This article presents a study on the length-dependent prediction of protein intrinsic disorder. The authors developed two new predictors, VSL2-M1 and VSL2-M2, to address the issue of length-dependent accuracy in predicting intrinsic protein disorder. These predictors are similar to the original VSL1 predictor used in the CASP6 experiment. The VSL2 predictors achieved well-balanced prediction accuracies of 81% on both short and long disordered regions. The results showed that VSL2 predictors were significantly more accurate than several existing predictors of intrinsic protein disorder. The study highlights the differences in amino acid compositions and sequence properties between short and long disordered regions. The VSL2 predictors are applicable to disordered regions of any length and can accurately identify short disordered regions that are often misclassified by previous disorder predictors. The success of the VSL2 predictors further confirmed the previously observed differences in amino acid compositions and sequence properties between short and long disordered regions, and justified the approaches for modeling short and long disordered regions separately. The VSL2 predictors are freely accessible for non-commercial use at http://www.ist.temple.edu/disprot/predictorVSL2.php. The study also discusses the importance of computationally expensive features in predicting intrinsic disorder, and the trade-offs between accuracy and computational cost. The results show that the inclusion of these features significantly improves the prediction accuracy of the VSL2 predictors. The study also examines the prediction accuracy on high-B-factor ordered regions, which were found to have a higher false positive error rate compared to low-B-factor residues. However, due to the small proportion of high-B-factor residues, the overall false positive rate was only slightly higher than on low-B-factor residues. The study concludes that the VSL2 predictors are effective in predicting intrinsic disorder and can be used for a wide range of applications in biomedical research.This article presents a study on the length-dependent prediction of protein intrinsic disorder. The authors developed two new predictors, VSL2-M1 and VSL2-M2, to address the issue of length-dependent accuracy in predicting intrinsic protein disorder. These predictors are similar to the original VSL1 predictor used in the CASP6 experiment. The VSL2 predictors achieved well-balanced prediction accuracies of 81% on both short and long disordered regions. The results showed that VSL2 predictors were significantly more accurate than several existing predictors of intrinsic protein disorder. The study highlights the differences in amino acid compositions and sequence properties between short and long disordered regions. The VSL2 predictors are applicable to disordered regions of any length and can accurately identify short disordered regions that are often misclassified by previous disorder predictors. The success of the VSL2 predictors further confirmed the previously observed differences in amino acid compositions and sequence properties between short and long disordered regions, and justified the approaches for modeling short and long disordered regions separately. The VSL2 predictors are freely accessible for non-commercial use at http://www.ist.temple.edu/disprot/predictorVSL2.php. The study also discusses the importance of computationally expensive features in predicting intrinsic disorder, and the trade-offs between accuracy and computational cost. The results show that the inclusion of these features significantly improves the prediction accuracy of the VSL2 predictors. The study also examines the prediction accuracy on high-B-factor ordered regions, which were found to have a higher false positive error rate compared to low-B-factor residues. However, due to the small proportion of high-B-factor residues, the overall false positive rate was only slightly higher than on low-B-factor residues. The study concludes that the VSL2 predictors are effective in predicting intrinsic disorder and can be used for a wide range of applications in biomedical research.