Intrinsically disordered regions in proteins have been shown to be important in protein function. However, not all proteins contain the same amount of intrinsic disorder. The variation in the levels ...of intrinsic disorder in different types of proteins has been extensively studied over the last two decades. It is now known that the levels of intrinsic disorder vary in proteins across organisms, functions, diseases, and cellular locations. This review consolidates the known trends in the abundance of intrinsic disorder identified in groups of proteins across varying conditions and functions. It also presents new data towards the understanding of intrinsic disorder in cell type-specific proteins.
The present research deals with the development of a novel bioinspired
fabrication of reduced graphene oxide (rGO)-silver nanoparticle (AgNPs) nanocomposite (rGO@AgNCs) using microbes namely
(PA) and
...(SA). The fabricated rGO@AgNCs were characterized using Ultraviolet-visible (UV-Vis) spectroscopy, Fourier-transform infrared spectroscopy (FTIR), particle size analysis, polydispersity index (PDI), zeta potential analysis, energy dispersive x-ray analysis (EDAX), Raman spectroscopy, powder x-ray diffraction (PXRD), high-resolution transmission electron microscopy (HR-TEM) analysis, etc. Furthermore, the rGO@AgNCs-PA and rGO@AgNCs-SA interaction with serum protein, pH stability study, and
dissolution of AgNPs were also performed. The research findings of the proposed study demonstrated the simultaneous reduction of graphene oxide (GO) and AgNPs and the formation of rGO@AgNCs in the presence of microbes. The
dissolution studies of rGO@AgNCs composites showed better AgNPs dissolution with controlled release and offered remarkable matrix integrity throughout the dissolution period. The size and stability of rGO@AgNCs-PA and rGO@AgNCs-SA had no significant changes at physiological pH 7.4. A minimal decrease in the zeta potential of rGO@AgNCs was observed, which may be due to the weak interaction of nanocomposites and albumin. The antibacterial application of the synthesized nanocomposite was evaluated against a pathogenic mastitis-forming bacterium. The obtained results suggested an admirable antibacterial activity of synthesized nanocomposites against the tested microbes. This knowledge will assist the scientific fraternity in designing novel antibacterial agents with enhanced antibacterial activity against various veterinary pathogens in near future.
Surface plasmon resonance (SPR) offers exceptional advantages such as label-free, in-situ and real-time measurement ability that facilitates the study of molecular or chemical binding events. ...Besides, SPR lacks in the detection of various binding events, particularly involving low molecular weight molecules. This drawback ultimately resulted in the development of several sensitivity enhancement methodologies and their application in the various area. Among graphene materials, graphene-based nanocomposites stands out owing to its significant properties such as strong adsorption of molecules, signal amplification by optical, high carrier mobility, electronic bridging, ease of fabrication and therefore, have established as an important sensitivity enhancement substrate for SPR. Also, graphene-based nanocomposites could amplify the signal generated by plasmon material and increase the sensitivity of molecular detection up to femto to atto molar level. This review focuses on the current important developments made in the potential research avenue of SPR and fiber optics based SPR for chemical and biological sensing. Latest trends and challenges in engineering and applications of graphene-based nanocomposites enhanced sensors for detecting minute and low concentration biological and chemical analytes are reviewed comprehensively. This review may aid in futuristic designing approaches and application of grapheneous sensor platforms for sensitive plasmonic nano-sensors.
Graphene nanocomposites based sensitivity enhancement methodologies for chemical and biological sensing. (a) oxidant and reducing gases sensing by oxygen atom of GO with surface reaction mechanism. (b) Protein binding interaction directly on GNC (grapheneous nanocomposites) surface. (c) Antigen: antibody (Ag:Ab) reaction of directly immobilized Ag:Ab on to GNC. (d) pi-pi stacking for planer structure. (e) Hydrogen or electrostatics attraction based binding (f) direct sensing of molecules (shown here for ammonia gas). Display omitted
•Presents graphene nanocomposite based SPR sensors.•Recent progresses in SPR signal enhancement by graphene nanocomposites has been overviewed.•Graphene nanocomposites based sensitivity enhancement methodologies for chemical and biological sensing is reviewed.•In this review, we discussed future trends and perspectives to lay down the future SPR based plasmonic nano-sensors.
