Abstract
The potential energy landscape perspective provides both a conceptual and a computational framework for predicting, understanding and designing molecular properties. In this Feature Article, we highlight some recent advances that greatly facilitate structure prediction and analysis of global thermodynamics and kinetics in proteins and nucleic acids. The geometry optimisation procedures, on which these calculations are based, can be accelerated significantly using local rigidification of selected degrees of freedom, and through implementations on graphics processing units. Results of progressive local rigidification are first summarised for trpzip1, including a systematic analysis of the heat capacity and rearrangement rates. Benchmarks for all the essential optimisation procedures are then provided for a variety of proteins. Applications are then illustrated from a study of how mutation affects the energy landscape for a coiled-coil protein, and for transitions in helix morphology for a DNA duplex. Both systems exhibit an intrinsically multifunnel landscape, with the potential to act as biomolecular switches.
Highlights
Degree of flexibility, which allows them to adopt stable structures, and switch between different conformations over a hierarchy of timescales, to perform different functions.[1,2] These results highlight the importance of a dynamical perspective
We provide a brief summary of the approaches employed to locate stationary points and construct kinetic transition networks; basin-hopping global optimisation[67,68,69,70] and discrete path sampling.[71,72,73]
In a recent article,[108] we investigated the systematic effects of local rigidification on the structure, thermodynamics and kinetics of trpzip1.119 The tryptophan side-chain rings, peptide bonds, trigonal planar centres and termini were grouped as rigid bodies, and, based on these sets, we formulated three local rigid body (LRB) schemes: I – rings, II – rings and peptide bonds, III – rings, peptide bonds, termini, trigonal planar centres treated as local rigid bodies
Summary
There are two aspects to the protein folding problem, which have been extensively discussed in previous reviews.[5,6,7,8,9] The first one concerns structure prediction: given an amino. The construction of kinetic transition networks provides a complementary way to study biomolecular energy landscapes.[49,50,51] We have actively developed methods based on the potential energy landscape perspective, which provides a convenient framework for building and analysing transition networks.[4,50] In this approach, the landscape is coarse-grained into a set of interconnected stationary points [minima (M) and transition states (TS)] This simplification is attractive because stationary points can be located using geometry optimisation, largely independent of energy barriers. The number of degrees of freedom poses a serious challenge to current simulation techniques, and even for a biomolecule of moderate size with around 1000 atoms, attaining true equilibrium is time consuming To circumvent this problem, a common approach is to coarse grain the atomistic representation of the molecule.[63,64,65] such reductionist schemes may not be able to represent key dynamical features, and can result in an artificially smooth landscape. Both examples exhibit multifunnel energy landscapes, which we associate with intrinsic multifunctional behaviour.[59]
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