4.8 Article

CAID prediction portal: a comprehensive service for predicting intrinsic disorder and binding regions in proteins

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NUCLEIC ACIDS RESEARCH
卷 51, 期 W1, 页码 W62-W69

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OXFORD UNIV PRESS
DOI: 10.1093/nar/gkad430

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Intrinsic disorder (ID) in proteins is a well-established phenomenon in structural biology, but measuring its behavior on a large scale is challenging. To address this issue, CAID benchmarks ID predictors and creates a web server, the CAID Prediction Portal, which executes all CAID methods on user-defined sequences. The server generates standardized output, facilitates comparison between methods, and provides a valuable resource for researchers studying ID in proteins.
Intrinsic disorder (ID) in proteins is well-established in structural biology, with increasing evidence for its involvement in essential biological processes. As measuring dynamic ID behavior experimentally on a large scale remains difficult, scores of published ID predictors have tried to fill this gap. Unfortunately, their heterogeneity makes it difficult to compare performance, confounding biologists wanting to make an informed choice. To address this issue, the Critical Assessment of protein Intrinsic Disorder (CAID) benchmarks predictors for ID and binding regions as a community blind-test in a standardized computing environment. Here we present the CAID Prediction Portal, a web server executing all CAID methods on user-defined sequences. The server generates standardized output and facilitates comparison between methods, producing a consensus prediction highlighting high-confidence ID regions. The website contains extensive documentation explaining the meaning of different CAID statistics and providing a brief description of all methods. Predictor output is visualized in an interactive feature viewer and made available for download in a single table, with the option to recover previous sessions via a private dashboard. The CAID Prediction Portal is a valuable resource for researchers interested in studying ID in proteins. The server is available at the URL: .

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