Prediction of the subcellular location of apoptosis proteins

Apoptosis proteins have a central role in the development and the homeostasis of an organism. These proteins are very important for understanding the mechanism of programmed cell death. The function of an apoptosis protein is closely related to its subcellular location. Based on the concept that the subcellular location of an apoptosis protein is mainly determined by its amino acid sequence, a new algorithm for prediction of the subcellular location of an apoptosis protein is proposed. By using of a distinctive set of information parameters derived from the primary sequence of 317 apoptosis proteins, the increment of diversity (ID), the sole prediction parameter, is calculated. The higher predictive success rates than the previous other algorithms is obtained by the jackknife tests using the expanded dataset. Our prediction results show that the local compositions of twin amino acids and hydropathy distribution are very useful to predict subcellular location of protein.     

Using neural networks for prediction of the subcellular location of proteins.

Neural networks have been trained to predict the subcellular location of proteins in prokaryotic or eukaryotic cells from their amino acid composition. For three possible subcellular locations in prokaryotic organisms a prediction accuracy of 81% can be achieved. Assigning a reliability index, 33% of the predictions can be made with an accuracy of 91%. For eukaryotic proteins (excluding plant sequences) an overall prediction accuracy of 66% for four locations was achieved, with 33% of the sequences being predicted with an accuracy of 82% or better. With the subcellular location restricting a protein's possible function, this method should be a useful tool for the systematic analysis of genome data and is available via a server on the world wide web.     

Crystallization of soluble proteins in vapor diffusion for x-ray crystallography

The preparation of protein single crystals represents one of the major obstacles in obtaining the detailed 3D structure of a biological macromolecule. The complete automation of the crystallization procedures requires large investments in terms of money and labor, which are available only to large dedicated infrastructures and is mostly suited for genomic-scale projects. On the other hand, many research projects from departmental laboratories are devoted to the study of few specific proteins. Here, we try to provide a series of protocols for the crystallization of soluble proteins, especially the difficult ones, tailored for small-scale research groups. An estimate of the time needed to complete each of the steps described can be found at the end of each section.     

GNBSL: a new integrative system to predict the subcellular location for Gram-negative bacteria proteins

This paper proposes a new integrative system (GNBSL--Gram-negative bacteria subcellular localization) for subcellular localization specifized on the Gram-negative bacteria proteins. First, the system generates a position-specific frequency matrix (PSFM) and a position-specific scoring matrix (PSSM) for each protein sequence by searching the Swiss-Prot database. Then different features are extracted by four modules from the PSFM and the PSSM. The features include whole-sequence amino acid composition, N- and C-terminus amino acid composition, dipeptide composition, and segment composition. Four probabilistic neural network (PNN) classifiers are used to classify these modules. To further improve the performance, two modules trained by support vector machine (SVM) are added in this system. One module extracts the residue-couple distribution from the amino acid sequence and the other module applies a pairwise profile alignment kernel to measure the local similarity between every two sequences. Finally, an additional SVM is used to fuse the outputs from the six modules. Test on a benchmark dataset shows that the overall success rate of GNBSL is higher than those of PSORT-B, CELLO, and PSLpred. A web server GNBSL can be visited from http://166.111.24.5/webtools/GNBSL/index.htm.     

Combining experimental and predicted datasets for determination of the subcellular location of proteins in Arabidopsis

Substantial experimental datasets defining the subcellular location of Arabidopsis (Arabidopsis thaliana) proteins have been reported in the literature in the form of organelle proteomes built from mass spectrometry data (approximately 2,500 proteins). Subcellular location for specific proteins has also been published based on imaging of chimeric fluorescent fusion proteins in intact cells (approximately 900 proteins). Further, the more diverse history of biochemical determination of subcellular location is stored in the entries of the Swiss-Prot database for the products of many Arabidopsis genes (approximately 1,800 proteins). Combined with the range of bioinformatic targeting prediction tools and comparative genomic analysis, these experimental datasets provide a powerful basis for defining the final location of proteins within the wide variety of subcellular structures present inside Arabidopsis cells. We have analyzed these published experimental and prediction data to answer a range of substantial questions facing researchers about the veracity of these approaches to determining protein location and their interrelatedness. We have merged these data to form the subcellular location database for Arabidopsis proteins (SUBA), providing an integrated understanding of protein location, encompassing the plastid, mitochondrion, peroxisome, nucleus, plasma membrane, endoplasmic reticulum, vacuole, Golgi, cytoskeleton structures, and cytosol (www.suba.bcs.uwa.edu.au). This includes data on more than 4,400 nonredundant Arabidopsis protein sequences. We also provide researchers with an online resource that may be used to query protein sets or protein families and determine whether predicted or experimental location data exist; to analyze the nature of contamination between published proteome sets; and/or for building theoretical subcellular proteomes in Arabidopsis using the latest experimental data.     

