Trajectory Statistical Learning of the Potential Mean of Force and Diffusion Coefficient from Molecular Dynamics Simulations.

Central to studying the conformational changes of a complex protein is understanding the dynamics and energetics involved. Phenomenologically, structural dynamics can be formulated using an overdamped Langevin model along an observable, e.g., the distance between two residues in the protein. The Langevin model is specified by the deterministic force (the potential of mean force, PMF) and stochastic force (characterized by the diffusion coefficient, D). It is therefore of great interest to be able to extract both PMF and D from an observable time series but under the same computational framework. Here, we approach this challenge in molecular dynamics (MD) simulations by treating it as a missing-data Bayesian estimation problem. An important distinction in our methodology is that the entire MD trajectory, as opposed to the individual data elements, is used as the statistical variable in Bayesian imputation. This idea is implemented through an eigen-decomposition procedure for a time-symmetrized Fokker-Planck equation, followed by maximizing the likelihood for parameter estimation. The mathematical expressions for the functional derivatives used in learning PMF and D also provide new physical insights for the manner by which the information on both the deterministic and stochastic forces is encoded in the dynamics data. An all-atom MD simulation of a nontrivial biomolecule case is used to illustrate the application of this approach. We show that, interestingly, the results of trajectory statistical learning can motivate new order parameters for an improved description of the kinetic bottlenecks in conformational changes. Complementing purely data-driven or black-box methods, this work underscores the advantages of physics-based machine learning in gaining chemical insights from quantitative parameter estimation.

Subdomain dynamics enable chemical chain reactions in non-ribosomal peptide synthetases.

Many peptide-derived natural products are produced by non-ribosomal peptide synthetases (NRPSs) in an assembly-line fashion. Each amino acid is coupled to a designated peptidyl carrier protein (PCP) through two distinct reactions catalysed sequentially by the single active site of the adenylation domain (A-domain). Accumulating evidence suggests that large-amplitude structural changes occur in different NRPS states; yet how these molecular machines orchestrate such biochemical sequences has remained elusive. Here, using single-molecule Förster resonance energy transfer, we show that the A-domain of gramicidin S synthetase I adopts structurally extended and functionally obligatory conformations for alternating between adenylation and thioester-formation structures during enzymatic cycles. Complementary biochemical, computational and small-angle X-ray scattering studies reveal interconversion among these three conformations as intrinsic and hierarchical where intra-A-domain organizations propagate to remodel inter-A-PCP didomain configurations during catalysis. The tight kinetic coupling between structural transitions and enzymatic transformations is quantified, and how the gramicidin S synthetase I A-domain utilizes its inherent conformational dynamics to drive directional biosynthesis with a flexibly linked PCP domain is revealed.

Mechanical codes of chemical-scale specificity in DNA motifs.

In gene transcription, certain sequences of double-stranded (ds)DNA play a vital role in nucleosome positioning and expression initiation. That dsDNA is deformed to various extents in these processes leads us to ask: Could the genomic DNA also have sequence specificity in its chemical-scale mechanical properties? We approach this question using statistical machine learning to determine the rigidity between DNA chemical moieties. What emerges for the polyA, polyG, TpA, and CpG sequences studied here is a unique trigram that contains the quantitative mechanical strengths between bases and along the backbone. In a way, such a sequence-dependent trigram could be viewed as a DNA mechanical code. Interestingly, we discover a compensatory competition between the axial base-stacking interaction and the transverse base-pairing interaction, and such a reciprocal relationship constitutes the most discriminating feature of the mechanical code. Our results also provide chemical-scale understanding for experimental observables. For example, the long polyA persistence length is shown to have strong base stacking while its complement (polyAc) exhibits high backbone rigidity. The mechanical code concept enables a direct reading of the physical interactions encoded in the sequence which, with further development, is expected to shed new light on DNA allostery and DNA-binding drugs.

An Ingestible Pill With CMOS Fluorescence Sensor Array, Bi-Directional Wireless Interface and Packaged Optics for in-Vivo Bio-Molecular Sensing.

