Principal Component Analysis of Lipid Molecule Conformational Changes in Molecular Dynamics Simulations

A review on description dynamics and conformational changes of proteins using combination of principal component analysis and molecular dynamics simulation

Understanding how proteins behave dynamically and undergo conformational changes is essential to comprehending their biological roles. This review article examines the potent tool of using Molecular Dynamics simulations in conjunction with Principal Component Analysis (PCA) to explore protein dynamics. Molecular dynamics data can be made easier to read by removing prominent patterns through the use of PCA, a sophisticated dimensionality reduction approach. Researchers can obtain critical insights into the fundamental principles governing protein function by using PCA on MD simulation data. We provide a systematic approach to PCA that includes data collection, input coordinate selection, and result interpretation. Protein collective movements and fundamental dynamics are made visible by PCA, which makes it possible to identify conformational substates that are crucial to function. By means of principal component analysis, scientists are able to observe and measure large-scale movements, like hinge bending and domain motions, as well as pinpoint areas of protein structural stiffness and flexibility. Moreover, PCA allows temporal separation, distinguishing slower global motions from faster local changes. A strong foundation for researching protein dynamics is provided by the combination of PCA and Molecular Dynamics simulations, which have applications in drug development and enhance our comprehension of intricate biological systems.

Evaluating Polarizable Biomembrane Simulations against Experiments

Owing to the increase of available computational capabilities and the potential for providing a more accurate description, polarizable molecular dynamics force fields are gaining popularity in modeling biomolecular systems. It is, however, crucial to evaluate how much precision is truly gained with increasing cost and complexity of the simulation. Here, we leverage the NMRlipids open collaboration and Databank to assess the performance of available polarizable lipid models─the CHARMM-Drude and the AMOEBA-based parameters─against high-fidelity experimental data and compare them to the top-performing nonpolarizable models. While some improvement in the description of ion binding to membranes is observed in the most recent CHARMM-Drude parameters, and the conformational dynamics of AMOEBA-based parameters are excellent, the best nonpolarizable models tend to outperform their polarizable counterparts for each property we explored. The identified shortcomings range from inaccuracies in describing the conformational space of lipids to excessively slow conformational dynamics. Our results provide valuable insights for the further refinement of polarizable lipid force fields and for selecting the best simulation parameters for specific applications.

Concurrent Optimizations of Efficacy and Blood-Brain Barrier Permeability in New Macrocyclic LRRK2 Inhibitors for Potential Parkinson's Disease Therapeutics

The elevated activity of leucine-rich repeat kinase 2 (LRRK2) is implicated in the pathogenesis of Parkinson's disease (PD). The quest for effective LRRK2 inhibitors has been impeded by the formidable challenge of crossing the blood-brain barrier (BBB). We leveraged structure-based de novo design and developed robust three-dimensional quantitative structure-activity relationship (3D-QSAR) models to predict BBB permeability, enhancing the likelihood of the inhibitor's brain accessibility. Our strategy involved the synthesis of macrocyclic molecules by linking the two terminal nitrogen atoms of HG-10-102-01 with an alkyl chain ranging from 2 to 4 units, laying the groundwork for innovative LRRK2 inhibitor designs. Through meticulous computational and synthetic optimization of both biochemical efficacy and BBB permeability, 9 out of 14 synthesized candidates demonstrated potent low-nanomolar inhibition and significant BBB penetration. Further assessments of in vitro and in vivo effectiveness, coupled with pharmacological profiling, highlighted 8 as the promising new lead compound for PD therapeutics.

