Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints

The inapplicability of amino acid covariation methods to small protein families has limited their use for structural annotation of whole genomes. Recently, deep learning has shown promise in allowing accurate residue-residue contact prediction even for shallow sequence alignments. Here we introduce DMPfold, which uses deep learning to predict inter-atomic distance bounds, the main chain hydrogen bond network, and torsion angles, which it uses to build models in an iterative fashion. DMPfold produces more accurate models than two popular methods for a test set of CASP12 domains, and works just as well for transmembrane proteins. Applied to all Pfam domains without known structures, confident models for 25% of these so-called dark families were produced in under a week on a small 200 core cluster. DMPfold provides models for 16% of human proteome UniProt entries without structures, generates accurate models with fewer than 100 sequences in some cases, and is freely available.Comment: JGG and SMK contributed equally to the wor

Services just for men? Insights from a national study of the well men services pilots.

Men continue to have a lower life expectancy in most countries compared to women. Explanations of this gendered health inequality tend to focus on male risk taking, unhealthy lifestyle choices and resistance to seeking help from health services. In the period 2005-2008 the Scottish Government funded a nationwide community health promotion programme aimed at improving men's health, called Well Men Service Pilots (henceforth WMS)

Differentiable simulation to develop molecular dynamics force fields for disordered proteins.

Acknowledgements: I thank the Sjors Scheres group and Conny Yu for useful discussions; all contributors to Molly.jl; William Moses and Valentin Churavy for support with Enzyme.jl; Jake Grimmett, Toby Darling and Ivan Clayson for help with high-performance computing; and D. E. Shaw Research for providing data. This work was supported by the Medical Research Council, as part of United Kingdom Research and Innovation (also known as UK Research and Innovation) [MC_UP_1201/33]. For the purpose of open access, the MRC Laboratory of Molecular Biology has applied a CC BY public copyright licence to any author accepted manuscript version arising.Implicit solvent force fields are computationally efficient but can be unsuitable for running molecular dynamics on disordered proteins. Here I improve the a99SB-disp force field and the GBNeck2 implicit solvent model to better describe disordered proteins. Differentiable molecular simulations with 5 ns trajectories are used to jointly optimise 108 parameters to better match explicit solvent trajectories. Simulations with the improved force field better reproduce the radius of gyration and secondary structure content seen in experiments, whilst showing slightly degraded performance on folded proteins and protein complexes. The force field, called GB99dms, reproduces the results of a small molecule binding study and improves agreement with experiment for the aggregation of amyloid peptides. GB99dms, which can be used in OpenMM, is available at https://github.com/greener-group/GB99dms. This work is the first to show that gradients can be obtained directly from nanosecond-length differentiable simulations of biomolecules and highlights the effectiveness of this approach to training whole force fields to match desired properties

Design of metalloproteins and novel protein folds using variational autoencoders

Abstract The design of novel proteins has many applications but remains an attritional process with success in isolated cases. Meanwhile, deep learning technologies have exploded in popularity in recent years and are increasingly applicable to biology due to the rise in available data. We attempt to link protein design and deep learning by using variational autoencoders to generate protein sequences conditioned on desired properties. Potential copper and calcium binding sites are added to non-metal binding proteins without human intervention and compared to a hidden Markov model. In another use case, a grammar of protein structures is developed and used to produce sequences for a novel protein topology. One candidate structure is found to be stable by molecular dynamics simulation. The ability of our model to confine the vast search space of protein sequences and to scale easily has the potential to assist in a variety of protein design tasks

From shame to blame: institutionalising oppression through the moralisation of mental distress in austerity England

This paper interrogates qualitative data regarding the changing experiences of men- tal health service and welfare state interventions for those who self-identify as experiencing long-term mental distress. We focus on austerity-related reforms in the English welfare and mental health policy architecture to explore the socio-cultural and material bases of benefit claims-making in relation to long-term illness and incapac- ity. Recent neoliberal social policy reforms contest the ontological status of mental distress, in effect recasting distress as a ‘moral’ status. This tendency is reinforced via three primary dynamics in contemporary mental health and welfare policy: the delegitimisation of sick role status in relation to mental distress; the foregrounding of individual responsibility and concomitant re-orientation of services towards self-help; and an increasing punitive conditionality. These intersecting processes represent an institutionalisation of ‘blame’ in various policy contexts (Scambler in Sociol Health Illn 31(3): 441–455, 2009; Sociol Rev Monogr 66(4):766–782, 2018), the moral stigmatisation of mental distress and escalating experiences of oppression for mental health service users and welfare recipients. Shifting conceptions of distress are thereby entwined with transformations in social policy regimes and political economies. Presenting distress as a personal failure legitimates austerity-related restrictions on benefit and service entitlements as part of a wider project of neoliberal welfare state transformation