Differentiable molecular simulation can learn all the parameters in a coarse-grained force field for proteins

Finding optimal parameters for force fields used in molecular simulation is a challenging and time-consuming task, partly due to the difficulty of tuning multiple parameters at once. Automatic differentiation presents a general solution: run a simulation, obtain gradients of a loss function with respect to all the parameters, and use these to improve the force field. This approach takes advantage of the deep learning revolution whilst retaining the interpretability and efficiency of existing force fields. We demonstrate that this is possible by parameterising a simple coarse-grained force field for proteins, based on training simulations of up to 2,000 steps learning to keep the native structure stable. The learned potential matches chemical knowledge and PDB data, can fold and reproduce the dynamics of small proteins, and shows ability in protein design and model scoring applications. Problems in applying differentiable molecular simulation to all-atom models of proteins are discussed along with possible solutions and the variety of available loss functions. The learned potential, simulation scripts and training code are made available at https://github.com/psipred/cgdms

Redistribution of β-catenin in response to EGF and lithium signalling in human oesophageal squamous carcinoma cell lines

BACKGROUND: The β-catenin link between membrane-bound cadherins and the actin cytoskeleton regulates cell adhesion and consequently metastasis. Abnormal stabilisation of β-catenin enhances its transcriptional activities. Factors affecting β-catenin's functions are important in understanding metastatic diseases such as oesophageal squamous cell carcinoma (SCC). RESULTS: In human oesophageal SCCs β-catenin localises predominantly to the plasma membrane. The presence of free β-catenin in the cytoplasm/nucleus was low. This indicates that β-catenin's activities are skewed towards cell-cell adhesion in these oesophageal SCCs. Exposure to EGF or Li alone, produced a slight increase in membrane concentrations but only Li induced β-catenin stabilisation in the cytoplasm. In combination, EGF and Li decreased membrane-associated β-catenin, concomitantly increasing cytoplasmic concentrations. Convergence of these signalling pathways appears to induce a β-catenin shift from the membrane into the cytoplasm. CONCLUSION: Therefore, although the adhesive role of β-catenin appears to be intact, exogenous signals increase the stability of free β-catenin thereby reducing cell-cell adhesion in these tumours

The Benefits of a Challenge Approach on Match Day: Investigating Cardiovascular Reactivity in Professional Academy Soccer Players.

This study assessed physiological (cardiovascular) and psychological (confidence, control, and approach focus) data in professional academy soccer players prior to performance in competitive matches. A challenge state is characterised by an increase in cardiac output (CO), and a decrease in total peripheral vascular resistance (TPR). Data were collected from 37 participants, with 19 of these providing data on two separate occasions. Performance was measured using coach and player self-ratings. Challenge reactivity was positively, and significantly, associated with performance. Participants who demonstrated blunted cardiovascular (CV) responses performed significantly worse than participants who displayed either challenge or threat reactivity. There was mixed consistency in CV reactivity for those participants whose data were collected on more than one occasion, suggesting that some participants responded differently across the competitive matches. The association between self-report data and CV responses was weak. This study supports previous research demonstrating that challenge reactivity is associated with superior performance

Recent Developments in Deep Learning Applied to Protein Structure Prediction

Although many structural bioinformatics tools have been using neural network models for a long time, deep neural network (DNN) models have attracted considerable interest in recent years. Methods employing DNNs have had a significant impact in recent CASP experiments, notably in CASP12 and especially CASP13. In this article, we offer a brief introduction to some of the key principles and properties of DNN models and discuss why they are naturally suited to certain problems in structural bioinformatics. We also briefly discuss methodological improvements that have enabled these successes. Using the contact prediction task as an example, we also speculate why DNN models are able to produce reasonably accurate predictions even in the absence of many homologues for a given target sequence, a result which can at first glance appear surprising given the lack of input information. We end on some thoughts about how and why these types of models can be so effective, as well as a discussion on potential pitfalls. This article is protected by copyright. All rights reserved

Ultrafast end-to-end protein structure prediction enables high-throughput exploration of uncharacterized proteins

Deep learning-based prediction of protein structure usually begins by constructing a multiple sequence alignment (MSA) containing homologs of the target protein. The most successful approaches combine large feature sets derived from MSAs, and considerable computational effort is spent deriving these input features. We present a method that greatly reduces the amount of preprocessing required for a target MSA, while producing main chain coordinates as a direct output of a deep neural network. The network makes use of just three recurrent networks and a stack of residual convolutional layers, making the predictor very fast to run, and easy to install and use. Our approach constructs a directly learned representation of the sequences in an MSA, starting from a one-hot encoding of the sequences. When supplemented with an approximate precision matrix, the learned representation can be used to produce structural models of comparable or greater accuracy as compared to our original DMPfold method, while requiring less than a second to produce a typical model. This level of accuracy and speed allows very large-scale three-dimensional modeling of proteins on minimal hardware, and we demonstrate this by producing models for over 1.3 million uncharacterized regions of proteins extracted from the BFD sequence clusters. After constructing an initial set of approximate models, we select a confident subset of over 30,000 models for further refinement and analysis, revealing putative novel protein folds. We also provide updated models for over 5,000 Pfam families studied in the original DMPfold paper

A guide to machine learning for biologists

The expanding scale and inherent complexity of biological data have encouraged a growing use of machine learning in biology to build informative and predictive models of the underlying biological processes. All machine learning techniques fit models to data; however, the specific methods are quite varied and can at first glance seem bewildering. In this Review, we aim to provide readers with a gentle introduction to a few key machine learning techniques, including the most recently developed and widely used techniques involving deep neural networks. We describe how different techniques may be suited to specific types of biological data, and also discuss some best practices and points to consider when one is embarking on experiments involving machine learning. Some emerging directions in machine learning methodology are also discussed

Identification of hydroxyapatite spherules provides new insight into subretinal pigment epithelial deposit formation in the aging eye.

