Sending Out an SMS: The Impact of Automatically Enrolling Consumers Into Overdraft Alerts

Incidental charges incurred by UK consumers on their Personal Current Account (PCA) are steep, especially for small amounts of unplanned borrowing and unpaid items. A recent policy mandates major UK banks to send consumers a text message alert of impending charges, allowing them to act before they incur a charge. Using a unique, large and detailed dataset covering the transactions of 1.5 million consumers across 6 banks, and by looking at large-scale automatic enrolment exercises carried out by two major banks, we estimate the effect of automatically enrolling consumers into these alerts. We find that automatic enrolment into alerts has large effects on charges: (i) automatic enrolment into unpaid item alerts (that inform customers of retry periods) reduces charges by 21-24% and (ii) automatic enrolment into unarranged overdraft alerts reduces charges by 25%. We also estimate average treatment effects for different types of consumers, grouped by their pre-alerts level of incidental charges (rare, occasional or heavy), and find that the benefits of automatic enrolment differ markedly between types of consumers. Those who rarely incur charges can avoid as much as half of charges thanks to alerts, whereas heavy users still incur substantial charges after automatic enrolment. We find strikingly similar patterns across the two banks, for both unpaid item and unarranged overdraft charges, providing reassurance that these findings are not specific to a particular customer base or firm implementation

Reevaluation of bromodomain ligands targeting BAZ2A

BAZ2A promotes migration and invasion in prostate cancer. Two chemical probes, the specific BAZ2-ICR, and the BAZ2/BRD9 cross-reactive GSK2801, interfere with the recognition of acetylated lysines in histones by the bromodomains of BAZ2A and of its BAZ2B paralog. The two chemical probes were tested in prostate cancer cell lines with opposite androgen susceptibility. BAZ2-ICR and GSK2801 showed different cellular efficacies in accordance with their unequal selectivity profiles. Concurrent inhibition of BAZ2 and BRD9 did not reproduce the effects observed with GSK2801, indicating possible off-targets for this chemical probe. On the other hand, the single BAZ2 inhibition by BAZ2-ICR did not phenocopy genetic ablation, demonstrating that bromodomain interference is not sufficient to strongly affect BAZ2A functionality and suggesting a PROTAC-based chemical ablation as an alternative optimization strategy and a possible therapeutic approach. In this context, we also present the crystallographic structures of BAZ2A in complex with the above chemical probes. Binding poses of TP-238 and GSK4027, chemical probes for the bromodomain subfamily I, and two ligands of the CBP/EP300 bromodomains identify additional headgroups for the development of BAZ2A ligands

Identification of a BAZ2A Bromodomain Hit Compound by Fragment Joining

The bromodomains of BAZ2A and BAZ2B (bromodomain adjacent to zinc finger domain proteins 2) are among the most hard to drug of the 61 human bromodomains. While little is known about the role of BAZ2B, there is strong evidence for the opportunity of targeting BAZ2A in various cancers. Here, a benzimidazole–triazole fragment that binds to the BAZ2A acetyl lysine pocket was identified by a molecular docking campaign and validated by competitive binding assays and X-ray crystallography. Another ligand was observed in close proximity by soaking experiments using the BAZ2A bromodomain preincubated with the benzimidazole–triazole fragment. The crystal structure of BAZ2A with the two ligands was employed to design a few benzimidazole–triazole derivatives with increased affinity. We also present the engineering of a BAZ2A bromodomain mutant for consistent, high-resolution crystallographic studies

PARP1 ADP-ribosylates lysine residues of the core histone tails

The chromatin-associated enzyme PARP1 has previously been suggested to ADP-ribosylate histones, but the specific ADP-ribose acceptor sites have remained enigmatic. Here, we show that PARP1 covalently ADP-ribosylates the amino-terminal histone tails of all core histones. Using biochemical tools and novel electron transfer dissociation mass spectrometric protocols, we identify for the first time K13 of H2A, K30 of H2B, K27 and K37 of H3, as well as K16 of H4 as ADP-ribose acceptor sites. Multiple explicit water molecular dynamics simulations of the H4 tail peptide into the catalytic cleft of PARP1 indicate that two stable intermolecular salt bridges hold the peptide in an orientation that allows K16 ADP-ribosylation. Consistent with a functional cross-talk between ADP-ribosylation and other histone tail modifications, acetylation of H4K16 inhibits ADP-ribosylation by PARP1. Taken together, our computational and experimental results provide strong evidence that PARP1 modifies important regulatory lysines of the core histone tail

