Analyzing the Impacts of Sequence Conservation on Protein RNA-binding Residue Prediction
Date Issued
2011
Date
2011
Author(s)
Chen, Ya-Ping
Abstract
Protein-RNA interactions play a vital role in many stages of gene expression such as pre-mRNA synthesis, mRNA splicing and translation. It is generally believed that binding domains or binding motifs enable RNA-binding proteins to recognize their target RNA. Since the corresponding nucleic acid type and the structure level recognized can be quite diverse, predicting RNA-binding residues from primary structure of proteins is indeed a challenging task.
In this thesis, we continue the work of ProteRNA and develop two classifiers, namely support vector machine (SVM) and random forests (RF), with the predicted protein disorder added as a new feature descriptor. For the post-processing procedure, we build a discriminator in order to improve the pattern quality by distinguishing RNA-binding residues from other functionally important ones in conserved regions. When considering the dataset preparation effects and variance in binding sites, the two classifiers achieve Matthew’s correlation coefficient (MCC) of 0.5288 and 0.4698 using five-fold cross-validation. Our approach outperforms other predictors which provide online service. Testing on the independent test dataset, the SVM model achieves an accuracy of 92.12%, sensitivity of 38.10%, specificity of 97.47%, precision of 59.89%, F-score of 0.4657 and MCC of 0.4381, while the RF model ranks second only to SVM, it achieves an accuracy of 90.08%, sensitivity of 34.47%, specificity of 95.59%, precision of 43.62%, F-score of 0.3851 and MCC of 0.3346.
We observe the measure trend in machine learning methods for datasets based on different sequence identities, and discuss the origin of performance increment and bottleneck. We find out that the homologous sequence, or even remote homologous in the same dataset as query sequence will probably make prediction result closer to the distribution of real binding sites. Besides, a method that identifies the nearest neighbor by sequence alignment and determines its binding residues accordingly may perform better than machine learning methods trained on PSSM in some cases. Nevertheless, when dealing with novel protein sequences, the excellent performance of machine learning methods shows great generalization ability.
Subjects
predicting RNA-binding residues
conserved regions
machine learning
sequence identities
Type
thesis
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