Prediction of DNA-binding specificity in zinc finger proteins

Zinc finger proteins interact via their individual fingers to three base pair subsites on the target DNA. The four key residue positions -1, 2, 3 and 6 on the alpha-helix of the zinc fingers have hydrogen bond interactions with the DNA. Mutating these key residues enables generation of a plethora of combinatorial possibilities that can bind to any DNA stretch of interest. Exploiting the binding specificity and affinity of the interaction between the zinc fingers and the respective DNA can help to generate engineered zinc fingers for therapeutic purposes involving genome targeting. Exploring the structure-function relationships of the existing zinc finger-DNA complexes can aid in predicting the probable zinc fingers that could bind to any target DNA. Computational tools ease the prediction of such engineered zinc fingers by effectively utilizing information from the available experimental data. A study of literature reveals many approaches for predicting DNA-binding specificity in zinc finger proteins. However, an alternative approach that looks into the physico-chemical properties of these complexes would do away with the difficulties of designing unbiased zinc fingers with the desired affinity and specificity. We present a physico-chemical approach that exploits the relative strengths of hydrogen bonding between the target DNA and all combinatorially possible zinc fingers to select the most optimum zinc finger protein candidate.     

DISPLAR: an accurate method for predicting DNA-binding sites on protein surfaces

Structural and physical properties of DNA provide important constraints on the binding sites formed on surfaces of DNA-targeting proteins. Characteristics of such binding sites may form the basis for predicting DNA-binding sites from the structures of proteins alone. Such an approach has been successfully developed for predicting protein–protein interface. Here this approach is adapted for predicting DNA-binding sites. We used a representative set of 264 protein–DNA complexes from the Protein Data Bank to analyze characteristics and to train and test a neural network predictor of DNA-binding sites. The input to the predictor consisted of PSI-blast sequence profiles and solvent accessibilities of each surface residue and 14 of its closest neighboring residues. Predicted DNA-contacting residues cover 60% of actual DNA-contacting residues and have an accuracy of 76%. This method significantly outperforms previous attempts of DNA-binding site predictions. Its application to the prion protein yielded a DNA-binding site that is consistent with recent NMR chemical shift perturbation data, suggesting that it can complement experimental techniques in characterizing protein–DNA interfaces.     

Site-specific DNA cleavage by artificial zinc finger-type nuclease with cerium-binding peptide

The addition of a new function to native proteins is one of the most attractive protein-based designs. In this study, we have converted a C(2)H(2)-type zinc finger as a DNA-binding motif into a novel zinc finger-type nuclease by connecting two distinct zinc finger proteins (Sp1 and GLI) with a functional linker possessing DNA cleavage activity. As a DNA cleavage domain, we chose an analogue of the metal-binding loop (12 amino acid residues), peptide P1, which has been reported to exhibit a strong binding affinity for a lanthanide ion and DNA cleavage ability in the presence of Ce(IV). Our newly designed nucleases, Sp1(P1)GLI and Sp1(P1G)GLI, can strongly bind to a lanthanide ion and show a unique DNA cleavage pattern, in which certain positions between the two DNA-binding sites are specifically cleaved. The present result provides useful information for expanding the design strategy for artificial nucleases.     

A structural model for the rolA protein and its interaction with DNA

The study of the plant oncogene rolA has been hampered by a lack of structural information. Here we show that, despite a lack of significant sequence similarity to proteins of known structure, the rolA sequence adopts a known fold; that of the papillomavirus E2 DNA-binding domain. This fold is reliably identified by modern threading programs, which consider predicted secondary structure, but not by others. Although the rolA sequence is only around 16% identical to those of the available template structures, a structural model could be built that performed well against protein structure verification programs. The adopted strategy involved alignment corrections, justified by multiple model building and evaluation, with particular attention paid to the hydrophobic core residues. We find that rolA protein is predicted to resemble the template proteins in two key aspects; existence as a dimer and ability to bind DNA. rolA protein has recently been shown experimentally to possess DNA binding ability. This model predicts Lys 24 and Arg 27 to be involved in sequence-specific interactions and eight other residues to hydrogen-bond phosphate groups of the DNA.     

