作者： stonewise

The low-voltage activated T-type calcium channels regulate cellular excitability and oscillatory behavior of resting membrane potential which trigger many physiological events and have been implicated with many diseases. Here, we determine structures of the human T-type CaV3.3 channel, in the absence and presence of antihypertensive drug mibefradil, antispasmodic drug otilonium bromide and antipsychotic drug pimozide. CaV3.3 contains a long bended S6 helix from domain III, with a positive charged region protruding into the cytosol, which is critical for T-type CaV channel activation at low voltage. The drug-bound structures clearly illustrate how these structurally different compounds bind to the same central cavity inside the CaV3.3 channel, but are mediated by significantly distinct interactions between drugs and their surrounding residues. Phospholipid molecules penetrate into the central cavity in various extent to shape the binding pocket and play important roles in stabilizing the inhibitor. These structures elucidate mechanisms of channel gating, drug recognition, and actions, thus pointing the way to developing potent and subtype-specific drug for therapeutic treatments of related disorders.

The increasing literature leads to formidable pressure for medical researchers. Most existing recommender approaches mainly depend on text-based information. How to extract and utilize the heterogeneous information, especially the graphic ones, to improve the recommender is worthy of further exploring. To this end, we establish a document-to-document recommender system for medical literature (D2D-MR). Specifically, we proposed HB-GED, the Half-branch GED algorithm, and the bipartite-graph-based algorithm for solving the molecule similarity and the paper similarity, respectively. Experimental results on real-world datasets demonstrate the effectiveness of the proposed recommender system.

AIScaffold , a web-based tool for scaffold diversification using the deep generative model, can perform large-scale (up to 500,000 molecules) diversification in several minutes and recommend the top 500 (top 0.1%) molecules. Features such as site-specific diversification are also supported. This tool can facilitate the scaffold diversification process for medicinal chemists, thereby accelerating drug design.