Protein surface roughness accounts for binding free energy of Plasmepsin II‐ligand complexes

The calculation of absolute binding affinities for protein‐inhibitor complexes remains as one of the main challenges in computational structure‐based ligand design. The present work explored the calculations of surface fractal dimension (as a measure of surface roughness) and the relationship with experimental binding free energies of Plasmepsin II complexes. Plasmepsin II is an attractive target for novel therapeutic compounds to treat malaria. However, the structural flexibility of this enzyme is a drawback when searching for specific inhibitors. Concerning that, we performed separate explicitly solvated molecular dynamics simulations using the available high‐resolution crystal structures of different Plasmepsin II complexes. Molecular dynamics simulations allowed a better approximation to systems dynamics and, therefore, a more reliable estimation of surface roughness. This constitutes a novel approximation in order to obtain more realistic values of fractal dimension, because previous works considered only x‐ray structures. Binding site fractal dimension was calculated considering the ensemble of structures generated at different simulation times. A linear relationship between binding site fractal dimension and experimental binding free energies of the complexes was observed within 20 ns. Previous studies of the subject did not uncover this relationship. Regression model, coined FD model, was built to estimate binding free energies from binding site fractal dimension values. Leave‐one‐out cross‐validation showed that our model reproduced accurately the absolute binding free energies for our training set (R² = 0.76; <|error|> =0.55 kcal/mol; SD_error = 0.19 kcal/mol). The fact that such a simple model may be applied raises some questions that are addressed in the article.     

Fractal dimension as a measure of surface roughness of G protein-coupled receptors: implications for structure and function

Protein surface roughness is a structural property associated with ligand-protein and protein-protein binding interfaces. In this work we apply for the first time the concept of surface roughness, expressed as the fractal dimension, to address structure and function of G protein-coupled receptors (GPCRs) which are an important group of drug targets. We calculate the exposure ratio and the fractal dimension for helix-forming residues of the β(2) adrenergic receptor (β(2)AR), a model system in GPCR studies, in different conformational states: in complex with agonist, antagonist and partial inverse agonists. We show that both exposure ratio and roughness exhibit periodicity which results from the helical structure of GPCRs. The pattern of roughness and exposure ratio of a protein patch depends on its environment: the residues most exposed to membrane are in general most rough whereas parts of receptors mediating interhelical contacts in a monomer or protein complex are much smoother. We also find that intracellular ends (TM3, TM5, TM6 and TM7) which are relevant for G protein binding and thus receptor signaling, are exposed but smooth. Mapping the values of residual fractal dimension onto receptor 3D structures makes it possible to conclude that the binding sites of orthosteric ligands as well as of cholesterol are characterized with significantly higher roughness than the average for the whole protein. In summary, our study suggests that identification of specific patterns of roughness could be a novel approach to spot possible binding sites which could serve as original drug targets for GPCRs modulation.     

In situ evaluation of surface roughness and micromorphology of temporary soft denture liner materials at different time intervals

Objective

To perform an in situ evaluation of surface roughness and micromorphology of two soft liner materials for dentures at different time intervals.

Background

The surface roughness of materials may influence the adhesion of micro‐organisms and inflammation of the mucosal tissues. The in situ evaluation of surface roughness and the micromorphology of soft liner materials over the course of time may present results different from those of in vitro studies, considering the constant presence of saliva and food, the changes in temperature and the pH level in the oral cavity.

Materials and methods

Forty‐eight rectangular specimens of each of the two soft liner materials were fabricated: a silicone‐based material (Mucopren Soft) and an acrylic resin‐based material (Trusoft). The specimens were placed in the dentures of 12 participants (n = 12), and the materials were evaluated for surface roughness and micromorphology at different time intervals: 0, 7, 30 and 60 days. Roughness (Ra) was evaluated by means of a roughness tester. Surface micromorphology was evaluated by scanning electron microscopy.

Results

Analysis of variance for randomised block design and Tukey's test showed that surface roughness values were lower in the groups using the silicone‐based material at all the time intervals (P < .0001). The average surface roughness was higher at time interval 0 than at the other intervals, for both materials (P < .0001). The surface micromorphology showed that the silicone material presented a more regular and smoother surface than the acrylic resin‐based material.

Conclusion

The surface roughness of acrylic resin‐based and silicone‐based denture soft liner materials decreased after 7 days of evaluation, leading to a smoother surface over time. The silicone‐based material showed lower roughness values and a smoother surface than the acrylic resin‐based material, thereby making it preferred when selecting more appropriate material, due its tendency to promote less biofilm build‐up.     

