Protein arginine methylation regulates multiple biological processes such as chromatin remodeling and RNA splicing. Malfunction of protein arginine methyltransferases (PRMTs) is correlated with many human diseases. Thus, small molecule inhibitors of protein arginine methylation are of great potential for therapeutic development. Herein, we report a type of compound that blocks PRMT1-mediated arginine methylation at micromolar potency through a unique mechanism. Most of the discovered compounds bear naphthalene and sulfonate groups and are structurally different from typical PRMT substrates, for example, histone H4 and glycine- and arginine-rich sequences. To elucidate the molecular basis of inhibition, we conducted a variety of kinetic and biophysical assays. The combined data reveal that this type of naphthyl-sulfo (NS) molecule directly targets the substrates but not PRMTs for the observed inhibition. We also found that suramin effectively inhibited PRMT1 activity. These findings about novel PRMT inhibitors and their unique inhibition mechanism provide a new way for chemical regulation of protein arginine methylation.

The localization problem of target nodes remains unresolved, especially in large-scale and complex environments. In this paper, we propose a particle centroid drift (PCD) algorithm to reduce the distance errors between nodes and obtain the particle aggregation region by using the drift vector. First, we use the particle quality prediction function to obtain the particles in a high-likelihood region. The high-quality particles have high probability in the calculation, which can increase the number of effective particles and enable avoiding particle degradation. Then, the centroid drift vector is used to make the particle distribution similar to the actual reference distribution. Experiments are conducted on state-space models: the local movement where 55% nodes are moving and the globe movement where 100% nodes are moving. The results show that the proposed algorithm has low estimation errors, a good tracking effect and an acceptable time complexity.

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The entanglement-assisted (EA) formalism allows arbitrary classical linear codes to transform into entanglement-assisted quantum error correcting codes (EAQECCs) by using pre-shared entanglement between the sender and the receiver. In this work, we propose a decomposition of the defining set of constacyclic codes. Using this method, we construct four classes of q-ary entanglement-assisted quantum MDS (EAQMDS) codes based on classical constacyclic MDS codes by exploiting less pre-shared maximally entangled states. We show that a class of q-ary EAQMDS have minimum distance upper limit greater than 3q − 1. Some of them have much larger minimum distance than the known quantum MDS (QMDS) codes of the same length. Most of these q-ary EAQMDS codes are new in the sense that their parameters are not covered by the codes available in the literature.