Abstract
The predictive power of simulation has become embedded in the infrastructure of modern economies. Computer-aided design is ubiquitous throughout industry. In aeronautical engineering, built infrastructure and materials manufacturing, simulations are routinely used to compute the performance of potential designs before construction. The ability to predict the behaviour of products is a driver of innovation by reducing the cost barrier to new designs, but also because radically novel ideas can be piloted with relatively little risk. Accurate weather forecasting is essential to guide domestic and military flight paths, and therefore the underpinning simulations are critical enough to have implications for national security. However, in the pharmaceutical and biotechnological industries, the application of computer simulations remains limited by the capabilities of the technology with respect to the complexity of molecular biology and human physiology. Over the last 30 years, molecular-modelling tools have gradually gained a degree of acceptance in the pharmaceutical industry. Drug discovery has begun to benefit from physics-based simulations. While such simulations have great potential for improved molecular design, much scepticism remains about their value. The motivations for such reservations in industry and areas where simulations show promise for efficiency gains in preclinical research are discussed. In this, the first of two complementary papers, the scientific and technical progress that needs to be made to improve the predictive power of biomolecular simulations, and how this might be achieved, is firstly discussed (Part 1). In Part2, the status of computer simulations in pharma is contrasted with aerodynamics modelling and weather forecasting, and comments are made on the cultural changes needed for equivalent computational technologies to become integrated into life-science industries.
Highlights
The predictive power of simulation has become embedded in the infrastructure of modern economies
In the pharmaceutical and biotechnological industries, the application of computer simulations remains limited by the capabilities of the technology with respect to the complexity of molecular biology and human physiology
The first of two complementary papers, the scientific and technical progress that needs to be made to improve the predictive power of biomolecular simulations, and how this might be achieved, is firstly discussed (Part 1)
Summary
An atomistic MD simulation of a protein is typically performed over microsecond timescales, which may take around one month of simulation time depending on the size of the protein and the computational resources available. Minor perturbations to the starting conditions, such as swapping around the atomic speeds at the beginning of the simulation, will result in subtly different simulation trajectories, and structures, being sampled from the same phase space To account for this inherent randomness, practioners run ‘repeat’. Brownian dynamics (Huber & McCammon, 2019) has been used in conjunction with atomistic methods to predict binding and unbinding kinetics (Jagger et al, 2018) and to investigate molecular crowding (McGuffee & Elcock, 2010) To achieve this improved efficiency, all of these simplified methods need to impose conformational restrictions on the flexibility of the protein. The capability to perform simulations of small proteins for multiple microseconds provides statistically converged trajectories, which will allow force-field deficiencies to be distinguished from sampling limitations; for example the folding free energy of Trp cage mutants (Piana et al, 2020). Advanced sampling applied to Gprotein coupled receptors has enabled the complex conformational landscape to be reconstructed, providing structures of previously unseen active intermediates and revealing state-dependent cholesterol hotspots that are potential allosteric regulatory sites (Lovera et al, 2019)
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