molecular interactions
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Mechano-adaptation in a large protein complex
Macromolecular protein complexes perform essential biological functions across life forms. A fundamental, though yet unsolved question in biology is how the function of such complexes is regulated by intracellular or extracellular signals. For instance, we have little understanding of how forces affect multi-protein machines whose function is often mechanical in nature. We address this question by studying the bacterial flagellar motor, a large complex that powers swimming motility in many bacteria. This rotary motor autonomously adapts to changes in mechanical load by adding or removing force-generating ‘stator’ units that power rotation. In the bacterium Escherichia coli, up to 11 units drive the motor at high load while all the units are released at low load. We manipulate motor load using electrorotation, a technique in which a rapidly rotating electric field applies an external torque on the motor. This allows us to change motor load at will and measure the resulting stator dynamics at single-unit resolution. We found that the force generated by the stator units controls their unbinding, forming a feedback loop that leads to autoregulation of the assembly. We complemented our experiments with theoretical models that provide insight into the underlying molecular interactions. Torque-dependent remodeling takes place within seconds, making it a highly responsive control mechanism, one that is mediated by the mechano-chemical tuning of protein interactions.
Neural network-like collective dynamics in molecules
Neural networks can learn and recognize subtle correlations in high dimensional inputs. However, neural networks are simply many-body systems with strong non-linearities and disordered interactions. Hence, many-body physical systems with similar interactions should be able to show neural network-like behavior. Here we show neural network-like behavior in the nucleation dynamics of promiscuously interacting molecules with multiple stable crystalline phases. Using a combination of theory and experiments, we show how the physics of the system dictates relationships between the difficulty of the pattern recognition task solved, time taken and accuracy. This work shows that high dimensional pattern recognition and learning are not special to software algorithms but can be achieved by the collective dynamics of sufficiently disordered molecular systems.
“Models for Liquid-liquid Phase Separation of Intrinsically Disordered Proteins”
Intrinsically disordered proteins (IDPs), lack of a well-defined folded structure, have been recently shown to be critical to forming membrane-less organelles via liquid-liquid phase separation (LLPS). Due to the flexible conformations of IDPs, it could be challenging to investigate IDPs with solely experimental techniques. Computational models can therefore provide complementary views at several aspects, including the fundamental physics underlying LLPS and the sequence determinants contributing to LLPS. In this presentation, I will start with our coarse-grained computational framework that can help generate sequence dependent phase diagrams. The coarse-grained model further led to the development of a polymer model with empirical parameters to quickly predict LLPS of IDPs. At last, I will show our preliminary efforts on addressing molecular interactions within LLPS of IDPs using all-atom explicit-solvent simulations.
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