Analysis of epidermal proteins for DNA-binding activity

A group of DNA-binding proteins from the soluble extract of newborn rat epidermis have been separated by chromatography using DNA-cellulose columns. The electrophoretogram of the DNA-binding proteins eluted from a single stranded DNA-cellulose column shows five major proteins of molecular weights ranging between 25K to 40K. Both the epidermal protein filaggrin and most keratins, except two high molecular weight keratins, do not show in vitro DNA-binding activity.     

Biochemical characterization of DNA-binding proteins from Pyrobaculum aerophilum and Aeropyrum pernix

Several representatives of the Crenarchaeal branch of the Archaea contain highly abundant, small, positively charged proteins exemplified by the Sso7d protein from Sulfolobus solfataricus. These proteins bind to DNA in a non-sequence-specific manner. Using publicly available genomic sequence information, we identified a second class of small Crenarchaeal DNA-binding proteins represented by the Pyrobaculum aerophilum open reading frame 3192–encoded (Pae3192) protein and its paralogs. We investigated the biochemical properties of the Pae3192 protein and an orthologous protein (Ape1322b) from Aeropyrum pernix in side-by-side experiments with the Sso7d protein. We demonstrate that the recombinant Ape1322b, Pae3192 and Sso7d proteins bind to DNA and that the DNA-protein complexes formed are slightly different for each protein. We show that like Sso7d, Pae3192 constrains negative supercoils in DNA. In addition, we show that all three proteins raise the melting temperature of duplex DNA upon binding. Finally, we present the equilibrium affinity constants and kinetic association constants of each protein for single-stranded and double-stranded DNA.     

Interaction of hepatitis B virus X protein with damaged DNA-binding protein p127: Structural analysis and identification of antagonists

The hepatitis B virus X protein is a multifunctional protein that is essential for natural infection and has also been implicated in liver cancer development. Previous studies have identified the DDB1 subunit of the damaged-DNA binding complex as a critical partner of X protein in the infection process, X-mediated cytotoxicity and stability of the viral protein. Here, we investigated the structural and functional constraints of X-DDB1 interaction using various mutational analyses. Our data show that the interaction interface of X with DDB1 is confined to a 15-residue epitope. All substitutions responsible for loss of binding mapped to this core-binding domain. In contrast, a marked increase in affinity for DDB1 resulted from substitutions at clustered positions lying close to the DDB1-binding epitope and correlated with loss of apoptotic potential. Selection of mutations in DDB1 that partially rescue the binding defect of an X mutant gave further insight into the contacts established between the two proteins. Importantly, both the core-binding domain of X and the gain-of-affinity X mutants inhibited DDB1-mediated stabilization of wild-type X protein. These X protein derivatives thus provide the basis for the development of therapeutic agents that antagonize X function through competitive inhibition of X-DDB1 interaction.     

Determination of the molecular weight of DNA-binding proteins using UV-crosslinking and SDS-PAGE

We describe the use of UV-crosslinking in combination with SDS-PAGE to determine the approximate molecular weight of DNA-binding proteins. A 5-bromo-2′-deoxyuridine (5-BrdU)-substituted, radioactively labeled double-stranded oligonucleotide representing the protein binding site is incubated with a crude nuclear extract containing the protein of interest. Following irradiation with a UV light source, the DNA/protein complex is subjected to SDS-PAGE and its molecular weight determined by comparison with appropriate protein standards.     

Simultaneous detection of DNA-binding proteins using exo-Taq-based reaction

The DNA-binding protein (DBP) has a wide range of roles such as those in DNA repair, recombination, and gene expression. Recently, a microarray-based method has been developed for the high-throughput analysis of DNA-protein interactions. However, to maximize the advantages of this method, the detection process should be improved so that the method can be applied to many proteins without the use of antibody or sample labeling. Previously, we presented a primary report on the detection of DBP, which is applicable to the microarray format. The system consists of three steps: first, the target DBP in the sample solution is incubated with a probe DNA; second, the probe is digested with Exo (Exonuclease) III; finally, the probe is extended withTaq DNA polymerase using fluorescent dye-labeled dUTP as a substrate. The binding DBP protects the probe from digestion by Exo III. Therefore, only the DBP-bound probe allows the following extension. In this study, the simultaneous detection of multiple DBPs was examined, and then the DBPs were analyzed using a crude extract of the cultured cells to demonstrate the general applicability of the method. Our method can be applied to many DBPs using the same procedure and components, whereas in the antibody-based method, the same number of antibodies as DBPs is needed to detect target DBPs in ELISA (enzyme-linked immunosorbent assay). These results suggest that our method is useful for the high-throughput detection of DBPs in the microarray format.     

Photochemical crosslinking of bacteriophage T4 single-stranded DNA-binding protein (gp32) to oligo-p(dT)8: identification of phenylalanine-183 as the site of crosslinking

Using ultraviolet light, both the 33,000-dalton single-stranded DNA-binding protein from T4 bacteriophage (gp32) as well as a 25,000-dalton limited trypsin cleavage product of gp32 (core gp32*) that retains high affinity for single-stranded DNA can be crosslinked to an oligodeoxynucleotide, p(dT)8. After photolysis, a single tryptic peptide crosslinked to p(dT)8 was isolated by anion-exchange high-performance liquid chromatography. Gas-phase sequencing of this modified peptide gave the following sequence: Gln-Val-Ser-Gly-(X)-Ser-Asn-Tyr-Asp-Glu-Ser-Lys, which corresponds to residues 179-190 in gp32. Based on the absence of the expected phenylthiohydantoin derivative of phenylalanine 183 at cycle 5 (X) we infer that crosslinking has occurred at this position and that phenylalanine 183 is at the interface of the gp32:p(dT)8 complex in an orientation that allows covalent bond formation with the thymine radical produced by ultraviolet irradiation.     

