The FAM13A Long Isoform Regulates Cilia Movement and Co-ordination in Airway Mucociliary Transport.

SNPs in the FAM13A locus are amongst the most commonly reported risk alleles associated with chronic obstructive pulmonary disease (COPD) and other respiratory diseases, however the physiological role of FAM13A is unclear. In humans, two major protein isoforms are expressed at the FAM13A locus: 'long' and 'short', but their functions remain unknown, partly due to a lack of isoform conservation in mice. We performed in-depth characterisation of organotypic primary human airway epithelial cell subsets and show that multiciliated cells predominantly express the FAM13A long isoform containing a putative N-terminal Rho GTPase activating protein (RhoGAP) domain. Using purified proteins, we directly demonstrate RhoGAP activity of this domain. In Xenopus laevis, which conserve the long isoform, Fam13a-deficiency impaired cilia-dependent embryo motility. In human primary epithelial cells, long isoform deficiency did not affect multiciliogenesis but reduced cilia co-ordination in mucociliary transport assays. This is the first demonstration that FAM13A isoforms are differentially expressed within the airway epithelium, with implications for the assessment and interpretation of SNP effects on FAM13A expression levels. We also show that the long FAM13A isoform co-ordinates cilia-driven movement, suggesting that FAM13A risk alleles may affect susceptibility to respiratory diseases through deficiencies in mucociliary clearance. This article is open access and distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).

De Novo Molecule Design by Translating from Reduced Graphs to SMILES.

A key component of automated molecular design is the generation of compound ideas for subsequent filtering and assessment. Recently deep learning approaches have been explored as alternatives to traditional de novo molecular design techniques. Deep learning algorithms rely on learning from large pools of molecules represented as molecular graphs (generally SMILES), and several approaches can be used to tailor the generated molecules to defined regions of chemical space. Cheminformatics has developed alternative higher-level representations that capture the key properties of a set of molecules, and it would be of interest to understand whether such representations can be used to constrain the output of molecule generation algorithms. In this work we explore the use of one such representation, the Reduced Graph, as a definition of target chemical space for a deep learning molecule generator. The Reduced Graph replaces functional groups with superatoms representing the pharmacophoric features. Assigning these superatoms to specific nonorganic element types allows the Reduced Graph to be represented as a valid SMILES string. The mapping from standard SMILES to Reduced Graph SMILES is well-defined, however, the inverse is not true, and this presents a particular challenge. Here we present the results of a novel seq-to-seq approach to molecule generation, where the one to many mapping of Reduced Graph to SMILES is learned on a large training set. This training needs to be performed only once. In a subsequent step, this model can be used to generate arbitrary numbers of compounds that have the same Reduced Graph as any input molecule. Through analysis of data sets in ChEMBL we show that the approach generates valid molecules and can extrapolate to Reduced Graphs unseen in the training set. The method offers an alternative deep learning approach to molecule generation that does not rely on transfer learning, latent space generation, or adversarial networks and is applicable to scaffold hopping and other cheminformatics applications in drug discovery.