bioRxivBiophysicsPreprint
IcmX Plug Ejection in L. pneumophila's Type IV Secretion System: Is It Possible?
Cayson HamiltoniD, Samuel D. Bills, Brenden Stark, Gus L. W. HartiD
Abstract
1Legionella pneumophila is a gram-negative bacterial pathogen that is the causative agent of several infectious diseases, notably the severe form of pneumonia known as Legionnaires Disease. L. pneumophila targets amoebas and, in humans, alveolar macrophages. L. pneumophila infects its host by envelopment into the host cell through phagocytosis followed by the activation of the Type IV Secretion System (T4SS). The T4SS secretes effector proteins into the host cell which deactivate cell defenses and reprogram cell function to support L. pneumophila reproduction. After reproduction, L. pneumophila lyses the host cell and the cycle repeats, causing swelling and destroying a primary cell in the hosts immune system--infections caused by L. pneumophila are notoriously hard to treat. The stages of the infection process are known but the physical mechanisms are poorly understood. The structure of the T4SS includes a 13 member polymer, DotG which protrudes from the outer leaflet of the bacterial outer membrane. Given this exposure and the necessity of T4SS for infection, DotG is a promising drug target. However, designing an effective drug requires understanding the physical mechanisms. Via MD simulations we have tested hypotheses regarding its function. Specifically, we show the feasibility that an applied force on IcmX, a plug-like penta-mer that initially blocks transport through the T4SS channel, can cause a structural change in DotG and allow for protein release. Further, this applied force could reasonably come from a build up of pressure in the channel from other effector proteins. We conclude that the process of secretion is dependent on the induced structural change in DotG. Inhibiting this change provides a possible drug development direction.
- organism: bacteria (other)
- research focus: protein folding & dynamics
- research focus: drug response / drug discovery
- approach: deep learning
- approach: statistical model
- Citations
- 0
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