The Yeast a1 and α2 Homeodomain Proteins Do Not Contribute Equally to Heterodimeric DNA Binding

In diploid cells of the yeast Saccharomyces cerevisiae, the α2 and a1 homeodomain proteins bind cooperatively to sites in the promoters of haploid cell-type-specific genes (hsg) to repress their expression. Although both proteins bind to the DNA, in the α2 homeodomain substitutions of residues that are involved in contacting the DNA have little or no effect on repression in vivo or cooperative DNA binding with a1 protein in vitro. This result brings up the question of the contribution of each protein in the heterodimer complex to the DNA-binding affinity and specificity. To determine the requirements for the a1-α2 homeodomain DNA recognition, we systematically introduced single base-pair substitutions in an a1-α2 DNA-binding site and examined their effects on repression in vivo and DNA binding in vitro. Our results show that nearly all substitutions that significantly decrease repression and DNA-binding affinity are at positions which are specifically contacted by either the α2 or a1 protein. Interestingly, an α2 mutant lacking side chains that make base-specific contacts in the major groove is able to discriminate between the wild-type and mutant DNA sites with the same sequence specificity as the wild-type protein. These results suggest that the specificity of α2 DNA binding in complex with a1 does not rely solely on the residues that make base-specific contacts. We have also examined the contribution of the a1 homeodomain to the binding affinity and specificity of the complex. In contrast to the lack of a defective phenotype produced by mutations in the α2 homeodomain, many of the alanine substitutions of residues in the a1 homeodomain have large effects on a1-α2-mediated repression and DNA binding. This result shows that the two proteins do not make equal contributions to the DNA-binding affinity of the complex.     

Interaction of c(60)-fullerene and carboxyfullerene with proteins: docking and binding site alignment

The unique properties of fullerenes have raised the interest of using them for biomedical applications. Within this framework, the interactions of fullerenes with proteins have been an exciting research target, yet little is known about how native proteins can bind fullerenes, and what is the nature of these interactions. Moreover, though some proteins have been shown to interact with fullerenes, up to date, no crystal structure of such complexes was obtained. Here we report docking studies aimed at examining the interactions of fullerene in two forms (C60 nonsubstituted fullerene and carboxyfullerene) with four proteins that are known to bind fullerene derivatives: HIV protease, fullerene-specific antibody, human serum albumin, and bovine serum albumin. Our work provides docking models with detailed binding pockets information, which closely match available experimental data. We further compare the predicted binding sites using a novel multiple binding site alignment method. A high similarity between the physicochemical properties and surface geometry was found for fullerene's binding sites of HIV protease and the human and bovine serum albumins.     

Differential affinity and cooperativity functions of the amino-terminal 70 residues of lambda integrase

The site-specific recombinase (Int) of bacteriophage lambda is a heterobivalent DNA-binding protein that binds two different classes of DNA-binding sites within its recombination target sites. The several functions of Int are apportioned between a large carboxy-terminal domain that cleaves and ligates DNA at each of its four "core-type" DNA-binding sites and a small amino-terminal domain, whose primary function is binding to each of its five "arm-type" DNA sites, which are distant from the core region. Int bridges between the two classes of binding sites are facilitated by accessory DNA-bending proteins that along with Int comprise higher-order recombinogenic complexes. We show here that although the 64 amino-terminal residues of Int bind efficiently to a single arm site, this protein cannot form doubly bound complexes on adjacent arm sites. However, 1-70 Int does show the same cooperative binding to adjacent arm sites as the full length protein. We also found that 1-70 Int specifies cooperative interactions with the accessory protein Xis when the two are bound to their adjacent cognate sites P2 and X1, respectively. To complement the finding that these two amino-terminal domain functions (along with arm DNA binding) are all specified by residues 1-70, we determined that Thr75 is the first residue of the minimal carboxy-terminal domain, thereby identifying a specific interdomain linker region. We have measured the affinity constants for Int binding to each of the five arm sites and the cooperativity factors for Int binding to the two pairs of adjacent arm sites, and we have identified several DNA structural features that contribute to the observed patterns of Int binding to arm sites. Taken together, the results highlight several interesting features of arm DNA binding that invite speculation about additional levels of complexity in the regulation of lambda site-specific recombination.     

