Critical assessment of protein intrinsic disorder prediction.
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ABSTRACT:
SUBMITTER: Necci M
PROVIDER: S-EPMC8105172 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Due to the functional importance of intrinsically disordered proteins or protein regions, prediction of intrinsic protein disorder from amino acid sequence has become an area of active research as witnessed in the 6th experiment on Critical Assessment of Techniques for Protein Structure Prediction (CASP6). Since the initial work by Romero et al. (Identifying disordered regions in proteins from amino acid sequences, IEEE Int. Conf. Neural Netw., 1997), our group has developed several predictors optimized for long disordered regions (>30 residues) with prediction accuracy exceeding 85%. However, these predictors are less successful on short disordered regions (< or =30 residues). A probable cause is a length-dependent amino acid compositions and sequence properties of disordered regions.We proposed two new predictor models, VSL2-M1 and VSL2-M2, to address this length-dependency problem in prediction of intrinsic protein disorder. These two predictors are similar to the original VSL1 predictor used in the CASP6 experiment. In both models, two specialized predictors were first built and optimized for short (< or = 30 residues) and long disordered regions (>30 residues), respectively. A meta predictor was then trained to integrate the specialized predictors into the final predictor model. As the 10-fold cross-validation results showed, the VSL2 predictors achieved well-balanced prediction accuracies of 81% on both short and long disordered regions. Comparisons over the VSL2 training dataset via 10-fold cross-validation and a blind-test set of unrelated recent PDB chains indicated that VSL2 predictors were significantly more accurate than several existing predictors of intrinsic protein disorder.The VSL2 predictors are applicable to disordered regions of any length and can accurately identify the short disordered regions that are often misclassified by our 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 our approaches for modelling 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.
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Project description:Besides being a common threat to farm animals and poultry, coronavirus (CoV) was responsible for the human severe acute respiratory syndrome (SARS) epidemic in 2002-4. However, many aspects of CoV behavior, including modes of its transmission, are yet to be fully understood. We show that the amount and the peculiarities of distribution of the protein intrinsic disorder in the viral shell can be used for the efficient analysis of the behavior and transmission modes of CoV. The proposed model allows categorization of the various CoVs by the peculiarities of disorder distribution in their membrane (M) and nucleocapsid (N). This categorization enables quick identification of viruses with similar behaviors in transmission, regardless of genetic proximity. Based on this analysis, an empirical model for predicting the viral transmission behavior is developed. This model is able to explain some behavioral aspects of important coronaviruses that previously were not fully understood. The new predictor can be a useful tool for better epidemiological, clinical, and structural understanding of behavior of both newly emerging viruses and viruses that have been known for a long time. A potentially new vaccine strategy could involve searches for viral strains that are characterized by the evolutionary misfit between the peculiarities of the disorder distribution in their shells and their behavior.
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Project description:Intrinsically disordered regions in proteins possess important biological roles including transcriptional regulation, molecular recognition, and provision of sites for posttranslational modification. In three-dimensional crystallization of both soluble and membrane proteins, identification and removal of disordered regions is often necessary for obtaining crystals possessing sufficient long-range order for structure determination. Disordered regions can be identified experimentally, with techniques such as limited proteolysis coupled with mass spectrometry, or computationally, by using disorder prediction programs, of which many are available. Although these programs use various methods to predict disorder from a protein's primary sequence, they all were developed using information derived from soluble protein structures. Therefore, their performance and accuracy when applied to integral membrane proteins remained an open question. We evaluated the performance of 13 disorder prediction programs on a dataset containing 343 membrane proteins, and upon subdatasets containing only ?-helical or ?-barrel proteins. These programs were ranked using multiple metrics, including metrics specifically created for membrane proteins. Analysis of these data shows a clear distinction between programs that accurately predict disordered regions in membrane proteins and programs which perform poorly, and allows for the robust integration of in silico disorder prediction into our PSI:Biology membrane protein structural genomics pipeline.
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