Molecular dynamics study of accelerated ion-induced shock waves in biological media

We present a molecular dynamics study of the effects of carbon- and iron-ion induced shock waves in DNA duplexes in liquid water. We use the CHARMM force field implemented within the MBN Explorer simulation package to optimize and equilibrate DNA duplexes in liquid water boxes of different sizes and shapes. The translational and vibrational degrees of freedom of water molecules are excited according to the energy deposited by the ions and the subsequent shock waves in liquid water are simulated. The pressure waves generated are studied and compared with an analytical hydrodynamics model which serves as a benchmark for evaluating the suitability of the simulation boxes. The energy deposition in the DNA backbone bonds is also monitored as an estimation of biological damage, something which is not possible with the analytical model

Extracellular loops 2 and 3 of the calcitonin receptor selectively modify agonist binding and efficacy.

Class B peptide hormone GPCRs are targets for the treatment of major chronic disease. Peptide ligands of these receptors display biased agonism and this may provide future therapeutic advantage. Recent active structures of the calcitonin (CT) and glucagon-like peptide-1 (GLP-1) receptors reveal distinct engagement of peptides with extracellular loops (ECLs) 2 and 3, and mutagenesis of the GLP-1R has implicated these loops in dynamics of receptor activation. In the current study, we have mutated ECLs 2 and 3 of the human CT receptor (CTR), to interrogate receptor expression, peptide affinity and efficacy. Integration of these data with insights from the CTR and GLP-1R active structures, revealed marked diversity in mechanisms of peptide engagement and receptor activation between the CTR and GLP-1R. While the CTR ECL2 played a key role in conformational propagation linked to Gs/cAMP signalling this was mechanistically distinct from that of GLP-1R ECL2. Moreover, ECL3 was a hotspot for distinct ligand- and pathway- specific effects, and this has implications for the future design of biased agonists of class B GPCRs

Key interactions by conserved polar amino acids located at the transmembrane helical boundaries in Class B GPCRs modulate activation, effector specificity and biased signalling in the glucagon-like peptide-1 receptor

Class B GPCRs can activate multiple signalling effectors with the potential to exhibit biased agonism in response to ligand stimulation. Previously, we highlighted key TM domain polar amino acids that were crucial for the function of the GLP-1 receptor, a key therapeutic target for diabetes and obesity. Using a combination of mutagenesis, pharmacological characterisation, mathematical and computational molecular modelling, this study identifies additional highly conserved polar residues located towards the TM helical boundaries of Class B GPCRs that are important for GLP-1 receptor stability and/or controlling signalling specificity and biased agonism. This includes (i) three positively charged residues (R3.30227, K4.64288, R5.40310) located at the extracellular boundaries of TMs 3, 4 and 5 that are predicted in molecular models to stabilise extracellular loop 2, a crucial domain for ligand affinity and receptor activation; (ii) a predicted hydrogen bond network between residues located in TMs 2 (R2.46176), 6 (R6.37348) and 7 (N7.61406 and E7.63408) at the cytoplasmic face of the receptor that is important for stabilising the inactive receptor and directing signalling specificity, (iii) residues at the bottom of TM 5 (R5.56326) and TM6 (K6.35346 and K6.40351) that are crucial for receptor activation and downstream signalling; (iv) residues predicted to be involved in stabilisation of TM4 (N2.52182 and Y3.52250) that also influence cell signalling. Collectively, this work expands our understanding of peptide-mediated signalling by the GLP-1 receptor

Comparative analyses of isolated sheaths of the archaebacteria Methanothrix concilii GP6 and Methanospirillum hungatei strains GP1 and JF1

Peer reviewed: YesNRC publication: Ye

Beyond validation: assessing the legitimacy of artificial neural network models

Artificial neural network models have been used extensively for prediction and forecasting over the last 25 years. As the data used to develop ANNs contain important information about the physical processes being modelled, it is generally implied that a model that has been calibrated (trained) and performs well on an independent set of validation data represents the underlying physical processes of the system being modelled. However, this is not necessarily the case, most likely due to problems with equifinality, where different combinations of model parameters (e.g. connection weights) result in similar predictive performance. Consequently, there is also a need to check the behaviour of calibrated ANN models as part of the validation process, which is commonly referred to as structural, conceptual or scientific validation (Figure 1). This checks whether the input-output relationship captured by the model is plausible in accordance with a priori system understanding. In this paper, the importance of considering structural validation is demonstrated. This is achieved by developing ANN models with different numbers of hidden nodes for two environmental modelling case studies from the literature namely, salinity forecasting in the River Murray in South Australia and the prediction of treated water turbidity at a water treatment plant based on raw water quality and the administered alum dose. The validation errors are then compared with corresponding model behaviours. This was done using the validann R-package, which caters to a range of structural validation approaches. Results show that ANN models producing the best fit to the data do not necessarily result in models that behave in accordance with underlying system understanding.</p

Exploding the myths: An introduction to artificial neural networks for prediction and forecasting

Available online 5 July 2023Artificial Neural Networks (ANNs), sometimes also called models for deep learning, are used extensively for the prediction of a range of environmental variables. While the potential of ANNs is unquestioned, they are surrounded by an air of mystery and intrigue, leading to a lack of understanding of their inner workings. This has led to the perpetuation of a number of myths, resulting in the misconception that applying ANNs primarily involves “throwing” a large amount of data at “black-box” software packages. While this is a convenient way to side-step the principles applied to the development of other types of models, this comes at significant cost in terms of the usefulness of the resulting models. To address these issues, this inroductory overview paper explodes a number of the common myths surrounding the use of ANNs and outlines state-of-the-art approaches to developing ANNs that enable them to be applied with confidence in practice.Holger R. Maier, Stefano Galelli, Saman Razavi, Andrea Castelletti, Andrea Rizzoli, Ioannis N. Athanasiadis, Miquel Sànchez-Marrè, Marco Acutis, Wenyan Wu, Greer B. Humphre