The Structural Features of MlaD Illuminate its Unique Ligand-Transporting Mechanism and Ancestry

The membrane-associated solute-binding protein (SBP) MlaD of the maintenance of lipid asymmetry (Mla) system has been reported to help the transport of phospholipids (PLs) between the outer and inner membranes of Gram-negative bacteria. Despite the availability of structural information, the molecular mechanism underlying the transport of PLs and the ancestry of the protein MlaD remain unclear. In this study, we report the crystal structures of the periplasmic region of MlaD from Escherichia coli (EcMlaD) at a resolution range of 2.3-3.2 Å. The EcMlaD protomer consists of two distinct regions, viz. N-terminal β-barrel fold consisting of seven strands (referred to as MlaD domain) and C-terminal α-helical domain (HD). The protein EcMlaD oligomerizes to give rise to a homo-hexameric ring with a central channel that is hydrophobic and continuous with a variable diameter. Interestingly, the structural analysis revealed that the HD, instead of the MlaD domain, plays a critical role in determining the oligomeric state of the protein. Based on the analysis of available structural information, we propose a working mechanism of PL transport, viz. "asymmetric protomer movement (APM)". Wherein half of the EcMlaD hexamer would rise in the periplasmic side along with an outward movement of pore loops, resulting in the change of the central channel geometry. Furthermore, this study highlights that, unlike typical SBPs, EcMlaD possesses a fold similar to EF/AMT-type beta(6)-barrel and a unique ancestry. Altogether, the findings firmly establish EcMlaD to be a non-canonical SBP with a unique ligand-transport mechanism.

Fed-batch fermentation of Mucor circinelloides reveals significant improvement in biomass and lipid accumulation through performance evaluation, chemical analysis, and expression profiling

This study aimed to improve the lipid and biomass yields of Mucor circinelloides WJ11 by implementing four different fed-batch fermentation strategies, varied in time and glucose concentration (S1-S4). The S1 fermentation strategy yielded the highest biomass, lipid, and fatty acid content (22 ± 0.7 g/L, 53 ± 1.2 %, and 28 ± 1.6 %) after 120 and 144 h, respectively. The γ-linolenic acid titer of 0.75 ± 0.0 g/L was greatest in S3 after 48 h. Quantitative reverse transcription polymerase chain reaction (RT-qPCR) was used to analyze the transcription of key genes involved in lipid accumulation. The glucose-6-phosphate dehydrogenase, 6-phosphogluconate dehydrogenase, and ATP-citrate lyase genes showed increased expression levels. Fourier-transform infrared (FTIR) spectroscopy was used to analyze the biochemical profile during fermentation strategies. Optimal abiotic factors for production efficiency included pH 6.5, 25-26 °C, 15 % (v/v) inoculum, 500 rpm, 20 %-30 % dissolved oxygen, and 120 h fermentation. Glucose co-feeding offers valuable insights to develop effective fermentation strategies for lipid production.

Overlay databank unlocks data-driven analyses of biomolecules for all

Tools based on artificial intelligence (AI) are currently revolutionising many fields, yet their applications are often limited by the lack of suitable training data in programmatically accessible format. Here we propose an effective solution to make data scattered in various locations and formats accessible for data-driven and machine learning applications using the overlay databank format. To demonstrate the practical relevance of such approach, we present the NMRlipids Databank-a community-driven, open-for-all database featuring programmatic access to quality-evaluated atom-resolution molecular dynamics simulations of cellular membranes. Cellular membrane lipid composition is implicated in diseases and controls major biological functions, but membranes are difficult to study experimentally due to their intrinsic disorder and complex phase behaviour. While MD simulations have been useful in understanding membrane systems, they require significant computational resources and often suffer from inaccuracies in model parameters. Here, we demonstrate how programmable interface for flexible implementation of data-driven and machine learning applications, and rapid access to simulation data through a graphical user interface, unlock possibilities beyond current MD simulation and experimental studies to understand cellular membranes. The proposed overlay databank concept can be further applied to other biomolecules, as well as in other fields where similar barriers hinder the AI revolution.

