The paper introduces PONDR-FIT, a meta-predictor for predicting intrinsically disordered amino acids in proteins. PONDR-FIT is developed by combining the outputs of several individual disorder predictors, including PONDR-VLXT, PONDR-VSL2, PONDR-VL3, FoldIndex, IUPred, and TopIDP. The meta-predictor was evaluated using two datasets: a fully ordered dataset (FOD) and a fully disordered dataset (FDD). By 8-fold cross-validation, PONDR-FIT showed improved prediction accuracy over individual predictors, with an average improvement of 11% compared to the best individual predictor. The errors in PONDR-FIT were analyzed, revealing that short disordered regions and residues close to order/disorder boundaries were the most challenging to predict. The study also highlights the importance of understanding the underlying mechanisms of meta-predictors to further improve disorder prediction accuracy. The access to PONDR-FIT is available at www.disprot.org.The paper introduces PONDR-FIT, a meta-predictor for predicting intrinsically disordered amino acids in proteins. PONDR-FIT is developed by combining the outputs of several individual disorder predictors, including PONDR-VLXT, PONDR-VSL2, PONDR-VL3, FoldIndex, IUPred, and TopIDP. The meta-predictor was evaluated using two datasets: a fully ordered dataset (FOD) and a fully disordered dataset (FDD). By 8-fold cross-validation, PONDR-FIT showed improved prediction accuracy over individual predictors, with an average improvement of 11% compared to the best individual predictor. The errors in PONDR-FIT were analyzed, revealing that short disordered regions and residues close to order/disorder boundaries were the most challenging to predict. The study also highlights the importance of understanding the underlying mechanisms of meta-predictors to further improve disorder prediction accuracy. The access to PONDR-FIT is available at www.disprot.org.