An updated dataset and a structure-based prediction model for protein-RNA binding affinity.

Understanding the process of protein-RNA interaction is essential for structural biology. The thermodynamic process is an important part to uncover the protein-RNA interaction mechanism. The regulatory networks between protein and RNA in organisms are dominated by the binding or dissociation in the cells. Therefore, determining the binding affinity for protein-RNA complexes can help us to understand the regulation mechanism of protein-RNA interaction. Since it is time-consuming and labor-intensive to determine the binding affinity for protein-RNA complexes by experimental methods, it is necessary and urgent to develop computational methods to predict that. To develop a binding affinity prediction model, first we update the dataset of protein-RNA binding affinity benchmark (PRBAB), which includes 145 complexes now. Second, we extract the structural features based on complex structure, and then we analyze and select the representative structural features to train the regression model. Third, we random select the subset from the PRBAB2.0 to fit the protein-RNA binding affinity determined by experiment. In the end, we tested our model on the nonredundant PDBbind dataset, and the results showed that Pearson correlation coefficient r = .57 and RMSE = 2.51 kcal/mol. The Pearson correlation coefficient achieves 0.7 while removing 5 complex structures with modified residues/nucleotides and metal ions. While testing on ProNAB, the results showed that 71.60% of the prediction achieves Pearson correlation coefficient r = .61 and RMSE = 1.56 kcal/mol with experiment values.

Two novel RNA-binding proteins identification through computational prediction and experimental validation.

Since RBPs play important roles in the cell, it's particularly important to find new RBPs. We performed iRIP-seq and CLIP-seq to verify two proteins, CLIP1 and DMD, predicted by RBPPred whether are RBPs or not. The experimental results confirm that these two proteins have RNA-binding activity. We identified significantly enriched binding motifs UGGGGAGG, CUUCCG and CCCGU for CLIP1 (iRIP-seq), DMD (iRIP-seq) and DMD (CLIP-seq), respectively. The computational KEGG and GO analysis show that the CLIP1 and DMD share some biological processes and functions. Besides, we found that the SNPs between DMD and its RNA partners may be associated with Becker muscular dystrophy, Duchenne muscular dystrophy, Dilated cardiomyopathy 3B and Cardiovascular phenotype. Among the thirteen cancers data, CLIP1 and another 300 oncogenes always co-occur, and 123 of these 300 genes interact with CLIP1. These cancers may be associated with the mutations occurred in both CLIP1 and the genes it interacts with.

Protein-DNA complex structure modeling based on structural template.

DNA-binding is an important feature of proteins, and protein-DNA interaction involves in many life processes. Various computational methods have been developed to predict protein-DNA complex structures due to the difficulty of experimentally obtaining protein-DNA complex structures. However, prediction of protein-DNA complex is still a challenging problem compared with prediction of protein-RNA complex, this may be due to the large conformational changes between bound and unbound structure in both protein and DNA. We extend PRIME 2.0 to PRIME 2.0.1 to model protein-DNA complex structures. By comparing sequence and structure alignment methods, we found that structure-based methods can find more templates than sequence-based methods. The results of all-to-all structure alignments showed that DNA structure plays an important role in prediction of protein-DNA complex structure. By exploring the relationship of sequence and structure, we found that in protein-DNA interaction, numerous structures with dissimilar sequences have similar 3D structures and perform the similar function.

P3DOCK: a protein-RNA docking webserver based on template-based and template-free docking.

MOTIVATION: The main function of protein-RNA interaction is to regulate the expression of genes. Therefore, studying protein-RNA interactions is of great significance. The information of three-dimensional (3D) structures reveals that atomic interactions are particularly important. The calculation method for modeling a 3D structure of a complex mainly includes two strategies: free docking and template-based docking. These two methods are complementary in protein-protein docking. Therefore, integrating these two methods may improve the prediction accuracy. RESULTS: In this article, we compare the difference between the free docking and the template-based algorithm. Then we show the complementarity of these two methods. Based on the analysis of the calculation results, the transition point is confirmed and used to integrate two docking algorithms to develop P3DOCK. P3DOCK holds the advantages of both algorithms. The results of the three docking benchmarks show that P3DOCK is better than those two non-hybrid docking algorithms. The success rate of P3DOCK is also higher (3-20%) than state-of-the-art hybrid and non-hybrid methods. Finally, the hierarchical clustering algorithm is utilized to cluster the P3DOCK's decoys. The clustering algorithm improves the success rate of P3DOCK. For ease of use, we provide a P3DOCK webserver, which can be accessed at www.rnabinding.com/P3DOCK/P3DOCK.html. An integrated protein-RNA docking benchmark can be downloaded from http://rnabinding.com/P3DOCK/benchmark.html. AVAILABILITY AND IMPLEMENTATION: www.rnabinding.com/P3DOCK/P3DOCK.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