Machine Learning-Assisted Computational Screening of Adhesive Molecules Derived from Dihydroxyphenyl Alanine

Marine mussels adhere to virtually any surface via 3,4-dihydroxyphenyl-L-alanines (L-DOPA), an amino acid largely contained in their foot proteins. The biofriendly, water-repellent, and strong adhesion of L-DOPA are unparalleled by any synthetic adhesive. Inspired by this, we computationally designed diverse derivatives of DOPA and studied their potential as adhesives or coating materials. We used first-principles calculations to investigate the adsorption of the DOPA derivatives on graphite. The presence of an electron-withdrawing group, such as nitrogen dioxide, strengthens the adsorption by increasing the π-π interaction between DOPA and graphite. To quantify the distribution of electron charge and to gain insights into the charge distribution at interfaces, we performed Bader charge analysis and examined charge density difference plots. We developed a quantitative structure-property relationship (QSPR) model using an artificial neural network (ANN) to predict the adsorption energy. Using the three-dimensional and quantum mechanical electrostatic potential of a molecule as a descriptor, the present quantum NN model shows promising performance as a predictive QSPR model.

Unravel the Tangle: Atomistic Insight into Ultrahigh Curcumin-Loaded Polymer Micelles

Amphiphilic ABA-triblock copolymers, comprised of poly(2-oxazoline) and poly(2-oxazine), can solubilize poorly water-soluble molecules in a structure-dependent manner forming micelles with exceptionally high drug loading. All-atom molecular dynamics simulations are conducted on previously experimentally characterized, curcumin-loaded micelles to dissect the structure-property relationships. Polymer-drug interactions for different levels of drug loading and variation in polymer structures of both the inner hydrophobic core and outer hydrophilic shell are investigated. In silico, the system with the highest experimental loading capacity shows the highest number of drug molecules encapsulated by the core. Furthermore, in systems with lower loading capacity outer A blocks show a greater extent of entanglement with the inner B blocks. Hydrogen bond analyses corroborate previous hypotheses: poly(2-butyl-2-oxazoline) B blocks, found experimentally to have reduced loading capacity for curcumin compared to poly(2-propyl-2-oxazine), establish fewer but longer-lasting hydrogen bonds. This possibly results from different sidechain conformations around the hydrophobic cargo, which is investigated by unsupervised machine learning to cluster monomers in smaller model systems mimicking different micelle compartments. Exchanging poly(2-methyl-2-oxazoline) with poly(2-ethyl-2-oxazoline) leads to increased drug interactions and reduced corona hydration; this suggests an impairment of micelle solubility or colloidal stability. These observations can help driving forward a more rational a priori nanoformulation design.

Derivation of Highly Predictive 3D-QSAR Models for hERG Channel Blockers Based on the Quantum Artificial Neural Network Algorithm

The hERG potassium channel serves as an annexed target for drug discovery because the associated off-target inhibitory activity may cause serious cardiotoxicity. Quantitative structure-activity relationship (QSAR) models were developed to predict inhibitory activities against the hERG potassium channel, utilizing the three-dimensional (3D) distribution of quantum mechanical electrostatic potential (ESP) as the molecular descriptor. To prepare the optimal atomic coordinates of dataset molecules, pairwise 3D structural alignments were carried out in order for the quantum mechanical cross correlation between the template and other molecules to be maximized. This alignment method stands out from the common atom-by-atom matching technique, as it can handle structurally diverse molecules as effectively as chemical derivatives that share an identical scaffold. The alignment problem prevalent in 3D-QSAR methods was ameliorated substantially by dividing the dataset molecules into seven subsets, each of which contained molecules with similar molecular weights. Using an artificial neural network algorithm to find the functional relationship between the quantum mechanical ESP descriptors and the experimental hERG inhibitory activities, highly predictive 3D-QSAR models were derived for all seven molecular subsets to the extent that the squared correlation coefficients exceeded 0.79. Given their simplicity in model development and strong predictability, the 3D-QSAR models developed in this study are expected to function as an effective virtual screening tool for assessing the potential cardiotoxicity of drug candidate molecules.

