The ability to predict local structural features of a protein from the primary sequence is of paramount importance for unraveling its function in absence of experimental structural information. Two main factors affect the utility of potential prediction tools: their accuracy must enable extraction of reliable structural information on the proteins of interest, and their runtime must be low to keep pace with sequencing data being generated at a constantly increasing speed. Here, we present NetSurfP-2.0, a novel tool that can predict the most important local structural features with unprecedented accuracy and runtime. NetSurfP-2.0 is sequence-based and uses an architecture composed of convolutional and long short-term memory neural networks trained on solved protein structures. Using a single integrated model, NetSurfP-2.0 predicts solvent accessibility, secondary structure, structural disorder, and backbone dihedral angles for each residue of the input sequences. We assessed the accuracy of NetSurfP-2.0 on several independent test datasets and found it to consistently produce state-of-the-art predictions for each of its output features. We observe a correlation of 80% between predictions and experimental data for solvent accessibility, and a precision of 85% on secondary structure 3-class predictions. In addition to improved accuracy, the processing time has been optimized to allow predicting more than 1000 proteins in less than 2 hours, and complete proteomes in less than 1 day.
Separation of proteins based on the physicochemical properties with different molecular weight and isoelectric points would be more accurate. In the current research, the 45-day-old seedlings were treated with 0 (control) and 12 dS m(-1) of sodium chloride in the hydroponic system. After 15 days of salt exposure, the total protein of the fresh leaves and roots was extracted and analyzed using two-dimensional electrophoresis system (2-DE). The analysis led to the detection of 32 induced proteins (19 proteins in leaf and 13 proteins in the root) as well as 12 upregulated proteins (four proteins in leaf and eight proteins in the root) in the salt-treated plants. Of the 44 detected proteins, 12 were sequenced, and three of them matched with superoxide dismutase, ascorbate peroxidase and ribulose-1, 5-bisphosphate oxygenase whereas the rest remained unknown. The three known proteins associate with plants response to environmental stresses and could represent the general stress proteins in the present study too. In addition, the proteomic feedback of different accessions of A. paniculata to salt stress can potentially be used to breed salt-tolerant varieties of the herb.
Gynura procumbens (Lour.) Merr. is a traditionally used medicinal plant to decrease cholesterol level, reduce high blood pressure, control diabetics, and for treatment of cancer. In our present study, a proteomic approach was applied to study the proteome of the plant that had never analyzed before. We have identified 92 abundantly expressed proteins from the leaves of G. procumbens (Lour.) Merr. Amongst the identified proteins was miraculin, a taste-masking agent with high commercial value. Miraculin made up ∼0.1% of the total protein extracted; the finding of miraculin gave a great commercial value to G. procumbens (Lour.) Merr. as miraculin's natural source is limited while the production of recombinant miraculin faced challenges of not being able to exhibit the taste-masking effect as in the natural miraculin. We believe the discovery of miraculin in G. procumbens (Lour.) Merr., provides commercial feasibility of miraculin in view of the availability of G. procumbens (Lour.) Merr. that grow wildly and easily in tropical climate.
Effective protein extraction is essential especially in producing a well-resolved proteome on 2D gels. A well-resolved placental cotyledon proteome, with good reproducibility, have allowed researchers to study the proteins underlying the physiology and pathophysiology of pregnancy. The aim of this study is to determine the best protein extraction protocol for the extraction of protein from placental cotyledons tissues for a two-dimensional gel electrophoresis (2D-GE). Based on widely used protein extraction strategies, 12 different extraction methodologies were carefully selected, which included one chemical extraction, two mechanical extraction coupled protein precipitations, and nine chemical extraction coupled protein precipitations. Extracted proteins were resolved in a one-dimensional gel electrophoresis and 2D-GE; then, it was compared with set criteria: extraction efficacy, protein resolution, reproducibility, and recovery efficiency. Our results revealed that a better profile was obtained by chemical extraction in comparison to mechanical extraction. We further compared chemical extraction coupled protein precipitation methodologies, where the DNase/lithium chloride-dense sucrose homogenization coupled dichloromethane-methanol precipitation (DNase/LiCl-DSH-D/MPE) method showed good protein extraction efficiency. This, however, was carried out with the best protein resolution and proteome reproducibility on 2D-gels. DNase/LiCl-DSH-D/MPE was efficient in the extraction of proteins from placental cotyledons tissues. In addition, this methodology could hypothetically allow the protein extraction of any tissue that contains highly abundant lipid and glycogen.
Palm oil is an edible vegetable oil derived from lipid-rich fleshy mesocarp tissue of oil palm (Elaeis guineensis Jacq.) fruit and is of global economic and nutritional relevance. While the understanding of oil biosynthesis in plants is improving, the fundamentals of oil biosynthesis in oil palm still require further investigations. To gain insight into the systemic mechanisms that govern oil synthesis during oil palm fruit ripening, the proteomics approach combining gel-based electrophoresis and mass spectrometry was used to profile protein changes and classify the patterns of protein accumulation during these complex physiological processes. Protein profiles from different stages of fruit ripening at 10, 12, 14, 15, 16, 18 and 20 weeks after anthesis (WAA) were analysed by two-dimensional gel electrophoresis (2DE). The proteome data were then visualised using a multivariate statistical analysis of principal component analysis (PCA) to get an overview of the proteome changes during the development of oil palm mesocarp. A total of 68 differentially expressed protein spots were successfully identified by matrix-assisted laser desorption/ionisation-time of flight (MALDI-TOF/TOF) and functionally classified using ontology analysis. Proteins related to lipid production, energy, secondary metabolites and amino acid metabolism are the most significantly changed proteins during fruit development representing potential candidates for oil yield improvement endeavors. Data are available via ProteomeXchange with identifier PXD009579. This study provides important proteome information for protein regulation during oil palm fruit ripening and oil synthesis.