The article presents for the first time a pill-based ingestible electronics with CMOS integrated multiplexed fluorescence bio-molecular sensor arrays, bi-directional wireless communication and packaged optics in a FDA-approved capsule for in-vivo bio-molecular sensing. The silicon chip integrates both the sensor array, and the ultra-low-power (ULP) wireless system that allows offloading sensor computing to an external base station that can reconfigure the sensor measurement time, and its dynamic range, allowing optimized high sensitivity measurement under low power consumption. The integrated receiver achieves -59 dBm receiver sensitivity dissipating 121 µW of power. The integrated transmitter operates in a dual mode FSK/OOK delivering -15 dBm of power. The 15-pixel fluorescence sensor array follows an electronic-optic co-design methodology and integrates the nano-optical filters with integrated sub-wavelength metal layers that achieves high extinction ratio (39 dB), thereby eliminating the need for bulky external optical filters. The chip integrates photo-detection circuitry and on-chip 10-bit digitation, and achieves measured sensitivity of 1.6 attomoles of fluorescence labels on surface, and between 100 pM to 1 nM of target DNA detection limit per pixel. The complete package includes a CMOS fluorescent sensor chip with integrated filter, a prototyped UV LED and optical waveguide, functionalized bioslip, off-chip power management and Tx/Rx antenna that fits in a standard FDA approved capsule size 000.

The trajectory patterns of single HIV-1 virus-like particle in live CD4 cells: A real time three-dimensional multi-resolution microscopy study using encapsulated nonblinking giant quantum dot.

BACKGROUND: The exploration of virology knowledge was limited by the optical technology for the observation of virus. Previously, a three-dimensional multi-resolution real-time microscope system (3D-MRM) was developed to observe the uptake of HIV-1-tat peptide-modified nanoparticles in cell membrane. In this study, we labeled HIV-1 virus-like particles (VLPs) with passivated giant quantum dots (gQDs) and recorded their interactive trajectories with human Jurkat CD4 cells through 3D-MRM. METHODS: The labeled of gQDs of the HIV-1 VLPs in sucrose-gradient purified viral lysates was first confirmed by Cryo-electronic microscopy and Western blot assay. After the infection with CD4 cells, the gQD-labeled VLPs were visualized and their extracellular and intracellular trajectories were recorded by 3D-MRM. RESULTS: A total of 208 prime trajectories was identified and classified into three distinct patterns: cell-free random diffusion pattern, directional movement pattern and cell-associated movement pattern, with distributions and mean durations were 72.6%/87.6 s, 9.1%/402.7 s and 18.3%/68.7 s, respectively. Further analysis of the spatial-temporal relationship between VLP trajectories and CD4 cells revealed the three stages of interactions: (1) cell-associated (extracellular) diffusion stage, (2) cell membrane surfing stage and (3) intracellular directional movement stage. CONCLUSION: A complete trajectory of HIV-1 VLP interacting with CD4 cells was presented in animation. This encapsulating method could increase the accuracy for the observation of HIV-1-CD4 cell interaction in real time and three dimensions.

Parabolic Potential Surfaces Localize Charge Carriers in Nonblinking Long-Lifetime "Giant" Colloidal Quantum Dots.

Materials for studying biological interactions and for alternative energy applications are continuously under development. Semiconductor quantum dots are a major part of this landscape due to their tunable optoelectronic properties. Size-dependent quantum confinement effects have been utilized to create materials with tunable bandgaps and Auger recombination rates. Other mechanisms of electronic structural control are under investigation as not all of a material's characteristics are affected by quantum confinement. Demonstrated here is a new structure-property concept that imparts the ability to spatially localize electrons or holes within a core/shell heterostructure by tuning the charge carrier's kinetic energy on a parabolic potential energy surface. This charge carrier separation results in extended radiative lifetimes and in continuous emission at the single-nanoparticle level. These properties enable new applications for optics, facilitate novel approaches such as time-gated single-particle imaging, and create inroads for the development of other new advanced materials.

Multicolor multifocal 3D microscopy using in-situ optimization of a spatial light modulator.