Elucidating lipid conformations in the ripple phase: Machine learning reveals four lipid populations

A new mixed radial-angular, three-particle correlation function method in combination with unsupervised machine learning was applied to examine the emergence of the ripple phase in dipalmitoylphosphatidylcholine (DPPC) lipid bilayers using data from atomistic molecular dynamics simulations of system sizes ranging from 128 to 4096 lipids. Based on the acyl tail conformations, the analysis revealed the presence of four distinct conformational populations of lipids in the ripple phases of the DPPC lipid bilayers. The expected gel-like (ordered; Lo) and fluid-like (disordered; Ld) lipids are found along with their splayed tail equivalents (Lo,s and Ld,s). These lipids differ, based on their gauche distribution and tail packing. The disordered (Ld) and disordered-splayed (Ld,s) lipids spatially cluster in the ripple in the groove side, that is, in an asymmetric manner across the bilayer leaflets. The ripple phase does not contain large numbers of Ld lipids; instead they only exist on the interface of the groove side of the undulation. The bulk of the groove side is a complex coexistence of Lo,Lo,s, and Ld,s lipids. The convex side of the undulation contains predominantly Lo lipids. Thus, the structure of the ripple phase is neither a simple coexistence of ordered and disordered lipids nor a coexistence of ordered interdigitating gel-like (Lo) and ordered-splayed (Lo,s) lipids, but instead a coexistence of an ordered phase and a complex mixed phase. Principal component analysis further confirmed the existence of the four lipid groups.

Best Practices in Constant pH MD Simulations: Accuracy and Sampling

Various approaches have been proposed to include the effect of pH in molecular dynamics (MD) simulations. Among these, the λ-dynamics approach proposed by Brooks and co-workers [Kong, X.; Brooks III, C. L. J. Chem. Phys.1996, 105, 2414−2423] can be performed with little computational overhead and hfor each typeence be used to routinely perform MD simulations at microsecond time scales, as shown in the accompanying paper [Aho, N. et al. J. Chem. Theory Comput.2022, DOI: 10.1021/acs.jctc.2c00516]. At such time scales, however, the accuracy of the molecular mechanics force field and the parametrization becomes critical. Here, we address these issues and provide the community with guidelines on how to set up and perform long time scale constant pH MD simulations. We found that barriers associated with the torsions of side chains in the CHARMM36m force field are too high for reaching convergence in constant pH MD simulations on microsecond time scales. To avoid the high computational cost of extending the sampling, we propose small modifications to the force field to selectively reduce the torsional barriers. We demonstrate that with such modifications we obtain converged distributions of both protonation and torsional degrees of freedom and hence consistent pK_a estimates, while the sampling of the overall configurational space accessible to proteins is unaffected as compared to normal MD simulations. We also show that the results of constant pH MD depend on the accuracy of the correction potentials. While these potentials are typically obtained by fitting a low-order polynomial to calculated free energy profiles, we find that higher order fits are essential to provide accurate and consistent results. By resolving problems in accuracy and sampling, the work described in this and the accompanying paper paves the way to the widespread application of constant pH MD beyond pK_a prediction.

High-pressure crystallography shows noble gas intervention into protein-lipid interaction and suggests a model for anaesthetic action

In this work we examine how small hydrophobic molecules such as inert gases interact with membrane proteins (MPs) at a molecular level. High pressure atmospheres of argon and krypton were used to produce noble gas derivatives of crystals of three well studied MPs (two different proton pumps and a sodium light-driven ion pump). The structures obtained using X-ray crystallography showed that the vast majority of argon and krypton binding sites were located on the outer hydrophobic surface of the MPs – a surface usually accommodating hydrophobic chains of annular lipids (which are known structural and functional determinants for MPs). In conformity with these results, supplementary in silico molecular dynamics (MD) analysis predicted even greater numbers of argon and krypton binding positions on MP surface within the bilayer. These results indicate a potential importance of such interactions, particularly as related to the phenomenon of noble gas-induced anaesthesia.

Noble gases are known to interact with proteins and can be good anaesthetics in hyperbaric conditions. This study identifies argon and krypton binding sites on membrane proteins and proposes as a hypothesis that noble gases, by altering protein/lipid contacts, may affect protein function.

