Nucleic Acid-Protein Recognition

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Structural features of protein-nucleic acid recognition sites.

Here, a histidine-tagged protein is allowed to interact with a pool of nucleic. Here, a histidine-tagged protein is allowed to interact with a pool of nucleic acids and the protein—nucleic acid complexes formed are retained on a Ni-NTA matrix. Nucleic acids with a low level of recognition by the protein are washed away. The pool of recovered nucleic acids is amplified by the polymerase chain reaction and is submitted to further rounds of selection. Each round of selection increases the proportion of sequences that are avidly bound by the protein of interest.

Understanding Nucleic Acid Binding Domains and Complex Interactions

The cloning and sequencing of these sequences finally completes their identification. Binding sequences, Sequence recognition, Nucleic acids ligands. The DPS protein compacts the eubacterial chromosome during stress. The histone fold and formation of the nucleosome.

Core histone tail modification regulates DNA compaction. About this free course 12 hours study. Level 3: Advanced. Course rewards. Free statement of participation on completion of these courses. Course content Course content. Nucleic acids and chromatin This free course is available to start right now. Free course Nucleic acids and chromatin. Figure 22 a and b Interaction between Drosophila Ubx protein and DNA showing the positioning of a recognition helix cyan in the major groove, supported by two other helices red and pink , in side and top-down views based on pdb file 1b8i.

Only one of the DNA binding domains is shown. In the case of Asn 51 and Gln 50 of Ubx, hydrogen bonding to the thymine involves a water molecule shown here as a blue circle. Answer Genetic engineering techniques such as site-directed mutagenesis could be used to create both novel zinc fingers and target DNAs for such studies.

SAQ 27 What techniques could be used to determine the strength of interaction binding between an engineered protein and its target DNA? Previous 6. Next 6.

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DNA-binding domain

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DNA protein interaction

Under the assumption that the protein receptor is large, the conventional specificity of discrimination of a nucleic acid binding to its protein receptor against other proteins can be transformed to the intrinsic specificity that the nucleic acid binds to the large receptor with a preferred pose over other poses.

By applying this concept, we have developed scoring functions for the interactions of bimolecular recognition, including protein-ligand interactions [21] and protein-protein interactions [22]. Also, the connection between the intrinsic specificity and conventional specificity was validated by studying a drug-target model [23] , where the conventional specificity is correlated with the intrinsic specificity. According to the theory of energy landscape [19] — [21] , [31] — [39] , the binding process of biomolecules can be visualized and quantified as a funnel-like energy landscape towards the native binding state with local roughness along the binding paths.

The native pose of protein-nucleic acid complex is the conformation with the lowest binding energy and the energies of the conformation ensemble follow a statistical Gaussian-like distribution. The intrinsic specificity ratio , where is the energy gap between the energy of native conformation and the average energy of conformation ensemble, is the energy roughness or the width of the energy distribution of the conformation ensemble, and is the conformational entropy can be defined to quantify the magnitude of intrinsic specificity.

With computationally generated non-native poses decoys , the ISR can be readily obtained. Therefore, without evaluating the conventional specificity through exploring the whole set of competitive partners, ISR physically provides a quantitative measure of the binding specificity.

In this work, based on our practical quantification of binding specificity, we have designed an optimization strategy to maximize both the affinity and specificity of native binding mode simultaneously for developing the scoring function of protein-nucleic acid interactions. The optimization strategy is to adjust the statistical knowledge-based potentials of atom pairs by iteration until the scoring function can effectively discriminate the native binding pose against the decoys.

The flow of developing procedures is shown in Figure S1 in File S1. We have tested the derived scoring function of protein-nucleic acid interactions SPA-PN via the performance on the prediction of binding affinity and the identification of native binding pose. The performance of SPA-PN demonstrated that the quantified specificity is necessary to be incorporated into the optimization of scoring functions of protein-nucleic acid interactions. The requirement of optimizing the knowledge-based statistical scoring function is to train a set of known structural data. To obtain a relatively high quality set of protein-nucleic complexes for the training dataset, X-ray structures with resolutions better than 3.