Multifocal microscopy enables high-speed three-dimensional (3D) volume imaging by using a multifocal grating in the emission path. This grating is typically designed to afford a uniform illumination of multifocal subimages for a single emission wavelength. Using the same grating for multicolor imaging results in non-uniform subimage intensities in emission wavelengths for which the grating is not designed. This has restricted multifocal microscopy applications for samples having multicolored fluorophores. In this paper, we present a multicolor multifocal microscope implementation which uses a Spatial Light Modulator (SLM) as a single multifocal grating to realize near-uniform multifocal subimage intensities across multiple wavelength emission bands. Using real-time control of an in-situ-optimized SLM implemented as a multifocal grating, we demonstrate multicolor multifocal 3D imaging over three emission bands by imaging multicolored particles as well as Escherichia coli (E. coli) interacting with human liver cancer cells, at [Formula: see text] multicolor 3D volumes per second acquisition speed. Our multicolor multifocal method is adaptable across SLM hardware, emission wavelength band locations and number of emission bands, making it particularly suited for researchers investigating fast processes occurring across a volume where multiple species are involved.

Sorting Nanoparticles by Valency with DNA Barcoding.

Self-assembly of DNA-labeled nanoparticles is an effective strategy to fabricate new nanocomposite materials and nanoscale devices from the bottom-up. To tailor the properties of the resulting material or device, one requires access to a wide range of nanoparticle sizes and shapes, as well as control over the number (valency) of DNA molecules on the nanoparticle surface. Currently, nanoparticles with a defined DNA valency can only be obtained in a narrow range of sizes, and in small quantities, limiting the properties of the resulting composite structures and their applications. Here, we leverage the digital information encoded in the number and sequence of short DNA barcodes to generate preparatory amounts of nanoparticles bearing a specific number of DNA molecules, irrespective of the identity of the nanocomponent. We show that this DNA valency sorting chromatography, which is driven by the selective affinity of Watson-Crick base pairs, is applicable to arbitrary DNA sequences and a broad range of nanoparticle sizes, shapes, and material compositions. To further demonstrate this fact, we use valency-sorted large gold nanospheres directly in self-assembly schemes to create, in one synthesis step, large amounts of several previously inaccessible molecule-like dimer and trimer nanostructures with unique optical properties. We anticipate that the expanded scope of DNA valency-defined nanoparticle reagents, and the increased scale at which they can be produced, will open new avenues for the molecularly precise manipulation of nanoscale matter.

Amplifying Intermolecular Events by Streptavidin-Induced Proximity.

Weak interactions between biomolecules play important roles in many cellular functions. Structural and kinetic analyses of these interactions, however, have been hindered by the transient nature of such events. Here, we pointed out a general approach to overcome this obstacle─anchoring the molecular partners to streptavidin hosts─and achieved constrained proximity and stoichiometry for the sought-after molecular coupling. We elaborated this idea through a series of DNA hybridization reactions and quantitatively characterized them using single-molecule experiments. Compared to a nominally 1 µM solution, for example, the streptavidin-induced proximity (SIP) amounted to an effective molarity of ∼10-30 µM for the binding partners. There was also a significantly increased proportion of molecular association, manifested in both ensemble population and single-molecule residence time. As an application example, we showed how SIP enabled the observation and quantitative characterization of an unstable complex between Cas9-RNA and noncognate DNA substrates, interactions that had been challenging to characterize previously. Conceptually simple and implementationally robust, SIP was shown to considerably enhance the efficacy in capturing weak interactions and, as demonstrated here, could empower scientists to see the otherwise unseeable.

Structure-mechanics statistical learning uncovers mechanical relay in proteins.

A protein's adaptive response to its substrates is one of the key questions driving molecular physics and physical chemistry. This work employs the recently developed structure-mechanics statistical learning method to establish a mechanical perspective. Specifically, by mapping all-atom molecular dynamics simulations onto the spring parameters of a backbone-side-chain elastic network model, the chemical moiety specific force constants (or mechanical rigidity) are used to assemble the rigidity graph, which is the matrix of inter-residue coupling strength. Using the S1A protease and the PDZ3 signaling domain as examples, chains of spatially contiguous residues are found to exhibit prominent changes in their mechanical rigidity upon substrate binding or dissociation. Such a mechanical-relay picture thus provides a mechanistic underpinning for conformational changes, long-range communication, and inter-domain allostery in both proteins, where the responsive mechanical hotspots are mostly residues having important biological functions or significant mutation sensitivity.

Sub-millisecond Translational and Orientational Dynamics of a Freely Moving Single Nanoprobe.