Improved prediction of subcellular location for apoptosis proteins by the dual-layer support vector machine

Apoptosis proteins play an important role in the development and homeostasis of an organism. The accurate prediction of subcellular location for apoptosis proteins is very helpful for understanding the mechanism of apoptosis and their biological functions. However, most of the existing predictive methods are designed by utilizing a single classifier, which would limit the further improvement of their performances. In this paper, a novel predictive method, which is essentially a multi-classifier system, has been proposed by combing a dual-layer support vector machine (SVM) with multiple compositions including amino acid composition (AAC), dipeptide composition (DPC) and amphiphilic pseudo amino acid composition (Am-Pse-AAC). As a demonstration, the predictive performance of our method was evaluated on two datasets of apoptosis proteins, involving the standard dataset ZD98 generated by Zhou and Doctor, and a larger dataset ZW225 generated by Zhang et al. With the jackknife test, the overall accuracies of our method on the two datasets reach 94.90% and 88.44%, respectively. The promising results indicate that our method can be a complementary tool for the prediction of subcellular location.     

When trafficking and signaling mix: How subcellular location shapes G protein‐coupled receptor activation of heterotrimeric G proteins

G protein‐coupled receptors (GPCRs) physically connect extracellular information with intracellular signal propagation. Membrane trafficking plays a supportive role by “bookending” signaling events: movement through the secretory pathway delivers GPCRs to the cell surface where receptors can sample the extracellular environment, while endocytosis and endolysosomal membrane trafficking provide a versatile system to titrate cellular signaling potential and maintain homeostatic control. Recent evidence suggests that, in addition to these important effects, GPCR trafficking actively shapes the cellular signaling response by altering the location and timing of specific receptor‐mediated signaling reactions. Here, we review key experimental evidence underlying this expanding view, focused on GPCR signaling mediated through activation of heterotrimeric G proteins located in the cytoplasm. We then discuss lingering and emerging questions regarding the interface between GPCR signaling and trafficking.      

The metabotropic glutamate receptor (MGR): pharmacology and subcellular location.

A pharmacological characterization of the metabotropic glutamate receptor (MGR) was performed in striatal neurons. Among the excitatory amino acid receptor antagonists tested, only D, L-2-amino-3-phosphonopropionate (D, L-AP3) inhibited QA-induced inositol phosphate (InsP) formation in a competitive manner (mean pKi = 4.45 +/- 0.43, n = 4). However, this drug was a partial agonist of MGR since it stimulated the inositol-phosphate formation. We found that D, L-AP3 also inhibited NMDA-induced calcium increase, in a competitive manner (mean pIC50 = 4.34 +/- 0.22, n = 8, and mean pKi = 3.7 +/- 0.11 n = 5). 1 mM of the ionotropic agonists alpha-amino-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA), kainate (KA) or domoate (DO) (100 microM or higher) induced a significant InsP formation in striatal neurons. The InsP responses induced by all these agonists were totally blocked by the phorbol ester phorbol-12,13-dibutyrate (PdBu), but not by atropine or prazosin. Agonist-induced increases of intracellular calcium concentrations ([Ca2+]i) were insensitive to PdBu, suggesting that all these substances were able to stimulate the MGR in striatal neurons. Trans-1-amino-cyclopentyl-1,3-dicarboxylate (trans-ACPD) evoked dose-dependent inositol phosphate formations with an EC50 of 29 microM but had no significant effect on NMDA or AMPA receptors, as measured by the patch clamp technique. In the presence of 30 microM of AMPA, trans-ACPD induced a significant release of arachidonic acid (AA) in striatal neurons. No important AA release was observed by any of these agonists alone. 56 mM K+ did not mimic AMPA in this associative ionotropic/metabotropic effect.(ABSTRACT TRUNCATED AT 250 WORDS)     

