Drena Dobbs

Selection-free zinc-finger-nuclease engineering by context-dependent assembly (CoDA)

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-generated ZFNs, we rapidly altered 20 genes in Danio rerio, Arabidopsis thaliana and Glycine max. The simplicity and efficacy of CoDA will enable broad adoption of ZFN technology and make possible large-scale projects focused on multigene pathways or genome-wide alterations.

Predicting linear B-cell epitopes using string kernels

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 homology‐reduced 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/. Copyright

High frequency targeted mutagenesis in Arabidopsis thaliana using zinc finger nucleases

We report here an efficient method for targeted mutagenesis of Arabidopsis genes through regulated expression of zinc finger nucleases (ZFNs)—enzymes engineered to create DNA double-strand breaks at specific target loci. ZFNs recognizing the Arabidopsis ADH1 and TT4 genes were made by Oligomerized Pool ENgineering (OPEN)—a publicly available, selection-based platform that yields high quality zinc finger arrays. The ADH1 and TT4 ZFNs were placed under control of an estrogen-inducible promoter and introduced into Arabidopsis plants by floral-dip transformation. Primary transgenic Arabidopsis seedlings induced to express the ADH1 or TT4 ZFNs exhibited somatic mutation frequencies of 7% or 16%, respectively. The induced mutations were typically insertions or deletions (1–142 bp) that were localized at the ZFN cleavage site and likely derived from imprecise repair of chromosome breaks by nonhomologous end-joining. Mutations were transmitted to the next generation for 69% of primary transgenics expressing the ADH1 ZFNs and 33% of transgenics expressing the TT4 ZFNs. Furthermore, ≈20% of the mutant-producing plants were homozygous for mutations at ADH1 or TT4, indicating that both alleles were disrupted. ADH1 and TT4 were chosen as targets for this study because of their selectable or screenable phenotypes (adh1, allyl alcohol resistance; tt4, lack of anthocyanins in the seed coat). However, the high frequency of observed ZFN-induced mutagenesis suggests that targeted mutations can readily be recovered by simply screening progeny of primary transgenic plants by PCR and DNA sequencing. Taken together, our results suggest that it should now be possible to obtain mutations in any Arabidopsis target gene regardless of its mutant phenotype.

ZiFiT (Zinc Finger Targeter): an updated zinc finger engineering tool

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/.

Zinc Finger Targeter (ZiFiT): an engineered zinc finger/target site design tool

Zinc Finger Targeter (ZiFiT) is a simple and intuitive web-based tool that facilitates the design of zinc finger proteins (ZFPs) that can bind to specific DNA sequences. The current version of ZiFiT is based on a widely employed method of ZFP design, the ‘modular assembly’ approach, in which pre-existing individual zinc fingers are linked together to recognize desired target DNA sequences. Several research groups have described experimentally characterized zinc finger modules that bind many of the 64 possible DNA triplets. ZiFiT leverages the combined capabilities of three of the largest and best characterized module archives by enabling users to select fingers from any of these sets. ZiFiT searches a query DNA sequence for target sites for which a ZFP can be designed using modules available in one or more of the three archives. In addition, ZiFiT output facilitates identification of specific zinc finger modules that are publicly available from the Zinc Finger Consortium. ZiFiT is freely available at http://bindr.gdcb.iastate.edu/ZiFiT/.

Standardized reagents and protocols for engineering zinc finger nucleases by modular assembly

Engineered zinc finger nucleases can stimulate gene targeting at specific genomic loci in insect, plant and human cells. Although several platforms for constructing artificial zinc finger arrays using “modular assembly” have been described, standardized reagents and protocols that permit rapid, cross-platform “mixing-and-matching” of the various zinc finger modules are not available. Here we describe a comprehensive, publicly available archive of plasmids encoding more than 140 well-characterized zinc finger modules together with complementary web-based software (termed ZiFiT) for identifying potential zinc finger target sites in a gene of interest. Our reagents have been standardized on a single platform, enabling facile mixing-and-matching of modules and transfer of assembled arrays to expression vectors without the need for specialized knowledge of zinc finger sequences or complicated oligonucleotide design. We also describe a bacterial cell-based reporter assay for rapidly screening the DNA-binding activities of assembled multi-finger arrays. This protocol can be completed in approximately 24–26 d.

