Efficient Characterization of Protein Cavities within Molecular Simulation Trajectories: trj_cavity.

Abstract

Protein cavities and tunnels are critical in determining phenomena such as ligand binding, molecular transport, and enzyme catalysis. Molecular dynamics (MD) simulations enable the exploration of the flexibility and conformational plasticity of protein cavities, extending the information available from static experimental structures relevant to, for example, drug design. Here, we present a new tool (trj_cavity) implemented within the GROMACS ( www.gromacs.org ) framework for the rapid identification and characterization of cavities detected within MD trajectories. trj_cavity is optimized for usability and computational efficiency and is applicable to the time-dependent analysis of any cavity topology, and optional specialized descriptors can be used to characterize, for example, protein channels. Its novel grid-based algorithm performs an efficient neighbor search whose calculation time is linear with system size, and a comparison of performance with other widely used cavity analysis programs reveals an orders-of-magnitude improvement in the computational cost. To demonstrate its potential for revealing novel mechanistic insights, trj_cavity has been used to analyze long-time scale simulation trajectories for three diverse protein cavity systems. This has helped to reveal, respectively, the lipid binding mechanism in the deep hydrophobic cavity of a soluble mite-allergen protein, Der p 2; a means for shuttling carbohydrates between the surface-exposed substrate-binding and catalytic pockets of a multidomain, membrane-proximal pullulanase, PulA; and the structural basis for selectivity in the transmembrane pore of a voltage-gated sodium channel (NavMs), embedded within a lipid bilayer environment. trj_cavity is available for download under an open-source license ( http://sourceforge.net/projects/trjcavity ). A simplified, GROMACS-independent version may also be compiled.