate the performance of this protocol by building and assessing the performance of DL and TL models of atomic energies and charges in various conformations of benzene, ethanol, formic acid dimer and the drug fomepizole. Our experiments suggest that TL models can be built one order of magnitude faster while preserving the quality of their DL analogs. Most importantly, when deployed in FFLUX simulations, TL models compete with or even outperform their DL analogs when it comes to performing FFLUX geometry optimization and computing harmonic vibrational modes.

Binding of Cholesterol to the N-Terminal Domain of the NPC1L1 Transporter: Analysis of the Epimerization-Related Binding Selectivity and Loop Mutations.

Cholesterol is a fat-like substance with a pivotal physiological relevance in humans, and its homeostasis is tightly regulated by various cellular processes, including the import in the small intestine and the reabsorption in the biliary ducts by the Niemann-Pick C1 Like 1 (NPC1L1) importer. NPC1L1 can mediate the absorption of a variety of sterols but strikingly exhibits a large sensitivity to cholesterol epimerization. This study examines the molecular basis of the epimerization-related selective binding of cholesterol by combining extended unbiased molecular dynamics simulations of the apo and holo species of the N-terminal domain of wild-type NPC1L1, in conjunction with relative binding free energy, umbrella sampling, and well-tempered metadynamics calculations. The analysis of the results discloses the existence of two distinct binding modes for cholesterol and epi-cholesterol. The former binds deeper in the cavity, forming key hydrogen-bond interactions with Q95, S56, and a water molecule. In contrast, epi-cholesterol is shifted ca. 3 Å to the mouth of the cavity and the transition to the Q95 site is prevented by an energetic barrier of 4.1 kcal·mol-1. Thus, the configuration of the hydroxyl group of cholesterol, together with the presence of a structural water molecule, is a key feature for effective absorption. Finally, whereas these findings may seemingly be challenged by single-point mutations that impair cholesterol transport but have a mild impact on the binding of cholesterol to the Q95 binding site, our results reveal that they have a drastic influence on the conformational landscape of the α8/β7 loop in the apo species compared to the wild-type protein. Overall, the results give support to the functional role played by the α8/β7 loop in regulating the access of ligands to NPC1L1, and hence to interpreting the impact of these mutations on diseases related to disruption of sterol absorption, paving the way to understanding certain physiological dysfunctions.

Decrypting Allostery in Membrane-Bound K-Ras4B Using Complementary In Silico Approaches Based on Unbiased Molecular Dynamics Simulations.

Protein functions are dynamically regulated by allostery, which enables conformational communication even between faraway residues, and expresses itself in many forms, akin to different "languages": allosteric control pathways predominating in an unperturbed protein are often unintuitively reshaped whenever biochemical perturbations arise (e.g., mutations). To accurately model allostery, unbiased molecular dynamics (MD) simulations require integration with a reliable method able to, e.g., detect incipient allosteric changes or likely perturbation pathways; this is because allostery can operate at longer time scales than those accessible by plain MD. Such methods are typically applied singularly, but we here argue their joint application─as a "multilingual" approach─could work significantly better. We successfully prove this through unbiased MD simulations (∼100 μs) of the widely studied, allosterically active oncotarget K-Ras4B, solvated and embedded in a phospholipid membrane, from which we decrypt allostery using four showcase "languages": Distance Fluctuation analysis and the Shortest Path Map capture allosteric hotspots at equilibrium; Anisotropic Thermal Diffusion and Dynamical Non-Equilibrium MD simulations assess perturbations upon, respectively, either superheating or hydrolyzing the GTP that oncogenically activates K-Ras4B. Chosen "languages" work synergistically, providing an articulate, mutually coherent, experimentally consistent picture of K-Ras4B allostery, whereby distinct traits emerge at equilibrium and upon GTP cleavage. At equilibrium, combined evidence confirms prominent allosteric communication from the membrane-embedded hypervariable region, through a hub comprising helix α5 and sheet β5, and up to the active site, encompassing allosteric "switches" I and II (marginally), and two proposed pockets. Upon GTP cleavage, allosteric perturbations mostly accumulate on the switches and documented interfaces.

Deciphering insights into the binding mechanism and plasticity of Telacebec with M. tuberculosis cytochrome bcc-aa3 supercomplex through an unbiased molecular dynamics simulation, free-energy analysis, and DFT study

The cytochrome bcc-aa3 supercomplex, a key component in the electron transport chain pathway involved in bacterial energy production and homeostasis, is a clinically validated target for tuberculosis (TB), leading to Telacebec (Q203). Telacebec is a potent candidate drug under Phase II clinical development for the treatment of drug-sensitive and drug-resistant TB. Recently, the cryo-electron microscopy structure of this supercomplex from Mycobacterium tuberculosis (Mtb) complexed with Q203 was resolved at 6.9 Å resolution (PDB ID: 7E1W). To understand the binding site (QP site) flexibility and Q203’s stability at the QP site of the Mtb cytochrome bcc complex, we conducted molecular dynamics (MD) simulation and free energy analysis on this complex in an explicit hydrated lipid bilayer environment for 500 ns. Through this study, the persistence of a range of direct and indirect interactions was observed over the course of the simulation. The significance of the interactions with His375, Tyr161, Ala178, Ala179, Ile183, His355, Leu356, and Thr313 is underlined. Electrostatic energy was the primary source of the net binding free energy, regardless of the important interacting residues. The overall binding free energy for Q203 was −112.84 ± 7.73 kcal/mol, of which the electrostatic and lipophilic energy contributions were −116.31 ± 1.14 and −21.32 ± 2.35 kcal/mol, respectively. Meanwhile, DFT calculations were utilized to elucidate Q203’s molecular properties. Overall, this study deciphers key insights into the cytochrome bcc-aa3 supercomplex with Q203 on the ground of molecular mechanics and quantum mechanics that may facilitate structure-based drug design and optimization for the discovery of the next-generation antitubercular drug(s). Communicated by Ramaswamy H. Sarma

Dynamics behavior of PE and PET oligomers in lipid bilayer simulations

In recent years many investigators have been concerned about the toxicity and potential health hazards of micro- and nanoplastics. However, we are still lacking a good understanding of the methods of their transport into the human body and subsequently within cells. This is especially true at the lower nanometer scale; these particles are potentially more dangerous than their micrometer counterparts due to their easier permeation into cells. In this study we used both unbiased molecular dynamics simulations and steered umbrella sampling simulations to explore the interactions of polyethylene terephthalate (PET) and polyethylene (PE) oligomers in phospholipid bilayers. Our simulations revealed that the bilayers did not represent significant energy barriers to the small oligomers; not only did they readily enter the cell membrane but they also became concentrated into specific parts of the membrane. The larger PET tetramers exhibited a strong aggregation in water but were the least likely to permeate through or into the membranes. It is possible that PE monomers and tetramers can become concentrated into membranes while PET monomers are more likely to pass through or concentrate just inside the membrane surface. Passive transport of microplastics into cells is, however, likely limited to particles of a few nanometers in diameter.