Sequence-Based Prediction of Protein-Carbohydrate Binding Sites Using Support Vector Machines.
Carbohydrate-binding proteins play significant roles in many diseases including cancer. Here, we established a machine-learning-based method (called sequence-based prediction of residue-level interaction sites of carbohydrates, SPRINT-CBH) to predict carbohydrate-binding sites in proteins using support vector machines (SVMs). We found that integrating evolution-derived sequence profiles with additional information on sequence and predicted solvent accessible surface area leads to a reasonably accurate, robust, and predictive method, with area under receiver operating characteristic curve (AUC) of 0.78 and 0.77 and Matthew's correlation coefficient of 0.34 and 0.29, respectively for 10-fold cross validation and independent test without balancing binding and nonbinding residues. The quality of the method is further demonstrated by having statistically significantly more binding residues predicted for carbohydrate-binding proteins than presumptive nonbinding proteins in the human proteome, and by the bias of rare alleles toward predicted carbohydrate-binding sites for nonsynonymous mutations from the 1000 genome project. SPRINT-CBH is available as an online server at http://sparks-lab.org/server/SPRINT-CBH .
School of Information and Communication Technology and ‡Institute for Glycomics, Griffith University , Parklands Drive, Southport, Queensland 4215, Australia.
Ghazaleh
G
Taherzadeh
School of Information and Communication Technology and ‡Institute for Glycomics, Griffith University , Parklands Drive, Southport, Queensland 4215, Australia.
Yaoqi
Y
Zhou
School of Information and Communication Technology and ‡Institute for Glycomics, Griffith University , Parklands Drive, Southport, Queensland 4215, Australia.
Alan Wee-Chung
AW
Liew
School of Information and Communication Technology and ‡Institute for Glycomics, Griffith University , Parklands Drive, Southport, Queensland 4215, Australia.
Yuedong
Y
Yang
D001665:Binding Sites; D050260:Carbohydrate Metabolism; D002241:Carbohydrates / Q000737:chemistry; D030562:Databases, Protein; D006801:Humans; D062105:Molecular Docking Simulation; D011485:Protein Binding; D011506:Proteins / Q000737:chemistry / Q000378:metabolism*; D012372:ROC Curve; D060388:Support Vector Machine
D002241:Carbohydrates; D011506:Proteins