1.
Proteomic and Bioinformatic Profiling of Transporters in Higher Plant Mitochondria.
Møller, IM, Rao, RSP, Jiang, Y, Thelen, JJ, Xu, D
Biomolecules. 2020;(8)
Abstract
To function as a metabolic hub, plant mitochondria have to exchange a wide variety of metabolic intermediates as well as inorganic ions with the cytosol. As identified by proteomic profiling or as predicted by MU-LOC, a newly developed bioinformatics tool, Arabidopsis thaliana mitochondria contain 128 or 143 different transporters, respectively. The largest group is the mitochondrial carrier family, which consists of symporters and antiporters catalyzing secondary active transport of organic acids, amino acids, and nucleotides across the inner mitochondrial membrane. An impressive 97% (58 out of 60) of all the known mitochondrial carrier family members in Arabidopsis have been experimentally identified in isolated mitochondria. In addition to many other secondary transporters, Arabidopsis mitochondria contain the ATP synthase transporters, the mitochondria protein translocase complexes (responsible for protein uptake across the outer and inner membrane), ATP-binding cassette (ABC) transporters, and a number of transporters and channels responsible for allowing water and inorganic ions to move across the inner membrane driven by their transmembrane electrochemical gradient. A few mitochondrial transporters are tissue-specific, development-specific, or stress-response specific, but this is a relatively unexplored area in proteomics that merits much more attention.
2.
Prediction and Analysis of Post-Translational Pyruvoyl Residue Modification Sites from Internal Serines in Proteins.
Jiang, Y, Li, BQ, Zhang, Y, Feng, YM, Gao, YF, Zhang, N, Cai, YD
PloS one. 2013;(6):e66678
Abstract
Most of pyruvoyl-dependent proteins observed in prokaryotes and eukaryotes are critical regulatory enzymes, which are primary targets of inhibitors for anti-cancer and anti-parasitic therapy. These proteins undergo an autocatalytic, intramolecular self-cleavage reaction in which a covalently bound pyruvoyl group is generated on a conserved serine residue. Traditional detections of the modified serine sites are performed by experimental approaches, which are often labor-intensive and time-consuming. In this study, we initiated in an attempt for the computational predictions of such serine sites with Feature Selection based on a Random Forest. Since only a small number of experimentally verified pyruvoyl-modified proteins are collected in the protein database at its current version, we only used a small dataset in this study. After removing proteins with sequence identities >60%, a non-redundant dataset was generated and was used, which contained only 46 proteins, with one pyruvoyl serine site for each protein. Several types of features were considered in our method including PSSM conservation scores, disorders, secondary structures, solvent accessibilities, amino acid factors and amino acid occurrence frequencies. As a result, a pretty good performance was achieved in our dataset. The best 100.00% accuracy and 1.0000 MCC value were obtained from the training dataset, and 93.75% accuracy and 0.8441 MCC value from the testing dataset. The optimal feature set contained 9 features. Analysis of the optimal feature set indicated the important roles of some specific features in determining the pyruvoyl-group-serine sites, which were consistent with several results of earlier experimental studies. These selected features may shed some light on the in-depth understanding of the mechanism of the post-translational self-maturation process, providing guidelines for experimental validation. Future work should be made as more pyruvoyl-modified proteins are found and the method should be evaluated on larger datasets. At last, the predicting software can be downloaded from http://www.nkbiox.com/sub/pyrupred/index.html.
3.
Predict potential drug targets from the ion channel proteins based on SVM.
Huang, C, Zhang, R, Chen, Z, Jiang, Y, Shang, Z, Sun, P, Zhang, X, Li, X
Journal of theoretical biology. 2010;(4):750-6
Abstract
The identification of molecular targets is a critical step in the drug discovery and development process. Ion channel proteins represent highly attractive drug targets implicated in a diverse range of disorders, in particular in the cardiovascular and central nervous systems. Due to the limits of experimental technique and low-throughput nature of patch-clamp electrophysiology, they remain a target class waiting to be exploited. In our study, we combined three types of protein features, primary sequence, secondary structure and subcellular localization to predict potential drug targets from ion channel proteins applying classical support vector machine (SVM) method. In addition, our prediction comprised two stages. In stage 1, we predicted ion channel target proteins based on whole-genome target protein characteristics. Firstly, we performed feature selection by Mann-Whitney U test, then made predictions to identify potential ion channel targets by SVM and designed a new evaluating indicator Q to prioritize results. In stage 2, we made a prediction based on known ion channel target protein characteristics. Genetic algorithm was used to select features and SVM was used to predict ion channel targets. Then, we integrated results of two stages, and found that five ion channel proteins appeared in both prediction results including CGMP-gated cation channel beta subunit and Gamma-aminobutyric acid receptor subunit alpha-5, etc., and four of which were relative to some nerve diseases. It suggests that these five proteins are potential targets for drug discovery and our prediction strategies are effective.