Hidden partners: Using cross‐docking calculations to predict binding sites for proteins with multiple interactions

Protein‐protein interactions control a large range of biological processes and their identification is essential to understand the underlying biological mechanisms. To complement experimental approaches, in silico methods are available to investigate protein‐protein interactions. Cross‐docking methods, in particular, can be used to predict protein binding sites. However, proteins can interact with numerous partners and can present multiple binding sites on their surface, which may alter the binding site prediction quality. We evaluate the binding site predictions obtained using complete cross‐docking simulations of 358 proteins with 2 different scoring schemes accounting for multiple binding sites. Despite overall good binding site prediction performances, 68 cases were still associated with very low prediction quality, presenting individual area under the specificity‐sensitivity ROC curve (AUC) values below the random AUC threshold of 0.5, since cross‐docking calculations can lead to the identification of alternate protein binding sites (that are different from the reference experimental sites). For the large majority of these proteins, we show that the predicted alternate binding sites correspond to interaction sites with hidden partners, that is, partners not included in the original cross‐docking dataset. Among those new partners, we find proteins, but also nucleic acid molecules. Finally, for proteins with multiple binding sites on their surface, we investigated the structural determinants associated with the binding sites the most targeted by the docking partners.     

New method for determining surface roughness of tongue

doi: 10.1111/j.1741‐2358.2011.00509.x New method for determining surface roughness of tongue Objective: The degree of atrophy of the lingual papillae in elderly individuals was evaluated using a quantitative method. Subjects and methods: One hundred and eighty‐two subjects living in nursing homes and 20 healthy adults as controls were studied. To express the degree of atrophy of the lingual papillae quantitatively, lingual surface roughness was determined by taking an impression with silicone dental material. Based on the impressions obtained from the elderly subjects, they were classified by three expert dentists into three groups: Normal, Smooth and Rough. The same determinations were also performed in the 20 healthy controls and compared with 38 of the elderly subjects who had agreement from all of the experts and without the presence of fissures (Normal, n = 6; Smooth, n = 12; Rough, n = 20). Results: The roughness average value for the controls was 65.0 μm, while that for the elderly subjects in the Normal, Smooth and Rough groups was 73.9 μm, 42.2 μm and 94.1 μm, respectively, which were significantly different. Conclusion: The present results indicate that the present technique of obtaining an impression of the tongue surface is simple and reliable for routine evaluation and quantification of the degree of atrophy as well as morphology of the lingual papillae.     

Analysis and interpretation of two-dimensional single-particle tracking microscopy measurements: effect of local surface roughness

Methodological advances in light microscopy have made it possible to record the motions of individual lipid and protein molecules resident in the membrane of living cells down to the nanometer level of precision in the x, y plane. Such measurement of a single molecule’s trajectory for a sufficiently long period of time or the measurement of multiple molecules’ trajectories for a shorter period of time can in principle provide the necessary information to derive the particle’s macroscopic two-dimensional-diffusion coefficient—a quantity of vital biological interest. However, one drawback of the light microscopy procedures used in such experiments is their relatively poor discriminatory capability for determining spatial differences along the z axis in comparison to those in the x, y plane. In this study we used computer simulation to examine the likely effect of local surface roughness over the nanometer to micrometer scale on the determination of diffusion constants in the membrane bilayer by the use of such optical-microscope-based single-particle tracking (SPT) procedures. We specifically examined motion of a single molecule along (i) a locally planar and (ii) a locally rough surface. Our results indicate a need for caution in applying overly simplistic analytical strategies to the analysis of data from SPT measurements and provide upper and lower bounds for the likely degree of error introduced on the basis of surface roughness effects alone. Additionally we present an empirical method based on an autocorrelation function approach that may prove useful in identifying the existence of surface roughness and give some idea of its extent.     

Weight loss and changes in surface roughness of denture base and reline materials after simulated toothbrushing in vitro

doi: 10.1111/j.1741‐2358.2010.00422.x
Weight loss and changes in surface roughness of denture base and reline materials after simulated toothbrushing in vitro Objective: To evaluate the weight loss and the surface roughness of acrylic resins after simulated brushing tests. Material and methods: Ten specimens of each material (Tokuyama Rebase II‐TR, New Truliner‐NT, Ufi Gel Hard‐UH and Lucitone 550‐L) were made. The wear loss (mg) by weight and the surface roughness (Ra μm) of each specimen was determined before and after brushing. The specimens were placed on the brushing machine and a total of 20 000 brushing cycles was performed. The results of weight loss and roughness values were submitted to the anova followed by the Tukey’s test (p = 0.05). Results: The mean weight loss of material L was statistically higher (p < 0.001) than that of the relines TR, UH and NT. No significant differences were found among the roughness values of resins TR, UH and L (p > 0.05). Only for L, toothbrushing increased the surface roughness. After toothbrushing, there was no significant difference between the roughness values of materials L and NT. The highest mean weight loss during the simulated toothbrushing tests was observed for L. Before the toothbrushing tests, the NT exhibited the highest mean roughness. Conclusion: Brushing resulted in increase in roughness only for resin L.     

