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

Article presenting a review, comparison, and critical assessment of published approaches for predicting RNA-binding residues in proteins using non-redundant databases

Comparison of SVMOpt, RNABindRPlus, RNABindR v2.0, BindN, BindN+ and PPRInt

<p>on the RB111 dataset using (A) ROC curves and (B) PR curves with a 5 Å distance cut-off for interface residues.</p

Boundaries of Safe, Twilight, and Dark Zones used by HomPRIP.

<p>Boundaries of Safe, Twilight, and Dark Zones used by HomPRIP.</p

RNABindRPlus flowchart.

<p>Flowchart showing the different components of RNABindRPlus.</p

Evaluation of Methods on the RB111 dataset.

<p>The first 6 methods are sequence-based methods. The last 2 methods (indicated by **) are structure-based methods. Methods in each category are sorted in descending order of MCC. The highest value in each column is shown in bold font.</p

Structure-based Methods for Predicting RNA-binding sites in Proteins.

<p>Structure-based Methods for Predicting RNA-binding sites in Proteins.</p

Comparison of SVMOpt, RNABindRPlus, and the Metapredictor

<p>on the RB44 dataset using (A) ROC curves and (B) PR curves with a 5 Å distance cut-off for interface residues.</p