ATPbind: Accurate Protein–ATP Binding Site Prediction by Combining Sequence-Profiling and Structure-Based Comparisons

Abstract

Protein–ATP interactions are ubiquitous in a wide variety of biological processes. Correctly locating ATP binding sites from protein information is an important but challenging task for protein function annotation and drug discovery. However, there is no method that can optimally identify ATP binding sites for different proteins. In this study, we report a new composite predictor, ATPbind, for ATP binding sites by integrating the outputs of two template-based predictors (i.e., S-SITE and TM-SITE) and three discriminative sequence-driven features of proteins: position specific scoring matrix, predicted secondary structure, and predicted solvent accessibility. In ATPbind, we assembled multiple support vector machines (SVMs) based on a random undersampling technique to cope with the serious imbalance phenomenon between the numbers of ATP binding sites and of non-ATP binding sites. We also constructed a new gold-standard benchmark data set consisting of 429 ATP binding proteins from the PDB database to evaluate and compare the proposed ATPbind with other existing predictors. Starting from a query sequence and predicted I-TASSER models, ATPbind can achieve an average accuracy of 72%, covering 62% of all ATP binding sites while achieving a Matthews correlation coefficient value that is significantly higher than that of other state-of-the-art predictors.

ccPDB 2.0: an updated version of datasets created and compiled from Protein Data Bank

“…Since the first version of ccPDB (18) published in 2011, there has been enormous growth in the development of improved methods in the field of secondary structure prediction (9, 19–24), irregular secondary structure prediction (10, 25–28), protein–ligand interactions (7, 15, 16, 29), DNA/RNA–protein interactions (13, 30, 31), protein crystallization and propensity prediction (32–35), dihedral angle prediction (6, 36–38), surface accessibility prediction (39), Rotamer libraries (8) and others (40–43). These methods have been found to annotate protein structure and function in comparison to earlier methods.…”

Spatiotemporal expression of regulatory kinases directs the transition from mitotic growth to cellular morphogenesis

“…ATPbind [67] was used to predict Bsd ATP binding residues; informatics predictions were compared to the VRK1 ATP binding pocket described in [43]. embryos that expressed wild-type Bsd in the mesoderm showed improved muscle morphology;…”

SAMbinder: A Web Server for Predicting S-Adenosyl-L-Methionine Binding Residues of a Protein From Its Amino Acid Sequence

“…Later on, it was realized that all ligands are not the same, and there is a wide variation in the shape and size of binding pockets. Therefore, researchers started developing ligand-specific methods (Chauhan et al, 2009;Chauhan et al, 2010;Chen et al, 2012;Yu et al, 2013a;Hu et al, 2016;Hu et al, 2018), and it was observed that these ligand-specific methods performed better than generalized methods (Chen et al, 2012;Yu et al, 2013b;Hu et al, 2016). Comprehensive information on the software developed for protein-small molecule interaction is reviewed in a paper by .…”