Predictors:

   Calcium ions serve as the messenger in many signal transduction pathways, including muscle cell contractions and fertilization. High-quality identification and validation of calcium-binding residues is important for the elucida-tion of intrinsic mechanisms of protein-calcium interac-tions. SCAMPER is a novel method for accurately predicting calcium-binding residues.
   Jian Zhang, Feng Zhou, Xingchen Liang, and Guifu Yang. 2022. SCAMPER: accurate type-specific prediction of calcium-binding residues using sequence-derived features, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 10.1109/TCBB.2022.3173437. link

   Protein-DNA interactions drive regulation of gene expression and DNA processing and repair. DNAgenie combines a custom-designed machine learning architecture and a comprehensive physiochemical profile extracted from the input protein sequence to accurately predict A-DNA, B-DNA and ssDNA binding residues.
   Jian Zhang, Sina Ghadermarzi, Akila Katuwawala and Lukasz Kurgan. 2021. DNAgenie: accurate prediction of DNA type specific binding residues in protein sequences, Briefings in Bioinformatics, 22(6), bbab336. link

   Cyclins control the progression in the cell cycle. Understanding the intrinsic mechanisms of uncontrolled cell proliferation promises the knowledge of manipulation or prevention of cancer cells. TYLER adopts motif- and machine learning-based models to accurately predict cyclin-dependent proteins.
   Jian Zhang, Xingchen Liang, Feng Zhou, Bo Li, and Yanling Li. 2021. TYLER, a fast method that accurately predicts cyclin-dependent proteins by using computation-based motifs and sequence-derived features, Mathematical Biosciences and Engineering, 18(5): 6410–6429. link

   Enzymatic proteins are widely distributed in organisms and cells and implicated in biochemical processes. Without these proteins, most biochemical reactions slowly occur at mild temperatures and pressures in living bodies. PEP is a novel method for predicting mammalian enzymatic proteins, particularly for two prevalent mammalian enzymatic proteins.
   Haiting Chai, and Jian Zhang. 2018. Identification of Mammalian Enzymatic Proteins Based on Sequence-derived Features and Species-specific Scheme, IEEE Access, 6, 8452-8458. link

   Secreted proteins widely spread in living organisms and cells. Since secreted proteins are easy to be detected in body fluids, urine, and saliva in clinical, they play important roles in biomarkers for disease diagnosis and vaccine production. In this study, we propose a novel predictor for accurate high-throughput identification of mammalian secreted proteins based on sequence-derived features.
   Jian Zhang, Haiting Chai, Song Guo, Huaping Guo, and Yanling Li. 2018. High-Throughput Identification of Mammalian Secreted Proteins Using Species-Specific Scheme and Application to Human Proteome, Molecules, 23(6), 1448. link

   Heme is an essential biomolecule that widely exists in numerous extant organisms. Accurately identifying heme binding residues (HEMEs) is of great importance in disease progression and drug development. HEMEsPred is a novel predictor which uses sequence- and structure-based features, fast-adaptive ensemble learning scheme, and ligand-specific models.
   Jian Zhang, Haiting Chai, Bo Gao, Guifu Yang, and Zhiqiang Ma. 2018. HEMEsPred: Structure-based Ligand-specific Heme Binding Residues Prediction by Using Fast-adaptive Ensemble Learning Scheme, IEEE-ACM Transactions on Computational Biology and Bioinformatics, 15(1), 147-156. link

   Bioluminescent proteins (BLPs) widely exist in many living organisms. As BLPs are featured by the capability of emitting lights, they can be served as biomarkers and easily detected in biomedical research, such as gene expression analysis and signal transduction pathways. PredBLP is a novel accurate predictor which uses sequence-based features and lineage-specific scheme.
   Jian Zhang, Haiting Chai, Guifu Yang, and Zhiqiang Ma, 2016. Prediction of bioluminescent proteins by using sequence-derived features and lineage-specific scheme, BMC bioinformatics, 18(1), 294. link

   DNA-binding proteins play a pivotal role in various biological activities. Identification of DNA-binding residues (DBRs) is of great importance for understanding the mechanism of gene regulations and chromatin remodeling. DQPred-DBR is a novel method, which uses large-scale extensible sample pool, dynamic query-driven learning scheme and evolution-based features.
   Haiting Chai, Jian Zhang, Guifu Yang, and Zhiqiang Ma. 2016. An evolution-based DNA- binding residues predictor by using dynamic query-driven learning scheme, Molecular BioSystems, 12(12), 3643-3650. link