A top-down approach to enhance the power of predicting human protein subcellular localization: Hum-mPLoc 2.0

Predicting subcellular localization of human proteins is a challenging problem, particularly when query proteins may have a multiplex character, i.e., simultaneously exist at, or move between, two or more different subcellular location sites. In a previous study, we developed a predictor called “Hum-mPLoc” to deal with the multiplex problem for the human protein system. However, Hum-mPLoc has the following shortcomings. (1) The input of accession number for a query protein is required in order to obtain a higher expected success rate by selecting to use the higher-level prediction pathway; but many proteins, such as synthetic and hypothetical proteins as well as those newly discovered proteins without being deposited into databanks yet, do not have accession numbers. (2) Neither functional domain nor sequential evolution information were taken into account in Hum-mPLoc, and hence its power may be reduced accordingly. In view of this, a top-down strategy to address these shortcomings has been implemented. The new predictor thus obtained is called Hum-mPLoc 2.0, where the accession number for input is no longer needed whatsoever. Moreover, both the functional domain information and the sequential evolution information have been fused into the predictor by an ensemble classifier. As a consequence, the prediction power has been significantly enhanced. The web server of Hum-mPLoc2.0 is freely accessible at http://www.csbio.sjtu.edu.cn/bioinf/hum-multi-2/.     

The Solution Structure of a Locked Nucleic Acid (LNA) Hybridized to DNA

Abstract

LNA (Locked Nucleic Acids) is a novel oligonucleotide analogue containing a conformationally restricted nucleotide with a 2′-0, 4′-C-methylene bridge that induces unprecedented thermal affinities when mixed with complementary single stranded DNA and RNA. We have used two-dimensional'H NMR spectroscopy obtained at 750 and 500 MHz to determine a high resolution solution structure of an LNA oligonucleotide hybridized to the complementary DNA strand. The determination of the structure was based on a complete relaxation matrix analysis of the NOESY cross peaks followed by restrained molecular dynamics calculations. Forty final structures were generated for the duplex from A-type and B-type dsDNA starting structures. The root-mean-square deviation (RMSD) of the coordinates for the forty structures of the complex was 0.32Å. The structures were analysed by use of calculated helix parameters. This showed that the values for rise and buckle in the LNA duplex is markedly different from canonical B-DNA at the modification site. A value of twist similar to A-DNA is also observed at the modification site. The overall length of the helix which is 27.3Å. The average twist over the sequence are 35.9° ± 0.3°. Consequently, the modification does not cause the helix to unwind. The bis-intercalation of the thiazole orange dye TOTO to the LNA duplex was also investigated by ¹H NMR spectroscopy to sense the structural change from the unmodified oligonucleotide. We observed that the bis-intercalation of TOTO is much less favourable in the 5′-CT^LAG-3′ site than in the unmodified 5′-CT^LAG-3′ site. This was related to the change in the base stacking of the LNA duplex compared to the unmodified duplex.     

pLoc-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by deep gene ontology learning via general PseAAC

Information of the proteins' subcellular localization is crucially important for revealing their biological functions in a cell, the basic unit of life. With the avalanche of protein sequences generated in the postgenomic age, it is highly desired to develop computational tools for timely identifying their subcellular locations based on the sequence information alone. The current study is focused on the Gram-negative bacterial proteins. Although considerable efforts have been made in protein subcellular prediction, the problem is far from being solved yet. This is because mounting evidences have indicated that many Gram-negative bacterial proteins exist in two or more location sites. Unfortunately, most existing methods can be used to deal with single-location proteins only. Actually, proteins with multi-locations may have some special biological functions important for both basic research and drug design. In this study, by using the multi-label theory, we developed a new predictor called “pLoc-mGneg” for predicting the subcellular localization of Gram-negative bacterial proteins with both single and multiple locations. Rigorous cross-validation on a high quality benchmark dataset indicated that the proposed predictor is remarkably superior to “iLoc-Gneg”, the state-of-the-art predictor for the same purpose. For the convenience of most experimental scientists, a user-friendly web-server for the novel predictor has been established at http://www.jci-bioinfo.cn/pLoc-mGneg/, by which users can easily get their desired results without the need to go through the complicated mathematics involved.     

A new hybrid approach to predict subcellular localization of proteins by incorporating gene ontology

Based on the recent development in the gene ontology and functional domain databases, a new hybridization approach is developed for predicting protein subcellular location by combining the gene product, functional domain, and quasi-sequence-order effects. As a showcase, the same prokaryotic and eukaryotic datasets, which were studied by many previous investigators, are used for demonstration. The overall success rate by the jackknife test for the prokaryotic set is 94.7% and that for the eukaryotic set 92.9%. These are so far the highest success rates achieved for the two datasets by following a rigorous cross-validation test procedure, suggesting that such a hybrid approach may become a very useful high-throughput tool in the area of bioinformatics, proteomics, as well as molecular cell biology. The very high success rates also reflect the fact that the subcellular localization of a protein is closely correlated with: (1). the biological objective to which the gene or gene product contributes, (2). the biochemical activity of a gene product, and (3). the place in the cell where a gene product is active.     

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.     

