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Binding a Meridional Ligand in a Facial Geometry: A Square Peg in a Round Hole.

ABSTRACT: The bis(pyridylimino)isoindoline (BPI) ligand is a tridentate chelate that binds to metals via a meridional coordination mode. However, when this ligand forms a complex with Re(CO)₃, an almost exclusively facial moiety, the BPI ligand deforms to coordinate in a facial mode. We have in-vestigated this deformation via structural and theoretical means, and the non-planar binding mode of the ligand bathochromically shifts the metal to ligand charge transfer (MLCT) transition.

SUBMITTER: Schrage BR

PROVIDER: S-EPMC8340874 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundComorbidity of musculoskeletal (MSK) and mental health (MH) problems is common but challenging to treat using conventional approaches. Integration of conventional with complementary approaches (CAM) might help address this challenge. Integration can aim to transform biomedicine into a new health paradigm or to selectively incorporate CAM in addition to conventional care. This study explored professionals' experiences and views of CAM for comorbid patients and the potential for integration into UK primary care.MethodsWe ran focus groups with GPs and CAM practitioners at three sites across England and focus groups and interviews with healthcare commissioners. Topics included experience of co-morbid MSK-MH and CAM/integration, evidence, knowledge and barriers to integration. Sampling was purposive. A framework analysis used frequency, specificity, intensity of data, and disconfirming evidence.ResultsWe recruited 36 CAM practitioners (4 focus groups), 20 GPs (3 focus groups) and 8 commissioners (1 focus group, 5 interviews). GPs described challenges treating MSK-MH comorbidity and agreed CAM might have a role. Exercise- or self-care-based CAMs were most acceptable to GPs. CAM practitioners were generally pro-integration. A prominent theme was different understandings of health between CAM and general practitioners, which was likely to impede integration. Another concern was that integration might fundamentally change the care provided by both professional groups. For CAM practitioners, NHS structural barriers were a major issue. For GPs, their lack of CAM knowledge and the pressures on general practice were barriers to integration, and some felt integrating CAM was beyond their capabilities. Facilitators of integration were evidence of effectiveness and cost effectiveness (particularly for CAM practitioners). Governance was the least important barrier for all groups. There was little consensus on the ideal integration model, particularly in terms of financing. Commissioners suggested CAM could be part of social prescribing.ConclusionsCAM has the potential to help the NHS in treating the burden of MSK-MH comorbidity. Given the challenges of integration, selective incorporation using traditional referral from primary care to CAM may be the most feasible model. However, cost implications would need to be addressed, possibly through models such as social prescribing or an extension of integrated personal commissioning.

Project description:Protein-ligand interactions are increasingly profiled at high-throughput, playing a vital role in lead compound discovery and drug optimization. Accurate prediction of binding pose and binding affinity constitutes a pivotal challenge in advancing our computational understanding of protein-ligand interactions. However, inherent limitations still exist, including high computational cost for conformational search sampling in traditional molecular docking tools, and the unsatisfactory molecular representation learning and intermolecular interaction modeling in deep learning-based methods. Here we propose a geometry-aware attention-based deep learning model, GAABind, which effectively predicts the pocket-ligand binding pose and binding affinity within a multi-task learning framework. Specifically, GAABind comprehensively captures the geometric and topological properties of both binding pockets and ligands, and employs expressive molecular representation learning to model intramolecular interactions. Moreover, GAABind proficiently learns the intermolecular many-body interactions and simulates the dynamic conformational adaptations of the ligand during its interaction with the protein through meticulously designed networks. We trained GAABind on the PDBbindv2020 and evaluated it on the CASF2016 dataset; the results indicate that GAABind achieves state-of-the-art performance in binding pose prediction and shows comparable binding affinity prediction performance. Notably, GAABind achieves a success rate of 82.8% in binding pose prediction, and the Pearson correlation between predicted and experimental binding affinities reaches up to 0.803. Additionally, we assessed GAABind's performance on the severe acute respiratory syndrome coronavirus 2 main protease cross-docking dataset. In this evaluation, GAABind demonstrates a notable success rate of 76.5% in binding pose prediction and achieves the highest Pearson correlation coefficient in binding affinity prediction compared with all baseline methods.

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Binding a Meridional Ligand in a Facial Geometry: A Square Peg in a Round Hole.

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