Enhanced Sampling for Conformational Changes and Molecular Mechanisms of Human NTHL1.

The functionalities of proteins rely on protein conformational changes during many processes. Identification of the protein conformations and capturing transitions among different conformations are important but extremely challenging in both experiments and simulations. In this work, we develop a machine learning based approach to identify a reaction coordinate that accelerates the exploration of protein conformational changes in molecular simulations. We implement our approach to study the conformational changes of human NTHL1 during DNA repair. Our results identified three distinct conformations: open (stable), closed (unstable), and bundle (stable). The existence of the bundle conformation can rationalize recent experimental observations. Comparison with an NTHL1 mutant demonstrates that a closely packed cluster of positively charged residues in the linker could be a factor to search when screening for genetic abnormalities. Results will lead to a better modulation of the DNA repair pathway to protect against carcinogenesis.

Prediction of the Peptide-TIM3 Binding Site in Inhibiting TIM3-Galectin 9 Binding Pathways.

T-cell immunoglobulin and mucin domain-containing protein-3 (TIM3) is an important receptor protein that modulates the immune system. The binding of TIM3 with Galectin 9 (GAL9) triggers immune system suppression, but the TIM3-GAL9 binding can be inhibited by binding of the peptide P26 to TIM3. A fast and accurate prediction of the P26-TIM3 binding site is crucial and a prerequisite for the investigation of P26-TIM3 interactions and TIM3-GAL9 binding pathways. Here, we present a machine learning approach, which considers protein conformational changes, to quickly identify the ligand-binding site on TIM3. Our results show that the P26 binding site is located near the C″-D loop of TIM3. Further simulations show that the binding pose is stabilized by a variety of electrostatic and hydrophobic interactions. Binding of P26 can alter the conformations of nearby glycan side chains on TIM3, providing possible mechanisms of how P26 inhibits TIM3-GAL9 binding pathways. The insights from this work will facilitate the identification of other peptides or antibodies that may also inhibit the TIM3-GAL9 pathways and eventually lead to improved attempts in the modulation of the TIM3-GAL9 immunosuppression pathways. The strategies and machine learning method can be generalized to study ligand-receptor binding when the conformational changes during the binding are important.

Effects of receptor properties on particle internalization through receptor-mediated endocytosis.

Receptor-mediated endocytosis (RME) is a highly complex process carried out by bioparticles, such as viruses and drug carriers, to enter cells. The discovery of both clathrin-dependent and clathrin-free pathways makes the RME process even more intriguing. Numerical models have been developed to facilitate the exploration of the process. However, the impacts of the receptor properties on RME have been less studied partially due to the oversimplifications of the receptor models. In this paper, we implement a stochastic model to systematically investigate the effects of mechanical (receptor flexure), geometrical (receptor length) and biochemical (ligand-receptor cutoff) properties of receptors, on RME with and without the existence of clathrin. Our simulation results show that the receptor's flexural rigidity plays an important role in RME with clathrin. There is a threshold beyond which particle internalization will not occur. Without clathrin, it is very difficult to achieve complete endocytosis with ligand-receptor interactions alone. A shorter receptor length and longer ligand-receptor reaction cutoff promote the formation of ligand-receptor bonds and facilitate particle internalization. Complete internalization can only be obtained with an extremely short receptor length and long reaction cutoff. Therefore, there are most likely some additional mechanisms to drive the membrane deformation in clathrin-free RME. Our results yield important fundamental insights into RME and provide crucial guidance when correlating the simulation results with experimental observations.

Effect of Slp4-a on Membrane Bending During Prefusion of Vesicles in Blood-Brain Barrier.

