A disulfide bond predictor. The most confident disulfide bonds are first identified and bonding prediction is then focused on the remaining cysteine residues based on SVR training. Compared to purely machine learning-based approaches, Cyscon improved the average accuracy of connectivity pattern prediction by 21.9%. For proteins with more than 5 disulfide bonds, Cyscon improved the accuracy by 585% on the benchmark set of PDBCYS. When applied to 158 non-redundant cysteine-rich proteins, Cyscon predictions helped increase (or decrease) the TM-score (or RMSD) of the ab initio QUARK modeling by 12.1% (or 14.4%). This result demonstrates a new avenue to improve the ab initio structure modeling for cysteine-rich proteins.
Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China; State Key Laboratory of Theoretical Physics, Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing, China; Department of Computational Medicine and Bioinformatics; University of Michigan, Ann Arbor, MI, USA; Department of Biological Chemistry, University of Michigan, Ann Arbor, MI, USA
Cyscon funding source(s)
This work was supported in part by the National Natural Science Foundation of China (61222306, 91130033, 61175024), Shanghai Science and Technology Commission (11JC1404800), and the National Institute of General Medical Sciences (GM083107).