Identification of a BAZ2A-Bromodomain Hit Compound by Fragment Growing

BAZ2A is an epigenetic regulator affecting transcription of ribosomal RNA. It is overexpressed in aggressive and recurrent prostate cancer, promoting cellular migration. Its bromodomain is characterized by a shallow and difficult-to-drug pocket. Here, we describe a structure-based fragment-growing campaign for the identification of ligands of the BAZ2A bromodomain. By combining docking, competition binding assays, and protein crystallography, we have extensively explored the interactions of the ligands with the rim of the binding pocket, and in particular ionic interactions with the side chain of Glu1820, which is unique to BAZ2A. We present 23 high-resolution crystal structures of the holo BAZ2A bromodomain and analyze common bromodomain/ligand motifs and favorable intraligand interactions. Binding of some of the compounds is enantiospecific, with affinity in the low micromolar range. The most potent ligand has an equilibrium dissociation constant of 7 μM and a good selectivity over the paralog BAZ2B bromodomain

Key opportunities and challenges for the use of big data in migration research and policy

Migration is one of the defining issues of the 21st century. Better data is required to improve understanding about how and why people are moving, target interventions and support evidence-based migration policy. Big data, defined as large, complex data from diverse sources, has been proposed as a solution to help address current gaps in knowledge. The authors participated in a workshop held in London, UK, in July 2019, that brought together experts from the UN, humanitarian NGOs, policy and academia to develop a better understanding of how big data could be used for migration research and policy. We identified six key areas regarding the application of big data in migration research and policy: accessing and utilising data; integrating data sources and knowledge; understanding environmental drivers of migration; improving healthcare access for migrant populations; ethical and security concerns; and addressing political narratives. We advocate the need for increased cross-disciplinary collaborations to advance the use of big data in migration research whilst safeguarding vulnerable migrant communities

Key opportunities and challenges for the use of big data in migration research and policy

Migration is one of the defining issues of the 21st century. Better data is required to improve understanding about how and why people are moving, target interventions and support evidencebased migration policy. Big data, defined as large, complex data from diverse sources, is regularly proposed as a solution to help address current gaps in knowledge. The authors participated in a workshop held in London, UK, in July 2019, that brought together experts from the United Nations (UN), humanitarian non-governmental organisations (NGOs), policy and academia to develop a better understanding of how big data could be used for migration research and policy. We identified six key areas regarding the application of big data in migration research and policy: accessing and utilising data; integrating data sources and knowledge; understanding environmental drivers of migration; improving healthcare access for migrant populations; ethical and security concerns around the use of big data; and addressing political narratives. We advocate the need for careful consideration of the challenges faced by the use of big data, as well as increased cross-disciplinary collaborations to advance the use of big data in migration research whilst safeguarding vulnerable migrant communities

PARP1 ADP-ribosylates lysine residues of the core histone tails

The chromatin-associated enzyme PARP1 has previously been suggested to ADP-ribosylate histones, but the specific ADP-ribose acceptor sites have remained enigmatic. Here, we show that PARP1 covalently ADP-ribosylates the amino-terminal histone tails of all core histones. Using biochemical tools and novel electron transfer dissociation mass spectrometric protocols, we identify for the first time K13 of H2A, K30 of H2B, K27 and K37 of H3, as well as K16 of H4 as ADP-ribose acceptor sites. Multiple explicit water molecular dynamics simulations of the H4 tail peptide into the catalytic cleft of PARP1 indicate that two stable intermolecular salt bridges hold the peptide in an orientation that allows K16 ADP-ribosylation. Consistent with a functional cross-talk between ADP-ribosylation and other histone tail modifications, acetylation of H4K16 inhibits ADP-ribosylation by PARP1. Taken together, our computational and experimental results provide strong evidence that PARP1 modifies important regulatory lysines of the core histone tails

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