Prediction of RNA-binding proteins from primary sequence by a support vector machine approach

Han, Lianyi; Cai, Cong; Lo, Siaw Ling; Chung, Maxey C. M.; Chen, Yu Zong

doi:10.1261/rna.5890304

Cited by 120 publications

(120 citation statements)

References 58 publications

(82 reference statements)

Supporting

Mentioning

118

Contrasting

Order By: Relevance

“…Statistical learning methods, such as support vector machines and neural networks, have emerged in the last few years as attractive methods for the prediction of protein functional classes (des Jardins et al, 1997;Jensen et al, 2002;Karchin et al, 2002;Cai et al, 2003aCai et al, , 2004Bhasin and Raghava, 2004;Han et al, 2004) and structural classes (Zhou and Assa-Munt, 2001;Cai et al, 2003b) without the use of sequence similarity. These classes contain proteins of diverse functions and structures.…”

Section: B Prediction Of Druggable Proteins By a Statistical Learninmentioning

confidence: 99%

Therapeutic Targets: Progress of Their Exploration and Investigation of Their Characteristics

et al. 2006

View full text Add to dashboard Cite

Section: B Prediction Of Druggable Proteins By a Statistical Learninmentioning

confidence: 99%

Therapeutic Targets: Progress of Their Exploration and Investigation of Their Characteristics

et al. 2006

View full text Add to dashboard Cite

“…In this work, nine feature properties are used to describe physicochemical characteristics of each protein, which have been used routinely for the prediction of RNA binding proteins (55) and other proteins (32,(35)(36)(37)(38). It has been reported that not all feature vectors contribute equally to the classification of proteins; some have been found to play relatively more prominent roles than others in specific aspects of proteins (36).…”

Section: Contribution Of Feature Properties To the Classification Of mentioning

confidence: 99%

Prediction of the functional class of lipid binding proteins from sequence-derived properties irrespective of sequence similarity

Lin

Han

Zhang

et al. 2006

Journal of Lipid Research

View full text Add to dashboard Cite

Lipid binding proteins play important roles in signaling, regulation, membrane trafficking, immune response, lipid metabolism, and transport. Because of their functional and sequence diversity, it is desirable to explore additional methods for predicting lipid binding proteins irrespective of sequence similarity. This work explores the use of support vector machines (SVMs) as such a method. SVM prediction systems are developed using 14,776 lipid binding and 133,441 nonlipid binding proteins and are evaluated by an independent set of 6,768 lipid binding and 64,761 nonlipid binding proteins. The computed prediction accuracy is 78.9, 79.5, 82.2, 79.5, 84.4, 76.6, 90.6, 79.0, and 89.9% for lipid degradation, lipid metabolism, lipid synthesis, lipid transport, lipid binding, lipopolysaccharide biosynthesis, lipoprotein, lipoyl, and all lipid binding proteins, respectively. The accuracy for the nonmember proteins of each class is 99.9, 99.2, 99.6, 99.8, 99.9, 99.8, 98.5, 99.9, and 97.0%, respectively. Comparable accuracies are obtained when homologous proteins are considered as one, or by using a different SVM kernel function. Our method predicts 86.8% of the 76 lipid binding proteins nonhomologous to any protein in the Swiss-Prot database and 89.0% of the 73 known lipid binding domains as lipid binding. These findings suggest the usefulness of SVMs for facilitating the prediction of lipid binding proteins. Our software can be accessed at the SVMProt server

show abstract

“…Three recent studies have reported the use of support vector machines (SVMs) to identify RNA binding proteins and assign them to functional classes (e.g., rRNA binding, mRNA binding, tRNA binding, viral RNA binding, etc.) using only the amino acid sequence (Han et al 2004), a combination of sequence and pseudo-amino acid composition as input (Cai and Lin 2003), or a variety of sequence-based information, including predicted solvent accessibility and predicted secondary structure (Yu et al 2006). Our previous work (Yan et al 2004a(Yan et al ,b, 2006 has demonstrated the feasibility of constructing classifiers for protein-protein and protein-DNA binding site identification using machine learning approaches.…”

Section: Introductionmentioning

confidence: 99%

Prediction of RNA binding sites in proteins from amino acid sequence

Terribilini¹,

Lee²,

Yan³

et al. 2006

RNA

164

196

View full text Add to dashboard Cite

RNA-protein interactions are vitally important in a wide range of biological processes, including regulation of gene expression, protein synthesis, and replication and assembly of many viruses. We have developed a computational tool for predicting which amino acids of an RNA binding protein participate in RNA-protein interactions, using only the protein sequence as input. RNABindR was developed using machine learning on a validated nonredundant data set of interfaces from known RNA-protein complexes in the Protein Data Bank. It generates a classifier that captures primary sequence signals sufficient for predicting which amino acids in a given protein are located in the RNA-protein interface. In leave-oneout cross-validation experiments, RNABindR identifies interface residues with >85% overall accuracy. It can be calibrated by the user to obtain either high specificity or high sensitivity for interface residues. RNABindR, implementing a Naive Bayes classifier, performs as well as a more complex neural network classifier (to our knowledge, the only previously published sequence-based method for RNA binding site prediction) and offers the advantages of speed, simplicity and interpretability of results. RNABindR predictions on the human telomerase protein hTERT are in good agreement with experimental data. The availability of computational tools for predicting which residues in an RNA binding protein are likely to contact RNA should facilitate design of experiments to directly test RNA binding function and contribute to our understanding of the diversity, mechanisms, and regulation of RNA-protein complexes in biological systems. (RNABindR is available as a Web tool from http://bindr.gdcb.iastate.edu.)

show abstract

Prediction of RNA-binding proteins from primary sequence by a support vector machine approach

Cited by 120 publications

References 58 publications

Therapeutic Targets: Progress of Their Exploration and Investigation of Their Characteristics

Therapeutic Targets: Progress of Their Exploration and Investigation of Their Characteristics

Prediction of the functional class of lipid binding proteins from sequence-derived properties irrespective of sequence similarity

Prediction of RNA binding sites in proteins from amino acid sequence

Contact Info

Product

Resources

About