首页|TransDFL:Identification of Disordered Flexible Linkers in Proteins by Transfer Learning
TransDFL:Identification of Disordered Flexible Linkers in Proteins by Transfer Learning
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万方数据
维普
Disordered flexible linkers(DFLs)are the functional disordered regions in proteins,which are the sub-regions of intrinsically disordered regions(IDRs)and play important roles in connecting domains and maintaining inter-domain interactions.Trained with the limited available DFLs,the existing DFL predictors based on the machine learning techniques tend to predict the ordered residues as DFLs,leading to a high false positive rate(FPR)and low prediction accuracy.Previous studies have shown that DFLs are extremely flexible disordered regions,which are usually predicted as disordered residues with high confidence[P(D)＞0.9]by an IDR predictor.Therefore,transferring an IDR predictor to an accurate DFL predictor is of great significance for understand-ing the functions of IDRs.In this study,we proposed a new predictor called TransDFL for iden-tifying DFLs by transferring the RFPR-IDP predictor for IDR identification to the DFL prediction.The RFPR-IDP was pre-trained with IDR sequences to learn the general features between IDRs and DFLs,which is helpful to reduce the false positives in the ordered regions.RFPR-IDP was fine-tuned with the DFL sequences to capture the specific features of DFLs so as to be transferred into the TransDFL.Experimental results of two application scenarios(predic-tion of DFLs only in IDRs or prediction of DFLs in entire proteins)showed that TransDFL con-sistently outperformed other existing DFL predictors with higher accuracy.The corresponding web server of TransDFL can be freely accessed at http://bliulab.net/TransDFL/.
万方数据
维普
