Identifying sequence regions undergoing conformational change via predicted continuum secondary structure

Motivation: Conformational flexibility is essential to the function of many proteins, e.g. catalytic activity. To assist efforts in determining and exploring the functional properties of a protein, it is desirable to automatically identify regions that are prone to undergo conformational changes. It was recently shown that a probabilistic predictor of continuum secondary structure is more accurate than categorical predictors for structurally ambivalent sequence regions, suggesting that such models are suited to characterize protein flexibility. Results: We develop a computational method for identifying regions that are prone to conformational change directly from the amino acid sequence. The method uses the entropy of the probabilistic output of an 8-class continuum secondary structure predictor. Results for 171 unique amino acid sequences with well-characterized variable structure (identified in the 'Macromolecular movements database') indicate that the method is highly sensitive at identifying flexible protein regions, but false positives remain a problem. The method can be used to explore conformational flexibility of proteins (including hypothetical or synthetic ones) whose structure is yet to be determined experimentally.

Prediction of protein continuum secondary structure with probabilistic models based on NMR solved structures

Background: The structure of proteins may change as a result of the inherent flexibility of some protein regions. We develop and explore probabilistic machine learning methods for predicting a continuum secondary structure, i.e. assigning probabilities to the conformational states of a residue. We train our methods using data derived from high-quality NMR models. Results: Several probabilistic models not only successfully estimate the continuum secondary structure, but also provide a categorical output on par with models directly trained on categorical data. Importantly, models trained on the continuum secondary structure are also better than their categorical counterparts at identifying the conformational state for structurally ambivalent residues. Conclusion: Cascaded probabilistic neural networks trained on the continuum secondary structure exhibit better accuracy in structurally ambivalent regions of proteins, while sustaining an overall classification accuracy on par with standard, categorical prediction methods.

Numerical Comparison of the Drag Models of Granular Flows Applied to the Fast Pyrolysis of Biomass

The paper presents a comparison between the different drag models for granular flows developed in the literature and the effect of each one of them on the fast pyrolysis of wood. The process takes place on an 100 g/h lab scale bubbling fluidized bed reactor located at Aston University. FLUENT 6.3 is used as the modeling framework of the fluidized bed hydrodynamics, while the fast pyrolysis of the discrete wood particles is incorporated as an external user defined function (UDF) hooked to FLUENT’s main code structure. Three different drag models for granular flows are compared, namely the Gidaspow, Syamlal O’Brien, and Wen-Yu, already incorporated in FLUENT’s main code, and their impact on particle trajectory, heat transfer, degradation rate, product yields, and char residence time is quantified. The Eulerian approach is used to model the bubbling behavior of the sand, which is treated as a continuum. Biomass reaction kinetics is modeled according to the literature using a two-stage, semiglobal model that takes into account secondary reactions.

On the 'classification' of neurodegenerative disorders:discrete entities, overlap or continuum?

The traditional method of classifying neurodegenerative diseases is based on the original clinico-pathological concept supported by 'consensus' criteria and data from molecular pathological studies. This review discusses first, current problems in classification resulting from the coexistence of different classificatory schemes, the presence of disease heterogeneity and multiple pathologies, the use of 'signature' brain lesions in diagnosis, and the existence of pathological processes common to different diseases. Second, three models of neurodegenerative disease are proposed: (1) that distinct diseases exist ('discrete' model), (2) that relatively distinct diseases exist but exhibit overlapping features ('overlap' model), and (3) that distinct diseases do not exist and neurodegenerative disease is a 'continuum' in which there is continuous variation in clinical/pathological features from one case to another ('continuum' model). Third, to distinguish between models, the distribution of the most important molecular 'signature' lesions across the different diseases is reviewed. Such lesions often have poor 'fidelity', i.e., they are not unique to individual disorders but are distributed across many diseases consistent with the overlap or continuum models. Fourth, the question of whether the current classificatory system should be rejected is considered and three alternatives are proposed, viz., objective classification, classification for convenience (a 'dissection'), or analysis as a continuum.

Effects of electric charge on osmotic flow across periodically arranged circular cylinders

An electrostatic model is developed for osmotic flow across a layer consisting of identical circular cylinders with a fixed surface charge, aligned parallel to each other so as to form an ordered hexagonal arrangement. The expression of the osmotic reflection coefficient is derived for spherical solutes with a fixed surface charge suspended in an electrolyte, based on low-Reynolds-number hydrodynamics and a continuum, point-charge description of the electric double layers. The repulsive electrostatic interaction between the surface charges with the same sign on the solute and the cylinders is shown to increase the exclusion region of solute from the cylinder surface, which enhances the osmotic flow. Applying the present model to the study of osmotic flow across the endothelial surface glycocalyx of capillary walls has revealed that this electrostatic model could account well for the reflection coefficients measured for charged macromolecules, such as albumin, in the physiological range of charge density and ion concentration.