Ancestral sequence reconstruction is a technique that is gaining widespread use in molecular evolution studies and protein engineering. Accurate reconstruction requires the ability to handle appropriately large numbers of sequences, as well as insertion and deletion (indel) events, but available approaches exhibit limitations. To address these limitations, we developed Graphical Representation of Ancestral Sequence Predictions (GRASP), which efficiently implements maximum likelihood methods to enable the inference of ancestors of families with more than 10,000 members. GRASP implements partial order graphs (POGs) to represent and infer insertion and deletion events across ancestors, enabling the identification of building blocks for protein engineering. To validate the capacity to engineer novel proteins from realistic data, we predicted ancestor sequences across three distinct enzyme families: glucose-methanol-choline (GMC) oxidoreductases, cytochromes P450, and dihydroxy/sugar acid dehydratases (DHAD). All tested ancestors demonstrated enzymatic activity. Our study demonstrates the ability of GRASP (1) to support large data sets over 10,000 sequences and (2) to employ insertions and deletions to identify building blocks for engineering biologically active ancestors, by exploring variation over evolutionary time.

We developed Graphical Representation of Ancestral Sequence Predictions (GRASP) to infer and explore ancestral variants of protein families with more than 10,000 members. GRASP uses partial order graphs to represent homology in very large datasets, which are intractable with current inference tools and may, for example, be used to engineer proteins by identifying ancient variants of enzymes. We demonstrate that (1) across three distinct enzyme families, GRASP predicts ancestor sequences, all of which demonstrate enzymatic activity, (2) within-family insertions and deletions can be used as building blocks to support the engineering of biologically active ancestors via a new source of ancestral variation, and (3) generous inclusion of sequence data encompassing great diversity leads to less variance in ancestor sequence.GRASP is the central tool in the GRASP-suite, which is freely available at

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The inter-relationship between arousal events and body and/or limb movements during sleep may significantly impact the performance and clinical interpretation of actigraphy. As such, the objective of this study was to quantify the temporal association between arousals and body/limb movement. From this, we aim to determine whether actigraphy can predict arousal events in children, and identify the impact of arousal-related movements on estimates of sleep/wake periods. Thirty otherwise healthy children (5-16 years, median 9 years, 21 male) with suspected sleep apnoea were studied using full polysomnography and customised raw tri-axial accelerometry measured at the left fingertip, left wrist, upper thorax, left ankle and left great toe. Raw data were synchronised to within 0.1 s of the polysomnogram. Movements were then identified using a custom algorithm. On average 67.5% of arousals were associated with wrist movement. Arousals associated with movement were longer than those without movement (mean duration: 12.2 s versus 7.9 s respectively, p < 0.01); movements during wake and arousal were longer than other sleep movements (wrist duration: 6.26 s and 9.89 s versus 2.35 s respectively, p < 0.01); and the movement index (movements/h) did not predict apnoea-hypopnoea index (ρ  =  -0.11). Movements associated with arousals are likely to unavoidably contribute to actigraphy's poor sensitivity for wake. However, as sleep-related movements tend to be shorter than those during wake or arousal, incorporating movement duration into the actigraphy scoring algorithm may improve sleep staging performance. Although actigraphy-based measurements cannot reliably predict all arousal events, actigraphy can likely identify longer events that may have the greatest impact on sleep quality.