RR3DD: an RNA global structure-based RNA three-dimensional structural classification database.

The three-dimensional (3D) structure of RNA usually plays an important role in the recognition with RNA-binding protein. Along with the discovering of RNAs, several RNA databases are developed to study the functions of RNA based on sequence, secondary structure, local 3D structural motif and global structure. Based on RNA function and structure, different RNAs are classified and stored in SCOR and DARTS, respectively. The classification of RNA structures is useful in RNA structure prediction and function annotation. However, the SCOR and DARTS are not updated any more. In this study, we present an RNA classification database RR3DD based on RNA fold with the global 3D structural similarity. The RR3DD includes 13,601 RNA chains from PDB and mmCIF format structures which are classified into 780 RNA folds. The RNA chains from PDB and mmCIF format structures are aligned and clustered into 675 and 220 RNA folds, respectively. By analysing the RNA structure in RR3DD, we find that there are 11 clusters with more than 50 members. These clusters include rRNAs, riboswitches, tRNAs and so on. By mapping RR3DD into Rfam, we found that some RNAs without annotation by Rfam can be annotated through structural alignment. For example, we analysed tRNAs and found that tRNA were successfully grouped in RR3DD for which Rfam did not classify them into one family. Finally, we provide a web interface of RR3DD offering functions of browsing RR3DD, annotating RNA 3D structure and finding templates for RNA homology modelling.

CPPred: coding potential prediction based on the global description of RNA sequence.

The rapid and accurate approach to distinguish between coding RNAs and ncRNAs has been playing a critical role in analyzing thousands of novel transcripts, which have been generated in recent years by next-generation sequencing technology. Previously developed methods CPAT, CPC2 and PLEK can distinguish coding RNAs and ncRNAs very well, but poorly distinguish between small coding RNAs and small ncRNAs. Herein, we report an approach, CPPred (coding potential prediction), which is based on SVM classifier and multiple sequence features including novel RNA features encoded by the global description. The CPPred can better distinguish not only between coding RNAs and ncRNAs, but also between small coding RNAs and small ncRNAs than the state-of-the-art methods due to the addition of the novel RNA features. A recent study proposes 1335 novel human coding RNAs from a large number of RNA-seq datasets. However, only 119 transcripts are predicted as coding RNAs by the CPPred. In fact, almost all proposed novel coding RNAs are ncRNAs (91.1%), which is consistent with previous reports. Remarkably, we also reveal that the global description of encoding features (T2, C0 and GC) plays an important role in the prediction of coding potential.

Rapid enzyme regeneration results in the striking catalytic longevity of an engineered, single species, biocatalytic biofilm.

