Substrate docking and molecular dynamic simulation for prediction of fungal enzymes from Trichoderma species-assisted extraction of nanocellulose from oil palm leaves

Abstract

Fungi of the Trichoderma species are valued industrial enzymes in support of the ‘zero-waste’ technology to convert agro-industrial biomass into valuable products, i.e. nanocellulose (NC). In this study, an in silico approach using substrate docking and molecular dynamic (MD) simulation was used to predict the order of which the multilayers of cellulosic polymers, i.e. lignin, hemicellulose and cellulose in oil palm leaves (OPL) are degraded by fungal enzymes, endocellulase and exocellulase. The study aimed to establish the catalytic tendencies of the enzymes to optimally degrade the cellulosic components of OPL for high yield production of NC. Energy minimized endocellulase and exocellulase models revealed satisfactory scores of PROCHECK (90.0% and 91.2%), Verify3D (97.23% and 98.85%) and ERRAT (95.24% and 91.00%) assessments. Active site prediction by blind docking, COACH meta-server and multiple sequence alignment indicated the catalytic triads for endocellulase and exocellulase were Ser116–His205–Glu249 and Ser382–Arg124–Asp385, respectively. Binding energy of endocellulase docked with hemicellulose (?6.0 ? kcal mol^?1) was the most favourable followed by lignin (?5.6 ? kcal mol^?1) and cellulose (?4.4 ? kcal mol^?1). Exocellulase, contrarily, bonded favorably with lignin (?8.7 ? kcal mol^?1), closely followed by cellulose (?8.5 ? kcal mol^?1) and hemicellulose (?8.4 ? kcal mol^?1). MDs simulations showed that interactions of complexes, endocellulase–hemicellulose and the exocellulase–cellulose being the most stable. Thus, the findings of the study successfully identified the specific actions of sugar-acting enzymes for NC production.

Communicated by Ramaswamy H. Sarma     

An Evidence-Based Combining Classifier for Brain Signal Analysis

Nowadays, brain signals are employed in various scientific and practical fields such as Medical Science, Cognitive Science, Neuroscience, and Brain Computer Interfaces. Hence, the need for robust signal analysis methods with adequate accuracy and generalizability is inevitable. The brain signal analysis is faced with complex challenges including small sample size, high dimensionality and noisy signals. Moreover, because of the non-stationarity of brain signals and the impacts of mental states on brain function, the brain signals are associated with an inherent uncertainty. In this paper, an evidence-based combining classifiers method is proposed for brain signal analysis. This method exploits the power of combining classifiers for solving complex problems and the ability of evidence theory to model as well as to reduce the existing uncertainty. The proposed method models the uncertainty in the labels of training samples in each feature space by assigning soft and crisp labels to them. Then, some classifiers are employed to approximate the belief function corresponding to each feature space. By combining the evidence raised from each classifier through the evidence theory, more confident decisions about testing samples can be made. The obtained results by the proposed method compared to some other evidence-based and fixed rule combining methods on artificial and real datasets exhibit the ability of the proposed method in dealing with complex and uncertain classification problems.     

Specific Localization of β-Arrestin2 in Myenteric Plexus of Mouse Gastrointestinal Tract

β-arrestin2 is a key molecule involved in signaling and internalization of activated G protein-coupled receptors including µ-opioid receptors (MOR). Previously we have shown that decreased expression of β-arrestin2 upon chronic morphine is associated with the development of opioid tolerance in the gastrointestinal tract. However, the localization of β-arrestin2 within the gastrointestinal wall is not known. In this study we found that β-arrestin2 is localized in the soma of a select group of neurons in the myenteric ganglia but not in smooth muscle. The density of β-arestin2 was significantly higher in the ileum than the colon. We identified four variants of β-arrestin2 in the ileum, with ARRB-005 and ARRB-013 being the most abundant. Further, the current study utilized multiple-labeling immunofluorescence to characterize the chemical coding of neurons expressing β-arrestin2 in the murine myenteric plexus and the co-localization of MOR₁ and β-arrestin2. β-arrestin2 co-localized with choline acetyltransferase and calretinin. In contrast, β-arrestin2 neither co-localized with substance P, nitric oxide synthase nor calbindin. Genetic deletion of β-arrestin2 did not affect cholinergic neuron activation by nicotine in the isolated ileum (-log M EC₅₀: wild type = 5.8 vs. β-arrestin2 knockout = 5.9). Our findings suggest specificity in the localization of β-arrestin2 in the myenteric plexus within MOR₁-expressing neurons and provide a relation for direct intracellular crosstalk between MOR₁ receptor activation and β-arrestin2 signaling in the myenteric neurons. β-arrestin2 deletion does not directly alter basal enteric cholinergic neuronal function.     

Optical Coherence Tomography and Autofluorescence Imaging of Human Tonsil

For the first time, we present co-registered autofluorescence imaging and optical coherence tomography (AF/OCT) of excised human palatine tonsils to evaluate the capabilities of OCT to visualize tonsil tissue components. Despite limited penetration depth, OCT can provide detailed structural information about tonsil tissue with much higher resolution than that of computed tomography, magnetic resonance imaging, and Ultrasound. Different tonsil tissue components such as epithelium, dense connective tissue, lymphoid nodules, and crypts can be visualized by OCT. The co-registered AF imaging can provide matching biochemical information. AF/OCT scans may provide a non-invasive tool for detecting tonsillar cancers and for studying the natural history of their development.