Prediction of nucleic acid binding probability in proteins: a neighboring residue network based score.
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.
Architecture et Réactivité de l'ARN, Université de Strasbourg, Institut de biologie moléculaire et cellulaire du CNRS, 15 Rue Descartes, 67000 Strasbourg, France.
Zhichao
Z
Miao
Architecture et Réactivité de l'ARN, Université de Strasbourg, Institut de biologie moléculaire et cellulaire du CNRS, 15 Rue Descartes, 67000 Strasbourg, France e.westhof@ibmc-cnrs.unistra.fr.
Eric
http://orcid.org/0000-0002-6172-5422
E
Westhof
D001665:Binding Sites; D004268:DNA-Binding Proteins / Q000737:chemistry* / Q000378:metabolism; D011485:Protein Binding; D012313:RNA / Q000737:chemistry / Q000378:metabolism; D016601:RNA-Binding Proteins / Q000737:chemistry* / Q000378:metabolism; D020539:Sequence Analysis, Protein; D012984:Software; D055672:Static Electricity; D040681:Structural Homology, Protein; D060388:Support Vector Machine
D004268:DNA-Binding Proteins; D016601:RNA-Binding Proteins; D012313:RNA