From Face to Interface Recognition: a Differential Geometric Approach to Distinguish DNA from RNA Binding Surfaces

Overview

Journal Nucleic Acids Res

Publisher Oxford University Press

Specialty Biochemistry

Date 2011 Jun 23

PMID 21693557

Citations 13

Authors

Shula Shazman

Gershon Elber

Yael Mandel-Gutfreund

Affiliations

Soon will be listed here.

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.

Citing Articles

Structure-based prediction of protein-nucleic acid binding using graph neural networks.

Sagendorf J, Mitra R, Huang J, Chen X, Rohs R Biophys Rev. 2024; 16(3):297-314.

PMID: 39345796 PMC: 11427629. DOI: 10.1007/s12551-024-01201-w.

Dissecting and predicting different types of binding sites in nucleic acids based on structural information.

Jiang Z, Xiao S, Liu R Brief Bioinform. 2021; 23(1).

PMID: 34624074 PMC: 8769709. DOI: 10.1093/bib/bbab411.

PredPSD: A Gradient Tree Boosting Approach for Single-Stranded and Double-Stranded DNA Binding Protein Prediction.

Tan C, Wang T, Yang W, Deng L Molecules. 2020; 25(1).

PMID: 31888057 PMC: 6982935. DOI: 10.3390/molecules25010098.

Analysis and prediction of single-stranded and double-stranded DNA binding proteins based on protein sequences.

Wang W, Sun L, Zhang S, Zhang H, Shi J, Xu T BMC Bioinformatics. 2017; 18(1):300.

PMID: 28606086 PMC: 5469069. DOI: 10.1186/s12859-017-1715-8.

Protein-RNA interactions: structural biology and computational modeling techniques.

Jones S Biophys Rev. 2017; 8(4):359-367.

PMID: 28510023 PMC: 5430296. DOI: 10.1007/s12551-016-0223-9.

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