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Affinity regression predicts the recognition code of nucleic acid-binding proteins.

ABSTRACT: Predicting the affinity profiles of nucleic acid-binding proteins directly from the protein sequence is a challenging problem. We present a statistical approach for learning the recognition code of a family of transcription factors or RNA-binding proteins (RBPs) from high-throughput binding data. Our method, called affinity regression, trains on protein binding microarray (PBM) or RNAcompete data to learn an interaction model between proteins and nucleic acids using only protein domain and probe sequences as inputs. When trained on mouse homeodomain PBM profiles, our model correctly identifies residues that confer DNA-binding specificity and accurately predicts binding motifs for an independent set of divergent homeodomains. Similarly, when trained on RNAcompete profiles for diverse RBPs, our model correctly predicts the binding affinities of held-out proteins and identifies key RNA-binding residues, despite the high level of sequence divergence across RBPs. We expect that the method will be broadly applicable to modeling and predicting paired macromolecular interactions in settings where high-throughput affinity data are available.

SUBMITTER: Pelossof R

PROVIDER: S-EPMC4871164 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

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Affinity regression predicts the recognition code of nucleic acid-binding proteins.

Pelossof Raphael R Singh Irtisha I Yang Julie L JL Weirauch Matthew T MT Hughes Timothy R TR Leslie Christina S CS

Nature biotechnology 20151116 12

Predicting the affinity profiles of nucleic acid-binding proteins directly from the protein sequence is a challenging problem. We present a statistical approach for learning the recognition code of a family of transcription factors or RNA-binding proteins (RBPs) from high-throughput binding data. Our method, called affinity regression, trains on protein binding microarray (PBM) or RNAcompete data to learn an interaction model between proteins and nucleic acids using only protein domain and probe ...[more]

PMID: 26571099

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Project description:Coronaviruses pose serious health threats to humans and other animals. Understanding the mechanisms of their replication has important implications for global health and economic stability. Nonstructural protein 9 (nsp9) is an essential RNA binding protein for coronavirus replication. However, the mechanisms of the dimerization and nucleic acid binding of nsp9 remain elusive. Here, we report four crystal structures, including wild-type porcine delta coronavirus (PDCoV) nsp9, PDCoV nsp9-ΔN7 (N-terminal 7 amino acids deleted), wild-type porcine epidemic diarrhea virus (PEDV) nsp9, and PEDV nsp9-C59A mutant. These structures reveal the diverse dimerization forms of coronavirus nsp9. We first found that the N-finger of nsp9 from PDCoV plays a critical role in dimerization. Meanwhile, PEDV nsp9 is distinguished by the presence of a disulfide bond in the dimer interface. Interestingly, size exclusion chromatography and analytical ultracentrifugation analyses indicate that the PDCoV nsp9-ΔN7 and PEDV nsp9-C59A mutants are monomeric in solution. In addition, electrophoretic mobility shift assays and microscale thermophoresis analysis indicate that the monomeric forms of PDCoV nsp9 and PEDV nsp9 still have nucleic acid binding affinity, although it is lower than that of the wild type. Our results show that the diverse dimerization forms of coronavirus nsp9 proteins enhance their nucleic acid binding affinity.IMPORTANCE Coronaviruses cause widespread respiratory, gastrointestinal, and central nervous system diseases in humans and other animals, threatening human health and causing economic loss. Coronavirus nsp9, a member of the replication complex, is an important RNA binding subunit in the RNA-synthesizing machinery of all coronaviruses. However, the mechanisms of the dimerization and nucleic acid binding of nsp9 remain elusive. In this study we determined the nsp9 crystal structures of PDCoV and PEDV. We first found that the N-finger of nsp9 from PDCoV plays a critical role in dimerization. Meanwhile, PEDV nsp9 is distinguished by the presence of a disulfide bond in the dimer interface. This study provides a structural and functional basis for understanding the mechanism of dimerization and shows that the diverse dimerization modes of coronavirus nsp9 proteins enhance their nucleic acid binding affinity. Importantly, these findings may provide a new insight for antiviral drug development.

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Affinity regression predicts the recognition code of nucleic acid-binding proteins.

Publications

Affinity regression predicts the recognition code of nucleic acid-binding proteins.

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