Influence of biologically inspired nanometer surface roughness on antigen-antibody interactions for immunoassay-biosensor applications

Current research efforts to improve immunoassay-biosensor functionality have centered on detection through the optimal design of microfluidic chambers, electrical circuitry, optical sensing elements, and so on. To date, little attention has been paid to the immunoassay-biosensor membrane surface on which interactions between antibodies and antigens must occur. For this reason, the objective of the present study was to manipulate the nanometer surface roughness of a model immunoassay-biosensor membrane to determine its role on sensitivity and specificity. It was hypothesized that surface roughness characteristics similar to those used by the body's own immune system with B-lymphocyte cell membranes would promote antigen-antibody interactions and minimize non-specific binding. To test this hypothesis, polystyrene 96-well plate surfaces were modified to possess similar topographies as those of B-lymphocyte cell membranes. This was accomplished by immobilizing Protein A conjugated gold particles and Protein A conjugated polystyrene particles ranging in sizes from 40 to 860 nm to the bottom of polystyrene wells. Atomic force microscopy results provided evidence of well-dispersed immunoassay-biosensor surfaces for all particles tested with high degrees of biologically inspired nanometer roughness. Testing the functionality of these immunosurfaces using antigenic fluorescent microspheres showed that specific antigen capture increased with greater nanometer surface roughness while nonspecific antigen capture did not correlate with surface roughness. In this manner, results from this study suggest that large degrees of biologically inspired nanometer surface roughness not only increases the amount of immobilized antibodies onto the immunosurface membrane, but it also enhances the functionality of those antibodies for optimal antigen capture, criteria critical for improving immunoassay-biosensor sensitivity and specificity.     

Changes in the surface roughness of snow from millimetre to metre scales

The roughness of snow influences the movement of air across the snow surface and resulting transfers of energy. Here we focus on the roughness of the snowpack surface to determine its range of variability for different snow conditions (e.g., time since last snowfall), across spatial scales that ranged from 0.01 cm (card) to more than 1000 cm (transect) or more than 5-orders of magnitude, and due to the deposition of aeolian constituents. Digital photogrammetry of snow surfaces was used to compute two roughness metrics at two mountain sites in north-central Colorado. These metrics are the random roughness (RR) that disregards the spatial structure and the fractal dimension (D) computed from variogram analysis.At the crystal scale, D is between 1.67 (card) and 1.60 (board), which increases to 1.77 between 0.1 and 1.0 cm. At longer scales, D is 1.53 (board) to 1.56 (transect). There was no significant change in surface roughness during the accumulation season, with RR values at about 0.002. During the melt season the surface roughness doubled, with the RR values increasing from about 0.002 to 0.004. Snow was more rough parallel to the wind when dunes were present, and roughness varied spatially. The average RR value computed for the white snow surface of 0.014 is substantially greater than the value computed for the red dust surface of 0.0032. Due to undulations of smaller amplitude and as a result of the dust itself, the red dust surface is more random (D is 2.62 versus 2.23 for the white snow). Our results show that there is consistency in roughness over different scales, yet large scale processes (e.g., wind and radiation activity) influence the magnitude of roughness metrics much more than small scale processes (e.g., crystal form and metamorphism).     

Analogies to demonstrate the effect of roughness on surface wettability

This article presents an analogy to illustrate the effect of surface roughness on surface wettability. I used a water-filled balloon to represent water droplet, a toothpick to represent surface roughness and Styrofoam as the surface. The analogies presented in this article will help visualize how roughness affects the wettability of the surface and therefore can be used to introduce surface wettability to high school students.     

