Dataset Information

From face to interface recognition: a differential geometric approach to distinguish DNA from RNA binding surfaces.

ABSTRACT: Protein nucleic acid interactions play a critical role in all steps of the gene expression pathway. Nucleic acid (NA) binding proteins interact with their partners, DNA or RNA, via distinct regions on their surface that are characterized by an ensemble of chemical, physical and geometrical properties. In this study, we introduce a novel methodology based on differential geometry, commonly used in face recognition, to characterize and predict NA binding surfaces on proteins. Applying the method on experimentally solved three-dimensional structures of proteins we successfully classify double-stranded DNA (dsDNA) from single-stranded RNA (ssRNA) binding proteins, with 83% accuracy. We show that the method is insensitive to conformational changes that occur upon binding and can be applicable for de novo protein-function prediction. Remarkably, when concentrating on the zinc finger motif, we distinguish successfully between RNA and DNA binding interfaces possessing the same binding motif even within the same protein, as demonstrated for the RNA polymerase transcription-factor, TFIIIA. In conclusion, we present a novel methodology to characterize protein surfaces, which can accurately tell apart dsDNA from an ssRNA binding interfaces. The strength of our method in recognizing fine-tuned differences on NA binding interfaces make it applicable for many other molecular recognition problems, with potential implications for drug design.

SUBMITTER: Shazman S

PROVIDER: S-EPMC3177183 | biostudies-literature | 2011 Sep

REPOSITORIES: biostudies-literature

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From face to interface recognition: a differential geometric approach to distinguish DNA from RNA binding surfaces.

Shazman Shula S Elber Gershon G Mandel-Gutfreund Yael Y

Nucleic acids research 20110621 17

Protein nucleic acid interactions play a critical role in all steps of the gene expression pathway. Nucleic acid (NA) binding proteins interact with their partners, DNA or RNA, via distinct regions on their surface that are characterized by an ensemble of chemical, physical and geometrical properties. In this study, we introduce a novel methodology based on differential geometry, commonly used in face recognition, to characterize and predict NA binding surfaces on proteins. Applying the method o ...[more]

PMID: 21693557

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Project description:BACKGROUND:The genus Cuscuta is a group of parasitic plants that are distributed world-wide. The process of parasitization starts with a Cuscuta plant coiling around the host stem. The parasite's haustorial organs then establish a vascular connection allowing for access to the phloem content. The host and the parasite form new cellular connections, suggesting coordination of developmental and biochemical processes. Simultaneous monitoring of gene expression in the parasite's and host's tissues may shed light on the complex events occurring between the parasitic and host cells and may help to overcome experimental limitations (i.e. how to separate host tissue from Cuscuta tissue at the haustorial connection). A novel approach is to use bioinformatic analysis to classify sequencing reads as either belonging to the host or to the parasite and to characterize the expression patterns. Owing to the lack of a comprehensive genomic dataset from Cuscuta spp., such a classification has not been performed previously. RESULTS:We first classified RNA-Seq reads from an interface region between the non-model parasitic plant Cuscuta japonica and the non-model host plant Impatiens balsamina. Without established reference sequences, we classified reads as originating from either of the plants by stepwise similarity search against de novo assembled transcript sets of C. japonica and I. balsamina, unigene sets of the same genus, and cDNA sequences of the same family. We then assembled de novo transcriptomes from the classified read sets. We assessed the quality of the classification by mapping reads to contigs of both plants, achieving a misclassification rate low enough (0.22-0.39%) to be used reliably for differential gene expression analysis. Finally, we applied our read classification method to RNA-Seq data from the interface between the non-model parasitic plant C. japonica and the model host plant Glycine max. Analysis of gene expression profiles at 5 parasitizing stages revealed differentially expressed genes from both C. japonica and G. max, and uncovered the coordination of cellular processes between the two plants. CONCLUSIONS:We demonstrated that reliable identification of differentially expressed transcripts in undissected interface region of the parasite-host association is feasible and informative with respect to differential-expression patterns.

Dataset Information

From face to interface recognition: a differential geometric approach to distinguish DNA from RNA binding surfaces.

Publications

From face to interface recognition: a differential geometric approach to distinguish DNA from RNA binding surfaces.

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