Remote prescribing consultations: exploring the principles of effective practice.

There has been a rapid increase in remote patient consultations, including remote prescribing - partly in response to the coronavirus disease 2019 (COVID-19) pandemic, but also as part of the move towards a 'digital first' NHS. There are various benefits associated with remote prescribing, such as convenience for patients and judicious use of healthcare resources. However, it is also associated with several risks, for example the use of inappropriate medicines or doses if the prescriber does not have full access to the patient's records. This article considers some of the benefits and challenges of remote prescribing, and discusses the main principles of effective practice in relation to patient safety, informed consent and documentation.

Critical Assessment of Methods for Predicting the 3D Structure of Proteins and Protein Complexes.

Advances in a scientific discipline are often measured by small, incremental steps. In this review, we report on two intertwined disciplines in the protein structure prediction field, modeling of single chains and modeling of complexes, that have over decades emulated this pattern, as monitored by the community-wide blind prediction experiments CASP and CAPRI. However, over the past few years, dramatic advances were observed for the accurate prediction of single protein chains, driven by a surge of deep learning methodologies entering the prediction field. We review the mainscientific developments that enabled these recent breakthroughs and feature the important role of blind prediction experiments in building up and nurturing the structure prediction field. We discuss how the new wave of artificial intelligence-based methods is impacting the fields of computational and experimental structural biology and highlight areas in which deep learning methods are likely to lead to future developments, provided that major challenges are overcome.

Tradeoff between cost and accuracy in large-scale surface water dynamic modeling.

Recent efforts have led to the development of the local inertia formulation (INER) for an accurate but still cost-efficient representation of surface water dynamics, compared to the widely used kinematic wave equation (KINE). In this study, both formulations are evaluated over the Amazon basin in terms of computational costs and accuracy in simulating streamflows and water levels through synthetic experiments and comparisons against ground-based observations. Varying time steps are considered as part of the evaluation and INER at 60-second time step is adopted as the reference for synthetic experiments. Five hybrid (HYBR) realizations are performed based on maps representing the spatial distribution of the two formulations that physically represent river reach flow dynamics within the domain. Maps have fractions of KINE varying from 35.6% to 82.8%. KINE runs show clear deterioration along the Amazon river and main tributaries, with maximum RMSE values for streamflow and water level reaching 7827m3.s-1 and 1379cm near the basin's outlet. However, KINE is at least 25% more efficient than INER with low model sensitivity to longer time steps. A significant improvement is achieved with HYBR, resulting in maximum RMSE values of 3.9-292m3.s-1 for streamflows and 1.1-28.5cm for water levels, and cost reduction of 6-16%, depending on the map used. Optimal results using HYBR are obtained when the local inertia formulation is used in about one third of the Amazon basin, reducing computational costs in simulations while preserving accuracy. However, that threshold may vary when applied to different regions, according to their hydrodynamics and geomorphological characteristics.

Cost-benefit analysis of the mechanisms that enable migrating cells to sustain motility upon changes in matrix environments.

Cells can move through extracellular environments with varying geometries and adhesive properties. Adaptation to these differences is achieved by switching between different modes of motility, including lamellipod-driven and blebbing motility. Further, cells can modulate their level of adhesion to the extracellular matrix (ECM) depending on both the level of force applied to the adhesions and cell intrinsic biochemical properties. We have constructed a computational model of cell motility to investigate how motile cells transition between extracellular environments with varying surface continuity, confinement and adhesion. Changes in migration strategy are an emergent property of cells as the ECM geometry and adhesion changes. The transition into confined environments with discontinuous ECM fibres is sufficient to induce shifts from lamellipod-based to blebbing motility, while changes in confinement alone within a continuous geometry are not. The geometry of the ECM facilitates plasticity, by inducing shifts where the cell has high marginal gain from a mode change, and conserving persistency where the cell can continue movement regardless of the motility mode. This regulation of cell motility is independent of global changes in cytoskeletal properties, but requires locally higher linkage between the actin network and the plasma membrane at the cell rear, and changes in internal cell pressure. In addition to matrix geometry, we consider how cells might transition between ECM of different adhesiveness. We find that this requires positive feedback between the forces cells apply on the adhesion points, and the strength of the cell-ECM adhesions on those sites. This positive feedback leads to the emergence of a small number of highly adhesive cores, similar to focal adhesions. While the range of ECM adhesion levels the cell can invade is expanded with this feedback mechanism; the velocities are lowered for conditions where the positive feedback is not vital. Thus, plasticity of cell motility sacrifices the benefits of specialization, for robustness.

