Protein and Peptide Letters - Volume 25, Issue 9, 2018

Background: Proteins through post translational modifications perform their biological process and cellular functions. Post-translational modifications play important roles in various biological process and cell functions. Identifying the PTMs sites in proteins is very significant to basic research and drug design. Experimental technique to identify post translational modifications is laborious. Computational identification of post translational modifications is a complementary way for its convenience. Conclusion: This review gives the processing to predict post-translational modification sites in proteins including feature construction, algorithms, evaluation measurement, and online webserver. There are two types of post translational modification in proteins. In the prediction of single PTM sites, we transformed it into binary classification learning. While in the prediction of crosstalk PTM sites, we transformed it into multi-label learning. This review summarized the steps on the two issues.

Background: Cysteine S-sulfenylation is a major type of dynamic post-translational modification of the protein that plays an important role in regulating many biological processes in both of prokaryotic and eukaryotic species. To understand the function of S-sulfenylated proteins, identification of S-sulfenylation sites is an essential step. Due to numerous restrictions of experimental methods, computational prediction of the potential S-sulfenylation sites becomes popular. In this review, we discuss the recent development and challenges in protein S-sulfenylation site prediction from the available datasets, algorithms and accessible services. We also demonstrate the encountered limitation and future perspective of the computational prediction tools. Conclusion: The development of S-sulfenylation site prediction and their application is an emerging field of protein bioinformatics research. Accurate predictors are expected to identify general and species-specific S-sulfenylation sites when more experimental annotation data are available. Combining experimental and computational technologies will definitely accelerate an understanding of protein S-sulfenylation, discovering regulatory networks in living organisms.

Background: In the post-genome age, it is more urgent to understand the functions of genes and proteins. Since experimental methods are usually costly and time consuming, computational predictions are recognized as an alternative approach. In developing a predictive method for functional genomics and proteomics, one of the most important steps is to represent biological sequences with a fixed length numerical form, which can be further analyzed using machine learning algorithms. Chou’s pseudo-amino acid compositions and the pseudo k-nucleotide compositions are algorithms for this purpose. Conclusion: Since the appearance of these algorithms, several software tools have been developed as implementations. These software tools facilitate the application of these algorithms. As these software tools are developed with different technologies and for different application scenarios, we will briefly review the technical aspect of these software tools in this short review.

Background: During recent years, a lot of experimental studies have shown that lncRNA-binding proteins play a key role in many biomedical processes. Therefore, it is important to predict the potential lncRNA-protein associations in biomedical researches. Objective: To predict the associations between lncRNAs and proteins more reliably and more efficiently. Method: Considering the limitations of previous computational methods, we introduce a predictive model called Random Walk for lncRNA-Protein Associations Prediction (RWLPAP). It belongs to semi-supervised learning algorithms, and thus RWLPAP successfully avoids the difficulty of extracting negative data sets and features. Results: By the leave-one-out cross validation, we compare RWLPAP with previous methods and conclude that RWLPAP has an AUC of 0.88, which is significantly higher than other three models. It suggests that RWLPAP is more reliable and effective in predicting the interactions between lncRNAs and proteins. Conclusion: In the case study, according to the rank of predictive scores, we can find that the scores of some lncRNA-protein associations are highly ranking by our method when is compared with other three methods. It indicates that our method is very effective and comprehensive. Therefore, we can expect that RWLPAP will be a useful bioinformatic tool in the future.

Background: Amyloids could be created under destabilizing conditions from various proteins. Having high chemical reactive groups makes the amyloid fibers suitable for enzyme stabilization. Imobilization of lipase as one of the stable classes of high catalytic power enzymes could be very valuable. Objective: In the present study, the lipase from Pseudomonas cepacia was immobilized on BSA amyloid nano-biofibrils and the kinetic parameters were compared with those of its free counterpart. The possibility of using this nano-material as a new nano-scaffold for lipase immobilization was investigated. Method: Response surface methodology was used in this study to produce the maximum amounts of amyloid fibrils using Design Expert 7 software. Transmission electron microscopy was employed to confirm the presence of amyloid fibers. The stabilization process was performed by glutaraldehyde mediated covalent cross–links between the enzyme and amyloid fibers. Kinetic parameters including activity, specific activity, optimal pH and temperature and thermal stability of immobilized enzyme were compared with the free counterpart. Results: The optimum conditions for fibrillogenesis were obtained at 4.36 mg.ml-1 of protein after 72 hours of mild agitation in a mixed citrate-phosphate buffer at the pH of 4.5 and the temperature of 80 ºC. The kinetic parameters of the immobilized lipase were improved in terms of activity, specific activity, Km and Vmax, optimal pH and temperature and thermal stability at 40 ºC. Amyloid fibrils with a diameter of less than 100 nm, as a new nano–scaffold, increased both the stability of lipase and other kinetic properties of the enzyme. Conclusion: Amyloid fibrils as a new chemically–rich nano–scaffold could be an appropriate matrix for lipase immobilization.

