Title: Comparing Sequence and Structure-based Classifiers for Predicting RNA Binding Sites in
Specific Families of RNA Binding Proteins
Authors: Michael Terribilini1, Cornelia Caragea2, Deepak Reyon3, Jeffry Sander4, Jae-Hyung Lee5,
Robert L. Jernigan6, Vasant Honavar7, and Drena Dobbs8
Short Abstract
We evaluate machine learning classifiers for predicting RNA-binding residues in proteins, using either
sequence-based information only, or a combination of sequence and structure-derived information and
quantitate relative contributions of these different input types to overall prediction performance. We
also present novel classifiers optimized for specific families of RNA binding proteins.
Abstract
Protein-RNA interactions play critical roles in a wide range of biological processes. Previously, we
developed a machine learning approach for predicting which amino acids of an RNA-binding protein
mediate protein-RNA interactions, using only the amino acid sequence of the protein as input
(Terribilini et al, 2006, RNA 12:1450; http://bindr.gdcb.iastate.edu/RNABindR/). Here we report an
evaluation of the relative contributions of sequence, structural features and evolutionary information
to performance of algorithms for predicting RNA binding residues in proteins. In this study we train
and test multiple classifiers using several benchmark datasets, including a non-redundant dataset of
181 RNA-binding polypeptide chains with <30% sequence identity (RB181), and “custom” datasets
comprising sets of related RNA-binding proteins.
We systematically compare results obtained using simple classifiers that use only one type of
information as input (e.g., Naïve Bayes classifier, using only amino acid sequence as input) with results
obtained using ensemble classifiers that exploit specific combinations of input information (e.g., an
ensemble of Naïve Bayes classifiers that use the amino acid sequence, information from sequence
homologs and/or the identities of spatial neighbors in known structures as input). Our results, partially
summarized in Table 1 below, indicate that the best “overall” performance, evaluated on the basis of
AUC for ROC curves, is obtained using ensemble classifiers using amino acid sequence information in
combination with either: i) PSSMs (derived from sequence homologs identified using BLAST) or ii)
spatial neighbor information (extracted from PDB structures of proteins). Also, we will report results
obtained using “custom” classifiers for predicting RNA-binding residues in specific families of RNA
binding proteins (i.e., those sharing similar sequences or structures).
Table 1: Comparison of Classifiers for RNA-binding Site Prediction
Sequencebased
Area Under
ROC Curve
1
2
3
4
5
6
7
8
0.74
Structurebased
0.77
PSSM-based
0.80
Bioinformatics & Computational Biology, Iowa State University, E-mail: terrible@iastate.edu
Computer Science, Iowa State University, E-mail: caragea@cs.iastate.edu
Bioinformatics & Computational Biology, Iowa State University, E-mail: dreyon@iastate.edu
Bioinformatics & Computational Biology, Iowa State University, E-mail: jdsander@iastate.edu
Bioinformatics & Computational Biology, Iowa State University, E-mail: jhlee777@iastate.edu
Biochemistry, Biophysics & Molecular Biology, Iowa State University, E-mail: jernigan@iastate.edu
Computer Science, Iowa State University, E-mail: honavar@cs.iastate.edu
Genetics, Development, & Cell Biology, Iowa State University, E-mail: ddobbs@iastate.edu
Ensemble
0.81
Scientific Justification
1. The ability to reliably predict which residues of a protein directly contribute to RNA binding
would significantly enhance our understanding of how proteins recognize RNA and
potentially generate new strategies for clinical intervention in both genetic and infectious
diseases. We will present our results on predictions made on several clinically important
RNA-binding proteins, such as the HIV-1 Rev and human telomerase reverse transcriptase
(hTERT) protein. We will also present a comparison of our results with those of other labs
2. Classifiers can be "tuned" to enhance either specificity or sensitivity of interface residue
prediction for specific subfamilies of related RNA binding proteins, thus facilitating the
design of experimental investigations.