Assessment of ligand binding residue predictions in CASP9

By Tobias Schmidt, J. Haas, Tiziano Gallo Cassarino, Torsten Schwede. 2011

83 citations recorded by the source corpus.

Interactions between proteins and their ligands play central roles in many physiological processes. The structural details for most of these interactions, however, have not yet been characterized experientially. Therefore, various computational tools have been developed to predict the location of binding sites and the amino acid residues interacting with ligands. In this manuscript, we assess the performance of 33 methods participating in the ligand‐binding site prediction category in CASP9. The overall accuracy of ligand‐binding site predictions in CASP9 appears rather high (average Matthews correlation coefficient of 0.62 for the 10 top performing groups) and compared to previous experiments more groups performed equally well. However, this should be seen in context of a strong bias in the test data toward easy template‐based models. Overall, the top performing methods have converged to a similar approach using ligand‐binding site inference from related homologous structures, which limits their applicability for difficult de novo prediction targets. Here, we present the results of the CASP9 assessment of the ligand‐binding site category, discuss examples for successful and challenging prediction targets in CASP9, and finally suggest changes in the format of the experiment to overcome the current limitations of the assessment. Proteins 2011. © 2011 Wiley‐Liss, Inc.

Subfields: Computational Theory and Mathematics, Molecular Biology

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Citations in the corpus

IntFOLD: an integrated web resource for high performance protein structure and function prediction (2019, positive): “Recently, the component methods have ranked among top independent servers in the Tertiary Structure (TS) prediction (5) and Estimates of Model Accuracy (EMA) categories (7), as well as ranking well in the historical categories of intrinsic disorder prediction and function prediction (26,27).”
COFACTOR: an accurate comparative algorithm for structure-based protein function annotation (2012, methodology): “One of the most common structure-based approaches in protein function annotation is to detect homologous template proteins by global structure comparisons and then transfer known functional annotations from the templates (2,4,5).”
The FunFOLD2 server for the prediction of protein–ligand interactions (2013, methodology): “An early implementation of the algorithm was used in the CASP9 competition (2010), where it ranked amongst the top 10 methods (21).”
Protein Structure and Function Prediction Using I-TASSER (2015, unknown): “In CASP9, COFACTOR achieved a Matthews correlation coefﬁcient of 0.69 for the ligand-binding site predictions of 31 targets, which was signiﬁcantly higher than all other participating methods (Schmidt et al., 2011).”
BioLiP: a semi-manually curated database for biologically relevant ligand–protein interactions (2012, methodology): “Recently, BioLiP has been successfully used for protein-ligand docking (18) and protein function prediction by COFACTOR (17,19), which was evaluated as the best ligand-binding prediction method in the Function Prediction Section in the CASP9 experiment (7).”
COFACTOR: improved protein function prediction by combining structure, sequence and protein–protein interaction information (2017, methodology): “Following the criterion used in the CASP experiment (27), a residue is defined as a binding site if it has at least one atom whose distance from the closest ligand atom is within 0.”
Protein-ligand binding site recognition using complementary binding-specific substructure comparison and sequence profile alignment (2013, background): “As shown in the recent community-wide CASP experiments (Schmidt et al., 2011), this type of template-based approaches represent by far the most accurate methods, especially for targets which have close homologs in the ligand–protein complex structure databases .”
IntFOLD: an integrated server for modelling protein structures and functions from amino acid sequences (2015, methodology): “The FunFOLD approach has performed well in recent CASP experiments (23,24).”
Critical assessment of methods of protein structure prediction—Round VIII (2009, unknown): “17 All these categories have been included in ABSTRACT This article is an introduction to the special issue of the journal Proteins , dedicated to the eighth CASP experiment to assess the state of the art in protein structure prediction.”