BACKGROUND: Engineering of single-species biofilms for enzymatic generation of fine chemicals is attractive. We have recently demonstrated the utility of an engineered Escherichia coli biofilm as a platform for synthesis of 5-halotryptophan. E. coli PHL644, expressing a recombinant tryptophan synthase, was employed to generate a biofilm. Its rapid deposition, and instigation of biofilm formation, was enforced by employing a spin-down method. The biofilm presents a large three-dimensional surface area, excellent for biocatalysis. The catalytic longevity of the engineered biofilm is striking, and we had postulated that this was likely to largely result from protection conferred to recombinant enzymes by biofilm's extracellular matrix. SILAC (stable isotopic labelled amino acids in cell cultures), and in particular dynamic SILAC, in which pulses of different isotopically labelled amino acids are administered to cells over a time course, has been used to follow the fate of proteins. To explore within our spin coated biofilm, whether the recombinant enzyme's longevity might be in part due to its regeneration, we introduced pulses of isotopically labelled lysine and phenylalanine into medium overlaying the biofilm and followed their incorporation over the course of biofilm development. RESULTS: Through SILAC analysis, we reveal that constant and complete regeneration of recombinant enzymes occurs within spin coated biofilms. The striking catalytic longevity within the biofilm results from more than just simple protection of active enzyme by the biofilm and its associated extracellular matrix. The replenishment of recombinant enzyme is likely to contribute significantly to the catalytic longevity observed for the engineered biofilm system. CONCLUSIONS: Here we provide the first evidence of a recombinant enzyme's regeneration in an engineered biofilm. The recombinant enzyme was constantly replenished over time as evidenced by dynamic SILAC, which suggests that the engineered E. coli biofilms are highly metabolically active, having a not inconsiderable energetic demand. The constant renewal of recombinant enzyme highlights the attractive possibility of utilising this biofilm system as a dynamic platform into which enzymes of interest can be introduced in a "plug-and-play" fashion and potentially be controlled through promoter switching for production of a series of desired fine chemicals.

Characterization of a new sn-1,3-regioselective triacylglycerol lipase from Malbranchea cinnamomea.

The thermophilic ascomycetous fungus Malbranchea cinnamomea produces lipases (EC 3.1.1.3) that allow it to grow efficiently on medium containing triacylglycerol substrates such as plant oils or tributyrin as sole carbon source. In the transcriptome of M. cinnamomea grown on olive oil, we found one cDNA sequence encoding a putative extracellular lipase. This gene, termed as MclipA, was cloned and heterologously expressed in Pichia pastoris. The recombinant protein, rMclipA, catalyzed the hydrolysis of short-chain fatty acid ester such as p-nitrophenyl butyrate (C4) and long-chain fatty acid ester such as p-nitrophenyl myristate (C14). These results indicate that MclipA is a true triacylglycerol lipase. For rMclipA, the optimum lipase activity was obtained at 45 °C, and more than 93% of enzyme activity was retained after 24 H of incubation at temperatures up to 50 °C. rMclipA was active toward p-nitrophenyl esters of various carbon chain lengths with peak activity on long-chain fatty acid (C14). rMclipA displayed high sn-1,3-regioselectivity on hydrolyzing triolein. rMclipA can catalyze oleic acid methyl ester synthesis resulting in a 71% esterification degree after 24 H of reaction at 40 °C. These properties suggest that rMclipA has potential application in, for example, selective hydrolysis of oil, modification of triacylglycerol, and production of biodiesel.

Selenium deficiency induced apoptosis via mitochondrial pathway caused by Oxidative Stress in porcine gastric tissues.

Selenium (Se) is an essential nutrient for the body, which can ensure GSH-Px activity and has antioxidant effect. Se deficiency may lead to apoptosis in various tissues and organs in animals. Pigs as major livestock in the farming industry, Se deficiency can cause various types of diseases such as white muscle disease, and mulberry heart disease.The aim of this experiment was to investigate the effect and mechanism of Se deficiency on apoptosis in porcine gastric tissue. Forty weaned piglets were randomly divided into Se deficiency group and control group, and fed with low Se diet and normal diet for six weeks respectively. The histochemical characteristics, antioxidant indexes, apoptotic genes and apoptotic protein expression of gastric cells in Se-deficient piglets were detected. The results of antioxidant index, TUNEL, RT-PCR and Western blot showed that Se deficiency decreased the activities of CAT, SOD and GSH-Px, increased the apoptotic rate of porcine gastric tissue, increased the expression of Bax and Caspase-3, and decreased the expression of Bcl-2. The results demonstrated that Se deficiency could induce apoptosis in porcine gastric tissue cells through oxidative stress-induced mitochondrial pathway. The stomach was a key target of Se deficiency and may play a key role in the response to Se deficiency. Our study may provide new ideas for the prevention and treatment of swine gastric diseases caused by Se deficiency and is beneficial to the development of pig farming industry.

PRIME-3D2D is a 3D2D model to predict binding sites of protein-RNA interaction.

