Featured publication: Specific cardiolipin–SecY interactions are required for proton-motive force stimulation of protein secretion

In a recent publication in PNAS, Corey et al (Collinson Group,  School of Biochemistry, University of Bristol) work on the bacterial form of the general secretion system – aka the SecY machinery. This complex carries out the bulk of pre-protein secretion at the bacterial plasma membrane, powered by both ATP and the proton-motive force (PMF). They were interested in the interaction of SecY with the energetically-important cardiolipin (CL) molecule. CL is thought to be involved in many different bioenergetics processes, and has been previously implicated in SecY activity.

To approach this problem, they coupled the high predictive power of coarse-grained (CG) molecular dynamics (MD) with experimental analyses. Considerable speed up on atomistic simulation can be achieved using CG force fields, such as the Martini force field for biomolecules. By reducing the degrees of freedom of a system, it is possible to achieve sampling orders of magnitude faster than atomistic simulation – driven primarily by an increase in permissible MD step size, a reduction in interactions to compute per step and a smoothing of the energy landscape.

The CG data revealed two distinct CL binding sites on the SecY surface, which they were able to validate using native mass spectrometry (nMS), with Dr Argyris Politis at King’s College London, and FRET-based analysis on carefully designed variants of SecY.

Cardiolipin bound to SecY.

They then used these knockout variants to more deeply understand the importance of the SecY-CL interactions. Using a range of biochemical assays, they reveal that the specific interaction of CL at these sites is responsible for the previously-recorded heightened activity of SecY. Moreover, they establish a hitherto unknown role for CL in the PMF-driven activity of SecY. This is the first direct evidence of CL acting directly with the PMF in any bioenergetic system.

Featured publication: applying graph theory to protein structure

Through analysis of known protein structures, it is possible to gain insight into the rules that dictate how proteins fold. However, the number of experimentally determined protein structures is large and growing rapidly, which makes even the categorisation of protein structure difficult to perform. Computational tools can ameliorate this process, through automated categorisation and analysis.

AtlasCC uses graph theory to enumerate all the possible and observed structure space for the α-helical coiled coil.

A team from the University of Bristol, led by Prof Dek Woolfson, have recently published an article on AtlasCC. This computational resource automatically analyses the PDB to find an important protein substructure called the α-helical coiled coil and uses graph theory enumerate all the possible and observed structures this fold can adopt. These data are made accessible using a user-friendly, interactive web application that enables users to browse the structures. The application also identifies regions of coiled-coil structure space that has not been explored by nature, indicating possible opportunities for de novo design.