Targeted Mutagenesis of Duplicated Genes in Soybean with Zinc-Finger Nucleases

We performed targeted mutagenesis of a transgene and nine endogenous soybean (Glycine max) genes using zinc-finger nucleases (ZFNs). A suite of ZFNs were engineered by the recently described context-dependent assembly platform—a rapid, open-source method for generating zinc-finger arrays. Specific ZFNs targeting DICER-LIKE (DCL) genes and other genes involved in RNA silencing were cloned into a vector under an estrogen-inducible promoter. A hairy-root transformation system was employed to investigate the efficiency of ZFN mutagenesis at each target locus. Transgenic roots exhibited somatic mutations localized at the ZFN target sites for seven out of nine targeted genes. We next introduced a ZFN into soybean via whole-plant transformation and generated independent mutations in the paralogous genes DCL4a and DCL4b. The dcl4b mutation showed efficient heritable transmission of the ZFN-induced mutation in the subsequent generation. These findings indicate that ZFN-based mutagenesis provides an efficient method for making mutations in duplicate genes that are otherwise difficult to study due to redundancy. We also developed a publicly accessible Web-based tool to identify sites suitable for engineering context-dependent assembly ZFNs in the soybean genome.

RNABindR: a server for analyzing and predicting RNA-binding sites in proteins

Understanding interactions between proteins and RNA is key to deciphering the mechanisms of many important biological processes. Here we describe RNABindR, a web-based server that identifies and displays RNA-binding residues in known protein–RNA complexes and predicts RNA-binding residues in proteins of unknown structure. RNABindR uses a distance cutoff to identify which amino acids contact RNA in solved complex structures (from the Protein Data Bank) and provides a labeled amino acid sequence and a Jmol graphical viewer in which RNA-binding residues are displayed in the context of the three-dimensional structure. Alternatively, RNABindR can use a Naive Bayes classifier trained on a non-redundant set of protein–RNA complexes from the PDB to predict which amino acids in a protein sequence of unknown structure are most likely to bind RNA. RNABindR automatically displays ‘high specificity’ and ‘high sensitivity’ predictions of RNA-binding residues. RNABindR is freely available at http://bindr.gdcb.iastate.edu/RNABindR.

Predicting DNA-binding sites of proteins from amino acid sequence

BackgroundUnderstanding the molecular details of protein-DNA interactions is critical for deciphering the mechanisms of gene regulation. We present a machine learning approach for the identification of amino acid residues involved in protein-DNA interactions.ResultsWe start with a Naïve Bayes classifier trained to predict whether a given amino acid residue is a DNA-binding residue based on its identity and the identities of its sequence neighbors. The input to the classifier consists of the identities of the target residue and 4 sequence neighbors on each side of the target residue. The classifier is trained and evaluated (using leave-one-out cross-validation) on a non-redundant set of 171 proteins. Our results indicate the feasibility of identifying interface residues based on local sequence information. The classifier achieves 71% overall accuracy with a correlation coefficient of 0.24, 35% specificity and 53% sensitivity in identifying interface residues as evaluated by leave-one-out cross-validation. We show that the performance of the classifier is improved by using sequence entropy of the target residue (the entropy of the corresponding column in multiple alignment obtained by aligning the target sequence with its sequence homologs) as additional input. The classifier achieves 78% overall accuracy with a correlation coefficient of 0.28, 44% specificity and 41% sensitivity in identifying interface residues. Examination of the predictions in the context of 3-dimensional structures of proteins demonstrates the effectiveness of this method in identifying DNA-binding sites from sequence information. In 33% (56 out of 171) of the proteins, the classifier identifies the interaction sites by correctly recognizing at least half of the interface residues. In 87% (149 out of 171) of the proteins, the classifier correctly identifies at least 20% of the interface residues. This suggests the possibility of using such classifiers to identify potential DNA-binding motifs and to gain potentially useful insights into sequence correlates of protein-DNA interactions.ConclusionNaïve Bayes classifiers trained to identify DNA-binding residues using sequence information offer a computationally efficient approach to identifying putative DNA-binding sites in DNA-binding proteins and recognizing potential DNA-binding motifs.

A two-stage classifier for identification of protein--protein interface residues

MOTIVATION The ability to identify protein-protein interaction sites and to detect specific amino acid residues that contribute to the specificity and affinity of protein interactions has important implications for problems ranging from rational drug design to analysis of metabolic and signal transduction networks. RESULTS We have developed a two-stage method consisting of a support vector machine (SVM) and a Bayesian classifier for predicting surface residues of a protein that participate in protein-protein interactions. This approach exploits the fact that interface residues tend to form clusters in the primary amino acid sequence. Our results show that the proposed two-stage classifier outperforms previously published sequence-based methods for predicting interface residues. We also present results obtained using the two-stage classifier on an independent test set of seven CAPRI (Critical Assessment of PRedicted Interactions) targets. The success of the predictions is validated by examining the predictions in the context of the three-dimensional structures of protein complexes.