The stressed life of a lipid in the Zika virus membrane

Protein-lipid interactions modulate a plethora of physiopathologic processes and have been the subject of countless studies. However, these kinds of interactions in the context of viral envelopes have remained relatively unexplored, partially because the intrinsically small dimensions of the molecular systems escape to the current resolution of experimental techniques. However, coarse-grained and multiscale simulations may fill that niche, providing nearly atomistic resolution at an affordable computational price. Here we use multiscale simulations to characterize the lipid-protein interactions in the envelope of the Zika Virus, a prominent member of the Flavivirus genus. Comparisons between the viral envelope and simpler molecular systems indicate that the viral membrane is under extreme pressures and asymmetric forces. Furthermore, the dense net of protein-protein contacts established by the envelope proteins creates poorly solvated regions that destabilize the external leaflet leading to a decoupled dynamics between both membrane layers. These findings lead to the idea that the Flaviviral membrane may store a significant amount of elastic energy, playing an active role in the membrane fusion process.

Biophysical Impact of Lipid A Modification Caused by Mobile Colistin Resistance Gene on Bacterial Outer Membranes

Expression of mobile colistin resistance gene mcr-1 results in the addition of phosphoethanolamine (pEtN) to the lipid A headgroup in the bacterial outer membrane (OM) of Gram-negative bacteria, increasing the resistance to the last-line polymyxins. However, the potential biological consequences of such modification remain unclear. Using coarse-grained molecular simulations with quantitative lipidomics models, we discovered pEtN modification of the lipid A headgroup caused substantial changes to the morphology and physicochemical properties of the OM. Single-lipid level structural and energetic analyses revealed that this modification resulted in lipid A-pEtN adopting an abnormally twisted and slanted conformation with a closer packing state because of strengthened inter-lipid attraction. The consequent accumulation of lipid A-pEtN produced a negative curvature of the OM and altered the membrane's tension, fluidity, and rigidity. Our results provide a key mechanistic connection between mcr-1 expression and biophysical changes in the bacterial OM.

Quantum Artificial Neural Network Approach to Derive a Highly Predictive 3D-QSAR Model for Blood-Brain Barrier Passage

A successful passage of the blood–brain barrier (BBB) is an essential prerequisite for the drug molecules designed to act on the central nervous system. The logarithm of blood–brain partitioning (LogBB) has served as an effective index of molecular BBB permeability. Using the three-dimensional (3D) distribution of the molecular electrostatic potential (ESP) as the numerical descriptor, a quantitative structure-activity relationship (QSAR) model termed AlphaQ was derived to predict the molecular LogBB values. To obtain the optimal atomic coordinates of the molecules under investigation, the pairwise 3D structural alignments were conducted in such a way to maximize the quantum mechanical cross correlation between the template and a target molecule. This alignment method has the advantage over the conventional atom-by-atom matching protocol in that the structurally diverse molecules can be analyzed as rigorously as the chemical derivatives with the same scaffold. The inaccuracy problem in the 3D structural alignment was alleviated in a large part by categorizing the molecules into the eight subsets according to the molecular weight. By applying the artificial neural network algorithm to associate the fully quantum mechanical ESP descriptors with the extensive experimental LogBB data, a highly predictive 3D-QSAR model was derived for each molecular subset with a squared correlation coefficient larger than 0.8. Due to the simplicity in model building and the high predictability, AlphaQ is anticipated to serve as an effective computational screening tool for molecular BBB permeability.

Inverse Conformational Selection in Lipid-Protein Binding

Interest in lipid interactions with proteins and other biomolecules is emerging not only in fundamental biochemistry but also in the field of nanobiotechnology where lipids are commonly used, for example, in carriers of mRNA vaccines. The outward-facing components of cellular membranes and lipid nanoparticles, the lipid headgroups, regulate membrane interactions with approaching substances, such as proteins, drugs, RNA, or viruses. Because lipid headgroup conformational ensembles have not been experimentally determined in physiologically relevant conditions, an essential question about their interactions with other biomolecules remains unanswered: Do headgroups exchange between a few rigid structures, or fluctuate freely across a practically continuous spectrum of conformations? Here, we combine solid-state NMR experiments and molecular dynamics simulations from the NMRlipids Project to resolve the conformational ensembles of headgroups of four key lipid types in various biologically relevant conditions. We find that lipid headgroups sample a wide range of overlapping conformations in both neutral and charged cellular membranes, and that differences in the headgroup chemistry manifest only in probability distributions of conformations. Furthermore, the analysis of 894 protein-bound lipid structures from the Protein Data Bank suggests that lipids can bind to proteins in a wide range of conformations, which are not limited by the headgroup chemistry. We propose that lipids can select a suitable headgroup conformation from the wide range available to them to fit the various binding sites in proteins. The proposed inverse conformational selection model will extend also to lipid binding to targets other than proteins, such as drugs, RNA, and viruses.

