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NAPS: a residue-level nucleic acid-binding prediction server.

ABSTRACT: Nucleic acid-binding proteins are involved in a great number of cellular processes. Understanding the mechanisms underlying these proteins first requires the identification of specific residues involved in nucleic acid binding. Prediction of NA-binding residues can provide practical assistance in the functional annotation of NA-binding proteins. Predictions can also be used to expedite mutagenesis experiments, guiding researchers to the correct binding residues in these proteins. Here, we present a method for the identification of amino acid residues involved in DNA- and RNA-binding using sequence-based attributes. The method used in this work combines the C4.5 algorithm with bootstrap aggregation and cost-sensitive learning. Our DNA-binding model achieved 79.1% accuracy, while the RNA-binding model reached an accuracy of 73.2%. The NAPS web server is freely available at http://proteomics.bioengr.uic.edu/NAPS.

SUBMITTER: Carson MB

PROVIDER: S-EPMC2896077 | biostudies-literature | 2010 Jul

REPOSITORIES: biostudies-literature

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NAPS: a residue-level nucleic acid-binding prediction server.

Carson Matthew B MB Langlois Robert R Lu Hui H

Nucleic acids research 20100516 Web Server issue

Nucleic acid-binding proteins are involved in a great number of cellular processes. Understanding the mechanisms underlying these proteins first requires the identification of specific residues involved in nucleic acid binding. Prediction of NA-binding residues can provide practical assistance in the functional annotation of NA-binding proteins. Predictions can also be used to expedite mutagenesis experiments, guiding researchers to the correct binding residues in these proteins. Here, we presen ...[more]

PMID: 20478832

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Project description: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.

Project description:Feline immunodeficiency virus (FIV) is a retrovirus that infects domestic cats, and is an excellent animal model for human immunodeficiency virus type 1 (HIV-1) pathogenesis. The nucleocapsid (NC) protein is critical for replication in both retroviruses. FIV NC has several structural features that differ from HIV-1 NC. While both NC proteins have a single conserved aromatic residue in each of the two zinc fingers, the aromatic residue on the second finger of FIV NC is located on the opposite C-terminal side relative to its location in HIV-1 NC. In addition, whereas HIV-1 NC has a highly charged cationic N-terminal tail and a relatively short C-terminal extension, the opposite is true for FIV NC. To probe the impact of these differences on the nucleic acid (NA) binding and chaperone properties of FIV NC, we carried out ensemble and single-molecule assays with wild-type (WT) and mutant proteins. The ensemble studies show that FIV NC binding to DNA is strongly electrostatic, with a higher effective charge than that observed for HIV-1 NC. The C-terminal basic domain contributes significantly to the NA binding capability of FIV NC. In addition, the non-electrostatic component of DNA binding is much weaker for FIV NC than for HIV-1 NC. Mutation of both aromatic residues in the zinc fingers to Ala (F12A/W44A) further increases the effective charge of FIV NC and reduces its non-electrostatic binding affinity. Interestingly, switching the location of the C-terminal aromatic residue to mimic the HIV-1 NC sequence (N31W/W44A) reduces the effective charge of FIV NC and increases its non-electrostatic binding affinity to values similar to HIV-1 NC. Consistent with the results of these ensemble studies, single-molecule DNA stretching studies show that while WT FIV NC has reduced stacking capability relative to HIV-1 NC, the aromatic switch mutant recovers the ability to intercalate between the DNA bases. Our results demonstrate that altering the position of a single aromatic residue switches the binding mode of FIV NC from primarily electrostatic binding to more non-electrostatic binding, conferring upon it NA interaction properties comparable to that of HIV-1 NC.

Project description:BACKGROUND: Recognition of binding sites in proteins is a direct computational approach to the characterization of proteins in terms of biological and biochemical function. Residue preferences have been widely used in many studies but the results are often not satisfactory. Although different amino acid compositions among the interaction sites of different complexes have been observed, such differences have not been integrated into the prediction process. Furthermore, the evolution information has not been exploited to achieve a more powerful propensity. RESULT: In this study, the residue interface propensities of four kinds of complexes (homo-permanent complexes, homo-transient complexes, hetero-permanent complexes and hetero-transient complexes) are investigated. These propensities, combined with sequence profiles and accessible surface areas, are inputted to the support vector machine for the prediction of protein binding sites. Such propensities are further improved by taking evolutional information into consideration, which results in a class of novel propensities at the profile level, i.e. the binary profiles interface propensities. Experiment is performed on the 1139 non-redundant protein chains. Although different residue interface propensities among different complexes are observed, the improvement of the classifier with residue interface propensities can be negligible in comparison with that without propensities. The binary profile interface propensities can significantly improve the performance of binding sites prediction by about ten percent in term of both precision and recall. CONCLUSION: Although there are minor differences among the four kinds of complexes, the residue interface propensities cannot provide efficient discrimination for the complicated interfaces of proteins. The binary profile interface propensities can significantly improve the performance of binding sites prediction of protein, which indicates that the propensities at the profile level are more accurate than those at the residue level.

Dataset Information

NAPS: a residue-level nucleic acid-binding prediction server.

Publications

NAPS: a residue-level nucleic acid-binding prediction server.

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