Polypeptides composed entirely of d-amino acids and the achiral amino acid glycine (d-proteins) inherently have in vivo properties that are proposed to be near-optimal for a large molecule therapeutic agent. Specifically, d-proteins are resistant to degradation by proteases and are anticipated to be nonimmunogenic. Furthermore, d-proteins are manufactured chemically and can be engineered to have other desirable properties, such as improved stability, affinity, and pharmacokinetics. Thus, a well-designed d-protein therapeutic would likely have significant advantages over l-protein drugs. Toward the goal of developing d-protein therapeutics, we previously generated RFX001.D, a d-protein antagonist of natural vascular endothelial growth factor A (VEGF-A) that inhibited binding to its receptor. However, RFX001.D is unstable at physiological temperatures (Tm = 33 °C). Here, we describe RFX037.D, a variant of RFX001.D with extreme thermal stability (Tm > 95 °C), high affinity for VEGF-A (Kd = 6 nM), and improved receptor blocking. Comparison of the two enantiomeric forms of RFX037 revealed that the d-protein is more stable in mouse, monkey, and human plasma and has a longer half-life in vivo in mice. Significantly, RFX037.D was nonimmunogenic in mice, whereas the l-enantiomer generated a strong immune response. These results confirm the potential utility of synthetic d-proteins as alternatives to therapeutic antibodies.

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As a revolutionary wireless transmission strategy, interference alignment (IA) can improve the capacity of the cell-edge users. However, the acquisition of the global channel state information (CSI) for IA leads to unacceptable overhead in the massive MIMO systems. To tackle this problem, in this paper, we propose an IA and soft-space-reuse (IA-SSR) based cooperative transmission scheme under the two-stage precoding framework. Specifically, the cell-center and the cell-edge users are separately treated to fully exploit the spatial degrees of freedoms (DoF). Then, the optimal power allocation policy is developed to maximize the sum-capacity of the network. Next, a low-cost channel estimator is designed for the proposed IA-SSR framework. Some practical issues in IA-SSR implementation are also discussed. Finally, plenty of numerical results are presented to show the efficiency of the proposed algorithm.

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