Web Tools for Predicting Metal Binding Sites in Proteins

Abstract

Approximately one third of proteins bind metal ions for stability and/or enzymatic function. However, on a structural level, only a small fraction of binding sites have been resolved. Metal binding site predictions can serve as a first step in putative function assignment for many unannotated proteins. Sequence based and structure based methods for metal binding site predictions are reviewed here. The CHED and SeqCHED methods of prediction from apo protein structures and translated gene sequences, respectively, are described in detail, including their web server applications. The relevance of SeqCHED to the analysis of single nucleotide polymorphisms (SNPs) associated with disease related metal binding sites is illustrated.

Identification of metal ion binding sites based on amino acid sequences

“…Deng et al [ 11 ] developed graph theory-based and geometry-based approaches to detecting calcium-binding sites and achieved a sensitivity of nearly 90% for 123 calcium binding proteins. The CHED algorithm was developed by Babor et al [ 12 , 13 ] based on the three-dimensional (3D) structure to predict transition metal-binding sites (Zn 2+ , Co 2+ , Ni 2+ , Fe 2+ , Cu 2+ , and Mn 2+ ) in 349 apoproteins and 82 holoproteins, achieving specificities of 95% and 96%, respectively. Jessica et al [ 14 ] developed a Bayesian classifier to predict zinc-binding sites in 349 zinc proteins and achieved a specificity of 99.8% and sensitivity of 75.5%.…”

DeepCys: Structure‐based multiple cysteine function prediction method trained on deep neural network: Case study on domains of unknown functions belonging to COX2 domains

“…Most of the existing cysteine prediction methods can predict one particular type of function, termed, here as "specific cysteine function prediction," such as, disulphide prediction, [22][23][24][25][26][27][28][29] metal-binding prediction, [30][31][32][33][34][35][36][37][38][39] and sulphenylation prediction. [40][41][42][43][44][45][46][47][48][49] Besides the specific cysteine function prediction methods, four multiple cysteine function prediction methods were known, namely, diamino acid neural network application (DiANNA), 50 COPA, 51 ASP-C, 52 and Cy-preds.…”

The Identification of Metal Ion Ligand-Binding Residues by Adding the Reclassified Relative Solvent Accessibility

“…Among the computational methods, many efforts were made to improve the databases, feature parameters, and algorithms. First, the databases were generally acquired from Protein Data Bank (PDB) (Tainer et al, 1991;Bernstein et al, 1997;Sodhi et al, 2004;Lin et al, 2005;Bordner, 2008;Babor et al, 2010;Lu et al, 2012), Structural Classification of Protein (SCOP) (Hubbard et al, 1997;Sodhi et al, 2004;Chauhan et al, 2010;Sobolev and Edelman, 2013), Ligand Protein Contact (LPC) (Sobolev et al, 1999;Chauhan et al, 2010), and BioLip (Yang et al, 2013a,b;Hu et al, 2016a,b, Wang et al, 2019. Second, the feature parameters generally contained the composition information of the amino acid (Cao et al, 2017;Wang et al, 2019), hydrophilicity-hydrophobicity (Lin et al, 2005;Lin et al, 2006;Cao et al, 2017), charge (Lin et al, 2005;Cao et al, 2017;Wang et al, 2019), position specific score matrix (PSSM) (Hu et al, 2016a), relative solvent accessibility (RSA) (Lin et al, 2006;Hu et al, 2016a;Cao et al, 2017;Wang et al, 2019) and three-dimensional structure information (Babor et al, 2010;Roy et al, 2012;Yang et al, 2015;Hu et al, 2016a).…”