Vesicle exocytosis is a promising pathway for brain drug delivery through the blood-brain barrier to treat neurodegenerative diseases. In vesicle exocytosis, the membrane fusion process is initiated by the calcium sensor protein named synaptotagmin-like protein4-a (Slp4-a). Understanding conformational changes of Slp4-a during the prefusion stage of exocytosis will help to develop vesicle-based drug delivery to the brain. In this work, we use molecular dynamics (MD) simulations with a hybrid force field coupling united-atom protein model with MARTINI coarse-grained (CG) solvent to capture the conformational changes of Slp4-a during the prefusion stage. These hybrid coarse-grained simulations are more efficient than all-atom MD simulations and can capture protein interactions and conformational changes. Our simulation results show that the calcium ions play critical roles during the prefusion stage. Only one calcium ion can remain in each calcium-binding pocket of Slp4-a C2 domains. The C2B domain of calcium-unbound Slp4-a remains parallel to the endothelial membrane, while the C2B domain of calcium-bound Slp4-a rotates perpendicular to the endothelial membrane to approach the vesicular membrane. For the calcium-bound case, three Slp4-a proteins can effectively bend lipid membranes at the prefusion stage, which could later trigger lipid stalk between membranes. This work provides a better understanding how C2 domains of Slp4-a operate during vesicle exocytosis from an endothelial cell.

LINES: Log-Probability Estimation via Invertible Neural Networks for Enhanced Sampling.

It is very challenging to sample a molecular process with large activation energies using molecular dynamics simulations. Current enhanced sampling methodologies, such as umbrella sampling and metadynamics, rely on the identification of appropriate reaction coordinates for a system. In this paper, we developed a method for log-probability estimation via invertible neural networks for enhanced sampling (LINES). This iterative scheme utilizes a normalizing flow machine learning model to learn the underlying free energy surface (FES) of a system as a function of molecular coordinates and then applies a gradient-based optimization method to the learned normalizing flow to identify reaction coordinates. A biasing potential is then evaluated over a tabulated grid of the reaction coordinate values, which can be applied to the next round of simulations for enhanced sampling, resulting in more efficient sampling. We tested the accuracy and efficiency of the LINES method in sampling the FES using the alanine dipeptide system. We also demonstrated the effectiveness of identification of reaction coordinates through simulation of cyclobutanol unbinding from ß-cyclodextrin and the folding/unfolding of CLN025─a variant of the peptide Chignolin. The LINES method can be extended to the study of large-scale protein systems with complex nonlinear reaction pathways.

Fine-tuning-based Transfer Learning for Characterization of Adeno-Associated Virus.

Accurate and precise identification of adeno-associated virus (AAV) vectors play an important role in dose-dependent gene therapy. Although solid-state nanopore techniques can potentially be used to characterize AAV vectors by capturing ionic current, the existing data analysis techniques fall short of identifying them from their ionic current profiles. Recently introduced machine learning methods such as deep convolutional neural network (CNN), developed for image identification tasks, can be applied for such classification. However, with smaller data set for the problem in hand, it is not possible to train a deep neural network from scratch for accurate classification of AAV vectors. To circumvent this, we applied a pre-trained deep CNN (GoogleNet) model to capture the basic features from ionic current signals and subsequently used fine-tuning-based transfer learning to classify AAV vectors. The proposed method is very generic as it requires minimal preprocessing and does not require any handcrafted features. Our results indicate that fine-tuning-based transfer learning can achieve an average classification accuracy between 90 and 99% in three realizations with a very small standard deviation. Results also indicate that the classification accuracy depends on the applied electric field (across nanopore) and the time frame used for data segmentation. We also found that the fine-tuning of the deep network outperforms feature extraction-based classification for the resistive pulse dataset. To expand the usefulness of the fine-tuning-based transfer learning, we have tested two other pre-trained deep networks (ResNet50 and InceptionV3) for the classification of AAVs. Overall, the fine-tuning-based transfer learning from pre-trained deep networks is very effective for classification, though deep networks such as ResNet50 and InceptionV3 take significantly longer training time than GoogleNet.

Electrokinetic actuation of an uncharged polarizable dielectric droplet in charged hydrogel medium.

