Summary Custom-made zinc-finger nucleases (ZFNs) can induce targeted genome modifications with high efficiency in cell types including Drosophila, C. elegans, plants, and humans. A bottleneck in the application of ZFN technology has been the generation of highly specific engineered zinc-finger arrays. Here we describe OPEN (Oligomerized Pool ENgineering), a rapid, publicly available strategy for constructing multi-finger arrays, which we show is more effective than the previously published modular assembly method. We used OPEN to construct 37 highly active ZFN pairs which induced targeted alterations with high efficiencies (1 to 50%) at 11 different target sites located within three endogenous human genes (VEGF-A, HoxB13, CFTR), an endogenous plant gene (tobacco SuRA), and a chromosomally-integrated EGFP reporter gene. In summary, OPEN provides an “open-source” method for rapidly engineering highly active zinc-finger arrays, thereby enabling broader practice, development, and application of ZFN technology for biological research and gene therapy.
The identification and characterization of B-cell epitopes play an important role in vaccine design, immunodiagnostic tests, and antibody production. Therefore, computational tools for reliably predicting linear B-cell epitopes are highly desirable. We evaluated Support Vector Machine (SVM) classifiers trained utilizing five different kernel methods using fivefold cross-validation on a homology-reduced data set of 701 linear B-cell epitopes, extracted from Bcipep database, and 701 non-epitopes, randomly extracted from SwissProt sequences. Based on the results of our computational experiments, we propose BCPred, a novel method for predicting linear B-cell epitopes using the subsequence kernel. We show that the predictive performance of BCPred (AUC = 0.758) outperforms 11 SVM-based classifiers developed and evaluated in our experiments as well as our implementation of AAP (AUC = 0.7), a recently proposed method for predicting linear B-cell epitopes using amino acid pair antigenicity. Furthermore, we compared BCPred with AAP and ABCPred, a method that uses recurrent neural networks, using two data sets of unique B-cell epitopes that had been previously used to evaluate ABCPred. Analysis of the data sets used and the results of this comparison show that conclusions about the relative performance of different B-cell epitope prediction methods drawn on the basis of experiments using data sets of unique B-cell epitopes are likely to yield overly optimistic estimates of performance of evaluated methods. This argues for the use of carefully homology-reduced data sets in comparing B-cell epitope prediction methods to avoid misleading conclusions about how different methods compare to each other. Our homologyreduced data set and implementations of BCPred as well as the APP method are publicly available through our web-based server, BCPREDS, at: http://ailab.cs.iastate.edu/bcpreds/.
BackgroundRNA-protein interactions (RPIs) play important roles in a wide variety of cellular processes, ranging from transcriptional and post-transcriptional regulation of gene expression to host defense against pathogens. High throughput experiments to identify RNA-protein interactions are beginning to provide valuable information about the complexity of RNA-protein interaction networks, but are expensive and time consuming. Hence, there is a need for reliable computational methods for predicting RNA-protein interactions.ResultsWe propose RPISeq, a family of classifiers for predicting RNA-protein interactions using only sequence information. Given the sequences of an RNA and a protein as input, RPIseq predicts whether or not the RNA-protein pair interact. The RNA sequence is encoded as a normalized vector of its ribonucleotide 4-mer composition, and the protein sequence is encoded as a normalized vector of its 3-mer composition, based on a 7-letter reduced alphabet representation. Two variants of RPISeq are presented: RPISeq-SVM, which uses a Support Vector Machine (SVM) classifier and RPISeq-RF, which uses a Random Forest classifier. On two non-redundant benchmark datasets extracted from the Protein-RNA Interface Database (PRIDB), RPISeq achieved an AUC (Area Under the Receiver Operating Characteristic (ROC) curve) of 0.96 and 0.92. On a third dataset containing only mRNA-protein interactions, the performance of RPISeq was competitive with that of a published method that requires information regarding many different features (e.g., mRNA half-life, GO annotations) of the putative RNA and protein partners. In addition, RPISeq classifiers trained using the PRIDB data correctly predicted the majority (57-99%) of non-coding RNA-protein interactions in NPInter-derived networks from E. coli, S. cerevisiae, D. melanogaster, M. musculus, and H. sapiens.ConclusionsOur experiments with RPISeq demonstrate that RNA-protein interactions can be reliably predicted using only sequence-derived information. RPISeq offers an inexpensive method for computational construction of RNA-protein interaction networks, and should provide useful insights into the function of non-coding RNAs. RPISeq is freely available as a web-based server at http://pridb.gdcb.iastate.edu/RPISeq/.
Engineered zinc-finger nucleases (ZFNs) enable targeted genome modification. Here we describe Context-Dependent Assembly (CoDA), a platform for engineering ZFNs using only standard cloning techniques or custom DNA synthesis. Using CoDA ZFNs, we rapidly altered 20 genes in zebrafish, Arabidopsis, and soybean. The simplicity and efficacy of CoDA will enable broad adoption of ZFN technology and make possible large-scale projects focused on multi-gene pathways or genome-wide alterations.
ZiFiT (Zinc Finger Targeter) is a simple and intuitive web-based tool that provides an interface to identify potential binding sites for engineered zinc finger proteins (ZFPs) in user-supplied DNA sequences. In this updated version, ZiFiT identifies potential sites for ZFPs made by both the modular assembly and OPEN engineering methods. In addition, ZiFiT now integrates additional tools and resources including scoring schemes for modular assembly, an interface with the Zinc Finger Database (ZiFDB) of engineered ZFPs, and direct querying of NCBI BLAST servers for identifying potential off-target sites within a host genome. Taken together, these features facilitate design of ZFPs using reagents made available to the academic research community by the Zinc Finger Consortium. ZiFiT is freely available on the web without registration at http://bindr.gdcb.iastate.edu/ZiFiT/.
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