Prediction of Intrinsic Disorder with Quality Assessment Using QUARTER

Liquid-liquid phase separation in tumor biology

Liquid–liquid phase separation (LLPS) is a novel principle for explaining the precise spatial and temporal regulation in living cells. LLPS compartmentalizes proteins and nucleic acids into micron-scale, liquid-like, membraneless bodies with specific functions, which were recently termed biomolecular condensates. Biomolecular condensates are executors underlying the intracellular spatiotemporal coordination of various biological activities, including chromatin organization, genomic stability, DNA damage response and repair, transcription, and signal transduction. Dysregulation of these cellular processes is a key event in the initiation and/or evolution of cancer, and emerging evidence has linked the formation and regulation of LLPS to malignant transformations in tumor biology. In this review, we comprehensively summarize the detailed mechanisms of biomolecular condensate formation and biophysical function and review the recent major advances toward elucidating the multiple mechanisms involved in cancer cell pathology driven by aberrant LLPS. In addition, we discuss the therapeutic perspectives of LLPS in cancer research and the most recently developed drug candidates targeting LLPS modulation that can be used to combat tumorigenesis.

QUARTERplus: Accurate disorder predictions integrated with interpretable residue-level quality assessment scores

A recent advance in the disorder prediction field is the development of the quality assessment (QA) scores. QA scores complement the propensities produced by the disorder predictors by identifying regions where these predictions are more likely to be correct. We develop, empirically test and release a new QA tool, QUARTERplus, that addresses several key drawbacks of the current QA method, QUARTER. QUARTERplus is the first solution that utilizes QA scores and the associated input disorder predictions to produce very accurate disorder predictions with the help of a modern deep learning meta-model. The deep neural network utilizes the QA scores to identify and fix the regions where the original/input disorder predictions are poor. More importantly, the accurate QUATERplus's predictions are accompanied by easy to interpret residue-level QA scores that reliably quantify their residue-level predictive quality. We provide these interpretable QA scores for QUARTERplus and 10 other popular disorder predictors. Empirical tests on a large and independent (low similarity) test dataset show that QUARTERplus predictions secure AUC = 0.93 and are statistically more accurate than the results of twelve state-of-the-art disorder predictors. We also demonstrate that the new QA scores produced by QUARTERplus are highly correlated with the actual predictive quality and that they can be effectively used to identify regions of correct disorder predictions. This feature empowers the users to easily identify which parts of the predictions generated by the modern disorder predictors are more trustworthy. QUARTERplus is available as a convenient webserver at http://biomine.cs.vcu.edu/servers/QUARTERplus/.