Electrokinetic transport of an uncharged nonconducting microsized liquid droplet in a charged hydrogel medium is studied. Dielectric polarization of the liquid drop under the action of an externally imposed electric field induces a non-homogeneous charge density at the droplet surface. The interactions of the induced surface charge of the droplet with the immobile charges of the hydrogel medium generates an electric force to the droplet, which actuates the drop through the charged hydrogel medium. A numerical study based on the first principle of electrokinetics is adopted. Dependence of the droplet velocity on its dielectric permittivity, bulk ionic concentration, and immobile charge density of the gel is analyzed. The surface conduction is significant in presence of charged gel, which creates a concentration polarization. The impact of the counterion saturation in the Debye layer due to the dielectric decrement of the medium is addressed. The modified Nernst-Planck equation for ion transport and the Poisson equation for the electric field is considered to take into account the dielectric polarization. A quadrupolar vortex around the uncharged droplet is observed when the gel medium is considered to be uncharged, which is similar to the induced charge electroosmosis around an uncharged dielectric colloid in free-solution. We find that the induced charge electrokinetic mechanism creates a strong recirculation of liquid within the droplet and the translational velocity of the droplet strongly depends on its size for the dielectric droplet embedded in a charged gel medium.

Adeno-associated virus characterization for cargo discrimination through nanopore responsiveness.

Solid-state nanopore (SSN)-based analytical methods have found abundant use in genomics and proteomics with fledgling contributions to virology - a clinically critical field with emphasis on both infectious and designer-drug carriers. Here we demonstrate the ability of SSN to successfully discriminate adeno-associated viruses (AAVs) based on their genetic cargo [double-stranded DNA (AAVdsDNA), single-stranded DNA (AAVssDNA) or none (AAVempty)], devoid of digestion steps, through nanopore-induced electro-deformation (characterized by relative current change; ΔI/I0). The deformation order was found to be AAVempty > AAVssDNA > AAVdsDNA. A deep learning algorithm was developed by integrating support vector machine with an existing neural network, which successfully classified AAVs from SSN resistive-pulses (characteristic of genetic cargo) with >95% accuracy - a potential tool for clinical and biomedical applications. Subsequently, the presence of AAVempty in spiked AAVdsDNA was flagged using the ΔI/I0 distribution characteristics of the two types for mixtures composed of ∼75 : 25% and ∼40 : 60% (in concentration) AAVempty : AAVdsDNA.

Effect of Calcium ion on synaptotagmin-like protein during pre-fusion of vesicle for exocytosis in blood-brain barrier.

BACKGROUND: Calcium signaling and membrane fusion play key roles in exocytosis of drug-containing vesicles through the blood-brain barrier (BBB). Identifying the role of synaptotagmin-like protein4-a (Slp4-a) in the presence of Ca2+ ions, at the pre-fusion stage of a vesicle with the basolateral membrane of endothelial cell, can reveal brain drug transportation across BBB. METHODS: We utilized molecular dynamics (MD) simulations with a coarse-grained PACE force field to investigate the behaviors of Slp4-a with vesicular and endothelial membranes at the pre-fusion stage of exocytosis since all-atom MD simulation or experiments are more time-consuming and expensive to capture these behaviors. RESULTS: The Slp4-a pulls lipid membranes (vesicular and endothelial) into close proximity and disorganizes lipid arrangement at contact points, which are predictors for initiation of fusion. Our MD results also indicate that Slp4-a needs Ca2+ to bind with weakly-charged POPE lipids (phosphatidylethanolamine). CONCLUSIONS: Slp4-a is an important trigger for membrane fusion in BBB exocytosis. It binds to lipid membranes at multiple binding sites and triggers membrane disruption for fusion in calcium-dependent case. GENERAL SIGNIFICANCE: Understanding the prefusion process of the vesicle will help to design better drug delivery mechanisms to the brain through formidable BBB.