This paper presents a new experiment with which we are able to measure the 3D translational motion of a single particle at 10 µs time resolution and with ∼10 nm spatial resolution while at the same time determining the 3D orientation of the same single particle with 250 µs time resolution. These high time resolutions are ∼40 times greater than previous simultaneous measurements of 3D position and 3D orientation. Detailed numerical simulations and experiments are used to demonstrate that the technique can measure 3D orientation at the shot-noise limit. The microscope is also able to simultaneously measure the length or width (with the other assumed) of the plasmonic nanorods used here in situ and nondestructively, which should yield a greater understanding of the underlying dynamics. This technique should be applicable to a broad range of problems where environments which change in space and time may perturb physical and chemical dynamics.

Mechanical couplings of protein backbone and side chains exhibit scale-free network properties and specific hotspots for function.

A backbone-side-chain elastic network model (bsENM) is devised in this contribution to decipher the network of molecular interactions during protein dynamics. The chemical details in 5 µs all-atom molecular dynamics (MD) simulation are mapped onto the bsENM spring constants by self-consistent iterations. The elastic parameters obtained by this structure-mechanics statistical learning are then used to construct inter-residue rigidity graphs for the chemical components in protein amino acids. A key discovery is that the mechanical coupling strengths of both backbone and side chains exhibit heavy-tailed distributions and scale-free network properties. In both rat trypsin and PDZ3 proteins, the statistically prominent modes of rigidity graphs uncover the sequence-specific coupling patterns and mechanical hotspots. Based on the contributions to graphical modes, our residue rigidity scores in backbone and side chains are found to be very useful metrics for the biological significance. Most functional sites have high residue rigidity scores in side chains while the biologically important glycines are generally next to mechanical hotspots. Furthermore, prominent modes in the rigidity graphs involving side chains oftentimes coincide with the co-evolution patterns due to evolutionary restraints. The bsENM specifically devised to resolve the protein chemical character thus provides useful means for extracting functional information from all-atom MD.

Information bounds in determining the 3D orientation of a single emitter or scatterer using point-detector-based division-of-amplitude polarimetry.

Determining the 3D orientation of a single molecule or particle, encoded in its polar and azimuthal angles, is of interest for a variety of fields, being relevant to a range of questions in elementary chemical reactivity, biomolecular motors, and nanorheology. A popular experimental method, known as division-of-amplitude polarimetry, for determining the real-time orientation of a single particle is to split the emitted/scattered light into multiple polarizations and to measure the light intensity using point detectors at these polarizations during a time interval Δt. Here, we derive the Cramér-Rao lower bounds for this method from the perspective of information theory in the cases of utilizing a chromophore or a scattering particle as a 3D orientation probe. Such Cramér-Rao lower bounds are new for using this experimental method to measure the full 3D orientation in both the scattering case and the fluorescence case. These results show that, for a scatterer, the information content of one photon is 1.16 deg-2 in the polar and 58.71 deg-2 in the azimuthal angles, respectively. For a chromophore, the information content of one photon is 2.54 deg-2 in the polar and 80.29 deg-2 in the azimuthal angles. In addition, the Cramér-Rao lower bound scales with the square root of the total signal photons. To determine orientation to an uncertainty of one degree requires 7.40 × 104 and 2.34 × 103 photons for the polar and the azimuthal angles, respectively, for fluorescence, whereas it takes 1.62 × 105 and 3.20 × 103 photons for scattering. This work provides experimentalists new guidelines by which future experiments can be designed and interpreted.

Improved Image Quality for Static BLADE Magnetic Resonance Imaging Using the Total-Variation Regularized Least Absolute Deviation Solver.