A vicilin-like seed protein of cycads: similarity to sucrose-binding proteins

Seed storage globulins of the 7S and 11S type are synthesized in the seeds of angiosperms and gymnosperms. We have isolated and characterized a vicilin-like gene expressed in the cycad Zamia furfuraceae. Sequence comparisons reveal clear similarities to a sucrose-binding protein isolated from soybean. We suggest the existence of a superfamily of related genes including both vicilin-like and legumin-like seed globulin genes as well as genes coding for spherulins, germins and sucrose-binding-proteins.     

A method for prediction of the locations of linker regions within large multifunctional proteins,and application to a type I polyketide synthase

Multifunctional proteins often appear to result from fusion of smaller proteins and in such cases typically can be separated into their ancestral components simply by cleaving the linker regions that separate the domains. Though possibly guided by sequence alignment, structural evidence, or light proteolysis, determination of the locations of linker regions remains empirical. We have developed an algorithm, named UMA, to predict the locations of linker regions in multifunctional proteins by quantification of the conservation of several properties within protein families, and the results agree well with structurally characterized proteins. This technique has been applied to a family of fungal type I iterative polyketide synthases (PKS), allowing prediction of the locations of all of the standard PKS domains, as well as two previously unidentified domains. Using these predictions, we report the cloning of the first fragment from the PKS norsolorinic acid synthase, responsible for biosynthesis of the first isolatable intermediate in aflatoxin production. The expression, light proteolysis and catalytic abilities of this acyl carrier protein-thioesterase didomain are discussed.     

A bioinformatic approach to the identification of bacterial proteins interacting with Toll-interleukin 1 receptor-resistance (TIR) homology domains

Members of the Toll-like receptor (TLR) family are currently under intense scrutiny for their role in the sampling and recognition of pathogens. It has already been reported that both vaccinia virus and Yersinia spp. express proteins that help them evade the TLR mediated immune response, acting through the Toll-interleukin-1 receptor-resistance (TIR) domain and leucine-rich repeat region of the host TLRs respectively. The TIR domain is involved in the dimerisation of the TLRs and their complexation with their adapter molecules. We tested here the hypothesis that bacteria have the ability to secrete proteins containing similar motifs to the intracellular TIR domains that are involved in the TIR-TIR interaction necessary for the subsequent signal transmission. Based upon their sequence homology, proteins expressing TIRs have been divided into three sub-classes, based around the TLRs, the TLR adapter proteins, and the interleukin-1 and -18 adapter proteins. The highly conserved regions from these separate sub-families were then used to identify similar bacterial proteins. The bacterial proteins identified were then included in an iterative MEME-BLAST process to broaden the search. Tollip, a known TLR antagonist and adapter protein, was included in this investigation although it does not fit into any of the three sub-classes outlined above. If suitable bacterial proteins had been identified, it would signify that certain bacteria had evolved a mechanism to aid them in avoiding detection by the innate immune system acting through the TIR domains. At this stage one has to conclude that there is no evidence currently available suggesting such a mechanism, when using the strategy applied here.     

A survey on cytosolic non-enzymic proteins involved in the metabolism of lipophilic compounds: from organic anion binders to new protein families

This review deals with recent advances in the research of cytosolic non-enzymic proteins involved in the metabolism of lipophilic compounds. Emphasis is given to the important contribution of structural data in the understanding of the functional properties of these proteins and in the emergence of new protein families. The possibility that many of the 'cytosolic' proteins might be structure-bound and structure-forming in the living cell is discussed, with references to so far available structural data and to recent investigations on the architecture and biochemical composition of the cytoplasm. The aim of this review is to present in a condensed form (227 references) the evolution in the study of cytosolic proteins binding and transferring lipophilic compounds and to enable interested investigators to become aware of current concepts and perspectives in this active and steadily growing area of research.     

Hypoglycosylation of dystroglycan due to T192M mutation: A molecular insight behind the fact

Abnormal glycosylation of dystroglycan (DG), a transmembrane glycoprotein, results in a group of diseases known as dystroglycanopathy. A severe dystroglycanopathy known as the limb girdle disease MDDGC9 [OMIM: 613818] occurs as a result of hypoglycosylation of alpha subunit of DG. Reasons behind this has been traced back to a point mutation (T192M) in DG that leads to weakening of interactions of DG protein with laminin and subsequent loss of signal flow through the DG protein. In this work we have tried to analyze the molecular details of the interactions between DG and laminin1 in order to propose a mechanism about the onset of the disease MDDGC9. We have observed noticeable changes between the modeled structures of wild type and mutant DG proteins. We also have employed molecular docking techniques to study and compare the binding interactions between laminin1 and both the wild type and mutant DG proteins. The docking simulations have revealed that the mutant DG has weaker interactions with laminin1 as compared to the wild type DG. Till date there are no previous reports that deal with the elucidation of the interactions of DG with laminin1 from the molecular level. Our study is therefore the first of its kind which analyzes the differences in binding patterns of laminin1 with both the wild type and mutant DG proteins. Our work would therefore facilitate analysis of the molecular mechanism of the disease MDDGC9. Future work based on our results may be useful for the development of suitable drugs against this disease.