Overcoming blood-brain barrier transport: Advances in nanoparticle-based drug delivery strategies.

The Blood-Brain Barrier (BBB), a unique structure in the central nervous system (CNS), protects the brain from bloodborne pathogens by its excellent barrier properties. Nevertheless, this barrier limits therapeutic efficacy and becomes one of the biggest challenges in new drug development for neurodegenerative disease and brain cancer. Recent breakthroughs in nanotechnology have resulted in various nanoparticles (NPs) as drug carriers to cross the BBB by different methods. This review presents the current understanding of advanced NP-mediated non-invasive drug delivery for the treatment of neurological disorders. Herein, the complex compositions and special characteristics of BBB are elucidated exhaustively. Moreover, versatile drug nanocarriers with their recent applications and their pathways on different drug delivery strategies to overcome the formidable BBB obstacle are briefly discussed. In terms of significance, this paper provides a general understanding of how various properties of nanoparticles aid in drug delivery through BBB and usher the development of novel nanotechnology-based nanomaterials for cerebral disease therapies.

Substrate-dependent transport mechanism in AcrB of multidrug resistant bacteria.

The multidrug resistance (MDR) system effectively expels antibiotics out of bacteria causing serious issues during bacterial infection. In addition to drug, indole, a common metabolic waste of bacteria, is expelled by MDR system of gram-negative bacteria for their survival. Experimental results suggest that AcrB, one of the key components of MDR system, undergoes large scale conformation changes during the pumping due to proton-motive process. However, due to extremely short time scale, it is difficult to observe (experimentally) those changes in the AcrB, which might facilitate the pumping process. Molecular simulations can shed light to understand the conformational changes for transport of indole in AcrB. Examination of conformational changes using all-atom simulation is, however, impractical. Here, we develop a hybrid coarse-grained force field to study the conformational changes of AcrB in presence of indole in the porter domain of monomer II. Using the coarse-grained force field, we investigated the conformational changes of AcrB for a number of model systems considering the effect of protonation in aspartic acid (Asp) residues Asp407 and Asp408 in the transmembrane domain of monomer II. Our results show that in the presence of indole, protonation of Asp408 or Asp407 residue causes conformational changes from binding state to extrusion state in monomer II, while remaining two monomers (I and III) approach access state in AcrB protein. We also observed that all three AcrB monomers prefer to go back to access state in the absence of indole. Steered molecular dynamics simulations were performed to demonstrate the feasibility of indole transport mechanism for protonated systems. Identification of indole transport pathway through AcrB can be very helpful in understanding the drug efflux mechanism used by the MDR bacteria.

Mechanical characterization of vesicles and cells: A review.

Vesicles perform many essential functions in all living organisms. They respond like a transducer to mechanical stress in converting the applied force into mechanical and biological responses. At the same time, both biochemical and biophysical signals influence the vesicular response in bearing mechanical loads. In recent years, liposomes, artificial lipid vesicles, have gained substantial attention from the pharmaceutical industry as a prospective drug carrier which can also serve as an artificial cell-mimetic system. The ability of these vesicles to enter through pores of even smaller size makes them ideal candidates for therapeutic agents to reach the infected sites effectively. Engineering of vesicles with desired mechanical properties that can encapsulate drugs and release as required is the prime challenge in this field. This requirement has led to the modifications of the composition of the bilayer membrane by adding cholesterol, sphingomyelin, etc. In this article, we review the manufacturing and characterization techniques of various artificial/synthetic vesicles. We particularly focus on the electric field-driven characterization techniques to determine different properties of vesicle and its membranes, such as bending rigidity, viscosity, capacitance, conductance, etc., which are indicators of their content and mobility. Similarities and differences between artificial vesicles, natural vesicles, and cells are highlighted throughout the manuscript since most of these artificial vesicles are intended for cell mimetic functions.