In order to improve the image quality of BLADE magnetic resonance imaging (MRI) using the index tensor solvers and to evaluate MRI image quality in a clinical setting, we implemented BLADE MRI reconstructions using two tensor solvers (the least-squares solver and the L1 total-variation regularized least absolute deviation (L1TV-LAD) solver) on a graphics processing unit (GPU). The BLADE raw data were prospectively acquired and presented in random order before being assessed by two independent radiologists. Evaluation scores were examined for consistency and then by repeated measures analysis of variance (ANOVA) to identify the superior algorithm. The simulation showed the structural similarity index (SSIM) of various tensor solvers ranged between 0.995 and 0.999. Inter-reader reliability was high (Intraclass correlation coefficient (ICC) = 0.845, 95% confidence interval: 0.817, 0.87). The image score of L1TV-LAD was significantly higher than that of vendor-provided image and the least-squares method. The image score of the least-squares method was significantly lower than that of the vendor-provided image. No significance was identified in L1TV-LAD with a regularization strength of λ= 0.4-1.0. The L1TV-LAD with a regularization strength of λ= 0.4-0.7 was found consistently better than least-squares and vendor-provided reconstruction in BLADE MRI with a SENSitivity Encoding (SENSE) factor of 2. This warrants further development of the integrated computing system with the scanner.

Leveraging lifetime information to perform real-time 3D single-particle tracking in noisy environments.

A microscopy platform that leverages the arrival time of individual photons to enable 3D single-particle tracking of fast-moving (translational diffusion coefficient of ≃3.3 µm2/s) particles in high-background environments is reported here. It combines a hardware-based time-gating module, which enables the rate of photon processing to be as high as 100 MHz, with a two-photon-excited 3D single-particle tracking confocal microscope to enable high sample penetration depth. Proof-of-principle experiments where single quantum dots are tracked in solutions containing dye-stained cellulose, are shown with tracking performance markedly improved using the hardware-based time-gating module. Such a microscope design is anticipated to be of use to a variety of communities who wish to track single particles in cellular environments, which commonly have high fluorescence and scattering background.

Intermediary conformations linked to the directionality of the aminoacylation pathway of nonribosomal peptide synthetases.

Nonribosomal peptide synthetases (NRPSs) are multifunctional megaenzymes that govern the stepwise biosynthesis of pharmaceutically important peptides. In an ATP-dependent assembly-line mechanism dedicated domains are responsible for each catalytic step. Crystal structures have provided insight into several conformations of interacting domains. However, the complete picture in solution of how domain dynamics and the timing of conformational changes effect a directional biosynthesis remains only poorly understood and will be important for the efficient reprogramming of NRPSs. Here we dissect the multiple conformational changes associated with the adenylation and thiolation reactions of the aminoacylation pathway under catalytic conditions. We used pyrophosphate (PP i ) to biochemically drive the conformational changes backward and forward while performing an online monitoring with a Förster resonance energy transfer (FRET) didomain sensor, consisting of adenylation (A) and peptidyl-carrier protein (PCP) domains. Notably, we found aminoacyl thioester formation to efficiently occur in the presence of PP i even at millimolar concentrations, despite the chemically and conformationally reversing effect of this metabolite and by-product. This finding settles conflicting reports and explains why intracellular PP i concentrations do not impair NRP biosynthesis. A conserved amino acid was identified to be important for the mechanism under these conditions. FRET time-course analyses revealed that the directionality of the aminoacylation catalysis is correlated with conformational kinetics. Complemented by equilibrium hydrogen-deuterium exchange (HDX) analyses, our data uncovered the existence of at least one new intermediary conformation that is associated with the rate-determining step. We propose an expanded model of conformational changes in the NRPS aminoacylation pathway.

Reproducibly Measuring Plasmon-Enhanced Fluorescence in Bulk Solution Across a 20-Fold Range of Optical Densities.

It is well-known that plasmonic nanoparticles can modify the spectroscopic properties of nearby optical probes, for example, enhanced emission of a fluorescent dye. Yet, the detection and quantification of this effect in bulk solution remain challenging even while size- and shape-controlled nanoparticles have become readily available. We investigated this problem and identified two main difficulties which we were able to overcome through systematic studies. For the detection of fluorescence emanating from optically dense nanoparticle solutions, we describe an analytical model that provides guidelines for experimentalists to maximize the fluorescence intensity by optimizing the concentration, light paths, and excitation-detection volume of the sample. For the quantification of enhancement, which critically hinges upon the comparison to an accurate reference sample, we exploit the tools of DNA nanotechnology to remove the fluorophore from plasmonic coupling on-demand, forming an in situ reference. Using a model system of fluorophore Cy3 and 80 nm gold nanoparticles, we show that these strategies enable the quantitative measurement of plasmonic enhancement across a 20-fold range of optical densities. We anticipate that the presented experimental framework will allow for routine, quantitative measurements for the research and development of plasmon-enhanced phenomena.

