PONDR-FIT is a meta-predictor for intrinsically disordered amino acids, combining outputs from multiple individual disorder predictors. It was evaluated using eight-fold cross-validation and showed improved accuracy (3-20%) compared to single predictors, with an average improvement of 11%. The meta-predictor outperformed individual predictors in most cases, particularly for longer disordered regions. However, it still struggled with short disordered regions and those near order/disorder boundaries. The study highlights the importance of combining multiple predictors to enhance accuracy in predicting protein disorder. PONDR-FIT is available at www.disprot.org. The paper discusses various disorder predictors, including PONDR series, DisEMBL, DISOPRED, NORSnet, and others, and evaluates their performance on different datasets. The results show that combining predictors improves accuracy, but the effectiveness depends on the dataset. PONDR-FIT was tested on multiple datasets, including fully ordered (FOD), fully disordered (FDD), and partially disordered (PDD) datasets. It demonstrated improved accuracy on PDD compared to FOD/FDD, suggesting that the choice of dataset affects prediction performance. The study also highlights the challenges of predicting disorder in boundary regions and short sequences. The meta-predictor was applied to several proteins, including p53, CREB, and CBP, showing its ability to identify functional domains. The results indicate that PONDR-FIT is a reliable tool for predicting protein disorder, with potential applications in proteomics research. The study emphasizes the importance of using multiple predictors and considering dataset characteristics to improve prediction accuracy.PONDR-FIT is a meta-predictor for intrinsically disordered amino acids, combining outputs from multiple individual disorder predictors. It was evaluated using eight-fold cross-validation and showed improved accuracy (3-20%) compared to single predictors, with an average improvement of 11%. The meta-predictor outperformed individual predictors in most cases, particularly for longer disordered regions. However, it still struggled with short disordered regions and those near order/disorder boundaries. The study highlights the importance of combining multiple predictors to enhance accuracy in predicting protein disorder. PONDR-FIT is available at www.disprot.org. The paper discusses various disorder predictors, including PONDR series, DisEMBL, DISOPRED, NORSnet, and others, and evaluates their performance on different datasets. The results show that combining predictors improves accuracy, but the effectiveness depends on the dataset. PONDR-FIT was tested on multiple datasets, including fully ordered (FOD), fully disordered (FDD), and partially disordered (PDD) datasets. It demonstrated improved accuracy on PDD compared to FOD/FDD, suggesting that the choice of dataset affects prediction performance. The study also highlights the challenges of predicting disorder in boundary regions and short sequences. The meta-predictor was applied to several proteins, including p53, CREB, and CBP, showing its ability to identify functional domains. The results indicate that PONDR-FIT is a reliable tool for predicting protein disorder, with potential applications in proteomics research. The study emphasizes the importance of using multiple predictors and considering dataset characteristics to improve prediction accuracy.