Eyeball-Like Yolk-Shell Bimetallic Nanoparticles for Synergistic Photodynamic-Photothermal Therapy.

Noble metal-based nanomaterials offer great potential as cargoes for multifunctional cancer treatment. In this research, Au eyeball-like nanoparticles (NPs) with open-mouthed Pd shells were synthesized and their surface was functionalized with cell-targeting ligand folic acid (FA) and photodynamic agent Chlorin e6 (Ce6). Due to the broad near-infrared (NIR) absorption band of eyeball-like bimetallic Au and Pd, the photothermal therapy effects of this nanomaterial were studied in MCF-7 cancer cells. The anchored Ce6 not only addressed the hypoxia issue of tumor cells but also exhibited remarkable photodynamic efficacy upon irradiation. Results showed that the obtained Au@Pd-PEG-FA-Ce6 (APPFC) NPs were selectively accumulated at the tumor site and induced cell apoptosis effectively due to the target specificity and synergistic phototherapy effect. The high specificity, desirable biosafety, fast delivery, and drug functionalization demonstrated eyeball-like Au@Pd NPs are promising candidate for multifunctional therapy of breast cancer.

Bayesian inference for parameter estimation in lactoferrin-mediated iron transport across blood-brain barrier.

BACKGROUND: In neurodegenerative diseases such as Alzheimer's and Parkinson's, excessive irons as well as lactoferrin (Lf), but not transferrin (Tf), have been found in and around the affected regions of the brain. These evidences suggest that lactoferrin plays a critical role during neurodegenerative diseases, although Lf-mediated iron transport across blood-brain barrier (BBB) is negligible compared to that of transferrin in normal condition. However, the kinetics of lactoferrins and lactoferrin-mediated iron transport are still unknown. METHOD: To determine the kinetic rate constants of lactoferrin-mediated iron transport through BBB, a mass-action based ordinary differential equation model has been presented. A Bayesian framework is developed to estimate the kinetic rate parameters from posterior probability density functions. The iron transport across BBB is studied by considering both Lf- and Tf-mediated pathways for both normal and pathologic conditions. RESULTS: Using the point estimates of kinetic parameters, our model can effectively reproduce the experimental data of iron transport through BBB endothelial cells. The robustness of the model and parameter estimation process are further verified by perturbation of kinetic parameters. Our results show that surge in high-affinity receptor density increases lactoferrin as well as iron in the brain. CONCLUSIONS: Due to the lack of a feedback loop such as iron regulatory proteins (IRPs) for lactoferrin, iron can transport to the brain continuously, which might increase brain iron to pathological levels and can contribute to neurodegeneration. GENERAL SIGNIFICANCE: This study provides an improved understanding of presence of lactoferrin and iron in the brain during neurodegenerative diseases.

Analytical Solution of Time-Periodic Electroosmotic Flow through Cylindrical Microchannel with Non-Uniform Surface Potential.

Time-periodic electroosmotic flow (EOF) with heterogeneous surface charges on channel walls can potentially be used to mix species or reagent molecules in microfluidic devices. Although significant research efforts have been placed to understand different aspects of EOF, its role in the mixing process is still poorly understood, especially for non-homogeneous surface charge cases. In this work, dynamic aspects of EOF in a cylindrical capillary are analyzed for heterogeneous surface charges. Closed form analytical solutions for time-periodic EOF are obtained by solving the Navier-Stokes equation. An analytical expression of induced pressure is also obtained from the velocity field solution. The results show that several vortices can be formed inside the microchannel with sinusoidal surface charge distribution. These vortices change their pattern and direction as the electric field change its strength and direction with time. In addition, the structure and strength of the vorticity depend on the frequency of the external electric field and the size of the channel. As the electric field frequency or channel diameter increases, vortices are shifted towards the channel surface and the perturbed flow region becomes smaller, which is not desired for effective mixing. Moreover, the number of vorticities depends on the periodicity of the surface charge.