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Prediction of nucleic acid binding probability in proteins: a neighboring residue network based score.

ABSTRACT: We describe a general binding score for predicting the nucleic acid binding probability in proteins. The score is directly derived from physicochemical and evolutionary features and integrates a residue neighboring network approach. Our process achieves stable and high accuracies on both DNA- and RNA-binding proteins and illustrates how the main driving forces for nucleic acid binding are common. Because of the effective integration of the synergetic effects of the network of neighboring residues and the fact that the prediction yields a hierarchical scoring on the protein surface, energy funnels for nucleic acid binding appear on protein surfaces, pointing to the dynamic process occurring in the binding of nucleic acids to proteins.

SUBMITTER: Miao Z

PROVIDER: S-EPMC4477668 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature

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Prediction of nucleic acid binding probability in proteins: a neighboring residue network based score.

Miao Zhichao Z Westhof Eric E

Nucleic acids research 20150504 11

We describe a general binding score for predicting the nucleic acid binding probability in proteins. The score is directly derived from physicochemical and evolutionary features and integrates a residue neighboring network approach. Our process achieves stable and high accuracies on both DNA- and RNA-binding proteins and illustrates how the main driving forces for nucleic acid binding are common. Because of the effective integration of the synergetic effects of the network of neighboring residue ...[more]

PMID: 25940624

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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.

Dataset Information

Prediction of nucleic acid binding probability in proteins: a neighboring residue network based score.

Publications

Prediction of nucleic acid binding probability in proteins: a neighboring residue network based score.

Similar Datasets

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