PDBe: improved accessibility of macromolecular structure data from PDB and EMDB

© 2015 The Authors. Published by OUP. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1093/nar/gkv1047The Protein Data Bank in Europe (http://pdbe.org) accepts and annotates depositions of macromolecular structure data in the PDB and EMDB archives and enriches, integrates and disseminates structural information in a variety of ways. The PDBe website has been redesigned based on an analysis of user requirements, and now offers intuitive access to improved and value-added macromolecular structure information. Unique value-added information includes lists of reviews and research articles that cite or mention PDB entries as well as access to figures and legends from full-text open-access publications that describe PDB entries. A powerful new query system not only shows all the PDB entries that match a given query, but also shows the 'best structures' for a given macromolecule, ligand complex or sequence family using data-quality information from the wwPDB validation reports. A PDBe RESTful API has been developed to provide unified access to macromolecular structure data available in the PDB and EMDB archives as well as value-added annotations, e.g. regarding structure quality and up-to-date cross-reference information from the SIFTS resource. Taken together, these new developments facilitate unified access to macromolecular structure data in an intuitive way for non-expert users and support expert users in analysing macromolecular structure data.The Wellcome Trust [88944, 104948]; UK Biotechnology and Biological Sciences Research Council [BB/J007471/1, BB/K016970/1, BB/M013146/1, BB/M011674/1]; National Institutes of Health [GM079429]; UK Medical Research Council [MR/L007835/1]; European Union [284209]; CCP4; European Molecular Biology Laboratory (EMBL). Funding for open access charge: The Wellcome Trust.Published versio

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Role of Antibody Paratope Conformational Flexibility in the Manifestation of Molecular Mimicry

Molecular mimicry is a recurrent theme in host defense processes. The correlation of functional mimicry with the structural features of the antibody paratope has been investigated, addressing the consequences of mimicry in host immune mechanisms. Two anti-mannopyranoside antibodies, 1H7 and 2D10, representing the possible extremes of the recognition spectrum with regard to peptide-carbohydrate mimicry were examined. Crystallographic and molecular dynamics simulation analyses established correlation between the antibody flexibility and the manifestation of mimicry. It was evident that monoclonal antibody (mAb) 1H7, which has a narrow specificity in favor of the immunizing antigen, exhibited structural invariance. On the other hand, the antigen-combining site of 2D10, the mimicry-recognizing antibody, showed substantial divergence in the complementarity determining region loops. The docking of mannopyranoside within the antibody paratope revealed multiple modes of binding of the carbohydrate antigen in mAb 2D10 vis à vis single docking mode in mAb 1H7, which overlapped with the common monosaccharide binding site defined in anti-carbohydrate antibodies. The presence of additional antigen binding modes is perhaps reflective of the utilization of conformational flexibility in molecular mimicry. A relatively broader recognition repertoire—attributable to paratope flexibility—may facilitate the recognition of altered antigens of invading pathogens while the antibodies with narrow recognition specificity maintain the fidelity of the response

Machine-OlF-Action: a unified framework for developing and interpreting machine-learning models for chemosensory research

Summary: Machine Learning-based techniques are emerging as state-of-the-art methods in chemoinformatics to selectively, effectively and speedily identify biologically relevant molecules from large databases. So far, a multitude of such techniques have been proposed, but unfortunately due to their sparse availability, and the dependency on high-end computational literacy, their wider adaptation faces challenges, at least in the context of G-Protein Coupled Receptors (GPCRs)-associated chemosensory research. Here, we report Machine-OlF-Action (MOA), a user-friendly, open-source computational framework, that utilizes user-supplied SMILES (simplified molecular input line entry system) of the chemicals, along with their activation status, to synthesize classification models. MOA integrates a number of popular chemical databases collectively harboring approximately 103 million chemical moieties. MOA also facilitates customized screening of user-supplied chemical datasets. A key feature of MOA is its ability to embed molecules based on the similarity of their local neighborhood, by utilizing a state-of-the-art model interpretability framework LIME. We demonstrate the utility of MOA in identifying previously unreported agonists for human and mouse olfactory receptors OR1A1 and MOR174-9 by leveraging the chemical features of their known agonists and non-agonists. In summary, here we develop an ML-powered software playground for performing supervisory learning tasks involving chemical compounds.</p