Identifying RNA-binding residues based on evolutionary conserved structural and energetic features

Abstract

Increasing numbers of protein structures are solved each year, but many of these structures belong to proteins whose sequences are homologous to sequences in the Protein Data Bank. Nevertheless, the structures of homologous proteins belonging to the same family contain useful information because functionally important residues are expected to preserve physico-chemical, structural and energetic features. This information forms the basis of our method, which detects RNA-binding residues of a given RNA-binding protein as those residues that preserve physico-chemical, structural and energetic features in its homologs. Tests on 81 RNA-bound and 35 RNA-free protein structures showed that our method yields a higher fraction of true RNA-binding residues (higher precision) than two structure-based and two sequence-based machine-learning methods. Because the method requires no training data set and has no parameters, its precision does not degrade when applied to ‘novel’ protein sequences unlike methods that are parameterized for a given training data set. It was used to predict the ‘unknown’ RNA-binding residues in the C-terminal RNA-binding domain of human CPEB3. The two predicted residues, F430 and F474, were experimentally verified to bind RNA, in particular F430, whose mutation to alanine or asparagine nearly abolished RNA binding. The method has been implemented in a webserver called DR_bind1, which is freely available with no login requirement at http://drbind.limlab.ibms.sinica.edu.tw.

Re-analysis of cryoEM data on HCV IRES bound to 40S subunit of human ribosome integrated with recent structural information suggests new contact regions between ribosomal proteins and HCV RNA

“…Protein -ligand interaction sites are observed to be conserved among similar folds 44,45 and nucleic acid binding proteins often employ specialized domains or motifs for interaction. [46][47][48][49] Hence the nucleic acid binding regions can be predicted by the structural analysis of homologs or analogous proteins with the same fold. This was done with the help of structure comparison techniques such as DALI.…”

A Max-margin Model for Predicting Residue–base Contacts in Protein–RNA Interactions

“…We compared our method with existing methods for predicting RNA-binding residues in proteins. DR bind1 (Chen et al, 2014), KYG (Kim et al, 2006), and OPRA (Perez-Cano and Fernandez-Recio, 2010) are structure-based methods that use 3D structures from the PDB to extract descriptors for prediction. BindN+ (Wang et al, 2010) and Pprint (Kumar et al, 2008) are sequencebased methods that employ evolutionary information instead of 3D structures.…”

“…We prepared our dataset in accordance with (Chen et al, 2014) and extracted RNA-bound proteins with X-ray resolution of ≤ 3.0Å from the Protein Data Bank (PDB) (Rose et al, 2011). To reduce dataset redundancy, we discarded some extracted data such that the dataset contains no protein pairs whose sequence identity is > 30%.…”

A Graph Approach to Mining Biological Patterns in the Binding Interfaces

“…Some of them used the machine-learning approach to train various computational methods to build classifiers that could predict RNA-binding sites on the protein structures using sequence, structural, and evolutionary features as input (Terribilini et al, 2006, 2007; Liu et al, 2010; Murakami et al, 2010; Chen et al, 2014; Yang et al, 2014). Although the classifiers reportedly achieved accurate predictions in many cases, a limitation of these methods is that the predicting process is like a black box, and it is hard to translate the rules used by the classifiers into knowledge to elucidate the affinity and specificity of the interactions.…”