Adeno-Associated Virus Type 2 Rep68 Can Bind to Consensus Rep-Binding Sites on the Herpes Simplex Virus 1 Genome

Adeno-associated virus type 2 is known to inhibit replication of herpes simplex virus 1 (HSV-1). This activity has been linked to the helicase- and DNA-binding domains of the Rep68/Rep78 proteins. Here, we show that Rep68 can bind to consensus Rep-binding sites on the HSV-1 genome and that the Rep helicase activity can inhibit replication of any DNA if binding is facilitated. Therefore, we hypothesize that inhibition of HSV-1 replication involves direct binding of Rep68/Rep78 to the HSV-1 genome.     

DBD-Hunter: a knowledge-based method for the prediction of DNA-protein interactions

The structures of DNA-protein complexes have illuminated the diversity of DNA-protein binding mechanisms shown by different protein families. This lack of generality could pose a great challenge for predicting DNA-protein interactions. To address this issue, we have developed a knowledge-based method, DNA-binding Domain Hunter (DBD-Hunter), for identifying DNA-binding proteins and associated binding sites. The method combines structural comparison and the evaluation of a statistical potential, which we derive to describe interactions between DNA base pairs and protein residues. We demonstrate that DBD-Hunter is an accurate method for predicting DNA-binding function of proteins, and that DNA-binding protein residues can be reliably inferred from the corresponding templates if identified. In benchmark tests on approximately 4000 proteins, our method achieved an accuracy of 98% and a precision of 84%, which significantly outperforms three previous methods. We further validate the method on DNA-binding protein structures determined in DNA-free (apo) state. We show that the accuracy of our method is only slightly affected on apo-structures compared to the performance on holo-structures cocrystallized with DNA. Finally, we apply the method to approximately 1700 structural genomics targets and predict that 37 targets with previously unknown function are likely to be DNA-binding proteins. DBD-Hunter is freely available at http://cssb.biology.gatech.edu/skolnick/webservice/DBD-Hunter/.     

Residue-level prediction of DNA-binding sites and its application on DNA-binding protein predictions

Protein-DNA interactions are crucial to many cellular activities such as expression-control and DNA-repair. These interactions between amino acids and nucleotides are highly specific and any aberrance at the binding site can render the interaction completely incompetent. In this study, we have three aims focusing on DNA-binding residues on the protein surface: to develop an automated approach for fast and reliable recognition of DNA-binding sites; to improve the prediction by distance-dependent refinement; use these predictions to identify DNA-binding proteins. We use a support vector machines (SVM)-based approach to harness the features of the DNA-binding residues to distinguish them from non-binding residues. Features used for distinction include the residue's identity, charge, solvent accessibility, average potential, the secondary structure it is embedded in, neighboring residues, and location in a cationic patch. These features collected from 50 proteins are used to train SVM. Testing is then performed on another set of 37 proteins, much larger than any testing set used in previous studies. The testing set has no more than 20% sequence identity not only among its pairs, but also with the proteins in the training set, thus removing any undesired redundancy due to homology. This set also has proteins with an unseen DNA-binding structural class not present in the training set. With the above features, an accuracy of 66% with balanced sensitivity and specificity is achieved without relying on homology or evolutionary information. We then develop a post-processing scheme to improve the prediction using the relative location of the predicted residues. Balanced success is then achieved with average sensitivity, specificity and accuracy pegged at 71.3%, 69.3% and 70.5%, respectively. Average net prediction is also around 70%. Finally, we show that the number of predicted DNA-binding residues can be used to differentiate DNA-binding proteins from non-DNA-binding proteins with an accuracy of 78%. Results presented here demonstrate that machine-learning can be applied to automated identification of DNA-binding residues and that the success rate can be ameliorated as more features are added. Such functional site prediction protocols can be useful in guiding consequent works such as site-directed mutagenesis and macromolecular docking.