Accumulation of protein- and lipid-containing deposits external to the retinal pigment epithelium (RPE) is common in the aging eye, and has long been viewed as the hallmark of age-related macular degeneration (AMD). The cause for the accumulation and retention of molecules in the sub-RPE space, however, remains an enigma. Here, we present fluorescence microscopy and X-ray diffraction evidence for the formation of small (0.5-20 μm in diameter), hollow, hydroxyapatite (HAP) spherules in Bruch's membrane in human eyes. These spherules are distinct in form, placement, and staining from the well-known calcification of the elastin layer of the aging Bruch's membrane. Secondary ion mass spectrometry (SIMS) imaging confirmed the presence of calcium phosphate in the spherules and identified cholesterol enrichment in their core. Using HAP-selective fluorescent dyes, we show that all types of sub-RPE deposits in the macula, as well as in the periphery, contain numerous HAP spherules. Immunohistochemical labeling for proteins characteristic of sub-RPE deposits, such as complement factor H, vitronectin, and amyloid beta, revealed that HAP spherules were coated with these proteins. HAP spherules were also found outside the sub-RPE deposits, ready to bind proteins at the RPE/choroid interface. Based on these results, we propose a novel mechanism for the growth, and possibly even the formation, of sub-RPE deposits, namely, that the deposit growth and formation begin with the deposition of insoluble HAP shells around naturally occurring, cholesterol-containing extracellular lipid droplets at the RPE/choroid interface; proteins and lipids then attach to these shells, initiating or supporting the growth of sub-RPE deposits

Comparative Morphology of the Penis and Clitoris in Four Species of Moles (Talpidae).

The penile and clitoral anatomy of four species of Talpid moles (broad-footed, star-nosed, hairy-tailed, and Japanese shrew moles) were investigated to define penile and clitoral anatomy and to examine the relationship of the clitoral anatomy with the presence or absence of ovotestes. The ovotestis contains ovarian tissue and glandular tissue resembling fetal testicular tissue and can produce androgens. The ovotestis is present in star-nosed and hairy-tailed moles, but not in broad-footed and Japanese shrew moles. Using histology, three-dimensional reconstruction, and morphometric analysis, sexual dimorphism was examined with regard to a nine feature masculine trait score that included perineal appendage length (prepuce), anogenital distance, and presence/absence of bone. The presence/absence of ovotestes was discordant in all four mole species for sex differentiation features. For many sex differentiation features, discordance with ovotestes was observed in at least one mole species. The degree of concordance with ovotestes was highest for hairy-tailed moles and lowest for broad-footed moles. In relationship to phylogenetic clade, sex differentiation features also did not correlate with the similarity/divergence of the features and presence/absence of ovotestes. Hairy-tailed and Japanese shrew moles reside in separated clades, but they exhibit a high degree of congruence. Broad-footed and hairy-tailed moles reside within the same clade but had one of the lowest correlations in features and presence/absence of ovotestes. Thus, phylogenetic affinity and the presence/absence of ovotestes are poor predictors for most sex differentiation features within mole external genitalia

Lepton Acceleration in Pulsar Wind Nebulae

Pulsar Wind Nebulae (PWNe) act as calorimeters for the relativistic pair winds emanating from within the pulsar light cylinder. Their radiative dissipation in various wavebands is significantly different from that of their pulsar central engines: the broadband spectra of PWNe possess characteristics distinct from those of pulsars, thereby demanding a site of lepton acceleration remote from the pulsar magnetosphere. A principal candidate for this locale is the pulsar wind termination shock, a putatively highly-oblique, ultra-relativistic MHD discontinuity. This paper summarizes key characteristics of relativistic shock acceleration germane to PWNe, using predominantly Monte Carlo simulation techniques that compare well with semi-analytic solutions of the diffusion-convection equation. The array of potential spectral indices for the pair distribution function is explored, defining how these depend critically on the parameters of the turbulent plasma in the shock environs. Injection efficiencies into the acceleration process are also addressed. Informative constraints on the frequency of particle scattering and the level of field turbulence are identified using the multiwavelength observations of selected PWNe. These suggest that the termination shock can be comfortably invoked as a principal injector of energetic leptons into PWNe without resorting to unrealistic properties for the shock layer turbulence or MHD structure.Comment: 19 pages, 5 figures, invited review to appear in Proc. of the inaugural ICREA Workshop on "The High-Energy Emission from Pulsars and their Systems" (2010), eds. N. Rea and D. Torres, (Springer Astrophysics and Space Science series