Protein-RNA interface residue prediction using machine learning: an assessment of the state of the art

BMC Bioinformatics

Research Article

Rasna R Walia¹  Vasant Honavar²  Benjamin A Lewis³  Drena Dobbs³  Cornelia Caragea⁴  Fadi Towfic⁵  Michael Terribilini⁶  Yasser El-Manzalawy⁷ 

[1] Bioinformatics and Computational Biology Program, Iowa State University, Ames, Iowa, USA;Department of Computer Science, Iowa State University, Ames, Iowa, USA;Bioinformatics and Computational Biology Program, Iowa State University, Ames, Iowa, USA;Department of Computer Science, Iowa State University, Ames, Iowa, USA;Center for Computational Intelligence, Learning and Discovery, Iowa State University, Ames, Iowa, USA;Bioinformatics and Computational Biology Program, Iowa State University, Ames, Iowa, USA;Department of Genetics, Development and Cell Biology, Ames, Iowa, USA;Center for Computational Intelligence, Learning and Discovery, Iowa State University, Ames, Iowa, USA;College of Information Sciences & Technology, The Pennsylvania State University, University Park, Pennsylvania, USA;Center for Computational Intelligence, Learning and Discovery, Iowa State University, Ames, Iowa, USA;The Broad Institute, Cambridge, Massachusetts, USA;Department of Biology, Elon University, Elon, North Carolina, USA;Department of Computer Science, Iowa State University, Ames, Iowa, USA;Center for Computational Intelligence, Learning and Discovery, Iowa State University, Ames, Iowa, USA;Department of Systems & Computer Engineering, Al-Azhar University, Cairo, Egypt;

关键词: Support Vector Machine Classifier;    Radial Basis Function Kernel;    Matthews Correlation Coefficient;    Interface Residue;    Target Residue;   

DOI  :  10.1186/1471-2105-13-89

 received in 2011-10-14, accepted in 2012-05-10,  发布年份 2012

来源: Springer

【 摘 要 】

BackgroundRNA molecules play diverse functional and structural roles in cells. They function as messengers for transferring genetic information from DNA to proteins, as the primary genetic material in many viruses, as catalysts (ribozymes) important for protein synthesis and RNA processing, and as essential and ubiquitous regulators of gene expression in living organisms. Many of these functions depend on precisely orchestrated interactions between RNA molecules and specific proteins in cells. Understanding the molecular mechanisms by which proteins recognize and bind RNA is essential for comprehending the functional implications of these interactions, but the recognition ‘code’ that mediates interactions between proteins and RNA is not yet understood. Success in deciphering this code would dramatically impact the development of new therapeutic strategies for intervening in devastating diseases such as AIDS and cancer. Because of the high cost of experimental determination of protein-RNA interfaces, there is an increasing reliance on statistical machine learning methods for training predictors of RNA-binding residues in proteins. However, because of differences in the choice of datasets, performance measures, and data representations used, it has been difficult to obtain an accurate assessment of the current state of the art in protein-RNA interface prediction.ResultsWe provide a review of published approaches for predicting RNA-binding residues in proteins and a systematic comparison and critical assessment of protein-RNA interface residue predictors trained using these approaches on three carefully curated non-redundant datasets. We directly compare two widely used machine learning algorithms (Naïve Bayes (NB) and Support Vector Machine (SVM)) using three different data representations in which features are encoded using either sequence- or structure-based windows. Our results show that (i) Sequence-based classifiers that use a position-specific scoring matrix (PSSM)-based representation (PSSMSeq) outperform those that use an amino acid identity based representation (IDSeq) or a smoothed PSSM (SmoPSSMSeq); (ii) Structure-based classifiers that use smoothed PSSM representation (SmoPSSMStr) outperform those that use PSSM (PSSMStr) as well as sequence identity based representation (IDStr). PSSMSeq classifiers, when tested on an independent test set of 44 proteins, achieve performance that is comparable to that of three state-of-the-art structure-based predictors (including those that exploit geometric features) in terms of Matthews Correlation Coefficient (MCC), although the structure-based methods achieve substantially higher Specificity (albeit at the expense of Sensitivity) compared to sequence-based methods. We also find that the expected performance of the classifiers on a residue level can be markedly different from that on a protein level. Our experiments show that the classifiers trained on three different non-redundant protein-RNA interface datasets achieve comparable cross-validation performance. However, we find that the results are significantly affected by differences in the distance threshold used to define interface residues.ConclusionsOur results demonstrate that protein-RNA interface residue predictors that use a PSSM-based encoding of sequence windows outperform classifiers that use other encodings of sequence windows. While structure-based methods that exploit geometric features can yield significant increases in the Specificity of protein-RNA interface residue predictions, such increases are offset by decreases in Sensitivity. These results underscore the importance of comparing alternative methods using rigorous statistical procedures, multiple performance measures, and datasets that are constructed based on several alternative definitions of interface residues and redundancy cutoffs as well as including evaluations on independent test sets into the comparisons.

【 授权许可】

CC BY   
© Walia et al.; licensee BioMed Central Ltd. 2012