Protein-RNA interaction participates in many biological processes. So, studying protein-RNA interaction can help us to understand the function of protein and RNA. Although the protein-RNA 3D3D model, like PRIME, was useful in building 3D structural complexes, it can't be used genome-wide, due to lacking RNA 3D structures. To take full advantage of RNA secondary structures revealed from high-throughput sequencing, we present PRIME-3D2D to predict binding sites of protein-RNA interaction. PRIME-3D2D is almost as good as PRIME at modeling protein-RNA complexes. PRIME-3D2D can be used to predict binding sites on PDB data (MCC = 0.75/0.70 for binding sites in protein/RNA) and transcription-wide (MCC = 0.285 for binding sites in RNA). Testing on PDB and yeast transcription-wide data show that PRIME-3D2D performs better than other binding sites predictor. So, PRIME-3D2D can be used to predict the binding sites both on PDB and genome-wide, and it's freely available.

Preparation of multi-functional magnetic-plasmonic nanocomposite for adsorption and detection of thiram using SERS.

Magnetic materials have been widely used for constructing substrate in surface enhanced Raman scattering (SERS) sensing due to the magnetic responsibility. Here, we reported a facile and effective approach to construct multi-functional SERS substrate based on assembling Ag nanoparticles (NPs) on porous Fe microspheres. The porous Fe microspheres were prepared through hydrogen reduction of Fe2O3 NPs with porous structure, in which the size and morphology of Fe could be well controlled. The surface of Fe was grafted with amino group, and then decorated with Ag NPs. The surface area and pore size of Fe microsphere were characterized by nitrogen adsorption and desorption. The Fe@Ag nanocomposite illustrated a good SERS activity. Furthermore, this substrate could be used for pesticide monitoring by portable Raman spectrometer. Especially, the porous Fe microsphere could adsorb analyte from target sample and the Fe@Ag could be concentrated by magnetic force to amplify the SERS signal for thiram detection.

Deep-RBPPred: Predicting RNA binding proteins in the proteome scale based on deep learning.

RNA binding protein (RBP) plays an important role in cellular processes. Identifying RBPs by computation and experiment are both essential. Recently, an RBP predictor, RBPPred, is proposed in our group to predict RBPs. However, RBPPred is too slow for that it needs to generate PSSM matrix as its feature. Herein, based on the protein feature of RBPPred and Convolutional Neural Network (CNN), we develop a deep learning model called Deep-RBPPred. With the balance and imbalance training set, we obtain Deep-RBPPred-balance and Deep-RBPPred-imbalance models. Deep-RBPPred has three advantages comparing to previous methods. (1) Deep-RBPPred only needs few physicochemical properties based on protein sequences. (2) Deep-RBPPred runs much faster. (3) Deep-RBPPred has a good generalization ability. In the meantime, Deep-RBPPred is still as good as the state-of-the-art method. Testing in A. thaliana, S. cerevisiae and H. sapiens proteomes, MCC values are 0.82 (0.82), 0.65 (0.69) and 0.85 (0.80) for balance model (imbalance model) when the score cutoff is set to 0.5, respectively. In the same testing dataset, different machine learning algorithms (CNN and SVM) are also compared. The results show that CNN-based model can identify more RBPs than SVM-based. In comparing the balance and imbalance model, both CNN-base and SVM-based tend to favor the majority class in the imbalance set. Deep-RBPPred forecasts 280 (balance model) and 265 (imbalance model) of 299 new RBP. The sensitivity of balance model is about 7% higher than the state-of-the-art method. We also apply deep-RBPPred to 30 eukaryotes and 109 bacteria proteomes downloaded from Uniprot to estimate all possible RBPs. The estimating result shows that rates of RBPs in eukaryote proteomes are much higher than bacteria proteomes.

Living GenoChemetics by hyphenating synthetic biology and synthetic chemistry in vivo.

