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There are many open questions regarding the hydration of solvent-exposed, non-polar tracts and pockets in proteins. Although water is predicted to de-wet purely repulsive surfaces and evacuate crevices, the extent of de-wetting is unclear when ubiquitous van der Waals interactions are in play. The structural simplicity of synthetic supramolecular hosts imbues them with considerable potential to address this issue. To this end, here we detail a combination of densimetry and molecular dynamics simulations of three cavitands, coupled with calorimetric studies of their complexes with short-chain carboxylates. Our results reveal the range of wettability possible within the ostensibly identical cavitand pockets — which differ only in the presence/position of the methyl groups that encircle the portal to their non-polar pockets. The results demonstrate the ability of macrocycles to template water cavitation within their binding sites and show how the orientation of methyl groups can trigger the drying of non-polar pockets in liquid water, suggesting new avenues to control guest complexation. 

Science still does not have the ability to accurately predict the affinity that ligands have for proteins. In an attempt to address this, the Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) series of blind predictive challenges is a community-wide exercise aimed at advancing computational techniques as standard predictive tools in rational drug design. In each cycle, a range of biologically relevant systems of different levels of complexity are selected to test the latest modeling methods. As part of this on-going exercise, and as a step towards understanding the important factors in context dependent guest binding, we challenged the computational community to determine the affinity of a series of negatively and positively charged guests to two constitutionally isomeric cavitand hosts: octa-acid 1 , and exo -octa acid 2 . Our affinity determinations, combined with molecular dynamics simulations, reveal asymmetries in affinities between host–guest pairs that cannot alone be explained by simple coulombic interactions, but also point to the importance of host–water interactions. Our work reveals the key facets of molecular recognition in water, emphasizes where improvements need to be made in modelling, and shed light on the complex problem of ligand-protein binding in the aqueous realm. 

Abstract Many measurements at the LHC require efficient identification of heavy-flavour jets, i.e. jets originating from bottom (b) or charm (c) quarks. An overview of the algorithms used to identify c jets is described and a novel method to calibrate them is presented. This new method adjusts the entire distributions of the outputs obtained when the algorithms are applied to jets of different flavours. It is based on an iterative approach exploiting three distinct control regions that are enriched with either b jets, c jets, or light-flavour and gluon jets. Results are presented in the form of correction factors evaluated using proton-proton collision data with an integrated luminosity of 41.5 fb -1 at  √s = 13 TeV, collected by the CMS experiment in 2017. The closure of the method is tested by applying the measured correction factors on simulated data sets and checking the agreement between the adjusted simulation and collision data. Furthermore, a validation is performed by testing the method on pseudodata, which emulate various mismodelling conditions. The calibrated results enable the use of the full distributions of heavy-flavour identification algorithm outputs, e.g. as inputs to machine-learning models. Thus, they are expected to increase the sensitivity of future physics analyses.