Control of glucocorticoid and progesterone receptor subcellular localization by the ligand-binding domain is mediated by distinct interactions with tetratricopeptide repeat proteins

The TPR proteins FKBP52, FKBP51, Cyp40, and PP5 are found in steroid receptor (SR) complexes, but their receptor-specific preferences and roles remain unresolved. We have undertaken a systematic approach to this problem by examining the contribution of all four TPRs to the localization properties of glucocorticoid (GR) and progesterone (PR) receptors. The GR of L929 cells was found in the cytoplasm in a complex containing PP5 and FKBP51, while the GR of WCL2 cells was nuclear and contained PP5 and FKBP52. Cyp40 did not interact with the GR in either cell line. To test whether FKBP interaction determined localization, we overexpressed Flag-tagged FKBP51 in WCL2 cells and Flag-FKBP52 in L929 cells. In WCL2 cells, the GR exhibited a shift to greater cytoplasmic localization that correlated with recruitment of Flag-FKBP51. In contrast, Flag-FKBP52 was not recruited to the GR of L929 cells, and no change in localization was observed, suggesting that both cell-type-specific mechanisms and TPR abundance contribute to the SR-TPR interaction. As a further test, GR-GFP and PR-GFP constructs were expressed in COS cells. The GR-GFP construct localized to the cytoplasm, while the PR-GFP construct was predominantly nuclear. Similar to L929 cells, the GR in COS interacted with PP5 and FKBP51, while PR interacted with FKBP52. Analysis of GR-PR chimeric constructs revealed that the ligand-binding domain of each receptor determines both TPR specificity and localization. Lastly, we analyzed GR and PR localization in cells completely lacking TPR. PR in FKBP52 KO cells showed a complete shift to the cytoplasm, while GR in FKBP51 KO and PP5 KO cells showed a moderate shift to the nucleus, indicating that both TPRs contribute to GR localization. Our results demonstrate that SRs have distinct preferences for TPR proteins, a property that resides in the LBD and which can now explain long-standing differences in receptor subcellular localization.     

Prediction of subcellular location apoptosis proteins with ensemble classifier and feature selection

Apoptosis proteins have a central role in the development and the homeostasis of an organism. These proteins are very important for understanding the mechanism of programmed cell death. The function of an apoptosis protein is closely related to its subcellular location. It is crucial to develop powerful tools to predict apoptosis protein locations for rapidly increasing gap between the number of known structural proteins and the number of known sequences in protein databank. In this study, amino acids pair compositions with different spaces are used to construct feature sets for representing sample of protein feature selection approach based on binary particle swarm optimization, which is applied to extract effective feature. Ensemble classifier is used as prediction engine, of which the basic classifier is the fuzzy K-nearest neighbor. Each basic classifier is trained with different feature sets. Two datasets often used in prior works are selected to validate the performance of proposed approach. The results obtained by jackknife test are quite encouraging, indicating that the proposed method might become a potentially useful tool for subcellular location of apoptosis protein, or at least can play a complimentary role to the existing methods in the relevant areas. The supplement information and software written in Matlab are available by contacting the corresponding author.     

Oestradiol and progesterone: soluble receptor levels and metabolism in the uterus of the ovariectomized ewe

Ovariectomized ewes received injections designed to mimic to some extent oestradiol and progesterone secretion during early pregnancy (maintenance progesterone), during oestrus (oestrous oestradiol) and during the luteal phase of the previous cycle (priming progesterone). The animals were killed at times equivalent to 1, 4 or 7 days after oestrus in those animals which had received oestrous oestradiol. The level of soluble oestradiol and progesterone receptors in whole uterus, and [3H]oestradiol and [3H]progesterone metabolism by uterus minces were measured. Oestradiol receptor level was highest on day 1 in those animals receiving oestrous oestradiol with no significant effect at any stage of the inclusion or omission of priming or maintenance progesterone. Progesterone receptor level was also high on day 1 in those animals receiving oestrous oestradiol with high levels maintained to day 4. Again, inclusion of priming or maintenance progesterone was without effect. In animals not receiving oestrous oestradiol the level of both receptors was uniformly low. Metabolism of [3H]oestradiol was low and not affected by treatment. [3H]Progesterone metabolism, although more variable, was also low and not affected by treatment.     