A High-Resolution Ternary Model Demonstrates How PEGylated 2D Nanomaterial Stimulates Integrin αv β8 on Cell Membrane

Bio-nano interfaces are integral to all applications of nanomaterials in biomedicine. In addition to peptide-ligand-functionalized nanomaterials, passivation on 2D nanomaterials has emerged as a new regulatory factor for integrin activation. However, the mechanisms underlying such ligand-independent processes are poorly understood. Here, using graphene oxide passivated with polyethylene glycol (GO-PEG) as a test bed, a ternary simulation model is constructed that also includes a membrane and both subunits of integrin αv β8 to characterize GO-PEG-mediated integrin activation on the cell membrane in a ligand-independent manner. Combined with the experimental findings, production simulations of the ternary model show a three-phase mechanotransduction process in the vertical interaction mode. Specifically, GO-PEG first induces lipid aggregation-mediated integrin proximity, followed by transmembrane domain rotation and separation, leading to the extension and activation of extracellular domains. Thus, this study presents a complete picture of the interaction between passivated 2D nanomaterials and cell membranes to mediate integrin activation, and provides insights into the potential de novo design and rational use of novel desirable nanomaterials at diverse bio-nano interfaces.

Two Coexisting Membrane Structures Are Defined by Lateral and Transbilayer Interactions between Sphingomyelin and Cholesterol

The structure of fully hydrated bilayers composed of equimolar proportions of palmitoylsphingomyelin (PSM) and cholesterol has been examined by synchrotron X-ray powder diffraction and atomistic molecular dynamics (MD) simulations. Two coexisting bilayer structures, which are distinguished by the transbilayer phosphate-phosphate distance of coupled PSM molecules, are observed by diffraction at 37 °C. The MD simulations reveal that PSM molecules in the thicker membrane are characterized by more ordered, more extended, and less interdigitated hydrocarbon tails compared to those in the thinner membrane. Intermolecular hydrogen bonds further distinguish the two bilayer structures, and we observe the disruption of a sphingomyelin intermolecular hydrogen bond network induced by the proximity of cholesterol. Through an unsupervised clustering of interatomic distances, we show for the first time that the asymmetry of phospholipids is important in driving their interactions with cholesterol. We identify four distinct modes of interaction, two of which lead to the dehydration of cholesterol. These two modes of interaction provide the first description of precise physical mechanisms underlying the umbrella model, which itself explains how phospholipids may shield cholesterol from water. The most dehydrating mode of interaction is particular to the N-acylated fatty acid moiety of PSM and thus may explain the long-held observation that cholesterol preferentially mixes with sphingomyelins over glycerophospholipids.

Principal component analysis highlights the influence of temperature, curvature and cholesterol on conformational dynamics of lipids

Membrane lipids are inherently highly dynamic molecules. Currently, it is difficult to probe the structures of individual lipids experimentally at the timescales corresponding to atomic motions, and consequently molecular dynamics simulations are used widely. In our previous work, we have introduced the principal component analysis (PCA) as a convenient framework for comprehensive quantitative description of lipid motions. Here, we present a newly developed open source script, PCAlipids, which automates the analysis and allows us to refine the approach and test its limitations. We use PCAlipids to determine the influence of temperature, cholesterol and curvature on individual lipids, and show that the most prominent lipid tail scissoring motion is strongly affected by these factors and allows tracking of phase transition. Addition of cholesterol affects the conformations and selectively changes the dynamics of lipid molecules, impacting the large-amplitude motions. Introduction of curvature biases the conformational ensembles towards more extended structures. We hope that the developed approach will be useful for understanding the molecular basis of different processes occurring in lipid membrane systems and will stimulate development of complementary experimental techniques probing the conformations of individual lipid molecules.