RaTrav: a tool for calculating mean first-passage times on biochemical networks.

BACKGROUND: The concept of mean first-passage times (MFPTs) occupies an important place in the theory of stochastic processes, with the methods of their calculation being equally important in theoretical physics, chemistry and biology. We present here a software tool designed to support computational biology studies where Markovian dynamics takes place and MFPTs between initial and single or multiple final states in network-like systems are used. Two methods are made available for which their efficiency is strongly dependent on the topology of the defined network: the combinatorial Hill technique and the Monte Carlo simulation method. RESULTS: After a brief introduction to RaTrav, we highlight the utility of MFPT calculations by providing two examples (accompanied by Additional file 1) where they are deemed to be of importance: analysis of a protein-protein docking funnel and interpretation of the free energy transduction between two coupled enzymatic reactions controlled by the dynamics of transition between enzyme conformational states. CONCLUSIONS: RaTrav is a versatile and easy to use software tool for calculating MFPTs across biochemical networks. The user simply prepares a text file with the structure of a given network, along with some additional basic parameters such as transition probabilities, waiting probabilities (if any) and local times (weights of edges), which define explicitly the stochastic dynamics on the network. The RaTrav tool can then be applied in order to compute desired MFPTs. For the provided examples, we were able to find the favourable binding path within a protein-protein docking funnel and to calculate the degree of coupling for two chemical reactions catalysed simultaneously by the same protein enzyme. However, the list of possible applications is much wider.

A Markov-chain model description of binding funnels to enhance the ranking of docked solutions.

Within the crowded, seemingly chaotic environment of the cell, proteins are still able to find their binding partners. This is achieved via an ensemble of trajectories, which funnel them towards their functional binding sites, the binding funnel. Here, we characterize funnel-like energy structures on the global energy landscape using time-homogeneous finite state Markov chain models. These models are based on the idea that transitions can occur between structurally similar docking solutions, with transition probabilities determined by their difference in binding energy. Funnel-like energy structures are those containing solutions with very high equilibrium populations. Although these are found surrounding both near-native and false positive binding sites, we show that the removal of nonfunnel-like energy structures, by filtering away solutions with low maximum equilibrium population, can significantly improve the ranking of docked poses.

Community-wide assessment of protein-interface modeling suggests improvements to design methodology.

The CAPRI (Critical Assessment of Predicted Interactions) and CASP (Critical Assessment of protein Structure Prediction) experiments have demonstrated the power of community-wide tests of methodology in assessing the current state of the art and spurring progress in the very challenging areas of protein docking and structure prediction. We sought to bring the power of community-wide experiments to bear on a very challenging protein design problem that provides a complementary but equally fundamental test of current understanding of protein-binding thermodynamics. We have generated a number of designed protein-protein interfaces with very favorable computed binding energies but which do not appear to be formed in experiments, suggesting that there may be important physical chemistry missing in the energy calculations. A total of 28 research groups took up the challenge of determining what is missing: we provided structures of 87 designed complexes and 120 naturally occurring complexes and asked participants to identify energetic contributions and/or structural features that distinguish between the two sets. The community found that electrostatics and solvation terms partially distinguish the designs from the natural complexes, largely due to the nonpolar character of the designed interactions. Beyond this polarity difference, the community found that the designed binding surfaces were, on average, structurally less embedded in the designed monomers, suggesting that backbone conformational rigidity at the designed surface is important for realization of the designed function. These results can be used to improve computational design strategies, but there is still much to be learned; for example, one designed complex, which does form in experiments, was classified by all metrics as a nonbinder.