Objective: To investigate the effect on zirconia surface of the post-fabrication surface treatments on the morphological characteristics and mechanical properties of CAD/CAM milled dental zirconia ...specimens as well as to identify the critical parameters in the measurement of oral retention under in vitro circumstances. Method: The zirconia specimens (N = 20, n = 4) were subjected to CAD/CAM milling and divided into five groups. The specifications were: Group G1—sintered; Group G2—sintered followed by a polishing process; Group G3—sintered followed by polishing and sandblasting with alumina particles Al2O3 (110 µm); Group G4—sintered followed by sandblasting; Group G5—sintered followed by sandblasting with polishing as the end process. All the groups were subjected to Fretting wear tests, 3-D surface roughness measurements, and Vickers’s Micro hardness tests. Investigation of the phase transformation using XRD, and surface feature examination using SEM were also carried out. Additionally, one-way ANOVA, Tukey, and Pearson correlations were statistically analysed. Results: The fabrication processes had a significant effect on the performance of zirconia specimens in all the groups (p > 0.05). Specimens that underwent polishing as the last process exhibited lower surface roughness. The monoclinic phase of zirconia was observed in all the specimens before and after wear except for those in the G2 and G5 groups, where polishing was the end process. In G5, the post-wear surface properties revealed lower surface roughness and hardness. Further, the SEM and 3-D topography show grooves as seen by the dale void volume (Vvv) values; shallow valley depth (Svk); micro craters; and wear track. Conclusion: Specimens in G5 that were subjected to multistep post-fabrication process, namely sandblasting followed by polishing, yielded better results when compared to those in the other groups (G1, G2, G3, and G4). G5 with an interlayer of alumina is recommended for clinical applications due to its enhanced surface properties, mechanical properties, and low wear.
Abstract
Motivation
Intrinsically disordered proteins lack stable 3-dimensional structure and play a crucial role in performing various biological functions. Key to their biological function are the ...molecular recognition features (MoRFs) located within long disordered regions. Computationally identifying these MoRFs from disordered protein sequences is a challenging task. In this study, we present a new MoRF predictor, OPAL, to identify MoRFs in disordered protein sequences. OPAL utilizes two independent sources of information computed using different component predictors. The scores are processed and combined using common averaging method. The first score is computed using a component MoRF predictor which utilizes composition and sequence similarity of MoRF and non-MoRF regions to detect MoRFs. The second score is calculated using half-sphere exposure (HSE), solvent accessible surface area (ASA) and backbone angle information of the disordered protein sequence, using information from the amino acid properties of flanks surrounding the MoRFs to distinguish MoRF and non-MoRF residues.
Results
OPAL is evaluated using test sets that were previously used to evaluate MoRF predictors, MoRFpred, MoRFchibi and MoRFchibi-web. The results demonstrate that OPAL outperforms all the available MoRF predictors and is the most accurate predictor available for MoRF prediction. It is available at http://www.alok-ai-lab.com/tools/opal/.
Supplementary information
Supplementary data are available at Bioinformatics online.
Intrinsically disordered proteins (IDPs) contain long unstructured regions, which play an important role in their function. These intrinsically disordered regions (IDRs) participate in binding events ...through regions called molecular recognition features (MoRFs). Computational prediction of MoRFs helps identify the potentially functional regions in IDRs. In this study, OPAL+, a novel MoRF predictor, is presented. OPAL+ uses separate models to predict MoRFs of varying lengths along with incorporating the hidden Markov model (HMM) profiles and physicochemical properties of MoRFs and their flanking regions. Together, these features help OPAL+ achieve a marginal performance improvement of 0.4–0.7% over its predecessor for diverse MoRF test sets. This performance improvement comes at the expense of increased run time as a result of the requirement of HMM profiles. OPAL+ is available for download at https://github.com/roneshsharma/OPAL-plus/wiki/OPAL-plus-Download.
Abstract
Motivation
Advances in next-generation sequencing have made it possible to carry out transcriptomic studies at single-cell resolution and generate vast amounts of single-cell RNA sequencing ...(RNA-seq) data rapidly. Thus, tools to analyze this data need to evolve as well as to improve accuracy and efficiency.
Results
We present FEATS, a Python software package, that performs clustering on single-cell RNA-seq data. FEATS is capable of performing multiple tasks such as estimating the number of clusters, conducting outlier detection and integrating data from various experiments. We develop a univariate feature selection-based approach for clustering, which involves the selection of top informative features to improve clustering performance. This is motivated by the fact that cell types are often manually determined using the expression of only a few known marker genes. On a variety of single-cell RNA-seq datasets, FEATS gives superior performance compared with the current tools, in terms of adjusted Rand index and estimating the number of clusters. It achieves a 22% improvement in clustering and more accurately estimates the number of clusters when compared with other tools. In addition to cluster estimation, FEATS also performs outlier detection and data integration while giving an excellent computational performance. Thus, FEATS is a comprehensive clustering tool capable of addressing the challenges during the clustering of single-cell RNA-seq data.