Assembly of Spinach Chloroplast ATP Synthase Rotor Ring Protein-Lipid Complex

Rotor ATPases are large multisubunit membrane protein complexes found in all kingdoms of life. The membrane parts of these ATPases include a ring-like assembly, so-called c-ring, consisting of several subunits c, plugged by a patch of phospholipids. In this report, we use a nature-inspired approach to model the assembly of the spinach (Spinacia oleracea) c₁₄ ring protein-lipid complex, where partially assembled oligomers are pulled toward each other using a biasing potential. The resulting assemblies contain 23 to 26 encapsulated plug lipids, general position of which corresponds well to experimental maps. However, best fit to experimental data is achieved with 15 to 17 lipids inside the c-ring. In all of the simulations, the lipids from one leaflet (loop side of the c subunit) are ordered and static, whereas the lipids from the other leaflet are disordered and dynamic. Spontaneous permeation of water molecules toward Glu61 at the active site is also observed. The presented assembly approach is expected to be generalizable to other protein complexes with encapsulated lipid patches.

Design, synthesis, biological evaluation and molecular dynamics simulation studies of (R)-5-methylthiazolidin-4-One derivatives as megakaryocyte protein tyrosine phosphatase 2 (PTP-MEG2) inhibitors for the treatment of type 2 diabetes

PTP-MEG2 plays a significant role in insulin production and is able to enhance insulin signaling and improve insulin sensitivity. So, PTP-MEG2 inhibitors are closely associated with type 2 diabetes therapy. A series of novel (R)-5-methylthiazolidin-4-one derivatives were designed and synthesized, and their PTP-MEG2 inhibitory activities (IC₅₀) were determined. Among the desired compounds, 1h shares the highest inhibitory activity (IC₅₀ = 1.34 μM) against PTP-MEG2. Additionally, various post-dynamic analyses confirmed that when compound 1h bound to the PTP-MEG2, the protein conformations became unstable and the function of the pTyr recognition loop (Asn331-Cys338) would be disturbed. And thus, the ideal conformations needed for the catalytic activity was difficult to be maintained. In brief, these might be how the compound 1h worked. Furthermore, we also found that the key residues Arg332 would play a critical role in disturbing the residue interactions. AbbreviationsDCCMdynamic cross-correlation mappingDMFN,N-dimethylformamideDSSPdefinition of secondary structure of proteinsFOXOforkhead transcription factorsMDmolecular dynamicsPCAprincipal component analysisPDBprotein data bankPTKsprotein tyrosine kinasesPTPsprotein tyrosine phosphatasesPTP-MEG2megakaryocyte protein tyrosine phosphatase 2RINresidue interaction networkRINGResidue Interaction Network GeneratorRMSDroot means square deviationRMSFroot mean square fluctuationCommunicated by Ramaswamy H. Sarma.

Development and application of a comprehensive machine learning program for predicting molecular biochemical and pharmacological properties

We establish a comprehensive quantitative structure-activity relationship (QSAR) model termed AlphaQ through the machine learning algorithm to associate the fully quantum mechanical molecular descriptors with various biochemical and pharmacological properties. Preliminarily, a novel method for molecular structural alignments was developed in such a way to maximize the quantum mechanical cross correlations among the molecules. Besides the improvement of structural alignments, three-dimensional (3D) distribution of the molecular electrostatic potential was introduced as the unique numerical descriptor for individual molecules. These dual modifications lead to a substantial accuracy enhancement in multifarious 3D-QSAR prediction models of AlphaQ. Most remarkably, AlphaQ has been proven to be applicable to structurally diverse molecules to the extent that it outperforms the conventional QSAR methods in estimating the inhibitory activity against thrombin, the water-cyclohexane distribution coefficient, the permeability across the membrane of the Caco-2 cell, and the metabolic stability in human liver microsomes. Due to the simplicity in model building and the high predictive capability for varying biochemical and pharmacological properties, AlphaQ is anticipated to serve as a valuable screening tool at both early and late stages of drug discovery.