Prior-Apprised Unsupervised Learning of Subpixel Curvilinear Features in Low Signal/Noise Images.

Many biophysical problems involve molecular and nanoscale targets moving next to a curvilinear track, e.g., a cytosolic cargo transported by motor proteins moving along a microtubule. For this type of problem, fluorescence imaging is usually the primary tool of choice. There is, however, an ∼20-fold mismatch between target-localization precision and track-imaging resolution such that questions requiring high-fidelity definition of the target's track remain inaccessible. On the other hand, if the contextual image of the tracks can be refined to a level comparable to that of the target, many intuitive yet mechanistically important issues can begin to be addressed. This work demonstrates that it is possible to statistically infer, to subpixel precision, curvilinear features in a low signal/noise image. This is achieved by a framework that consists of three stages: the Hessian-based feature enhancement, the subimage feature sampling and registration, and the statistical learning of the underlying curvilinear structure using a new, to our knowledge, method developed here for inferring the principal curves. In each stage, the descriptive prior information that the features come from curvilinear elements is explicitly taken into account. It is fully automated without user supervision, which is distinctly different from approaches that require user seeding or well-defined training data sets. Computer simulations of realistic images are used to investigate the performance of the framework and its implementation. The characterization results suggest that curvilinear features are refined to the same order of precision as that of the target and that the bootstrap confidence intervals from the analysis allow an estimate for the statistical bounds of the simulated "true" curve. Also shown are analyses of experimental images from three different microscopy modalities: two-photon laser-scanning microscopy, epifluorescence microscopy, and total internal reflection fluorescence microscopy. The practical application of this prior-apprised unsupervised learning framework as well as its potential outlook are discussed.

Uniform intensity in multifocal microscopy using a spatial light modulator.

Multifocal microscopy (MFM) offers high-speed three-dimensional imaging through the simultaneous image capture from multiple focal planes. Conventional MFM systems use a fabricated grating in the emission path for a single emission wavelength band and one set of focal plane separations. While a Spatial Light Modulator (SLM) can add more flexibility as a replacement to the fabricated grating, the relatively small number of pixels in the SLM chip, cross-talk between the pixels, and aberrations in the imaging system can produce non-uniform intensity in the different axially separated image planes. We present an in situ iterative SLM calibration algorithm that overcomes these optical- and hardware-related limitations to deliver near-uniform intensity across all focal planes. Using immobilized gold nanoparticles under darkfield illumination, we demonstrate superior intensity evenness compared to current methods. We also demonstrate applicability across emission wavelengths, axial plane separations, imaging modalities, SLM settings, and different SLM manufacturers. Therefore, our microscope design and algorithms provide an alternative to the use of fabricated gratings in MFM, as they are relatively simple and could find broad applications in the wider research community.

Structure-mechanics statistical learning unravels the linkage between local rigidity and global flexibility in nucleic acids.

The mechanical properties of nucleic acids underlie biological processes ranging from genome packaging to gene expression, but tracing their molecular origin has been difficult due to the structural and chemical complexity. We posit that concepts from machine learning can help to tackle this long-standing challenge. Here, we demonstrate the feasibility and advantage of this strategy through developing a structure-mechanics statistical learning scheme to elucidate how local rigidity in double-stranded (ds)DNA and dsRNA may lead to their global flexibility in bend, stretch, and twist. Specifically, the mechanical parameters in a heavy-atom elastic network model are computed from the trajectory data of all-atom molecular dynamics simulation. The results show that the inter-atomic springs for backbone and ribose puckering in dsRNA are stronger than those in dsDNA, but are similar in strengths for base-stacking and base-pairing. Our analysis shows that the experimental observation of dsDNA being easier to bend but harder to stretch than dsRNA comes mostly from the respective B- and A-form topologies. The computationally resolved composition of local rigidity indicates that the flexibility of both nucleic acids is mostly due to base-stacking. But for properties like twist-stretch coupling, backbone springs are shown to play a major role instead. The quantitative connection between local rigidity and global flexibility sets foundation for understanding how local binding and chemical modification of genetic materials effectuate longer-ranged regulatory signals.