Monitoring tablet surface roughness during the film coating process

The purpose of this study was to evaluate the change of surface roughness and the development of the film during the film coating process using laser profilometer roughness measurements, SEM imaging, and energy dispersive X-ray (EDX) analysis. Surface roughness and texture changes developing during the process of film coating tablets were studied by noncontact laser profilometry and scanning electron microscopy (SEM). An EDX analysis was used to monitor the magnesium stearate and titanium dioxide of the tablets. The tablet cores were film coated with aqueous hydroxypropyl methylcellulose, and the film coating was performed using an instrumented pilot-scale side-vented drum coater. The SEM images of the film-coated tablets showed that within the first 30 minutes, the surface of the tablet cores was completely covered with a thin film. The magnesium signal that was monitored by SEM-EDX disappeared after ∼15 to 30 minutes, indicating that the tablet surface was homogeneously covered with film coating. The surface roughness started to increase from the beginning of the coating process, and the increase in the roughness broke off after 30 minutes of spraying. The results clearly showed that the surface roughness of the tablets increased until the film coating covered the whole surface area of the tablets, corresponding to a coating time period of 15 to 30 minutes (from the beginning of the spraying phase). Thereafter, the film only became thicker. The methods used in this study were applicable in the visualization of the changes caused by the film coating on the tablet surfaces.     

The effect of surface roughness on claw and adhesive hair performance in the dock beetle Gastrophysa viridula

Abstract Natural adhesive systems are adapted to attach to rough surfaces, but the underlying mechanisms have not been fully clarified. Attachment forces for the beetle Gastrophysa viridula were recorded on epoxy casts of surfaces with different roughness using a centrifuge device. Replicas were made of standardized polishing paper with asperity sizes ranging from 0.05 to 30 μm and of dock leaves (Rumex obtusifolius). Beetles adhered with a safety factor of up to 36 times body weight on smooth substrates or on casts of leaves of their host plant. On the rough substrates, forces were much lower and a minimum at small scale roughness (0.05 μm asperity size, with a mean safety factor of 5) was observed. Removal of the claws led to a significant reduction in force for rough substrates with asperity sizes ≥ 12 μm. Attachment forces of the hairy adhesive system itself (without the claws) slightly increased from small‐scale to large‐scale surface roughness, but remained below the level seen on the smooth substrate. This is explained by the inability of setal tips to make full contact to the surface.     

An experimental investigation into the surface and hydrodynamic characteristics of marine coatings with mimicked hull roughness ranges

Abstract

There are limited scientific data on contributors to the added drag of in-service ships, represented by modern-day coating roughness and biofouling, either separately or combined. This study aimed to gain an insight into roughness and hydrodynamic performance of typical coatings under in-service conditions of roughened ships’ hull surfaces. Comprehensive and systematic experimental data on the boundary layer and drag characteristics of antifouling coating systems with different finishes are presented. The coating types investigated were linear-polishing polymers, foul-release and controlled-depletion polymers. The data were collected through state-of-the-art equipment, including a 2-D laser Doppler velocimetry (LDV) system for hydrodynamic data in a large circulating water tunnel. Three coating systems were first applied on flat test panels with ‘normal’ finishes in the first test campaign to represent coating applications under idealised laboratory conditions. In order to address more realistic roughness conditions, as typically observed on ships’ hulls, ‘low’ and ‘high’ roughness densities were introduced into the same types of coating, in the second test campaign. The data collected from the first test campaign served as the baseline to demonstrate the effect on the surface roughness and hydrodynamic drag characteristics of these coating types as a result of ‘in-service’ or ‘severely flawed’ coating application scenarios. Data collected on coatings with a range of in-service surface conditions provided a basis to establish correlation between the surface roughness characteristics and hydrodynamic performance (roughness function). The findings of the study indicate that the estimations of drag penalties based on well-applied, relatively smooth coating conditions underestimate the importance of hull roughness, which although undesirable, is commonplace in the world’s commercial fleet.     

Decoding visual roughness perception: an fMRI study

Abstract

The neural substrates of tactile roughness perception have been investigated by many neuroimaging studies, while relatively little effort has been devoted to the investigation of neural representations of visually perceived roughness. In this human fMRI study, we looked for neural activity patterns that could be attributed to five different roughness intensity levels when the stimuli were perceived visually, i.e., in absence of any tactile sensation. During functional image acquisition, participants viewed video clips displaying a right index fingertip actively exploring the sandpapers that had been used for the behavioural experiment. A whole brain multivariate pattern analysis found four brain regions in which visual roughness intensities could be decoded: the bilateral posterior parietal cortex (PPC), the primary somatosensory cortex (S1) extending to the primary motor cortex (M1) in the right hemisphere, and the inferior occipital gyrus (IOG). In a follow-up analysis, we tested for correlations between the decoding accuracies and the tactile roughness discriminability obtained from a preceding behavioural experiment. We could not find any correlation between both although, during scanning, participants were asked to recall the tactilely perceived roughness of the sandpapers. We presume that a better paradigm is needed to reveal any potential visuo-tactile convergence. However, the present study identified brain regions that may subserve the discrimination of different intensities of visual roughness. This finding may contribute to elucidate the neural mechanisms related to the visual roughness perception in the human brain.     