Predicting rRNA-, RNA-, and DNA-binding proteins from primary structure with support vector machines

In the post-genome era, the prediction of protein function is one of the most demanding tasks in the study of bioinformatics. Machine learning methods, such as the support vector machines (SVMs), greatly help to improve the classification of protein function. In this work, we integrated SVMs, protein sequence amino acid composition, and associated physicochemical properties into the study of nucleic-acid-binding proteins prediction. We developed the binary classifications for rRNA-, RNA-, DNA-binding proteins that play an important role in the control of many cell processes. Each SVM predicts whether a protein belongs to rRNA-, RNA-, or DNA-binding protein class. Self-consistency and jackknife tests were performed on the protein data sets in which the sequences identity was < 25%. Test results show that the accuracies of rRNA-, RNA-, DNA-binding SVMs predictions are approximately 84%, approximately 78%, approximately 72%, respectively. The predictions were also performed on the ambiguous and negative data set. The results demonstrate that the predicted scores of proteins in the ambiguous data set by RNA- and DNA-binding SVM models were distributed around zero, while most proteins in the negative data set were predicted as negative scores by all three SVMs. The score distributions agree well with the prior knowledge of those proteins and show the effectiveness of sequence associated physicochemical properties in the protein function prediction. The software is available from the author upon request.     

A structure-based method for identifying DNA-binding proteins and their sites of DNA-interaction

A classification model of a DNA-binding protein chain was created based on identification of alpha helices within the chain likely to bind to DNA. Using the model, all chains in the Protein Data Bank were classified. For many of the chains classified with high confidence, previous documentation for DNA-binding was found, yet no sequence homology to the structures used to train the model was detected. The result indicates that the chain model can be used to supplement sequence based methods for annotating the function of DNA-binding. Four new candidates for DNA-binding were found, including two structures solved through structural genomics efforts. For each of the candidate structures, possible sites of DNA-binding are indicated by listing the residue ranges of alpha helices likely to interact with DNA.     

Left-handed helix of polyproline ii type in linker regions of DNA-binding proteins

Linker segments assuming the polyproline II type conformation within DNA-protein complexes were sought in protein and linker databases. Seventy-three linker-DNA complexes were found. The mean length of polyproline II type segments was six residues, and prolines were not predominant there. It was shown that the symmetrical position of prolines in these segments prevented the formation of the cooperative water network involving amide groups. An example of specific proline location in some motility apparatus proteins is presented.     

Facile determination of DNA-binding nuclear factor-kappaB by chemiluminescence detection

A simple, rapid, and sensitive method for the assay of a sequence-specific DNA-binding protein, nuclear factor-kappaB (NF-kappaB), has been developed by using a DNA-detectable chemiluminogenic reagent and a centrifugal filter that distinguishes different molecular sizes. After the formation of a complex between NF-kappaB and DNA, the unbound DNA is separated from the complex by the centrifugal filter. The amount of the bound NF-kappaB is estimated by chemiluminescence detection of the bound DNA. This detection is performed within 2 min at room temperature by the use of a chemiluminogenic reagent, 3',4',5'-trimethoxyphenylglyoxal, which selectively recognizes guanine moiety in oligonucleotides or DNAs. This method does not require any labeled probes or antibodies and can determine a concentration as low as 5 nM of DNA-binding NF-kappaB. The sensitivity is nearly the same as that of other methods such as gel shift assay using fluorescence-labeled probes and enzyme-linked immunosorbent assay. Therefore, the current method provides a convenient tool for surveying various DNA-binding proteins.     

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.     

Enabling full-length evolutionary profiles based deep convolutional neural network for predicting DNA-binding proteins from sequence

Sequence based DNA-binding protein (DBP) prediction is a widely studied biological problem. Sliding windows on position specific substitution matrices (PSSMs) rows predict DNA-binding residues well on known DBPs but the same models cannot be applied to unequally sized protein sequences. PSSM summaries representing column averages and their amino-acid wise versions have been effectively used for the task, but it remains unclear if these features carry all the PSSM's predictive power, traditionally harnessed for binding site predictions. Here we evaluate if PSSMs scaled up to a fixed size by zero-vector padding (pPSSM) could perform better than the summary based features on similar models. Using multilayer perceptron (MLP) and deep convolutional neural network (CNN), we found that (a) Summary features work well for single-genome (human-only) data but are outperformed by pPSSM for diverse PDB-derived data sets, suggesting greater summary-level redundancy in the former, (b) even when summary features work comparably well with pPSSM, a consensus on the two outperforms both of them (c) CNN models comprehensively outperform their corresponding MLP models and (d) actual predicted scores from different models depend on the choice of input feature sets used whereas overall performance levels are model-dependent in which CNN leads the accuracy.     

Arabidopsis HY5 protein functions as a DNA-binding tag for purification and functional immobilization of proteins on agarose/DNA microplate

Protein microarray is considered to be one of the key analytical tools for high-throughput protein function analysis. Here, we report that the Arabidopsis HY5 functions as a novel DNA-binding tag (DBtag) for proteins. We also demonstrate that the DBtagged proteins could be immobilized and purified on a newly designed agarose/DNA microplate. Furthermore, we show three applications using the microarray: (1) detection of autophosphorylation activity of DBtagged human protein kinases and inhibition of their activity by staurosporine, (2) specific cleavage of DBtagged proteins by a virus protease and caspase 3, and (3) detection of a protein-protein interaction between the DBtagged UBE2N and UBE2v1. Thus, this method may facilitate rapid functional analysis of a wide range of proteins.