Marrying synthetic biology with synthetic chemistry provides a powerful approach toward natural product diversification, combining the best of both worlds: expediency and synthetic capability of biogenic pathways and chemical diversity enabled by organic synthesis. Biosynthetic pathway engineering can be employed to insert a chemically orthogonal tag into a complex natural scaffold affording the possibility of site-selective modification without employing protecting group strategies. Here we show that, by installing a sufficiently reactive handle (e.g., a C-Br bond) and developing compatible mild aqueous chemistries, synchronous biosynthesis of the tagged metabolite and its subsequent chemical modification in living culture can be achieved. This approach can potentially enable many new applications: for example, assay of directed evolution of enzymes catalyzing halo-metabolite biosynthesis in living cells or generating and following the fate of tagged metabolites and biomolecules in living systems. We report synthetic biological access to new-to-nature bromo-metabolites and the concomitant biorthogonal cross-coupling of halo-metabolites in living cultures.Coupling synthetic biology and chemical reactions in cells is a challenging task. The authors engineer bacteria capable of generating bromo-metabolites, develop a mild Suzuki-Miyaura cross-coupling reaction compatible with cell growth and carry out the cross-coupling chemistry in live cell cultures.

Hydrolysis of wheat arabinoxylan by two acetyl xylan esterases from Chaetomium thermophilum.

The thermophilic filamentous ascomycete Chaetomium thermophilum produces functionally diverse hemicellulases when grown on hemicellulose as carbon source. Acetyl xylan esterase (EC 3.1.1.72) is an important accessory enzyme in hemicellulose biodegradation. Although the genome of C. thermophilum has been sequenced, its carbohydrate esterases are not annotated yet. We applied peptide pattern recognition (PPR) tool for sequence analysis of the C. thermophilum genome, and 11 carbohydrate esterase genes were discovered. Furthermore, we cloned and heterologously expressed two putative acetyl xylan esterase genes, CtAxeA and CtAxeB, in Pichia pastoris. The recombinant proteins, rCtAxeA and rCtAxeB, released acetic acids from p-nitrophenyl acetate and water-insoluble wheat arabinoxylan. These results indicate that CtAxeA and CtAxeB are true acetyl xylan esterases. For both recombinant esterases, over 93 % of the initial activity was retained after 24 h of incubation at temperatures up to 60 °C, and over 90 % of the initial activity was retained after 24 h of incubation in different buffers from pH 4.0 to 9.0 at 4 and 50 °C. The overall xylose yield from wheat arabinoxylan hydrolysis was 8 % with xylanase treatment and increased to 34 % when xylanase was combined with rCtAxeA and rCtAxeB. In sum, the present study first report the biochemical characterization of two acetyl xylan esterases from C. thermophilum, which are efficient in hydrolyzing hemicellulose with potential application in biomass bioconversion to high value chemicals or biofuels.

The importance of fungi and of mycology for a global development of the bioeconomy.

The vision of the European common research programme for 2014-2020, called Horizon 2020, is to create a smarter, more sustainable and more inclusive society. However, this is a global endeavor, which is important for mycologists all over the world because it includes a special role for fungi and fungal products. After ten years of research on industrial scale conversion of biowaste, the conclusion is that the most efficient and gentle way of converting recalcitrant lignocellulosic materials into high value products for industrial purposes, is through the use of fungal enzymes. Moreover, fungi and fungal products are also instrumental in producing fermented foods, to give storage stability and improved health. Climate change will lead to increasingly severe stress on agricultural production and productivity, and here the solution may very well be that fungi will be brought into use as a new generation of agricultural inoculants to provide more robust, more nutrient efficient, and more drought tolerant crop plants. However, much more knowledge is required in order to be able to fully exploit the potentials of fungi, to deliver what is needed and to address the major global challenges through new biological processes, products, and solutions. This knowledge can be obtained by studying the fungal proteome and metabolome; the biology of fungal RNA and epigenetics; protein expression, homologous as well as heterologous; fungal host/substrate relations; physiology, especially of extremophiles; and, not the least, the extent of global fungal biodiversity. We also need much more knowledge and understanding of how fungi degrade biomass in nature.The projects in our group in Aalborg University are examples of the basic and applied research going on to increase the understanding of the biology of the fungal secretome and to discover new enzymes and new molecular/bioinformatics tools.However, we need to put Mycology higher up on global agendas, e.g. by positioning Mycology as a candidate for an OECD Excellency Program. This could pave the way for increased funding of international collaboration, increased global visibility, and higher priority among decision makers all over the world.