Background: Marine sponges, belonging to the phylum Porifera, are gaining more attention by researchers and industrial sectors from all over the world due to their ability to produce a variety of bioactive secondary metabolites that have many applications including drug discovery. Marine sponges are a promising source of bioactive lectins, which are structurally diverse, many of them in the form of glycoproteins. Objective: To purify and characterize lectin from a marine sponge Fasciospongia cavernosa and to study its antibacterial activity. Method: Lectin from a marine sponge Fasciospongia cavernosa was purified by guar gum affinity chromatography and checked for its biophysical and antibacterial properties. The lectin was subjected to evaluation for inhibition of microbial growth against bacteria by aggregation test. The activity of FCL against the biofilms formed by P. aeruginosa was also carried out. Biofilm is defined as the undesirable accumulation of microorganisms on artificial surfaces immersed in a common matrix. The effect of FCL on biofilm-forming gram negative bacteria P. aeruginosa was tested by crystal violet assay. Results: This lectin, named FCL, has a molecular weight of 80 KDa approximately, was found to agglutinate human ABO, rat, rabbit and chicken erythrocytes. The hemagglutinating activity of FCL was reduced by demetallisation with E.D.T.A and regained by the addition of Ca2+, Mg2+, Mn2+, Ba2+ and Fe2+, which shows the metal dependency of the lectin. The hemagglutinating activity by the lectin was inhibited by D-galactose and N-acetyl-D-galactosamine. The lectin was stable over a range of pH from 2 to 10.5, and up to a temperature 70°C for 20 min. FCL agglutinated B. subtilis, S. aureus and P. aeruginosa and was able to reduce biofilm mass formed by P. aeruginosa. Thus, the marine sponge F. cavernosa lectin, FCL could be used as an antibacterial agent. FCL significantly reduced the biomass of bacterial biofilm tested. Biofilm mass of P. aeruginosa, K. pneumoniae and E. coli were decreased in a dose dependent manner. Conclusion: A novel lectin was isolated and purified from marine sponge F. cavernosa. FCL, a galactose-binding lectin displayed considerable antimicrobial activity in vitro, particularly against gram-positive bacteria and also exhibited a strong antibiofilm activity.

Background: Rheumatoid arthritis is the most common inflammatory autoimmune disease in the world. Recently new targets for its detection were developed as alternatives to classic biomarkers, including the M-12 peptide, that mimics carbonic anhydrase III. Thus, the application of this peptide for the development of new detection devices is attractive. Objective: Our goal was to construct a modified electrode for immobilization of M-12 peptide and detection of a rheumatoid arthritis biomarker in serum of patients. Methods: 3-Hydroxybenzoic acid was electropolymerized onto graphite electrodes, and M-12 peptide was immobilized by adsorption. Negative and positive serum samples for rheumatoid arthritis were diluted and applied onto the electrode. Detection was carried in potassium ferrocyanide/ ferricyanide solution by differential pulse voltammetry. Atomic force microscopy and scanning electron microscopy were used to evaluate electrode surfaces. Results: Cyclic voltammograms indicated the poly(3-hydroxybenzoic acid) formation and increase of electroactive area. Immobilization of M-12 probe increased current by 1.2 times, and negative serum addition caused no suitable difference. However, positive serum showed expressive decrease in the current signal of about 2.2 times, possibly due to steric hindrance when the anti-CA3 antibody interacts with the M-12 peptide, decreasing the electron transfer. Microscopies images corroborated with the electrochemical detection, showing evident changes in the morphology of the electrode surfaces. Conclusion: The bioelectrode was able to discriminate positive and negative serum samples of rheumatoid arthritis by a considerable decrease in the current signal value. Morphological analyses supported the electrochemical results. Thus, the constructed bioelectrode offers a new platform for detection of rheumatoid arthritis.