Name
Title | ||
|---|---|---|
Prediction Of Protein-Binding Residues: Dichotomy Of Sequence-Based Methods Developed Using Structured Complexes Versus Disordered Proteins |
Abstract | ||
|---|---|---|
Motivation: There are over 30 sequence-based predictors of the protein-binding residues (PBRs). They use either structure-annotated or disorder-annotated training datasets, potentially creating a dichotomy where the structure-/disorder-specific models may not be able to cross-over to accurately predict the other type. Moreover, the structure-trained predictors were shown to substantially cross-predict PBRs among residues that interact with non-protein partners (nucleic acids and small ligands). We address these issues by performing first-of-its-kind comparative study of a representative collection of disorder- and structure-trained predictors using a comprehensive benchmark set with the structure- and disorder-derived annotations of PBRs (to analyze the cross-over) and the protein-, nucleic acid- and small ligand-binding proteins (to study the cross-predictions).Results: Three predictors provide accurate results: SCRIBER, ANCHOR and disoRDPbind. Some of the structure-trained methods make accurate predictions on the structure-annotated proteins. Similarly, the disorder-trained predictors predict well on the disorder-annotated proteins. However, the considered predictors generally fail to crossover, with the exception of SCRIBER. Our study also reveals that virtually all methods substantially cross-predict PBRs, except for SCRIBER for the structure-annotated proteins and disoRDPbind for the disorder-annotated proteins. We formulate a novel hybrid predictor, hybridPBRpred, that combines results produced by disoRDPbind and SCRIBER to accurately predict disorder- and structure-annotated PBRs. HybridPBRpred generates accurate results that cross-over structure- and disorder-annotated proteins and produces relatively low amount of cross-predictions, offering an accurate alternative to predict PBRs. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1093/bioinformatics/btaa573 | BIOINFORMATICS |
DocType | Volume | Issue |
Journal | 36 | 18 |
ISSN | Citations | PageRank |
1367-4803 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jian Zhang | 1 | 1 | 1.03 |
| Sina Ghadermarzi | 2 | 0 | 1.35 |
| Lukasz Kurgan | 3 | 7 | 2.47 |