ExPASy Research Articles

Variability in linear polypeptide stabilizes proteoglycan than zinc finger protein in vascular smooth muscle cells: An In Silico approach

Background: Structural and physicochemical topologies of proteins play a considerable role in differentiating the functional properties of the biological system. We aimed to study the physicochemical similarities, structural and functional differences of versican (VCAN) and early growth response (Egr) proteins involved in vascular injuries. Methods: For the primary structure prediction, the proteomic tools Expasy's Protparam is used, likewise, for secondary structure and content prediction SOPM and SOPMA tool is used. The transmembrane regions in VCAN and EGR proteins are predicted through SOSUI (Classification and Secondary Structure Prediction of Membrane Proteins) server. The CYSREC tool is used to identify the presence of disulphide bonds in all the VCAN and EGR proteins, additionally through homology modelling the disulphide bonds are visualized and structure of the modelled proteins are validated through Rampage (Ramachandran plot), ProQ (Protein Quality Server) and ProSA (Protein Structure Analysis) server. Results: VCAN and Egr proteins resemble hydrophilic in nature, similarly negative score of the grand average of hydropathicity index confirms hydrophilic nature. The maximum molecular weight for VCAN is observed as 39265 and 61623 Dalton for EGR protein. VCAN proteins showed a higher level of basic residues except Q86W61, while all the Egr proteins were acidic residues. The extinction coefficient (EC) has unique absorbance at 280 nm wavelength. Based on the aliphatic index (AI ≥ 45) and instability index (II ≥ 40) most of the VCAN and Egr proteins were unstable. The Classification and Secondary Structure Prediction of Membrane Proteins server classifies all Egr and few VCAN and proteins are soluble nature. Secondary structure content prediction and SOPM server show most of the VCAN proteins are beta sheets and many Egr proteins are alpha-helical, while few with mixed structures. Besides these differences, the VCAN protein stability was identified by most probable disulfide (SS) bridges using CYS_REC tool and confirmed by homology modeling in tertiary structure. Whereas the probable disulfide bonds in Egr proteins were not identified. Conclusion: The findings with these functional and structural properties will add an extra room in understanding their dual role.

Structure and function prediction analysis of Leptin

Objective Leptin is an important protein hormone produced by white adipose tissue, which participates in the regulation of energy metabolism. Its research on improving metabolic mechanism after bariatric surgery is attracting more and more attention. The purpose of this study is to analyze Leptin’s structure and predict its function by bioinformatics, in order to further explore the role of leptin in the treatment of obesity by bariatric surgery. Methods Tools of ExPASy Server were employed to analyze the physicochemical properties of Leptin protein. The secondary structure was predicted by Jpred 4 and its three-level structure by Swiss-Mode. STRINGS database is used to predict interactions between Leptin and other proteins. Results The Leptin protein consisted of 167 amino acids with an isoelectric point of 5.88, an instability index of 47.52, and a fat coefficient of 110.84. There were 5 α-helicesand 1 β-sheet in the secondary structure prediction. The number of amino acids constituting alpha helix accounted for 50.9% of the total protein. Three possible tertiary structure models were simulated. From the prediction model, the protein was mainly alpha-helix, which was consistent with the prediction results of secondary structure. According to the trend of similar waveform, prediction model A is the most stable. The protein interaction network found that the protein families interacting with Leptin include Leptin receptor (LEPR), Signal transducer and activator of transcription 3 (STAT3), Peroxisome proliferator-activated receptor gamma (PPARG), Suppressor of cytokine signaling 3 (SOCS3), Glucagon (GCG), Tyrosine-protein kinase JAK2, Insulin receptor substrate 1 (IRS1), C-reactive protein (CRP), Tyrosine-protein phosphatase non-receptor type 1 (PTPN1) and Insulin (INS). Conclusion Leptin protein is an unstable lipophilic protein mainly composed of alpha-helix. It is secreted by white adipose tissue and mainly acts on the outside of cells. It inhibits the synthesis of adipocytes by participating in the regulation of sugar, fat and energy metabolism, thus reducing body weight. Key words: Leptin; Bariatric Surgery; Bioinformatics

