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Register a new userNew algorithm to determine a minimal representation of the molecular surface
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Most proteins perform their biological function by interacting with one or more molecular partners. In this respect, characterizing the features of the molecular surface, especially in the portions where the interaction takes place, turned out to be a crucial step in the investigation of the mechanisms of recognition and binding between molecules. Predictive methods often rely on extensive samplings of molecular patches with the aim to identify hot spots on the surface. In this framework, analysis of large proteins and/or many molecular dynamics frames is often unfeasible due to the high computational cost. Thus, finding optimal ways to reduce the number of points to be sampled maintaining the biological information carried by the molecular surface is pivotal. Here, we present a new theoretical and computational algorithm with the aim of determining a subset of surface points, appropriately selected in space, in order to maximize the information of the overall shape of the molecule by minimizing the number of total points. We test our procedure by looking at the local shape of the surface through a recently developed method based on the formalism of Zernike polynomials in two dimensions, which is able to characterize the local shape properties of portions of molecular surfaces. The results of this method show that a remarkably higher ability of this algorithm to reproduce the information of the complete molecular surface compared to uniform random sampling.
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Implicit solvent models, such as Poisson-Boltzmann models, play important roles in computational studies of biomolecules. A vital step in almost all implicit solvent models is to determine the solvent-solute interface, and the solvent excluded surface (SES) is the most widely used interface definition in these models. However, classical algorithms used for computing SES are geometry-based, thus neither suitable for parallel implementations nor convenient for obtaining surface derivatives. To address the limitations, we explored a machine learning strategy to obtain a level-set formulation for the SES. The training process was conducted in three steps, eventually leading to a model with over 95% agreement with the classical SES. Visualization of tested molecular surfaces shows that the machine-learned SES overlaps with the classical SES on almost all situations. We also implemented the machine-learned SES into the Amber/PBSA program to study its performance on reaction field energy calculation. The analysis shows that the two sets of reaction field energies are highly consistent with 1% deviation on average. Given its level-set formulation, we expect the machine-learned SES to be applied in molecular simulations that require either surface derivatives or high efficiency on parallel computing platforms.
During development, organisms acquire three-dimensional shapes with important physiological consequences. While the basic mechanisms underlying morphogenesis are known in eukaryotes, it is often difficult to manipulate them in vivo. To circumvent this issue, here we present a study of developing Vibrio cholerae biofilms grown on agar substrates in which the spatiotemporal morphological patterns were altered by varying the agar concentration. Expanding biofilms are initially flat, but later experience a mechanical instability and become wrinkled. Whereas the peripheral region develops ordered radial stripes, the central region acquires a zigzag herringbone-like wrinkle pattern. Depending on the agar concentration, the wrinkles initially appear either in the peripheral region and propagate inward (low agar concentration) or in the central region and propagate outward (high agar concentration). To understand these experimental observations, we developed a model that considers diffusion of nutrients and their uptake by bacteria, bacterial growth/biofilm matrix production, mechanical deformation of both the biofilm and the agar, and the friction between them. Our model demonstrates that depletion of nutrients beneath the central region of the biofilm results in radially-dependent growth profiles, which in turn, produce anisotropic stresses that dictate the morphology of wrinkles. Furthermore, we predict that increasing surface friction (agar concentration) reduces stress anisotropy and shifts the location of the maximum compressive stress, where the wrinkling instability first occurs, toward the center of the biofilm, in agreement with our experimental observations. Our results are broadly applicable to bacterial biofilms with similar morphologies and also provide insight into how other bacterial biofilms form distinct wrinkle patterns.
Despite ablation and drag processes associated with atmospheric entry of meteoroids were a subject of intensive study over the last century, little attention was devoted to interpret the observed fireball terminal height. This is a key parameter because it not only depends on the initial mass, but also on the bulk physical properties of the meteoroids and hence of their ability to ablate in the atmosphere. In this work we have developed a new approach that is tested using the fireball terminal heights observed by the Meteorite Observation and Recovery Project operated in Canada between 1970-1985 (hereafter referred as MORP). We then compare them to the calculation made. Our results clearly show that the new methodology is able to forecast the degree of deepening of meteoroids in the Earths atmosphere. Then, this approach has important applications in predicting the impact hazard from cm- to meter-sized bodies that are represented, in part, in the MORP bolide list.
In this paper, we will describe a new factorization algorithm based on the continuous representation of Gauss sums, generalizable to orders j>2. Such an algorithm allows one, for the first time, to find all the factors of a number N in a single run without precalculating the ratio N/l, where l are all the possible trial factors. Continuous truncated exponential sums turn out to be a powerful tool for distinguishing factors from non-factors (we also suggest, with regard to this topic, to read an interesting paper by S. Woelk et al. also published in this issue [Woelk, Feiler, Schleich, J. Mod. Opt. in press]) and factorizing different numbers at the same time. We will also describe two possible M-path optical interferometers, which can be used to experimentally realize this algorithm: a liquid crystal grating and a generalized symmetric Michelson interferometer.
The reduction of graphene oxide is one of the most facile methods to fabricate a large amount of graphene and the reduction rate of graphene oxide is related with the quality of synthesized graphene for its possible application. The reduction rate is usually determined by using various spectroscopy measurements such as Raman spectroscopy, Fourier transform infrared spectroscopy, and X-ray photoelectron spectroscopy. Here we propose that the magnetic data can be used as a means of determining the quality of graphene oxide (GO) and reduced graphene oxide (RGO) by the investigation of close relation between magnetic moment and chemical bonding state. Our experimental findings and previous theoretical studies suggest that hydroxyl functional groups in GO mainly contribute to Langevin paramagnetism, carboxyl functional groups in RGO1 act as the source for Pauli paramagnetism, and sp2 bonding state in RGO2 plays a major role on the diamagnetism. Especially in terms of mass production, the magnetic data is useful for decomposing the chemical bonding electronic states in graphene-like samples and judging their quality.
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