Atomic structures of anthrax toxin protective antigen channels bound to partially unfolded lethal and edema factors

Following assembly, the anthrax protective antigen (PA) forms an oligomeric translocon that unfolds and translocates either its lethal factor (LF) or edema factor (EF) into the host cell. Here, we report the cryo-EM structures of heptameric PA channels with partially unfolded LF and EF at 4.6 and 3.1-Å resolution, respectively. The first α helix and β strand of LF and EF unfold and dock into a deep amphipathic cleft, called the α clamp, which resides at the interface of two PA monomers. The α-clamp-helix interactions exhibit structural plasticity when comparing the structures of lethal and edema toxins. EF undergoes a largescale conformational rearrangement when forming the complex with the channel. A critical loop in the PA binding interface is displaced for about 4 Å, leading to the weakening of the binding interface prior to translocation. These structures provide key insights into the molecular mechanisms of translocation-coupled protein unfolding and translocation

Towards a learnt neural body schema for dexterous coordination of action in humanoid and industrial robots

During any goal oriented behavior the dual processes of generation of dexterous actions and anticipation of the consequences of potential actions must seamlessly alternate. This article presents a unified neural framework for generation and forward simulation of goal directed actions and validates the architecture through diverse experiments on humanoid and industrial robots. The basic idea is that actions are consequences of an simulation process that animates the internal model of the body (namely the body schema), in the context of intended goals/constraints. Specific focus is on (a) Learning: how the internal model of the body can be acquired by any robotic embodiment and extended to coordinated tools; (b) Configurability: how diverse forward/inverse models of action can be ‘composed’ at runtime by coupling/decoupling different body (body + tool) chains with task relevant goals and constraints represented as multi-referential force fields; and (c) Computational simplicity: how both the synthesis of motor commands to coordinate highly redundant systems and the ensuing forward simulations are realized through well-posed computations without kinematic inversions. The performance of the neural architecture is demonstrated through a range of motor tasks on a 53-DoFs robot iCub and two industrial robots performing real world assembly with emphasis on dexterity, accuracy, speed, obstacle avoidance, multiple task-specific constraints, task-based configurability. Putting into context other ideas in motor control like the Equilibrium Point Hypothesis, Optimal Control, Active Inference and emerging studies from neuroscience, the relevance of the proposed framework is also discussed