Influence of Bilayer Size and Number in Multi-Bilayer DOPC Simulations at Full and Low Hydration

Biophysical studies of model cell membranes at full and low hydration are usually carried out using scattering measurements on multi-bilayer systems. Molecular simulations of lipid bilayers aimed at reproducing those experimental conditions are usually conducted using single bilayers with different amounts of water. These simulation conditions may lead to artifacts arising from size effects and self-interactions because of periodic boundary conditions. We have tested the influence of the size and number of bilayers on membrane properties using the Lipid14 force field for lipids in molecular dynamics simulations of 1,2-dioleoyl- sn-glycero-3-phosphocholine bilayers at full hydration (44 water molecules per lipid), low hydration (18 water molecules per lipid), and dehydration (9 water molecules per lipid). A number of additional simulations were conducted with the Slipids force field for comparison. We have found that the average area per lipid (APL), thickness, mass density profiles, and acyl tail order parameters are insensitive to the size and the number of bilayers for all hydration states. The Lipid14 force field can also successfully reproduce the experimentally observed decrease in APL and corresponding increase in bilayer thickness upon dehydration, reflecting the increase in ordering as the system becomes more gel-like. Additionally, decreasing hydration levels were associated with a trend away from normal lateral diffusion and toward more subdiffusive regimes across both force fields. In summary, at least for the Lipid14 force field, the use of a single bilayer with 128 phospholipid molecules provides an adequate representation of multi-bilayer systems at varying levels of hydration.

Effects of Coarse Graining and Saturation of Hydrocarbon Chains on Structure and Dynamics of Simulated Lipid Molecules

Molecular dynamics simulations are used extensively to study the processes on biological membranes. The simulations can be conducted at different levels of resolution: all atom (AA), where all atomistic details are provided; united atom (UA), where hydrogen atoms are treated inseparably of corresponding heavy atoms; and coarse grained (CG), where atoms are grouped into larger particles. Here, we study the behavior of model bilayers consisting of saturated and unsaturated lipids DOPC, SOPC, OSPC and DSPC in simulations performed using all atom CHARMM36 and coarse grained Martini force fields. Using principal components analysis, we show that the structural and dynamical properties of the lipids are similar, both in AA and CG simulations, although the unsaturated molecules are more dynamic and favor more extended conformations. We find that CG simulations capture 75 to 100% of the major collective motions, overestimate short range ordering, result in more flexible molecules and 5–7 fold faster sampling. We expect that the results reported here will be useful for comprehensive quantitative comparisons of simulations conducted at different resolution levels and for further development and improvement of CG force fields.

DPPC-cholesterol phase diagram using coarse-grained Molecular Dynamics simulations

Cholesterol-phospholipid bilayers continue to be the current state of the art in membrane models and serve as representative systems for studying the effect of cholesterol on the cell membrane. As the mixing of different lipid species requires long spatio-temporal scales, coarse-grained models have gained increasing popularity in modeling such membrane systems. In this paper, a systematic study of the MARTINI coarse-grained model for the DPPC-cholesterol binary system has been performed. We construct the phase diagram of DPPC lipid bilayers in the presence of different cholesterol concentrations and at different temperatures using coarse-grained Molecular Dynamics (MD) simulations with the MARTINI force field. The phase diagram based on the condensation effect is directly comparable to available experimental data and demonstrates qualitative agreement over all cholesterol concentrations. Self-assembled bilayers quantitatively reproduce experimental observables, such as lateral diffusion of lipids, electron density, area per lipid and lipid order parameters. The phase diagram of the DPPC-cholesterol binary system also reveals the profound effect of cholesterol on the physical properties of phospholipid bilayers such lipid order, diffusion, and fluidity. Cholesterol induces the liquid-ordered phase, which increases the fluidity of the phospholipid hydrocarbon chains above the gel to liquid-crystalline phase transition temperature and decreases it below the phase transition. The present study suggests that the MARTINI force field can be successfully used to obtain molecular level insights into cholesterol-DPPC model membranes.