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An electronic fabric based on stretchable capacitive sensor arrays detecting tactile and multimodal mechanical stimuli is presented.

SUMMARY The immune checkpoint receptor PD-1 and its ligand, PD-L1, have emerged as key regulators of anti-tumor immunity in humans. Recently, we reported an ultra high-affinity PD-1 mutant, termed HAC PD-1, which shows superior therapeutic efficacy in mice compared to antibodies. However, the molecular details underlying the action of this agent remain incompletely understood, and a molecular view of PD-1/PD-L1 interactions in general is only beginning to emerge. Here, we report the structure of HAC PD-1 in complex with PD-L1, showing it binds PD-L1 using a unique set of polar interactions. Biophysical studies and long-timescale molecular dynamics experiments reveal the mechanisms by which ten point mutations confer a 35,000-fold enhancement in binding affinity, and offer atomic-scale views of the role of conformational dynamics in PD-1/PD-L1 interactions. Finally, we show that the HAC PD-1 exhibits pH-dependent affinity, with pseudo-irreversible binding in a low pH setting akin to the tumor microenvironment.

Interest in atomically-detailed simulations has grown significantly with recent advances in computational hardware and Markov state modeling (MSM) methods, yet outstanding questions remain that hinder their widespread adoption. Namely, how do alternative sampling strategies explore conformational space and how might this influence predictions generated from the data? Here, we seek to answer these questions for four commonly used sampling methods: 1) a single long simulation, 2) many short simulations run in parallel, 3) adaptive sampling, and 4) our recently developed goal-oriented sampling algorithm, FAST. We first develop a theoretical framework for analytically calculating the probability of discovering select states on simple landscapes, where we uncover the drastic effects of varying the number and length of simulations. We then use kinetic Monte Carlo simulations on a variety of physically inspired landscapes to characterize the probability of discovering particular states and transition pathways for each of the four methods. Consistently, we find that FAST simulations discover each target state with the highest probability, while traversing realistic pathways. Furthermore, we uncover the potential pathology that short parallel simulations sometimes predict an incorrect transition pathway by crossing large energy barriers that long simulations would typically circumnavigate. We refer to this pathology as “pathway tunneling”. To protect against this phenomenon when using adaptive-sampling and FAST simulations, we introduce the FAST-string method. This method enhances sampling along the highest-flux transition paths to refine an MSMs transition probabilities and discriminate between competing pathways. Additionally, we compare the performance of a variety of MSM estimators in describing accurate thermodynamics and kinetics. For adaptive sampling, we recommend simply normalizing the transition counts out of each state after adding small pseudo-counts to avoid creating sources or sinks. Lastly, we evaluate whether our insights from simple landscapes hold for all-atom molecular dynamics simulations of the folding of the λ-repressor protein. Remarkably, we find that FAST-contacts predicts the same folding pathway as a set of long simulations but with orders of magnitude less simulation time.

Ribonucleic acid (RNA) molecules play central roles in a variety of biological processes and, hence, are attractive targets for therapeutic intervention. In recent years, molecular docking techniques have become one of the most popular and successful approaches in drug discovery; however, almost all docking programs are protein based. The adaptability of popular docking programs in RNA world has not been systematically evaluated. This paper describes the comprehensive evaluation of two widely used protein-based docking programs--GOLD and Glide--for their docking and virtual screening accuracies against RNA targets. Using multiple docking strategies, both GOLD 4.0 and Glide 5.0 successfully reproduced most binding modes of the 60 tested RNA complexes. Applying different docking/scoring combinations, significant enrichments from the simulated virtual and fragment screening experiments were achieved against tRNA decoding A site of 16S rRNA (rRNA A-site). Our study demonstrated that current protein-based docking programs can fulfill general docking tasks against RNA, and these programs are very helpful in RNA-based drug discovery and design.