Availability
The installation instructions and documentation of FEATS is available at https://edwinv87.github.io/feats/.
Supplementary Data
Supplementary data are available online at https://academic.oup.com/bib.
•MoRFpred-plus: a new sequence based MoRF predictor to identify MoRF residues in disordered protein sequences.•Hidden Markov model (HMM) profiles of amino acids is used for identifying Molecular ...Recognition Features (MoRFs) in Intrinsically Disordered Proteins (IDR) sequences.•Local physicochemical properties of amino acids is used for identifying Molecular Recognition Features (MoRFs) in Intrinsically Disordered Proteins (IDR) sequences.
Motivation. Intrinsically Disordered Proteins (IDPs) lack stable tertiary structure and they actively participate in performing various biological functions. These IDPs expose short binding regions called Molecular Recognition Features (MoRFs) that permit interaction with structured protein regions. Upon interaction they undergo a disorder-to-order transition as a result of which their functionality arises. Predicting these MoRFs in disordered protein sequences is a challenging task.
Method. In this study, we present MoRFpred-plus, an improved predictor over our previous proposed predictor to identify MoRFs in disordered protein sequences. Two separate independent propensity scores are computed via incorporating physicochemical properties and HMM profiles, these scores are combined to predict final MoRF propensity score for a given residue. The first score reflects the characteristics of a query residue to be part of MoRF region based on the composition and similarity of assumed MoRF and flank regions. The second score reflects the characteristics of a query residue to be part of MoRF region based on the properties of flanks associated around the given residue in the query protein sequence. The propensity scores are processed and common averaging is applied to generate the final prediction score of MoRFpred-plus.
Results. Performance of the proposed predictor is compared with available MoRF predictors, MoRFchibi, MoRFpred, and ANCHOR. Using previously collected training and test sets used to evaluate the mentioned predictors, the proposed predictor outperforms these predictors and generates lower false positive rate. In addition, MoRFpred-plus is a downloadable predictor, which makes it useful as it can be used as input to other computational tools.
Availability.https://github.com/roneshsharma/MoRFpred-plus/wiki/MoRFpred-plus:-Download
We define how chronic cigarette smoke-induced time-dependent epigenetic alterations can sensitize human bronchial epithelial cells for transformation by a single oncogene. The smoke-induced chromatin ...changes include initial repressive polycomb marking of genes, later manifesting abnormal DNA methylation by 10 months. At this time, cells exhibit epithelial-to-mesenchymal changes, anchorage-independent growth, and upregulated RAS/MAPK signaling with silencing of hypermethylated genes, which normally inhibit these pathways and are associated with smoking-related non-small cell lung cancer. These cells, in the absence of any driver gene mutations, now transform by introducing a single KRAS mutation and form adenosquamous lung carcinomas in mice. Thus, epigenetic abnormalities may prime for changing oncogene senescence to addiction for a single key oncogene involved in lung cancer initiation.
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•Chronic smoke exposure causes sequential chromatin changes leading to gene silencing•Silenced genes are normally polycomb controlled but adopt abnormal DNA methylation•Gene methylation causes sequential upregulation of key signal transduction pathways•Epigenetic alterations sensitize cells to transformation by a single oncogenic event
Vaz et al. show that long-term exposure of untransformed human bronchial epithelial cells to cigarette smoke condensate induces epigenetic changes, consistent with those commonly seen in smoking-related non-small cell lung cancer, that sensitize the cells to transformation with a single KRAS mutation.
Molecular Recognition Features (MoRFs) are short protein regions present in intrinsically disordered protein (IDPs) sequences. MoRFs interact with structured partner protein and upon interaction, ...they undergo a disorder-to-order transition to perform various biological functions. Analyses of MoRFs are important towards understanding their function.
Performance is reported using the MoRF dataset that has been previously used to compare the other existing MoRF predictors. The performance obtained in this study is equivalent to the benchmarked OPAL predictor, i.e., OPAL achieved AUC of 0.815, whereas the model in this study achieved AUC of 0.819 using TEST set.
Achieving comparable performance, the proposed method can be used as an alternative approach for MoRF prediction.
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