MSLoc-DT: A new method for predicting the protein subcellular location of multispecies based on decision templates

Revealing the subcellular location of newly discovered protein sequences can bring insight to their function and guide research at the cellular level. The rapidly increasing number of sequences entering the genome databanks has called for the development of automated analysis methods. Currently, most existing methods used to predict protein subcellular locations cover only one, or a very limited number of species. Therefore, it is necessary to develop reliable and effective computational approaches to further improve the performance of protein subcellular prediction and, at the same time, cover more species. The current study reports the development of a novel predictor called MSLoc-DT to predict the protein subcellular locations of human, animal, plant, bacteria, virus, fungi, and archaea by introducing a novel feature extraction approach termed Amino Acid Index Distribution (AAID) and then fusing gene ontology information, sequential evolutionary information, and sequence statistical information through four different modes of pseudo amino acid composition (PseAAC) with a decision template rule. Using the jackknife test, MSLoc-DT can achieve 86.5, 98.3, 90.3, 98.5, 95.9, 98.1, and 99.3% overall accuracy for human, animal, plant, bacteria, virus, fungi, and archaea, respectively, on seven stringent benchmark datasets. Compared with other predictors (e.g., Gpos-PLoc, Gneg-PLoc, Virus-PLoc, Plant-PLoc, Plant-mPLoc, ProLoc-Go, Hum-PLoc, GOASVM) on the gram-positive, gram-negative, virus, plant, eukaryotic, and human datasets, the new MSLoc-DT predictor is much more effective and robust. Although the MSLoc-DT predictor is designed to predict the single location of proteins, our method can be extended to multiple locations of proteins by introducing multilabel machine learning approaches, such as the support vector machine and deep learning, as substitutes for the K-nearest neighbor (KNN) method. As a user-friendly web server, MSLoc-DT is freely accessible at http://bioinfo.ibp.ac.cn/MSLOC_DT/index.html.     

Visualization of the subcellular location of sporulation proteins in Bacillus subtilis using immunofluorescence microscopy

We describe the application of immunofluorescence microscopy to visualization of the subcellular localization of proteins involved in coat morphogenesis and chromosome packaging during the process of sporulation in Bacillus subtilis . In confirmation and extension of previous findings, we show that SpolVA, which is responsible for guiding coat formation to the surface of the outer membrane that surrounds the developing spore, assembles into a shell that is located close to or on the surface of this enveloping membrane. CotE, which is responsible for the formation of the outer layer of the coat, assembles into a second shell of apparently larger diameter. Assembly of SpolVA could be detected as early as the morphological stage of polar septation and closely followed the enveloping membrane of the mother cell during the stage of engulfment, thereby providing a sensitive and diagnostic marker for this phagocytic-like process. Surprisingly, the chromosome of the developing spore and the small, acid-soluble proteins, known as α/β-type SASPs, that are known to coat the spore chromosome, were found to co-localize to a doughnut-like ring of approximately 1 µm in diameter. The use of a double mutant lacking the α/β-type SASP demonstrated that these high abundance, DNA-binding proteins are responsible for packaging the chromosome of the developing spore into this unusual structure. We conclude that sporulation in B. subtilis is a fertile system for addressing cell biological problems in a bacterium and that immunofluorescence microscopy provides a sensitive method for visualizing protein subcellular localization at high resolution.     

A theoretical method for distinguishing between soluble and membrane proteins

A method for distinguishing between membrane and soluble proteins in an amino acid sequence was developed, using only two parameters associated with the hydrophobicity: the average hydrophobicity and the power spectral density of period longer than 30 residues. The power spectral density was calculated by a maximum entropy method of Fourier transformation. Membrane proteins could be distinguished from soluble proteins with a distinction rate as high as 97%. This fact strongly suggests that the morphology of proteins, i.e., membrane or soluble forms, is determined thermodynamically through the hydrophobicity of polypeptides.     

