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Publication - Professor Adrian Mulholland

    Biomolecular simulations

    from dynamics and mechanisms to computational assays of biological activity

    Citation

    Huggins, DJ, Biggin, PC, Dämgen, MA, Essex, JW, Harris, SA, Henchman, RH, Khalid, S, Kuzmanic, A, Laughton, CA, Michel, J, Mulholland, AJ, Rosta, E, Sansom, MS & Kamp, MWvd, 2018, ‘Biomolecular simulations: from dynamics and mechanisms to computational assays of biological activity’. Wiley Interdisciplinary Reviews: Computational Molecular Science.

    Abstract

    Biomolecular simulation is increasingly central to understanding and designing biological molecules and their interactions. Detailed, physics-based simulation methods are demonstrating rapidly growing impact in areas as diverse as biocatalysis, drug delivery, biomaterials, biotechnology, and drug design. Simulations offer the potential of uniquely detailed, atomic-level insight into mechanisms, dynamics, and processes, as well as increasingly accurate predictions of molecular properties. Simulations can now be used as computational assays of biological activity, for example, in predictions of drug resistance. Methodological and algorithmic developments, combined with advances in computational hardware, are transforming the scope and range of calculations. Different types of methods are required for different types of problem. Accurate methods and extensive simulations promise quantitative comparison with experiments across biochemistry. Atomistic simulations can now access experimentally relevant timescales for large systems, leading to a fertile interplay of experiment and theory and offering unprecedented opportunities for validating and developing models. Coarse-grained methods allow studies on larger length- and timescales, and theoretical developments are bringing electronic structure calculations into new regimes. Multiscale methods are another key focus for development, combining different levels of theory to increase accuracy, aiming to connect chemical and molecular changes to macroscopic observables. In this review, we outline biomolecular simulation methods and highlight examples of its application to investigate questions in biology. This article is categorized under: Molecular and Statistical Mechanics > Molecular Dynamics and Monte-Carlo Methods Structure and Mechanism > Computational Biochemistry and Biophysics Molecular and Statistical Mechanics > Free Energy Methods.

    Full details in the University publications repository