Correction: Membranes Linked by Trans-Snare Complexes Require Lipids Prone to Non-Bilayer Structure for Progression to Fusion

Like other intracellular fusion events, the homotypic fusion of yeast vacuoles requires a Rab GTPase, a large Rab effector complex, SNARE proteins which can form a 4-helical bundle, and the SNARE disassembly chaperones Sec17p and Sec18p. In addition to these proteins, specific vacuole lipids are required for efficient fusion in vivo and with the purified organelle. Reconstitution of vacuole fusion with all purified components reveals that high SNARE levels can mask the requirement for a complex mixture of vacuole lipids. At lower, more physiological SNARE levels, neutral lipids with small headgroups that tend to form non-bilayer structures (phosphatidylethanolamine, diacylglycerol, and ergosterol) are essential. Membranes without these three lipids can dock and complete trans -SNARE pairing but cannot rearrange their lipids for fusion

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Projected increase in continental runoff due to plant responses to increasing carbon dioxide

In addition to influencing climatic conditions directly through radiative forcing, increasing carbon dioxide concentration influences the climate system through its effects on plant physiology1. Plant stomata generally open less widely under increased carbon dioxide concentration2, which reduces transpiration1, 3, 4, 5, 6 and thus leaves more water at the land surface7. This driver of change in the climate system, which we term 'physiological forcing', has been detected in observational records of increasing average continental runoff over the twentieth century8. Here we use an ensemble of experiments with a global climate model that includes a vegetation component to assess the contribution of physiological forcing to future changes in continental runoff, in the context of uncertainties in future precipitation. We find that the physiological effect of doubled carbon dioxide concentrations on plant transpiration increases simulated global mean runoff by 6 per cent relative to pre-industrial levels; an increase that is comparable to that simulated in response to radiatively forced climate change (11 6 per cent). Assessments of the effect of increasing carbon dioxide concentrations on the hydrological cycle that only consider radiative forcing9, 10, 11 will therefore tend to underestimate future increases in runoff and overestimate decreases. This suggests that freshwater resources may be less limited than previously assumed under scenarios of future global warming, although there is still an increased risk of drought. Moreover, our results highlight that the practice of assessing the climate-forcing potential of all greenhouse gases in terms of their radiative forcing potential relative to carbon dioxide does not accurately reflect the relative effects of different greenhouse gases on freshwater resources