Biopython Project Update 2017

The Biopython Project is a long-running distributed collaborative effort, supported by the Open Bioinformatics Foundation, which develops a freely available Python library for biological computation [1]. We present here details of the Biopython releases since BOSC 2016, namely Biopython 1.68, 1.69 and 1.70. Together these had 82 named contributors including 51 newcomers which reflects our policy of trying to encourage even small contributions.Biopython 1.68 (August 2016) was a relatively small release, with the main new feature being support for RSSB’s new binary Macromolecular Transmission Format (MMTF) for structural data.Biopython 1.69 (April 2017) represents the start of our re-licensing plan, to transition away from our liberal but unique Biopython License Agreement to the similar but very widely used 3-Clause BSD License. We are reviewing the code base authorship file-by-file, in order to gradually dual license the entire project. Major new features include: a new parser for the ExPASy Cellosaurus cell line database, catalogue and ontology; support for the UCSC Multiple Alignment Format (MAF), FSA sequencing files, version 4 of the Affymetrix CEL format; updates to the REBASE February 2017 restriction enzyme list; Bio.PDB.PDBList now can download more formats including MMTF; enhanced PyPy support by taking advantage of NumPy and compiling most of the Biopython C code modules.Biopython 1.70 (July 2017) has internal changes to better support the now standard pip tool for Python package installation. Major new features include: support for Mauve’s eXtended Multi-FastA (XMFA) file format, updates to our BLAST XML and MEME parsers, ExPASy support, and phylogenetic distance matrices. This release is noteworthy for our new logo, contributed by Patrick Kunzmann. This draws on our original double helix logo, and the blue and yellow colors of the current Python logo.All releases fixed miscellaneous bugs, enhanced the test suite, and continued efforts to follow the PEP8 and PEP257 coding style guidelines which is now checked automatically with GitHub-integrated continuous integration testing using TravisCI. We now also use AppVeyor for continuous integration testing under Windows. Current efforts include improving the unit test coverage, which is easily viewed online at CodeCov.io.

In silico sequence analysis of predicted beta-amylase 7-like protein in Juglans regia L.

Walnut ( Juglans regia L.) is a deciduous tree of the Juglandaceae family. Beta-amylase (β-amylase, EC 3.2.1.2) is an enzyme that catalyses hydrolysis of glycosidic bonds in polysaccharides. In this study; sequence, physicochemical, and three-dimensional analyses of predicted β-amylase 7-like protein in Juglans regia using various bioinformatic tools were conducted. The physicochemical properties of the predict β-amylase 7-like protein were analyzed by using ExPASy ProtParam tool that revealed the molecular weight (MW), Isoelectric Points ( pI ), total number of negatively charged residues (Asp + Glu), total number of positively charged residues (Arg + Lys), instability index, aliphatic index, and GRAVY (Grand Average of Hydropathy) values. Subcellular localization using CELLO v.2.5, putative phosphorylation sites using NetPhos 3.1 server, domain analysis using Pfam, and secondary structure prediction using SOPMA were accom-plished. To predict the 3D structure of the predict β-amylase 7-like protein, homology models were applied using PSIPRED, RAMPAGE, and PyMOL programs. The results of our study provide insight into fundamental characteristics of the predicted β-amylase 7-like protein in Juglans regia.

Proteomics of Marburg and Ebola glycoproteins: Insights into their physicochemical similarities and irregularities

Ebola (EBO) and Marburg (MBG) form the two major genus of a class of negatively stranded RNA viruses responsible for viral hemorrhagic fevers (VHF) in equatorial Africa called filoviridae. Pathogenesis of VHF is thought to be mediated by the envelope glycoproteins (GP). Although both viruses have been found to be closely related, with a side by side alignment of the gene sequences of the Ebola and Marburg viruses and comparison of protein products showing similarities, there is absence of immunological cross reactivity. The physicochemical properties of the two GP may in part explain this. Using a 676 aa Ebola and 681 aa Marburg GP protein sequences; and the ExPASy Server ProtParam, the physicochemical properties of both proteins were derived and compared. The MBG-GP molecular weight was bigger and more negatively charged than EBO (MW=74481 vs.74464.4 and overall charge=-11 vs.-9). Other constants analyzed respectively: for MBG versus EBO (1) theoretical PI: 5.85 vs. 6.16, (2) extinction coefficient (82110/1.102 vs 100100/1.344 M-1 cm-1 at 280 nm/H2O). The Estimated half life for both glycoproteins was 30 h, although MBG-GP was unstable compared to EBO-GP (The instability index [II]; MBG vs EBO = 42.82 vs. 38.36). These variations must be taken into account in research and development of a common vaccine against both MBG and EBO. Key words: Ebola, Marburg, glycoproteins, proteomics, vaccine research.