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  1. Free, publicly-accessible full text available July 1, 2023
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  4. As the core component of the adherens junction in cell–cell adhesion, the cadherin–catenin complex transduces mechanical tension between neighboring cells. Structural studies have shown that the cadherin–catenin complex exists as an ensemble of flexible conformations, with the actin-binding domain (ABD) of α-catenin adopting a variety of configurations. Here, we have determined the nanoscale protein domain dynamics of the cadherin–catenin complex using neutron spin echo spectroscopy (NSE), selective deuteration, and theoretical physics analyses. NSE reveals that, in the cadherin–catenin complex, the motion of the entire ABD becomes activated on nanosecond to submicrosecond timescales. By contrast, in the α-catenin homodimer, only the smaller disordered C-terminal tail of ABD is moving. Molecular dynamics (MD) simulations also show increased mobility of ABD in the cadherin–catenin complex, compared to the α-catenin homodimer. Biased MD simulations further reveal that the applied external forces promote the transition of ABD in the cadherin–catenin complex from an ensemble of diverse conformational states to specific states that resemble the actin-bound structure. The activated motion and an ensemble of flexible configurations of the mechanosensory ABD suggest the formation of an entropic trap in the cadherin–catenin complex, serving as negative allosteric regulation that impedes the complex from binding to actin under zeromore »force. Mechanical tension facilitates the reduction in dynamics and narrows the conformational ensemble of ABD to specific configurations that are well suited to bind F-actin. Our results provide a protein dynamics and entropic explanation for the observed force-sensitive binding behavior of a mechanosensitive protein complex.

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  5. In this community review report, we discuss applications and techniques for fast machine learning (ML) in science—the concept of integrating powerful ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. We also present overlapping challenges across the multiple scientific domains where common solutions can be found. This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions. This is followed by a high-level overview and organization of technical advances, including an abundance of pointers to source material, which can enable these breakthroughs.
    Free, publicly-accessible full text available April 12, 2023