46-kDa mannose 6-phosphate-specific receptor: biosynthesis, processing, subcellular location and topology

Synthesis of the cation-dependent mannose 6-phosphate-specific receptor was followed in cells of human (fibroblasts, Hep G2 cells, U937 monocytes, blood-derived macrophages) or rat (Morris hepatoma 7777 cells) origin. The mature form of the receptor has an apparent molecular size of 46 kDa except in fibroblasts, where the apparent molecular size was 43 kDa. The receptor contains 7-8 N-linked oligosaccharide chains, about 5 of which are converted into endo H-resistant forms within 2 h of synthesis. A small fraction of the receptor (about 3% of total in U937 monocytes) is located at the cell surface while the bulk of the receptor resides in internal membranes. Part of the internal receptors (20% in fibroblasts) resides in membranes of the endocytic pathway. The receptor was not detectable in dense lysosomes. The receptor is a hydrophobic transmembrane protein partitioning with Triton X-114. The cytosolic portion of the receptor comprises a molecular size of about 5 kDa and contains the C-terminus. The luminal (or external) portion of the receptor comprises a molecular size of greater than or equal to 37.5 kDa, of which more than half is represented by carbohydrate. Cross-linking experiments suggest that the mature receptor exists in membranes as a dimer.     

Predicting subcellular location of apoptosis proteins based on wavelet transform and support vector machine

Apoptosis proteins have a central role in the development and homeostasis of an organism. These proteins are very important for understanding the mechanism of programmed cell death. As a result of genome and other sequencing projects, the gap between the number of known apoptosis protein sequences and the number of known apoptosis protein structures is widening rapidly. Because of this extremely unbalanced state, it would be worthwhile to develop a fast and reliable method to identify their subcellular locations so as to gain better insight into their biological functions. In view of this, a new method, in which the support vector machine combines with discrete wavelet transform, has been developed to predict the subcellular location of apoptosis proteins. The results obtained by the jackknife test were quite promising, and indicated that the proposed method can remarkably improve the prediction accuracy of subcellular locations, and might also become a useful high-throughput tool in characterizing other attributes of proteins, such as enzyme class, membrane protein type, and nuclear receptor subfamily according to their sequences.     

Prediction of the subcellular location of prokaryotic proteins based on the hydrophobicity index of amino acids

An algorithm of predicting the subcellular location of prokaryotic proteins is proposed in this paper. In addition to the amino acid composition, the auto-correlation functions based on the hydrophobicity profile of amino acids along the primary sequence of the query protein have been used. Consequently, the best predictive accuracy to date has been achieved. Of the 997 prokaryotic proteins in the database used here, 688 cytoplasmic, 107 extracellular and 202 periplasmic proteins, the overall predictive accuracies are as high as 97.7 and 90.4% in the resubstitution and jackknife tests, respectively, using the hydrophilicity value of Hopp and Woods. The underlying mechanism of the improvement is also discussed. This work would be useful for a systematic analysis of the great amounts of prokaryotic genome sequences. The computer programs used in this paper are available on request via email.     

A simplified method for purification of recombinant soluble DnaA proteins

An improved, simplified method for the purification of recombinant, tagged DnaA proteins is described. The presented protocol allowed us to purify soluble DnaA proteins from two different bacterial species: Helicobacter pylori and Streptomyces coelicolor, but it can most likely also be used for the isolation of DnaA proteins from other bacteria, as it was adapted for Mycobacterium tuberculosis DnaA. The isolation procedure consists of protein precipitation with ammonium sulphate followed by affinity chromatography. The composition of the buffers used at each purification step is crucial for the successful isolation of the recombinant DnaA proteins. The universality of the method in terms of its application to differently tagged proteins (His-tagged or GST-tagged) as well as different properties of purified proteins (e.g., highly aggregating truncated forms) makes the protocol highly useful for all studies requiring purified and active DnaA proteins.