By removing the entry overlaps with the testing datasets below, the resulting training dataset contains complexes, including protein-DNA structures and protein-RNA structures Table S1 in File S1. To validate the performance of a novel scoring function, two kinds of tests are needed. This dataset for binding affinity prediction was employed from the binding database which is a modified version of Zhang et al.


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Second, in order to evaluate the ability of SPA-PN on discriminating the native conformation from decoy conformations, SPA-PN was tested on our collected benchmark of protein-nucleic acid complexes. The collected benchmark for binding pose prediction was obtained from available benchmarks of protein-nucleic acid complexes. The second protein-DNA benchmark [46] contains 47 complexes which covers almost all major groups of DNA-binding proteins according to the classification of Luscombe et al [47]. The first protein-RNA benchmark [48] contains 45 complexes covering all major groups of protein-RNA complexes according to the classification of Bahadur et al [49].

The second protein-RNA benchmark [50] is an extended set of the first one and contains protein-RNA complexes, it was obtained from both experimental and homology modeling data. This collected testing dataset were also filtered with the criteria as composed on the training dataset. In addition, one entry was kept if there are overlaps among the different benchmarks.

For the optimization of SPA-PN, an ensemble of decoys for each complex are needed to calculate the ISR for specificity and carry out the iteration algorithm. The RosettaDock v3.

Three steps were performed. First, each docking partner of the complex was prepared in isolation for optimizing their side-chain conformations prior to docking using the prepacking protocol. Second, the prepacked complexes were relaxed and minimized with high resolution by the refinement protocol. Third, the refined structures were taken as the starting structures for the docking using the local docking perturbation protocol. The smaller partner was defined as the docking ligand in the complex and the other was assigned as the receptor which was kept fixed during docking.

Other docking parameters were set as default. The generated decoys are structured diversely to explore the underlying binding energy landscape. The knowledge-based scoring function consists of a set of statistical distance-dependent atom-pair potentials to quantify the interactions. Normally, the observed atom-pair potentials were directly derived from the Boltzmann relation widely applied in the derivation of knowledge-based statistical potentials for the protein-ligand, protein-protein and protein-nucleic acid interactions [53] — [57] , the Boltzmann relation is written as 1 where is the observed atom pair distribution function quantified by.

It can be directly extracted from the structural database of protein-nucleic acid complexes. It was obtained based on the approximation that the atom-pair is uniformly distributed in the sphere of the reference state [58]. In total, there are 20 spherical shells with bin size 0. This lead to 95 effective types of atom pairs for the protein-nucleic acid interactions Table S5 in File S1. In addition, if the atom pair has no occurrence in a particular spherical shell, the corresponding pair potential was set as the van der Waals interaction within this shell.

The observed statistical potentials from the known structures has its limitations as the statistical potentials extracted from equation 1 is not exactly the expected potentials that nature employs to stabilize the complexes [61]. The origin of this problem is attributed to the construction of the reference state where the atom pairs are uniformly distributed and independent of each other [58].

Structural Features of Protein−Nucleic Acid Recognition Sites | Biochemistry

In reality, the protein-nucleic acid interactions involve the excluded volume, sequences and connectivity. Thus the observed statistical potentials are generally not equal to the expected potentials [62].

Contributing signatures

To circumvent the reference state issue and improve the statistical potentials, earlier efforts [42] , [61] , [63] — [65] have taken different approaches to optimize the statistical potentials. An effective way is to take into account both native and nonnative conformations decoys [61] , [63] , [65] based on the energy landscape theory that the native conformation should be sufficiently favored over alternative nonnative structures thermodynamically.


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  6. However, these earlier works hasn't combined both the specificity and affinity to discriminate native conformation over nonnative conformations. In our recent papers on the study of the protein-ligand [21] and protein-protein interactions [22] , we considered the importance of both the affinity for stabilizing the native conformation and the specificity of discrimination over nonnative conformations, and combined them into the optimization processes of scoring function.

    Here we expand this concept to optimize the statistical potentials of protein-nucleic acid interactions. The expected statistical potentials are calculated similarly as the observed statistical potentials, which is 5 where is the expected atom-pair distribution function from all the native and non-native conformations, which is.

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