Diversity of dermal denticle structure in sharks: Skin surface roughness and three‐dimensional morphology

Shark skin is covered with numerous placoid scales or dermal denticles. While previous research has used scanning electron microscopy and histology to demonstrate that denticles vary both around the body of a shark and among species, no previous study has quantified three‐dimensional (3D) denticle structure and surface roughness to provide a quantitative analysis of skin surface texture. We quantified differences in denticle shape and size on the skin of three individual smooth dogfish sharks (Mustelus canis) using micro‐CT scanning, gel‐based surface profilometry, and histology. On each smooth dogfish, we imaged between 8 and 20 distinct areas on the body and fins, and obtained further comparative skin surface data from leopard, Atlantic sharpnose, shortfin mako, spiny dogfish, gulper, angel, and white sharks. We generated 3D images of individual denticles and measured denticle volume, surface area, and crown angle from the micro‐CT scans. Surface profilometry was used to quantify metrology variables such as roughness, skew, kurtosis, and the height and spacing of surface features. These measurements confirmed that denticles on different body areas of smooth dogfish varied widely in size, shape, and spacing. Denticles near the snout are smooth, paver‐like, and large relative to denticles on the body. Body denticles on smooth dogfish generally have between one and three distinct ridges, a diamond‐like surface shape, and a dorsoventral gradient in spacing and roughness. Ridges were spaced on average 56 µm apart, and had a mean height of 6.5 µm, comparable to denticles from shortfin mako sharks, and with narrower spacing and lower heights than other species measured. We observed considerable variation in denticle structure among regions on the pectoral, dorsal, and caudal fins, including a leading‐to‐trailing edge gradient in roughness for each region. Surface roughness in smooth dogfish varied around the body from 3 to 42 microns.     

On the relationship between common amplitude surface roughness parameters and surface area: Implications for the study of cell–material interactions

In order to show that surface area is not always a quantity proportional to the surface roughness, we have constructed simple surfaces consisting of boxes of the same height equally spaced, and rms roughness and surface area have been computed. We have shown how we can get examples of surface configurations for which an increment in the surface roughness corresponds to a decrease in the surface area, although this is observed only for surfaces having similar rms roughness. We have also shown that even in the more intuitive situations where an increase in the surface roughness leads to an increase in the surface area, this increase is not necessarily equivalent. Analogous conclusions have been found when roughness was evaluated through the average roughness. These results could be interesting when analyzing interfacial phenomena such as cell adhesion, especially from a microscopic point of view, where the exact contact area between interacting phases governs these phenomena, and an exact-as-possible approximation to its real value is desirable. Also, the results of this paper could be of interest in various biomedical applications where the modulation of material surface-by-surface roughness may play a significant role. It can be concluded that care should be taken when using roughness parameters as estimators or indicators of the contact area between phases, since the relationship is not always simple.     

Effect of surface roughness on squeeze film poroelastic bearings with special reference to synovial joints

A simplified mathematical model has been developed for understanding the combined effects of surface roughness and couple stresses on lubrication aspects of synovial joints. The modified Reynolds equation which incorporates the elastic as well as randomized surface roughness structure of cartilage with couple-stress fluid as lubricant is derived. The mean pressure, load carrying capacity and time of approach as functions of film thickness during normal articulation of joints are obtained by using Christensen stochastic theory with the assumption that the roughness asperity heights are to be small compared to the film thickness. The effects of surface roughness and elasticity are considerably pronounced for the poroelastic bearings with couple-stress fluid as lubricant compared with classical case.     

Drag force and surface roughness measurements on freshwater biofouled surfaces

The detrimental effect of biofilms on skin friction for near wall flows is well known. The diatom genera Gomphonema and Tabellaria dominated the biofilm mat in the freshwater open channels of the Tarraleah Hydropower Scheme in Tasmania, Australia. A multi-faceted approach was adopted to investigate the drag penalty for biofouled 1.0 m × 0.6 m test plates which incorporated species identification, drag measurement in a recirculating water tunnel and surface characterisation using close-range photogrammetry. Increases in total drag coefficient of up to 99% were measured over clean surface values for biofouled test plates incubated under flow conditions in a hydropower canal. The effective roughness of the biofouled surfaces was found to be larger than the physical roughness; the additional energy dissipation was caused in part by the vibration of the biofilms in three-dimensions under flow conditions. The data indicate that there was a roughly linear relationship between the maximum peak-to-valley height of a biofilm and the total drag coefficient.